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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: amd64 Version: 1.62.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3538 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomicranges, r-bioc-shortread, r-cran-lattice Suggests: r-bioc-bsgenome, r-bioc-genomicfeatures, r-bioc-txdb.mmusculus.ucsc.mm9.knowngene, r-bioc-bsgenome.mmusculus.ucsc.mm9, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/noble/main/r-bioc-chipseq_1.62.0-1.ca2404.1_amd64.deb Size: 2592682 MD5sum: e762eedefac61b091497aba81389740f SHA1: d8c1465d8451385eee3826257280ea22146c41ef SHA256: 3036f2ba62a554c82bf20ee74dbb2598d407191e5badb0884f472e5cc20d114b SHA512: f2db05246f19555ac4e09ea55ed7d6c68cc36107df2239ff715b97b1ab089a0f8f2b670391c37d95805fe5d633610f58445b72aae1071954f9e8f9919b3b686a Homepage: https://cran.r-project.org/package=chipseq Description: Bioc Package 'chipseq' (chipseq: A package for analyzing chipseq data) Tools for helping process short read data for chipseq experiments. Package: r-bioc-chromvar Architecture: amd64 Version: 1.34.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1752 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomicranges, r-cran-ggplot2, r-cran-nabor, r-bioc-biocparallel, r-bioc-biocgenerics, r-bioc-biostrings, r-bioc-pwalign, r-bioc-tfbstools, r-bioc-rsamtools, r-bioc-s4vectors, r-cran-rcpp, r-cran-plotly, r-cran-shiny, r-cran-miniui, r-cran-dt, r-cran-rtsne, r-cran-matrix, r-bioc-summarizedexperiment, r-cran-rcolorbrewer, r-bioc-bsgenome, r-cran-rcpparmadillo Suggests: r-bioc-jaspar2016, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-cran-readr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pheatmap, r-bioc-motifmatchr Filename: pool/dists/noble/main/r-bioc-chromvar_1.34.1-1.ca2404.1_amd64.deb Size: 1253064 MD5sum: c14ad8030ffd60df7d08820b586064f4 SHA1: 4ef8f0d545ba18a3fd795703b95d852b25223ac3 SHA256: edee6c2cbe4ff1002346a491e9b8f140be0a2b0f3c9b31e52207b654b4b7bee6 SHA512: 9008e36f724ef5f2b20848d6fcf8ffdd1c4c64b35ee200de3bd29320a613d904055bf47d85334525a93468c9fe295a7ea0f593b2d83da89195cbf09217bfeff6 Homepage: https://cran.r-project.org/package=chromVAR Description: Bioc Package 'chromVAR' (Chromatin Variation Across Regions) Determine variation in chromatin accessibility across sets of annotations or peaks. Designed primarily for single-cell or sparse chromatin accessibility data, e.g. from scATAC-seq or sparse bulk ATAC or DNAse-seq experiments. Package: r-bioc-cigarillo Architecture: amd64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 866 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-biostrings Suggests: r-bioc-rsamtools, r-bioc-genomicalignments, r-bioc-rnaseqdata.hnrnpc.bam.chr14, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-cigarillo_1.2.0-1.ca2404.1_amd64.deb Size: 339180 MD5sum: 769d82e26a16ca5d3851011cf0d20551 SHA1: 5a3a1c6faea4de23092752600f1d407710355313 SHA256: 16ec92bd2dc15b9ae6f2027acb781e699850d2182323be29295909edd0fb4af3 SHA512: c83ab8c0030288428602649083d7004d954ff5ace5f430eb27cbe640cf1ee89ccf1cd9519c1d257046ff442b34e1269ba2ff25fe2e315af9481d2d9ada34f8e1 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. 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Package: r-bioc-cner Architecture: amd64 Version: 1.42.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 19935 Depends: libc6 (>= 2.38), r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biostrings, r-bioc-pwalign, r-cran-dbi, r-cran-rsqlite, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-rtracklayer, r-bioc-xvector, r-bioc-genomicalignments, r-bioc-s4vectors, r-bioc-iranges, r-cran-readr, r-bioc-biocgenerics, r-cran-reshape2, r-cran-ggplot2, r-cran-powerlaw, r-bioc-annotate, r-bioc-go.db, r-cran-r.utils, r-bioc-keggrest Suggests: r-bioc-gviz, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-bsgenome.drerio.ucsc.danrer10, r-bioc-bsgenome.hsapiens.ucsc.hg38, r-bioc-txdb.drerio.ucsc.danrer10.refgene, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-bsgenome.ggallus.ucsc.galgal3 Filename: pool/dists/noble/main/r-bioc-cner_1.42.0-1.ca2404.1_amd64.deb Size: 9724858 MD5sum: 7f49e8ea0809dc0c24491fe8ef0e9e6e SHA1: 2431fb8999891aca17029119c7d5d82ce3a30297 SHA256: d59dd33a49dee3608b84e921b097f58ce2a2f681858a09dbd20d96ea3380ca09 SHA512: 4dfac0464db4a1b2583be3bcc112ac557e7c2d52ca4852275160ab79271f6cebb78acb502a84772e69d7b214d2018e1d5a66ae0446b6242948890418ed294101 Homepage: https://cran.r-project.org/package=CNEr Description: Bioc Package 'CNEr' (CNE Detection and Visualization) Large-scale identification and advanced visualization of sets of conserved noncoding elements. Package: r-bioc-csaw Architecture: amd64 Version: 1.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2284 Depends: libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), libgcc-s1 (>= 3.0), liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 13.1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-cran-rcpp, r-cran-matrix, r-bioc-biocgenerics, r-bioc-rsamtools, r-bioc-edger, r-bioc-limma, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo, r-bioc-biocparallel, r-bioc-metapod, r-bioc-rhtslib Suggests: r-bioc-annotationdbi, r-bioc-org.mm.eg.db, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene, r-cran-testthat, r-bioc-genomicfeatures, r-bioc-genomicalignments, r-cran-knitr, r-bioc-biocstyle, r-cran-rmarkdown, r-cran-biocmanager Filename: pool/dists/noble/main/r-bioc-csaw_1.46.0-1.ca2404.1_amd64.deb Size: 1183570 MD5sum: 3e7ecd567add819824696569d4271526 SHA1: 7f2709ab7c73c3ea3ab83d5ba8565d4b5594ce2c SHA256: c631be95769ab2bdabeb5ed3caa47715ae2d516152bf87c08b691dd46875ab1e SHA512: 2f0c43422cbc42a5808b0c991cd52946e167870e25f6a432485fbe8bc0098e0f26748820340098b0b404958da78eacadaabba114af72b702adfe125c353eba5f Homepage: https://cran.r-project.org/package=csaw Description: Bioc Package 'csaw' (ChIP-Seq Analysis with Windows) Detection of differentially bound regions in ChIP-seq data with sliding windows, with methods for normalization and proper FDR control. Package: r-bioc-cytolib Architecture: amd64 Version: 2.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10902 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-rprotobuflib, r-cran-bh, r-bioc-rhdf5lib Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-cytolib_2.24.0-1.ca2404.1_amd64.deb Size: 1381812 MD5sum: fe4245bdec2212e76c9684de6698d8ed SHA1: 7fd023c6854ce0c53ed16d59e85fdc67d388b6ef SHA256: 73875dd3e79ea14e92e6d030ec89e58d2df025be394c2fd07d2cc3164b50b200 SHA512: 71fcfe8c22fc4dccf34b028ba15156a4aadcb181ebbca737e6e7874044b473deba3505e0e588f9b189f703062203568707e6b0ba75e318e441ccfd402793d3c8 Homepage: https://cran.r-project.org/package=cytolib Description: Bioc Package 'cytolib' (C++ infrastructure for representing and interacting with thegated cytometry data) This package provides the core data structure and API to represent and interact with the gated cytometry data. 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Package: r-bioc-dada2 Architecture: amd64 Version: 1.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4281 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-bioc-biostrings, r-cran-ggplot2, r-cran-reshape2, r-bioc-shortread, r-cran-rcppparallel, r-bioc-iranges, r-bioc-xvector, r-bioc-biocgenerics Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-dada2_1.40.0-1.ca2404.1_amd64.deb Size: 3412080 MD5sum: 7d043f71b56fa092738e012ff3d61acd SHA1: b14c33cbdbf0c92a267bfaaa0ccb4cbf940b66a9 SHA256: 0095fc040d02a5e815cf5720bc988f98227f43baa00e00ee2455e43a7dd00bed SHA512: d162635be0ff7e8c6bd1e8699c15eec9ac26179da59ad33c3f01aa952cb42364894b01426cc12ddefec39e6b81ed596895dcfd164f6e74ecd6bd526ce8acf2ab Homepage: https://cran.r-project.org/package=dada2 Description: Bioc Package 'dada2' (Accurate, high-resolution sample inference from ampliconsequencing data) The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and species-level assignment to 16S rRNA gene fragments by exact matching. Package: r-bioc-decipher Architecture: amd64 Version: 3.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 20647 Depends: libc6 (>= 2.14), libgomp1 (>= 6), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biostrings, r-cran-dbi, r-bioc-s4vectors, r-bioc-iranges, r-bioc-xvector Suggests: r-cran-rsqlite Filename: pool/dists/noble/main/r-bioc-decipher_3.8.0-1.ca2404.1_amd64.deb Size: 17708728 MD5sum: 82df779484b72c89fba0e0be15e53b5c SHA1: 8db5b81f000c8b00ebbac3445a0b1ca98ee3c146 SHA256: 9178f8028b4deaf084213d3ae7314159135d591072ab1157f8c17caefc6d8ba4 SHA512: f468f4955ad0e6345a6f42ffd28e60db7e52512251d310f6aa5befb0daea4641427ef3f1975a482999633af58e3970a798faf602085e091b0e3f4f498956f19b Homepage: https://cran.r-project.org/package=DECIPHER Description: Bioc Package 'DECIPHER' (Tools for curating, analyzing, and manipulating biologicalsequences) A toolset for deciphering and managing biological sequences. 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In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, ordinary arrays and, data frames. Package: r-bioc-densvis Architecture: amd64 Version: 1.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2995 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-bioc-basilisk, r-cran-assertthat, r-cran-reticulate, r-cran-rtsne, r-cran-irlba Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-ggplot2, r-cran-uwot, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-densvis_1.22.0-1.ca2404.1_amd64.deb Size: 1776780 MD5sum: bf19eea983d771a0ce4e12d940509edb SHA1: 457dbf96c3af9575fd6cfcddf9317ac644f67736 SHA256: 29fdf410bff183e0475cdfcb928204b6030a0a7edf1473c72a0794d1c5032dab SHA512: 7cfb0053e51115f26719e96d7e78f7c1d2a09b64ddb6b92ce5154f56052e7d6a1d1b5bc8a065fe3c4062a6686e6158056f2525ebbe4eb2ba19a16e80d2b1d0cf Homepage: https://cran.r-project.org/package=densvis Description: Bioc Package 'densvis' (Density-Preserving Data Visualization via Non-LinearDimensionality Reduction) Implements the density-preserving modification to t-SNE and UMAP described by Narayan et al. (2020) . The non-linear dimensionality reduction techniques t-SNE and UMAP enable users to summarise complex high-dimensional sequencing data such as single cell RNAseq using lower dimensional representations. These lower dimensional representations enable the visualisation of discrete transcriptional states, as well as continuous trajectory (for example, in early development). However, these methods focus on the local neighbourhood structure of the data. In some cases, this results in misleading visualisations, where the density of cells in the low-dimensional embedding does not represent the transcriptional heterogeneity of data in the original high-dimensional space. den-SNE and densMAP aim to enable more accurate visual interpretation of high-dimensional datasets by producing lower-dimensional embeddings that accurately represent the heterogeneity of the original high-dimensional space, enabling the identification of homogeneous and heterogeneous cell states. This accuracy is accomplished by including in the optimisation process a term which considers the local density of points in the original high-dimensional space. This can help to create visualisations that are more representative of heterogeneity in the original high-dimensional space. Package: r-bioc-deseq2 Architecture: amd64 Version: 1.52.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4774 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-biocgenerics, r-bioc-biobase, r-bioc-biocparallel, r-cran-matrixstats, r-cran-locfit, r-cran-ggplot2, r-cran-rcpp, r-bioc-matrixgenerics, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-vsn, r-cran-pheatmap, r-cran-rcolorbrewer, r-bioc-apeglm, r-cran-ashr, r-bioc-tximport, r-bioc-tximeta, r-bioc-tximportdata, r-cran-readr, r-cran-pbapply, r-bioc-airway, r-bioc-glmgampoi, r-cran-biocmanager Filename: pool/dists/noble/main/r-bioc-deseq2_1.52.0-1.ca2404.1_amd64.deb Size: 3172550 MD5sum: 8fd4a821e8df42aa9641e71574915182 SHA1: 2aebb370b76128d8f0db9359ac44c28bd0bdd60b SHA256: 35a501361d36ee0f75111e0153e689a579ff964c482e5d1da25d3d4186d6e94b SHA512: bc91a952155a9c928d6a997aac31a83faf703e068f36907a3590197591c8c11a8ff9a048ab3fe612f8f0f96b5c9e18195b68657530912249f784bb7ad17b00a2 Homepage: https://cran.r-project.org/package=DESeq2 Description: Bioc Package 'DESeq2' (Differential gene expression analysis based on the negativebinomial distribution) Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. Package: r-bioc-destiny Architecture: amd64 Version: 3.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3174 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen, r-cran-rspectra, r-cran-irlba, r-bioc-pcamethods, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-summarizedexperiment, r-bioc-singlecellexperiment, r-cran-ggplot2, r-cran-ggplot.multistats, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect, r-cran-ggthemes, r-cran-vim, r-cran-knn.covertree, r-cran-proxy, r-cran-rcpphnsw, r-cran-smoother, r-cran-scales, r-cran-scatterplot3d Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-igraph, r-cran-testthat, r-cran-fnn, r-cran-tidyverse, r-cran-gridextra, r-cran-cowplot, r-cran-conflicted, r-cran-viridis, r-cran-rgl, r-bioc-scrnaseq, r-bioc-org.mm.eg.db, r-bioc-scran, r-cran-repr Filename: pool/dists/noble/main/r-bioc-destiny_3.22.0-1.ca2404.1_amd64.deb Size: 1456310 MD5sum: 38c910e0908109cb07cfc49fb9aea7b7 SHA1: 43fcdfefc61c144ee5ad34dc8aba1fc3d3a1b760 SHA256: e447eb45add475bb9da0cf5670b8ec1cae6bd142abbfbe7f8d729395c0d17179 SHA512: deee3ecc424b5bfd59092ee9fcd2b98939fb38047fc5957193e19e12f328fef02a38b211f0fef9c9ffef4c6314d6df2acc9777071ce5ba80ce2e3753cacbc97a Homepage: https://cran.r-project.org/package=destiny Description: Bioc Package 'destiny' (Creates diffusion maps) Create and plot diffusion maps. 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Package: r-bioc-enrichedheatmap Architecture: amd64 Version: 1.42.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1904 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-complexheatmap, r-bioc-genomicranges, r-cran-matrixstats, r-cran-getoptlong, r-cran-rcpp, r-cran-locfit, r-cran-circlize, r-bioc-iranges Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-bioc-genefilter, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-bioc-enrichedheatmap_1.42.0-1.ca2404.1_amd64.deb Size: 1841454 MD5sum: f83ceed1aabe1a75c1651099d7ef21b6 SHA1: 8f6e9f22d90578fb6dfa227cfa3417e1d40c7617 SHA256: 840f5a406e0fa7ae3caa38317cd7a0243c8f0941e0eed38f4fdc518dba41adca SHA512: 973afd237107a9376fdd79d11e95d9aa4007bd1606444f3d2e36cc6146d914acd14a8208a7a7f2180da35a4cca8711ca1bdec916591ae5d33294262952c42f42 Homepage: https://cran.r-project.org/package=EnrichedHeatmap Description: Bioc Package 'EnrichedHeatmap' (Making Enriched Heatmaps) Enriched heatmap is a special type of heatmap which visualizes the enrichment of genomic signals on specific target regions. Here we implement enriched heatmap by ComplexHeatmap package. Since this type of heatmap is just a normal heatmap but with some special settings, with the functionality of ComplexHeatmap, it would be much easier to customize the heatmap as well as concatenating to a list of heatmaps to show correspondance between different data sources. Package: r-bioc-fabia Architecture: amd64 Version: 2.58.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1535 Depends: libc6 (>= 2.38), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase Filename: pool/dists/noble/main/r-bioc-fabia_2.58.0-1.ca2404.1_amd64.deb Size: 1181282 MD5sum: 4db50524b811d479f5b3f2b182a41d14 SHA1: db27e2c74c133ad41bc404da4cf375877c8959bc SHA256: d9593bebdf6b904b559db6eca0050ff3bd2e07232461210e32207a4a594319f7 SHA512: 0ba32677530c4471254eab8f02485494c739720337e7da06688804ee69a2d10623328d3ce68a016746a741e9e0c05932d7ffc518eee514632621a580126d04bf Homepage: https://cran.r-project.org/package=fabia Description: Bioc Package 'fabia' (FABIA: Factor Analysis for Bicluster Acquisition) Biclustering by "Factor Analysis for Bicluster Acquisition" (FABIA). FABIA is a model-based technique for biclustering, that is clustering rows and columns simultaneously. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. It captures realistic non-Gaussian data distributions with heavy tails as observed in gene expression measurements. FABIA utilizes well understood model selection techniques like the EM algorithm and variational approaches and is embedded into a Bayesian framework. FABIA ranks biclusters according to their information content and separates spurious biclusters from true biclusters. The code is written in C. Package: r-bioc-fastseg Architecture: amd64 Version: 1.58.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1412 Depends: libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges Suggests: r-bioc-dnacopy, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/noble/main/r-bioc-fastseg_1.58.0-1.ca2404.1_amd64.deb Size: 753228 MD5sum: 82759e6abbbcfbdc206359fab6f2847f SHA1: 5746709079e614c097cdc0a359090b5ab561be3c SHA256: 2d778d87f10733c4c16562bd7478a3d3405185c9699237e79c43142973ca9821 SHA512: 221526c465c380b1465bab0f1be1b9429ffa0702e04116b017d515755589608f18d8299acb20e00fa4bb7f91dc7a0123e63a5411ef4c35482e4470e659d1d9aa Homepage: https://cran.r-project.org/package=fastseg Description: Bioc Package 'fastseg' (fastseg - a fast segmentation algorithm) fastseg implements a very fast and efficient segmentation algorithm. It has similar functionality as DNACopy (Olshen and Venkatraman 2004), but is considerably faster and more flexible. fastseg can segment data from DNA microarrays and data from next generation sequencing for example to detect copy number segments. Further it can segment data from RNA microarrays like tiling arrays to identify transcripts. Most generally, it can segment data given as a matrix or as a vector. Various data formats can be used as input to fastseg like expression set objects for microarrays or GRanges for sequencing data. The segmentation criterion of fastseg is based on a statistical test in a Bayesian framework, namely the cyber t-test (Baldi 2001). The speed-up arises from the facts, that sampling is not necessary in for fastseg and that a dynamic programming approach is used for calculation of the segments' first and higher order moments. Package: r-bioc-fgsea Architecture: amd64 Version: 1.38.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9907 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-bioc-biocparallel, r-cran-ggplot2, r-cran-cowplot, r-cran-fastmatch, r-cran-matrix, r-cran-scales, r-cran-bh Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-reactome.db, r-bioc-annotationdbi, r-bioc-org.mm.eg.db, r-bioc-limma, r-bioc-geoquery, r-cran-msigdbr, r-cran-aggregation, r-cran-seurat Filename: pool/dists/noble/main/r-bioc-fgsea_1.38.0-1.ca2404.1_amd64.deb Size: 5814562 MD5sum: 07219fc7ff4f8e85a785e7a3fb0eabe3 SHA1: 55e2ef40bda1e176629eff57494c2f0678de5045 SHA256: 8e2c12475fb6c6d1606904e23b273c45a4a48723a200ab6a07edd93838db09df SHA512: 44ade705ef8e6b35e7ef4bda50100f812f497f2bc837e63383999e97c1608db43a782d43212e0a0cb61af335d20e8a6706aa8fa3e9c4bba62f62322200a19f1c Homepage: https://cran.r-project.org/package=fgsea Description: Bioc Package 'fgsea' (Fast Gene Set Enrichment Analysis) The package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm allows to make more permutations and get more fine grained p-values, which allows to use accurate stantard approaches to multiple hypothesis correction. Package: r-bioc-flowclust Architecture: amd64 Version: 3.50.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2752 Depends: libc6 (>= 2.38), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-biobase, r-bioc-graph, r-bioc-flowcore Suggests: r-cran-testthat, r-bioc-flowworkspace, r-bioc-flowworkspacedata, r-cran-knitr, r-cran-rmarkdown, r-bioc-opencyto, r-bioc-flowstats Filename: pool/dists/noble/main/r-bioc-flowclust_3.50.0-1.ca2404.1_amd64.deb Size: 1263342 MD5sum: 0d4fde649709b16c5eff45356777d18f SHA1: 032bccab04d4755b5d02a846f258df9f61169ac5 SHA256: 3f2f34264942cef6489b110b57aea1e0779e92fba77836baf13c892a0bc9b582 SHA512: f57903801ec34bb53dac7be361bbd2c00a80d5dccfa7eeab2742e5be9bd27e33e1fcc13cff6d558e3d1075e4f55a1cb353a62ea5a60560721c4901a7fafb9184 Homepage: https://cran.r-project.org/package=flowClust Description: Bioc Package 'flowClust' (Clustering for Flow Cytometry) Robust model-based clustering using a t-mixture model with Box-Cox transformation. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. Package: r-bioc-flowcore Architecture: amd64 Version: 2.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15258 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libcurl4t64 (>= 7.16.2), libgcc-s1 (>= 12), liblapack3 | liblapack.so.3, libssl3t64 (>= 3.0.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-biocgenerics, r-cran-rcpp, r-cran-matrixstats, r-bioc-cytolib, r-bioc-s4vectors, r-cran-cpp11, r-cran-bh, r-bioc-rprotobuflib Suggests: r-bioc-rgraphviz, r-bioc-flowviz, r-bioc-flowstats, r-cran-testthat, r-bioc-flowworkspace, r-bioc-flowworkspacedata, r-bioc-opencyto, r-cran-knitr, r-bioc-ggcyto, r-cran-gridextra Filename: pool/dists/noble/main/r-bioc-flowcore_2.24.0-1.ca2404.1_amd64.deb Size: 10315438 MD5sum: a4820cb9f8c48bcd90b13a48f4e9ee48 SHA1: ca9472ff0993a902feb6f6d4fc5c28ba3bb9e22d SHA256: f6a945dc163a3ccf80562db440545a6bb74f3ae10bfbeabdd6348ca0ac000bb6 SHA512: c33e7c06f78c0ced18f97c93a890ea4d3584164ebfb9565ccbbe30c97578370daf2d2c47345291a0c190d2c17f0a072ccefe41921d68f7f549744bd55ca87d99 Homepage: https://cran.r-project.org/package=flowCore Description: Bioc Package 'flowCore' (flowCore: Basic structures for flow cytometry data) Provides S4 data structures and basic functions to deal with flow cytometry data. Package: r-bioc-flowsom Architecture: amd64 Version: 2.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6111 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-igraph, r-cran-colorramps, r-bioc-consensusclusterplus, r-cran-dplyr, r-bioc-flowcore, r-cran-ggforce, r-cran-ggnewscale, r-cran-ggplot2, r-cran-ggpubr, r-cran-magrittr, r-cran-rlang, r-cran-rtsne, r-cran-tidyr, r-bioc-biocgenerics, r-cran-xml Suggests: r-bioc-biocstyle, r-cran-testthat, r-bioc-cytoml, r-bioc-flowworkspace, r-cran-ggrepel, r-cran-scattermore, r-cran-pheatmap, r-cran-ggpointdensity, r-bioc-complexheatmap Filename: pool/dists/noble/main/r-bioc-flowsom_2.20.0-1.ca2404.1_amd64.deb Size: 4888192 MD5sum: b0c9039243e44c2a525ea1a51f7e85dc SHA1: ff40c9e00b1166a238b1c4d11a834ac25adda2dd SHA256: ebcdc4ae4097da0902377d2516d22d2c2932cc4289778395c17f685a6a45dbfc SHA512: 7c481ce9b2bf7ff8334579cb6173793d1911ed4226ae62fd47ef1a5ca4d1520928f021e3e5be306f9c9459fd9ed7d9c7dd234d88417bd72c86beddc36fd1f763 Homepage: https://cran.r-project.org/package=FlowSOM Description: Bioc Package 'FlowSOM' (Using self-organizing maps for visualization and interpretationof cytometry data) FlowSOM offers visualization options for cytometry data, by using Self-Organizing Map clustering and Minimal Spanning Trees. Package: r-bioc-flowworkspace Architecture: amd64 Version: 4.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13189 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libcurl4t64 (>= 7.16.2), libgcc-s1 (>= 12), liblapack3 | liblapack.so.3, libssl3t64 (>= 3.0.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-cytolib, r-cran-xml, r-cran-ggplot2, r-bioc-graph, r-bioc-rbgl, r-bioc-rgraphviz, r-cran-data.table, r-cran-dplyr, r-cran-scales, r-cran-matrixstats, r-bioc-rprotobuflib, r-bioc-flowcore, r-bioc-ncdfflow, r-bioc-delayedarray, r-bioc-s4vectors, r-cran-cpp11, r-cran-bh, r-bioc-rhdf5lib Suggests: r-cran-testthat, r-bioc-flowworkspacedata, r-cran-knitr, r-cran-rmarkdown, r-bioc-ggcyto, r-bioc-cytoml, r-bioc-opencyto Filename: pool/dists/noble/main/r-bioc-flowworkspace_4.24.0-1.ca2404.1_amd64.deb Size: 5081412 MD5sum: 6d7ed96386d429789ffa5f245ccd7e62 SHA1: 4de29e62d6eb1f31ab7ff486eef7544b5c463f0d SHA256: e9fbfcd43e5e90a776a82baa180de1933b3a319df35766a10b7e2a2314aae54e SHA512: ad00f56b86bfb330a600244d7b19e736b6dc8f6bd1eb88ebd709409946339d03676bfe74dc8c35c59efcc3f7bfce3ae497e13dccfe679924a0a1e197fbcf52fd Homepage: https://cran.r-project.org/package=flowWorkspace Description: Bioc Package 'flowWorkspace' (Infrastructure for representing and interacting with gated andungated cytometry data sets.) This package is designed to facilitate comparison of automated gating methods against manual gating done in flowJo. This package allows you to import basic flowJo workspaces into BioConductor and replicate the gating from flowJo using the flowCore functionality. Gating hierarchies, groups of samples, compensation, and transformation are performed so that the output matches the flowJo analysis. Package: r-bioc-fmcsr Architecture: amd64 Version: 1.54.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1949 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-chemminer, r-cran-runit, r-bioc-biocgenerics Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-knitcitations, r-cran-knitrbootstrap, r-cran-rmarkdown, r-cran-codetools Filename: pool/dists/noble/main/r-bioc-fmcsr_1.54.0-1.ca2404.1_amd64.deb Size: 952744 MD5sum: 67bf69ac09c22dc1a88b673f5c66656b SHA1: 8e9e9534d621470e916084bbbf3b6d9062c894c6 SHA256: b37033e1f96a26a3ed65db93cb19dd7fbe7c2d02aaf7d3b0872b728ad207db6b SHA512: 8b532fb1ce98dbe0e51a258410e28414280734717f1e9d31c81f85dba5e07ba64fa988387cea9ae1d9b8fcb2a2bef9e49a6bee01f8fb1b053035a0b9db762ea4 Homepage: https://cran.r-project.org/package=fmcsR Description: Bioc Package 'fmcsR' (Mismatch Tolerant Maximum Common Substructure Searching) The fmcsR package introduces an efficient maximum common substructure (MCS) algorithms combined with a novel matching strategy that allows for atom and/or bond mismatches in the substructures shared among two small molecules. The resulting flexible MCSs (FMCSs) are often larger than strict MCSs, resulting in the identification of more common features in their source structures, as well as a higher sensitivity in finding compounds with weak structural similarities. The fmcsR package provides several utilities to use the FMCS algorithm for pairwise compound comparisons, structure similarity searching and clustering. Package: r-bioc-fmrs Architecture: amd64 Version: 1.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival Suggests: r-bioc-biocgenerics, r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-bioc-fmrs_1.22.0-1.ca2404.1_amd64.deb Size: 193540 MD5sum: 60143e29ce45ae759be4445b396cea89 SHA1: 0b4c532437e69560c380bdcc22150700d9816da4 SHA256: ae07a568a286f59ca991df96f3790ee4912bbf8a36d88de67ea307f10d69d964 SHA512: 49e25501147c4043c2f8749c43ce52c92912dc094a6d52a97c88cb8732f77bd2ffbe364606e9609d804cced9c1a90aa8b97140e955650c3d823a220b95460904 Homepage: https://cran.r-project.org/package=fmrs Description: Bioc Package 'fmrs' (Variable Selection in Finite Mixture of AFT Regression and FMRModels) The package obtains parameter estimation, i.e., maximum likelihood estimators (MLE), via the Expectation-Maximization (EM) algorithm for the Finite Mixture of Regression (FMR) models with Normal distribution, and MLE for the Finite Mixture of Accelerated Failure Time Regression (FMAFTR) subject to right censoring with Log-Normal and Weibull distributions via the EM algorithm and the Newton-Raphson algorithm (for Weibull distribution). More importantly, the package obtains the maximum penalized likelihood (MPLE) for both FMR and FMAFTR models (collectively called FMRs). A component-wise tuning parameter selection based on a component-wise BIC is implemented in the package. Furthermore, this package provides Ridge Regression and Elastic Net. Package: r-bioc-gcrma Architecture: amd64 Version: 2.84.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-affy, r-bioc-biobase, r-bioc-affyio, r-bioc-xvector, r-bioc-biostrings, r-cran-biocmanager Suggests: r-bioc-affydata, r-bioc-hgu95av2cdf, r-bioc-hgu95av2probe Filename: pool/dists/noble/main/r-bioc-gcrma_2.84.0-1.ca2404.1_amd64.deb Size: 396360 MD5sum: b05a51fc7e2f40fe011eb28cc11a0b02 SHA1: 9fd48b3617aff491d6a15f868548b2f138bbc0ee SHA256: c5a488dd5882354bae7fa69dbfda3cde9b92545695aca615a4c15baac051fb2f SHA512: b3fae5f553ec849212e15eaefb56b8f891115efeb84415875fe24c50153a863d340072b8e96ab9e9c648c28c04b4c926dc67b5e53ff8d9348fe95fac488a31cf Homepage: https://cran.r-project.org/package=gcrma Description: Bioc Package 'gcrma' (Background Adjustment Using Sequence Information) Background adjustment using sequence information Package: r-bioc-gdsfmt Architecture: amd64 Version: 1.48.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5994 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-digest, r-cran-matrix, r-cran-crayon, r-cran-runit, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-bioc-biocgenerics Filename: pool/dists/noble/main/r-bioc-gdsfmt_1.48.1-1.ca2404.1_amd64.deb Size: 1579842 MD5sum: d4ef8db7764a4b9920e11e5d1c412cd2 SHA1: 96aa3dda769f49aabc913b85ac9974c6c91acb6b SHA256: fc62ddcfb9e306071fd43cafbc5d324cf9947cfdc25cd4eb5d621f8372f40c1b SHA512: e3961f5abd2ed0e9769b59ae86c65e3c2c79632aefa4bef4dd240f605315c3f345a4e5549976a77cbf8c9e9adbcbfecd4f6f3694355fdcefa610b2b680efe1a3 Homepage: https://cran.r-project.org/package=gdsfmt Description: Bioc Package 'gdsfmt' (R Interface to CoreArray Genomic Data Structure (GDS) Files) Provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files. GDS is portable across platforms with hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers the efficient operations specifically designed for integers of less than 8 bits, since a diploid genotype, like single-nucleotide polymorphism (SNP), usually occupies fewer bits than a byte. Data compression and decompression are available with relatively efficient random access. It is also allowed to read a GDS file in parallel with multiple R processes supported by the package parallel. Package: r-bioc-genefilter Architecture: amd64 Version: 1.94.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2520 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-matrixgenerics, r-bioc-annotationdbi, r-bioc-annotate, r-bioc-biobase, r-cran-survival Suggests: r-cran-class, r-bioc-hgu95av2.db, r-bioc-tkwidgets, r-bioc-all, r-bioc-roc, r-cran-rcolorbrewer, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/noble/main/r-bioc-genefilter_1.94.0-1.ca2404.1_amd64.deb Size: 1231988 MD5sum: aab90d1c876b49a78888996e3596dac7 SHA1: 17554ef803ed8dc1d12cad36a8841f7a91f7698b SHA256: f2711aed28a13417c1f7e0ca46c324ea3858addd581f75c334e73d4dba7cddcb SHA512: 3abb398fd18244a607eb1ed112685158b2b8353d183026ceeb307c91eb0e3f422e9331efde03313db106e1603b7981249aeacff39788cb33e8a563784f841f6c Homepage: https://cran.r-project.org/package=genefilter Description: Bioc Package 'genefilter' (genefilter: methods for filtering genes from high-throughputexperiments) Some basic functions for filtering genes. Package: r-bioc-genesis Architecture: amd64 Version: 2.42.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7250 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-biocparallel, r-bioc-gwastools, r-bioc-gdsfmt, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-bioc-seqarray, r-bioc-seqvartools, r-bioc-snprelate, r-cran-data.table, r-cran-igraph, r-cran-matrix, r-cran-reshape2 Suggests: r-cran-compquadform, r-cran-compoissonreg, r-cran-poibin, r-cran-spatest, r-cran-survey, r-cran-testthat, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-gwasdata, r-cran-dplyr, r-cran-ggplot2, r-cran-ggally, r-cran-rcolorbrewer, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-genomeinfodb Filename: pool/dists/noble/main/r-bioc-genesis_2.42.0-1.ca2404.1_amd64.deb Size: 3664180 MD5sum: 1b34821925deebb3bcf64825b500558b SHA1: ab1fa6595a72f900cb7249fa9c196b094322d9a2 SHA256: 93e6cb69077bdcf4ba8640a5423d7a2d5555fbc0d1fbd4d765fff53adddc6e2d SHA512: 228a062c131fe4b81174cb5e344d1ee09d41c439e532d40344713f2f9f6e0518c1f9e28c6bc3d78f54a6560212695c889efd1d40d68ff7dff5f17b2c971a571f Homepage: https://cran.r-project.org/package=GENESIS Description: Bioc Package 'GENESIS' (GENetic EStimation and Inference in Structured samples(GENESIS): Statistical methods for analyzing genetic data fromsamples with population structure and/or relatedness) The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses. The current implementation provides functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a Principal Components Analysis on genome-wide SNP data for the detection of population structure in a sample that may contain known or cryptic relatedness. Unlike standard PCA, PC-AiR accounts for relatedness in the sample to provide accurate ancestry inference that is not confounded by family structure. PC-Relate uses ancestry representative principal components to adjust for population structure/ancestry and accurately estimate measures of recent genetic relatedness such as kinship coefficients, IBD sharing probabilities, and inbreeding coefficients. Additionally, functions are provided to perform efficient variance component estimation and mixed model association testing for both quantitative and binary phenotypes. Package: r-bioc-genie3 Architecture: amd64 Version: 1.34.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 749 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-reshape2, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-foreach, r-cran-dorng, r-cran-doparallel, r-bioc-biobase, r-bioc-summarizedexperiment, r-cran-testthat, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-genie3_1.34.0-1.ca2404.1_amd64.deb Size: 251212 MD5sum: 94c470c67fea7c01b33700ed1dfbf806 SHA1: 6eb935f6744af7a52cf5ba109800615e95acb927 SHA256: 1422218429f31726a4e552873cbefb18a08250d81d5b208dc907396b313b50e2 SHA512: 188de13496bbc106bf044da1197f464e577a7a50306623434d1445013c7d60d9f580f9a1d30097f71830f279846deb203f9e2f00b51a755f9d7e1c25d364d2a2 Homepage: https://cran.r-project.org/package=GENIE3 Description: Bioc Package 'GENIE3' (GEne Network Inference with Ensemble of trees) This package implements the GENIE3 algorithm for inferring gene regulatory networks from expression data. Package: r-bioc-genomation Architecture: amd64 Version: 1.44.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6377 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biostrings, r-bioc-bsgenome, r-cran-data.table, r-bioc-seqinfo, r-bioc-genomicranges, r-bioc-genomicalignments, r-bioc-s4vectors, r-cran-ggplot2, r-cran-gridbase, r-bioc-impute, r-bioc-iranges, r-cran-matrixstats, r-cran-plotrix, r-cran-plyr, r-cran-readr, r-cran-reshape2, r-bioc-rsamtools, r-bioc-seqpattern, r-bioc-rtracklayer, r-cran-rcpp Suggests: r-bioc-biocgenerics, r-bioc-genomationdata, r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-runit Filename: pool/dists/noble/main/r-bioc-genomation_1.44.0-1.ca2404.1_amd64.deb Size: 2955210 MD5sum: 44b59dea3af37e893fef30e146c48ee7 SHA1: 756739c7fcf9ae5ffec4175871325a7cef38159e SHA256: f3526d37f04456103a98037eb86f8f643170322ec317e0101435f59059f5cb01 SHA512: 7eed9e071da514607403bca43b6e5311693d3e036c955e7fa9975a6927b6b724e5aa5660124bea0894ef7726c5ed553b14efd2ddc07bc0651bc7165f0e957de7 Homepage: https://cran.r-project.org/package=genomation Description: Bioc Package 'genomation' (Summary, annotation and visualization of genomic data) A package for summary and annotation of genomic intervals. Users can visualize and quantify genomic intervals over pre-defined functional regions, such as promoters, exons, introns, etc. The genomic intervals represent regions with a defined chromosome position, which may be associated with a score, such as aligned reads from HT-seq experiments, TF binding sites, methylation scores, etc. The package can use any tabular genomic feature data as long as it has minimal information on the locations of genomic intervals. In addition, It can use BAM or BigWig files as input. Package: r-bioc-genomicalignments Architecture: amd64 Version: 1.48.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3349 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-biostrings, r-bioc-rsamtools, r-bioc-biocparallel, r-bioc-cigarillo Suggests: r-bioc-shortread, r-bioc-rtracklayer, r-bioc-bsgenome, r-bioc-genomicfeatures, r-bioc-rnaseqdata.hnrnpc.bam.chr14, r-bioc-pasillabamsubset, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.dmelanogaster.ucsc.dm3.ensgene, r-bioc-bsgenome.dmelanogaster.ucsc.dm3, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-deseq2, r-bioc-edger, r-cran-runit, r-cran-knitr, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-genomicalignments_1.48.0-1.ca2404.1_amd64.deb Size: 2126406 MD5sum: 5d8dd9d20dcf133ae70fd2342313b25e SHA1: 3dbda1693c9cc0c88d0adde8f979d128b2f766e8 SHA256: 6c69992eb1e6e5567ab0d19ff3e6a3a75fa8bba94a3328e773705936f48043c1 SHA512: 2e978f2186ee92ab7ed11f99115d4f0400ce23365c718c051fd0fec305cbbb1cfceb313932120c93eedeb31e8b9d95f32fa153dfb5094ab47912eb0e0eca97d9 Homepage: https://cran.r-project.org/package=GenomicAlignments Description: Bioc Package 'GenomicAlignments' (Representation and manipulation of short genomic alignments) Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments. Package: r-bioc-genomicfeatures Architecture: amd64 Version: 1.64.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2452 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomicranges, r-bioc-annotationdbi, r-cran-dbi, r-bioc-xvector, r-bioc-biostrings, r-bioc-rtracklayer Suggests: r-bioc-genomeinfodb, r-bioc-txdbmaker, r-bioc-org.mm.eg.db, r-bioc-org.hs.eg.db, r-bioc-bsgenome, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-bsgenome.celegans.ucsc.ce11, r-bioc-bsgenome.dmelanogaster.ucsc.dm3, r-bioc-fdb.ucsc.trnas, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.celegans.ucsc.ce11.ensgene, r-bioc-txdb.dmelanogaster.ucsc.dm3.ensgene, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene, r-bioc-txdb.hsapiens.ucsc.hg19.lincrnastranscripts, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-snplocs.hsapiens.dbsnp144.grch38, r-bioc-rsamtools, r-bioc-pasillabamsubset, r-bioc-genomicalignments, r-bioc-ensembldb, r-bioc-annotationfilter, r-cran-runit, r-bioc-biocstyle, r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-bioc-genomicfeatures_1.64.0-1.ca2404.1_amd64.deb Size: 1345048 MD5sum: fa0eb2c9abc842a2bc30c59cc4e87cfd SHA1: e70d0829a5c3a21c29ab8dacf0df02178598d153 SHA256: 825c395dae6947507e492c5559b1b5e73b49acea2677d0bb4781420eaac96ddc SHA512: 415dd51656737eeacb9b06c7d8ac8109a047e35f5abde3b7c19590d785f9324b1c9140c756a1513a53635fbe118456f4ce20a286ceb795f16d4be8c4ef708d3c Homepage: https://cran.r-project.org/package=GenomicFeatures Description: Bioc Package 'GenomicFeatures' (Query the gene models of a given organism/assembly) Extract the genomic locations of genes, transcripts, exons, introns, and CDS, for the gene models stored in a TxDb object. A TxDb object is a small database that contains the gene models of a given organism/assembly. Bioconductor provides a small collection of TxDb objects in the form of ready-to-install TxDb packages for the most commonly studied organisms. Additionally, the user can easily make a TxDb object (or package) for the organism/assembly of their choice by using the tools from the txdbmaker package. Package: r-bioc-genomicranges Architecture: amd64 Version: 1.62.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3610 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo Suggests: r-bioc-genomeinfodb, r-bioc-biobase, r-bioc-annotationdbi, r-bioc-annotate, r-bioc-biostrings, r-bioc-summarizedexperiment, r-bioc-rsamtools, r-bioc-genomicalignments, r-bioc-bsgenome, r-bioc-genomicfeatures, r-bioc-ucsc.utils, r-bioc-txdbmaker, r-bioc-gviz, r-bioc-variantannotation, r-bioc-annotationhub, r-bioc-deseq2, r-bioc-dexseq, r-bioc-edger, r-bioc-kegggraph, r-bioc-rnaseqdata.hnrnpc.bam.chr14, r-bioc-pasillabamsubset, r-bioc-keggrest, r-bioc-hgu95av2.db, r-bioc-hgu95av2probe, r-bioc-bsgenome.scerevisiae.ucsc.saccer2, r-bioc-bsgenome.hsapiens.ucsc.hg38, r-bioc-bsgenome.mmusculus.ucsc.mm10, r-bioc-txdb.athaliana.biomart.plantsmart22, r-bioc-txdb.dmelanogaster.ucsc.dm3.ensgene, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene, r-cran-runit, r-cran-digest, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-genomicranges_1.62.1-1.ca2404.1_amd64.deb Size: 2296008 MD5sum: a5930a20bf66608b8023d30ff0018f49 SHA1: 28693a75a3360b3847326fd673115440b5f372d1 SHA256: a557de9c59809d618ed099562f4b19f91704f4c2916beecc30e3d681f6f262fa SHA512: 5e07d3e498b0371188ea36568483024d44325b9c6f8812aca310a1ce5e12d2c9ad26e69ac174db71ee19d6a1e0803e76dd8737734533b246b9401d060ee66183 Homepage: https://cran.r-project.org/package=GenomicRanges Description: Bioc Package 'GenomicRanges' (Representation and manipulation of genomic intervals) The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose containers for storing and manipulating genomic intervals and variables defined along a genome. More specialized containers for representing and manipulating short alignments against a reference genome, or a matrix-like summarization of an experiment, are defined in the GenomicAlignments and SummarizedExperiment packages, respectively. Both packages build on top of the GenomicRanges infrastructure. Package: r-bioc-glmgampoi Architecture: amd64 Version: 1.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3346 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-bioc-beachmat, r-bioc-delayedmatrixstats, r-cran-matrixstats, r-bioc-matrixgenerics, r-bioc-sparsearray, r-bioc-s4vectors, r-bioc-delayedarray, r-bioc-hdf5array, r-cran-matrix, r-bioc-summarizedexperiment, r-bioc-singlecellexperiment, r-bioc-biocgenerics, r-cran-rlang, r-cran-vctrs, r-cran-rcpparmadillo, r-bioc-assorthead Suggests: r-cran-testthat, r-cran-zoo, r-bioc-deseq2, r-bioc-edger, r-bioc-limma, r-cran-mass, r-cran-statmod, r-cran-ggplot2, r-cran-bench, r-bioc-biocparallel, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-bioc-tenxpbmcdata, r-bioc-muscdata, r-bioc-scran, r-cran-dplyr Filename: pool/dists/noble/main/r-bioc-glmgampoi_1.24.0-1.ca2404.1_amd64.deb Size: 1688870 MD5sum: 591132139c40a01463475d120bc1167c SHA1: 24964ff3ae2551537b8496f91cf07d96b2469a76 SHA256: af956e2651c186c523c0ff2a7ad1d65a640f26011e203e6d5cf37cd38997e9ef SHA512: 846ff3d8b285e1e498578c4d9a7c651eecc867b8645e9c32fda1cc9ed5f0f423f9e139a8f94c601538f85e8a7961c9f37d0abe6cb832eb6ac48d151ffabec045 Homepage: https://cran.r-project.org/package=glmGamPoi Description: Bioc Package 'glmGamPoi' (Fit a Gamma-Poisson Generalized Linear Model) Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments. Package: r-bioc-globalancova Architecture: amd64 Version: 4.30.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1838 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-corpcor, r-bioc-globaltest, r-bioc-annotate, r-bioc-annotationdbi, r-bioc-biobase, r-cran-dendextend, r-bioc-gseabase, r-cran-vgam Suggests: r-bioc-go.db, r-bioc-golubesets, r-bioc-hu6800.db, r-bioc-vsn, r-bioc-rgraphviz Filename: pool/dists/noble/main/r-bioc-globalancova_4.30.0-1.ca2404.1_amd64.deb Size: 1621116 MD5sum: 777e6c18fdd0af2fbf64be21a40504e4 SHA1: 25f84df44fb0baf99f0c4e91ed84c971386d4944 SHA256: ac0ce76df8f370fac723d9b808d96d5116e896e6f1065fb738b98d9f914f4c01 SHA512: 36bff235654105813bc71a57975ac89a8d3b1a045ad21fe1e60fa29d74c32ecd84db30b2f2a52b7006871caf986e6f58da9fd1e2908461dd36f25fe88f86e530 Homepage: https://cran.r-project.org/package=GlobalAncova Description: Bioc Package 'GlobalAncova' (Global test for groups of variables via model comparisons) The association between a variable of interest (e.g. two groups) and the global pattern of a group of variables (e.g. a gene set) is tested via a global F-test. We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. The framework is generalized to groups of categorical variables or even mixed data by a likelihood ratio approach. Closed and hierarchical testing procedures are supported. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany. Package: r-bioc-gosemsim Architecture: amd64 Version: 2.38.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1359 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi, r-cran-dbi, r-cran-digest, r-bioc-go.db, r-cran-rlang, r-cran-yulab.utils, r-cran-rcpp Suggests: r-bioc-annotationhub, r-cran-biocmanager, r-bioc-clusterprofiler, r-bioc-dose, r-cran-knitr, r-bioc-org.hs.eg.db, r-cran-prettydoc, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-tidyselect, r-cran-rocr Filename: pool/dists/noble/main/r-bioc-gosemsim_2.38.0-1.ca2404.1_amd64.deb Size: 1116300 MD5sum: c4ff6810d2e0d491f07c78cabfcd4cb7 SHA1: bb41dff7fc08e651378cbad5603a74cdf7a23111 SHA256: 9e302e944366e463527f13237af09f055be9d567e4880f8ad50fc621e6295801 SHA512: 606273b5cea455de83f82aee755e60fb9ad6887c7b4419db4ddada8aca0133d3f43856555743aa2972e5f8f336858777d0765e79a0c9e27939557447932f7181 Homepage: https://cran.r-project.org/package=GOSemSim Description: Bioc Package 'GOSemSim' (GO-terms Semantic Similarity Measures) The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R package for semantic similarity computation among GO terms, sets of GO terms, gene products and gene clusters. GOSemSim implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively. Package: r-bioc-graph Architecture: amd64 Version: 1.90.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4919 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics Suggests: r-cran-sparsem, r-cran-xml, r-bioc-rbgl, r-cran-runit, r-cran-cluster, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/noble/main/r-bioc-graph_1.90.0-1.ca2404.1_amd64.deb Size: 1291090 MD5sum: 412c7930a47173f06988387f080505bb SHA1: 073797ba5b9fc7d49ec30b5ea841e25602a80bdb SHA256: c55c7ac020a9264b1c6418e211b7feb7cd39ddd1bb317703abc9b5a8ba73703e SHA512: 6635a6b3e0df247282505b97dd2b7af6a2f78a0e3c315545027171d16c59f68aba6ba2df49d2f81aed73266d5634d358a832d1981f7fe8b3d0800240161c71bf Homepage: https://cran.r-project.org/package=graph Description: Bioc Package 'graph' (graph: A package to handle graph data structures) A package that implements some simple graph handling capabilities. Package: r-bioc-gsva Architecture: amd64 Version: 2.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5690 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-matrixgenerics, r-bioc-s4vectors, r-bioc-s4arrays, r-bioc-hdf5array, r-bioc-sparsearray, r-bioc-delayedarray, r-bioc-iranges, r-bioc-biobase, r-bioc-summarizedexperiment, r-bioc-gseabase, r-cran-matrix, r-bioc-delayedmatrixstats, r-bioc-biocparallel, r-bioc-singlecellexperiment, r-bioc-biocsingular, r-bioc-spatialexperiment, r-bioc-sparsematrixstats, r-cran-cli, r-cran-memuse Suggests: r-cran-runit, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-limma, r-cran-rcolorbrewer, r-bioc-org.hs.eg.db, r-bioc-genefilter, r-bioc-edger, r-bioc-gsvadata, r-bioc-sva, r-bioc-tenxpbmcdata, r-bioc-tenxvisiumdata, r-bioc-scrapper, r-bioc-bluster, r-cran-igraph, r-cran-shiny, r-cran-shinydashboard, r-cran-ggplot2, r-cran-data.table, r-cran-plotly, r-cran-future, r-cran-promises, r-cran-shinybusy, r-cran-shinyjs Filename: pool/dists/noble/main/r-bioc-gsva_2.6.2-1.ca2404.1_amd64.deb Size: 2323168 MD5sum: 46f0bea47359b9336753104f079fe820 SHA1: b11c5c648694675bae77d9a910fccdd5f7c75ee6 SHA256: 3e4cc2a2d34291dc8693138c722e65767978675226431d365ad3d8e785ebb856 SHA512: 94814cf0dcf386d866fa4fc4e270c41fd98f78963dbfd5816752c1285aec715daadc7274a324a429cae561a17e7b5f2fa06a59d6c73f1dbe42683c8d6cf98fdb Homepage: https://cran.r-project.org/package=GSVA Description: Bioc Package 'GSVA' (Gene Set Variation Analysis for Microarray and RNA-Seq Data) Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner. Package: r-bioc-h5mread Architecture: amd64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8482 Depends: libc6 (>= 2.38), libcurl4t64 (>= 7.16.2), libssl3t64 (>= 3.0.0), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-rhdf5, r-bioc-biocgenerics, r-bioc-sparsearray, r-bioc-rhdf5filters, r-bioc-s4vectors, r-bioc-iranges, r-bioc-s4arrays, r-bioc-rhdf5lib Suggests: r-bioc-biocparallel, r-bioc-experimenthub, r-bioc-tenxbraindata, r-bioc-hdf5array, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-h5mread_1.4.0-1.ca2404.1_amd64.deb Size: 4515458 MD5sum: 23bf131176a7240c59d7a6b4a997cbce SHA1: e65cc195dad30c8d375c222528d62b5def0979ad SHA256: 4b88b91972ec9bc4e63a5ab7618ac67913a7bd686b12164a9741cc5d9113d716 SHA512: c2730540c8c7137b36af64be6b8f8a5ce5f05defa1f1c78ad97c16167659d61ec37e30e2efbb9b2240e207656155245aaf0842b6cd6226046c75912860acdf4f Homepage: https://cran.r-project.org/package=h5mread Description: Bioc Package 'h5mread' (A fast HDF5 reader) The main function in the h5mread package is h5mread(), which allows reading arbitrary data from an HDF5 dataset into R, similarly to what the h5read() function from the rhdf5 package does. In the case of h5mread(), the implementation has been optimized to make it as fast and memory-efficient as possible. 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It provides a collection of functions assembled into a pipeline to filter and normalize the data, predict the compartments and visualize the results. It accepts several type of data: tabular `.tsv` files, Cooler `.cool` or `.mcool` files, Juicer `.hic` files or HiC-Pro `.matrix` and `.bed` files. Package: r-bioc-hopach Architecture: amd64 Version: 2.72.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3014 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-bioc-biobase, r-bioc-biocgenerics Filename: pool/dists/noble/main/r-bioc-hopach_2.72.0-1.ca2404.1_amd64.deb Size: 1018350 MD5sum: 65fafd3937bf5202368590133e7ee652 SHA1: 7f12c548b7362558648f64e7e0ea8c343c0f4a94 SHA256: 05299097b61df75e3c6954b1f7e508505c0ea8db99ec910a30197acd5bad3599 SHA512: aa7fc47f38f517ea437dad1214817f5d573dbaa3411995ae297497d3e7f084ba20239aece2c28f57243d5dbd932c736155c40fdab3713ea54170d089349a509e Homepage: https://cran.r-project.org/package=hopach Description: Bioc Package 'hopach' (Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH)) The HOPACH clustering algorithm builds a hierarchical tree of clusters by recursively partitioning a data set, while ordering and possibly collapsing clusters at each level. The algorithm uses the Mean/Median Split Silhouette (MSS) criteria to identify the level of the tree with maximally homogeneous clusters. It also runs the tree down to produce a final ordered list of the elements. The non-parametric bootstrap allows one to estimate the probability that each element belongs to each cluster (fuzzy clustering). Package: r-bioc-ibbig Architecture: amd64 Version: 1.54.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1479 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-biclust, r-cran-xtable, r-cran-ade4 Filename: pool/dists/noble/main/r-bioc-ibbig_1.54.0-1.ca2404.1_amd64.deb Size: 1080548 MD5sum: e89b05ee703565fdd1060d5faa7f268d SHA1: 0dc5e499a170f316eaba6493b7d2910653845a5f SHA256: 0af09f9476caa8b472cab126440961d3855801aa67b2760aa7c42da1075f0e0d SHA512: ecd191c0b292ac2fd9cbee5c50b31069213291b9835f94b86d8b48a89842548b239a5752df10aca10c514d352d1e77dcfd9583be855872f5dfe0aaabbe5ae3a0 Homepage: https://cran.r-project.org/package=iBBiG Description: Bioc Package 'iBBiG' (Iterative Binary Biclustering of Genesets) iBBiG is a bi-clustering algorithm which is optimizes for binary data analysis. We apply it to meta-gene set analysis of large numbers of gene expression datasets. The iterative algorithm extracts groups of phenotypes from multiple studies that are associated with similar gene sets. iBBiG does not require prior knowledge of the number or scale of clusters and allows discovery of clusters with diverse sizes Package: r-bioc-iclusterplus Architecture: amd64 Version: 1.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18263 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-runit, r-bioc-biocgenerics Filename: pool/dists/noble/main/r-bioc-iclusterplus_1.46.0-1.ca2404.1_amd64.deb Size: 16619858 MD5sum: a23dd353dd2fb67c63bcdc07b68d86cd SHA1: 4af70f4bac99c3cd637b7895f4a49dbeebdeb19e SHA256: 27311945a896dbb3a7c3f61746b6bbc39ae4cb0fdadf26db057f4cd4c4c124d5 SHA512: a7cb387523edcba1e62b29191472375804dd63718db8a42941b0b220256fea40715919e49efe155e25cd388eebaa803bb7bf8ebce8b7a3783d493d31effa062c Homepage: https://cran.r-project.org/package=iClusterPlus Description: Bioc Package 'iClusterPlus' (Integrative clustering of multi-type genomic data) Integrative clustering of multiple genomic data using a joint latent variable model. Package: r-bioc-illuminaio Architecture: amd64 Version: 0.54.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-base64 Suggests: r-cran-runit, r-bioc-biocgenerics, r-bioc-illuminadatatestfiles, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-illuminaio_0.54.0-1.ca2404.1_amd64.deb Size: 502578 MD5sum: 552e31bdd2ec860f924a3ffd4c9c5d1d SHA1: aa992b3c901caa3ffed41cb442822afdc8c425c5 SHA256: 6959e6dbb3d281cfd3a7304583d28d498e1ecc05f491f5e9e49dee067fa51bc0 SHA512: 6c506a31d4052d26ac008799f5ea75e0d0f1cb7e7f89240689d6cf5bcdabdbaea66bb2dc1744baa73d1ea13d7bfe8fb577258d63695195b22b89dd6ae5c57c8c Homepage: https://cran.r-project.org/package=illuminaio Description: Bioc Package 'illuminaio' (Parsing Illumina Microarray Output Files) Tools for parsing Illumina's microarray output files, including IDAT. 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The package includes pre-processing of sequences, unifying gene nomenclature usage, encoding sequences, and combining models. This package will serve as the basis of future immune receptor sequence functions/packages/models compatible with the scRepertoire ecosystem. 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Implements an algebra of range operations, including efficient algorithms for finding overlaps and nearest neighbors. Defines efficient list-like classes for storing, transforming and aggregating large grouped data, i.e., collections of atomic vectors and DataFrames. Package: r-bioc-lea Architecture: amd64 Version: 3.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1340 Depends: libc6 (>= 2.38), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-bioc-lea_3.24.0-1.ca2404.1_amd64.deb Size: 950408 MD5sum: cc98018778a100553b18c22d69cb0ca5 SHA1: 90b3b7c36d92ad2918284d99a395ae77a91ce64a SHA256: ee275692afb89a69c70f130a9e4d169c38306a726a136f7d14ecceafa3ab2549 SHA512: 2db980ed8e91cc134ef92a1571f490c76995782726c680935c46c2a899f41d89bba37f0e553789599144043ca99680f06038dda7ac3552a999c7e508f4307f5a Homepage: https://cran.r-project.org/package=LEA Description: Bioc Package 'LEA' (LEA: an R package for Landscape and Ecological AssociationStudies) LEA is an R package dedicated to population genomics, landscape genomics and genotype-environment association tests. LEA can run analyses of population structure and genome-wide tests for local adaptation, and also performs imputation of missing genotypes. The package includes statistical methods for estimating ancestry coefficients from large genotypic matrices and for evaluating the number of ancestral populations (snmf). It performs statistical tests using latent factor mixed models for identifying genetic polymorphisms that exhibit association with environmental gradients or phenotypic traits (lfmm2). In addition, LEA computes values of genetic offset statistics based on new or predicted environments (genetic.gap, genetic.offset). LEA is mainly based on optimized programs that can scale with the dimensions of large data sets. Package: r-bioc-lfa Architecture: amd64 Version: 2.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 504 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0, r-cran-corpcor, r-cran-rspectra, r-cran-bedmatrix, r-cran-genio Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-lfa_2.12.0-1.ca2404.1_amd64.deb Size: 423502 MD5sum: 16dae47c18bff6e87443c2b316b9bacd SHA1: edbb0897845d5314cb1bcaebf1752038f77d71e8 SHA256: 0085fdcfba84f8c3ffbc4e94962b9152e0d5c36b64b9259083e0a7214213e494 SHA512: 1025edbb639cdac7e14cae4dc53ed13d72f188f0daa62aae7cf220467355b66a4ee6871363e9cf5acb9b07612f77bfee3ab52c76b61868b83920b62c9919783f Homepage: https://cran.r-project.org/package=lfa Description: Bioc Package 'lfa' (Logistic Factor Analysis for Categorical Data) Logistic Factor Analysis is a method for a PCA analogue on Binomial data via estimation of latent structure in the natural parameter. The main method estimates genetic population structure from genotype data. There are also methods for estimating individual-specific allele frequencies using the population structure. Lastly, a structured Hardy-Weinberg equilibrium (HWE) test is developed, which quantifies the goodness of fit of the genotype data to the estimated population structure, via the estimated individual-specific allele frequencies (all of which generalizes traditional HWE tests). Package: r-bioc-limma Architecture: amd64 Version: 3.68.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4017 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-statmod Suggests: r-cran-biasedurn, r-cran-ellipse, r-cran-gplots, r-cran-knitr, r-cran-locfit, r-cran-mass, r-bioc-affy, r-bioc-annotationdbi, r-bioc-biobase, r-bioc-biocstyle, r-bioc-go.db, r-bioc-illuminaio, r-bioc-org.hs.eg.db, r-bioc-vsn Filename: pool/dists/noble/main/r-bioc-limma_3.68.3-1.ca2404.1_amd64.deb Size: 3070504 MD5sum: 6d9fbaa543405f5dd5869d5717ee9fca SHA1: 4a2384e2ea672385e19eaef2da00d3b5f3cb9571 SHA256: 335eb8cf5b9f38b6e530714ed8a8ddc13b5cb9a6a24963fc3ef75fa1e9b70e26 SHA512: b7a2f4191b71995ebd415b0a18db0f6d6b2f3efa9d00d35e063d7ede6d516428f7eda1c0e0ccb6ce3703137d8f4c3637e4108eda2c7f2906e4a877276be6dd72 Homepage: https://cran.r-project.org/package=limma Description: Bioc Package 'limma' (Linear Models for Microarray and Omics Data) Data analysis, linear models and differential expression for omics data. Package: r-bioc-lpsymphony Architecture: amd64 Version: 1.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4086 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-lpsymphony_1.40.0-1.ca2404.1_amd64.deb Size: 1829384 MD5sum: c74dfbffac6065a9d387fac2dcd2a228 SHA1: 50972d34a1afe7d0438f36f377592b7abcc3e2e5 SHA256: e8386fc0879140d5095c0b614ec1d16ba196925429142f6541330c3e2e61c984 SHA512: e0272987b59c0966bcba88b4539587277d74e7ccaa62d7c217f8149df5c350bea252146db06b19238240143ef84024fe97e6a659c2bdab8003afd447458302aa Homepage: https://cran.r-project.org/package=lpsymphony Description: Bioc Package 'lpsymphony' (Symphony integer linear programming solver in R) This package was derived from Rsymphony_0.1-17 from CRAN. These packages provide an R interface to SYMPHONY, an open-source linear programming solver written in C++. The main difference between this package and Rsymphony is that it includes the solver source code (SYMPHONY version 5.6), while Rsymphony expects to find header and library files on the users' system. Thus the intention of lpsymphony is to provide an easy to install interface to SYMPHONY. For Windows, precompiled DLLs are included in this package. Package: r-bioc-maftools Architecture: amd64 Version: 2.28.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18828 Depends: libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), liblzma5 (>= 5.1.1alpha+20120614), zlib1g (>= 1:1.1.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-rcolorbrewer, r-bioc-rhtslib, r-cran-survival, r-bioc-dnacopy, r-cran-pheatmap Suggests: r-cran-berryfunctions, r-bioc-biostrings, r-bioc-bsgenome, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-genomicranges, r-bioc-iranges, r-cran-knitr, r-cran-mclust, r-bioc-multiassayexperiment, r-cran-nmf, r-cran-r.utils, r-bioc-raggedexperiment, r-cran-rmarkdown, r-bioc-s4vectors Filename: pool/dists/noble/main/r-bioc-maftools_2.28.0-1.ca2404.1_amd64.deb Size: 12063080 MD5sum: 388e2987203adcc8e5f83a6dbf0d6fbe SHA1: cb4f8900d4a11a377859e208a02f0b3dbc8839d8 SHA256: 66b7e210363ff24dd6ad0fbd56881497c86c51e2f7466851c56193053033b2cd SHA512: 1f130284f7d8b4757d3e5eda60f97c8610412c2988689fc87f37a2ad6fb4f35850b64e66c4a27542f70ae1ecba8fa4cae2b41a84caf2165471d21b139e82e4a1 Homepage: https://cran.r-project.org/package=maftools Description: Bioc Package 'maftools' (Summarize, Analyze and Visualize MAF Files) Analyze and visualize Mutation Annotation Format (MAF) files from large scale sequencing studies. This package provides various functions to perform most commonly used analyses in cancer genomics and to create feature rich customizable visualzations with minimal effort. Package: r-bioc-massspecwavelet Architecture: amd64 Version: 1.78.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3858 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-signal, r-cran-waveslim, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-runit, r-cran-bench Filename: pool/dists/noble/main/r-bioc-massspecwavelet_1.78.0-1.ca2404.1_amd64.deb Size: 2033904 MD5sum: 693e3504eee4f6d36b6a9d77227d1ad8 SHA1: 50c40a9834acb4525e6ded4c4d34d9a0c6bc7eaf SHA256: 00d78b70c644002d6a75cc2474df54e570c778810f4fb1af9112c32a7db15af3 SHA512: 36f6a08ba05fc9194bb13820e3f226dcf8cb186b6dcb02fd0239bfb33a1d5e686f87273286e4137821b7b2c1e5bb7c1a8dfdf9512a0eee8a737bf59fd276bfe8 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. 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Package: r-bioc-metapod Architecture: amd64 Version: 1.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1352 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-bioc-biocstyle, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-metapod_1.20.0-1.ca2404.1_amd64.deb Size: 485282 MD5sum: 00aa213141a78367679f75bb2805f63e SHA1: 93ae39863e984bceb969f0371ac10dea9725419f SHA256: 7fea9f5a006b5ee5c9d26479ad72540f8bbe6f6046871b0cae99372d2691a17a SHA512: 36976b17ea6c47372c21e04cb3b73edc81adf805646d29b93a385b26bd7d61dd91e24d04e02bade895f5efd7a99f6113d33020d8d499489cc6412f6c5fd32b24 Homepage: https://cran.r-project.org/package=metapod Description: Bioc Package 'metapod' (Meta-Analyses on P-Values of Differential Analyses) Implements a variety of methods for combining p-values in differential analyses of genome-scale datasets. Functions can combine p-values across different tests in the same analysis (e.g., genomic windows in ChIP-seq, exons in RNA-seq) or for corresponding tests across separate analyses (e.g., replicated comparisons, effect of different treatment conditions). Support is provided for handling log-transformed input p-values, missing values and weighting where appropriate. Package: r-bioc-methylkit Architecture: amd64 Version: 1.38.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5270 Depends: libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), libgcc-s1 (>= 3.0), liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 13.1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-bioc-iranges, r-cran-data.table, r-bioc-s4vectors, r-bioc-seqinfo, r-cran-kernsmooth, r-bioc-qvalue, r-cran-emdbook, r-bioc-rsamtools, r-cran-gtools, r-bioc-fastseg, r-bioc-rtracklayer, r-cran-mclust, r-cran-mgcv, r-cran-rcpp, r-cran-r.utils, r-bioc-limma, r-bioc-rhtslib Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-genomation, r-cran-biocmanager Filename: pool/dists/noble/main/r-bioc-methylkit_1.38.0-1.ca2404.1_amd64.deb Size: 2505984 MD5sum: 54fc5890724631f5381cfac820fab3ab SHA1: 7ebf1c17ac7ce2314e5b7e3e150da961f91b2431 SHA256: a521fd9ee408786730b8baaf4f89ed48219002b694265dd28b66865fdda2e899 SHA512: 228735dd9fdad6adbb6c528ac80e3225c68cc3f2fce37c061c97302e00e45e78690bed81ec0abfb56ffb1763a8a6794cdfd52f1a4791aebadc9ea908bfb95232 Homepage: https://cran.r-project.org/package=methylKit Description: Bioc Package 'methylKit' (DNA methylation analysis from high-throughput bisulfitesequencing results) methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods and whole genome bisulfite sequencing. It also has functions to analyze base-pair resolution 5hmC data from experimental protocols such as oxBS-Seq and TAB-Seq. Methylation calling can be performed directly from Bismark aligned BAM files. Package: r-bioc-mia Architecture: amd64 Version: 1.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6197 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-multiassayexperiment, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-bioc-treesummarizedexperiment, r-cran-ape, r-bioc-biocgenerics, r-bioc-biocparallel, r-bioc-biostrings, r-bioc-bluster, r-bioc-decipher, r-bioc-decontam, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-bioc-dirichletmultinomial, r-cran-dplyr, r-bioc-iranges, r-cran-mass, r-bioc-matrixgenerics, r-cran-ecodive, r-cran-rlang, r-bioc-s4vectors, r-bioc-scater, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-vegan, r-cran-rcpp Suggests: r-cran-ade4, r-bioc-biocstyle, r-bioc-biomformat, r-bioc-dada2, r-cran-knitr, r-cran-mediation, r-bioc-miatime, r-bioc-miaviz, r-bioc-microbiomedatasets, r-cran-nmf, r-cran-patchwork, r-bioc-philr, r-bioc-phyloseq, r-cran-reldist, r-bioc-rhdf5, r-cran-rmarkdown, r-bioc-scuttle, r-cran-testthat, r-cran-topicdoc, r-cran-topicmodels, r-cran-yaml Filename: pool/dists/noble/main/r-bioc-mia_1.20.0-1.ca2404.1_amd64.deb Size: 4692078 MD5sum: 2e4f81a40f85f295efd5e3e918d075d8 SHA1: e4e240a61acd57cffc1e93654235ae1cf37ad4f7 SHA256: 7f509dbaa25462936031e9b912d9fb3350fc2cfffbf28019f03a51b4dc67be22 SHA512: 7a0d744a96ee5ba1238b340026b420e654bb1f43c1df68e32d7e5c749d0cd992b71c6cc3e0485885c608edb844c52ab042390a7c725ffb47981a2de6a38b1382 Homepage: https://cran.r-project.org/package=mia Description: Bioc Package 'mia' (Microbiome analysis) mia implements tools for microbiome analysis based on the SummarizedExperiment, SingleCellExperiment and TreeSummarizedExperiment infrastructure. Data wrangling and analysis in the context of taxonomic data is the main scope. Additional functions for common task are implemented such as community indices calculation and summarization. Package: r-bioc-minet Architecture: amd64 Version: 3.70.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-infotheo Filename: pool/dists/noble/main/r-bioc-minet_3.70.0-1.ca2404.1_amd64.deb Size: 96512 MD5sum: bd6fce64b9fe19461ee750d55492f683 SHA1: 4bd7a62903297fd6294fe886e3c743fb7562cd95 SHA256: f00f05bbacc7d746d789da3bcab2511392ad1f178a0a6d8feea5b3d8c7339ad9 SHA512: 63e2cf0ca394566c66ed86376e7d73a833b8bb2ad927c99b0f5d427a4c4f68c444aae285f3321fec23ffd7d9d89f9fc81c271df10b1647b2f8309e02eba53c94 Homepage: https://cran.r-project.org/package=minet Description: Bioc Package 'minet' (Mutual Information NETworks) This package implements various algorithms for inferring mutual information networks from data. Package: r-bioc-mofa2 Architecture: amd64 Version: 1.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8443 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-rhdf5, r-cran-dplyr, r-cran-tidyr, r-cran-reshape2, r-cran-pheatmap, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-cowplot, r-cran-ggrepel, r-cran-reticulate, r-bioc-hdf5array, r-cran-magrittr, r-cran-forcats, r-cran-corrplot, r-bioc-delayedarray, r-cran-rtsne, r-cran-uwot, r-bioc-basilisk, r-cran-stringi Suggests: r-cran-knitr, r-cran-testthat, r-cran-seurat, r-cran-seuratobject, r-cran-ggpubr, r-cran-foreach, r-cran-psych, r-bioc-multiassayexperiment, r-bioc-summarizedexperiment, r-bioc-singlecellexperiment, r-cran-ggrastr, r-cran-mvtnorm, r-cran-ggally, r-cran-rmarkdown, r-cran-data.table, r-cran-tidyverse, r-bioc-biocstyle, r-cran-matrix, r-cran-markdown Filename: pool/dists/noble/main/r-bioc-mofa2_1.22.0-1.ca2404.1_amd64.deb Size: 4631666 MD5sum: 3a448515fb215cece4ccc23c8a982a1a SHA1: c92dc780b2da58a1e5362007e627777042cd7825 SHA256: 400b26c71d5e633d37d227c17efe4a4d9b16b6892a7ffadb85dc8a8ea3bc84e8 SHA512: a51ac2c2452584dca1acb5b4c9ff27f4849c29d6c92995a94bfe1ee30fe4c5ac4b0162c11d895c402f9ccf92fcc89e53fce3063ba3a6d6c21d13c4eda46d9acc Homepage: https://cran.r-project.org/package=MOFA2 Description: Bioc Package 'MOFA2' (Multi-Omics Factor Analysis v2) The MOFA2 package contains a collection of tools for training and analysing multi-omic factor analysis (MOFA). MOFA is a probabilistic factor model that aims to identify principal axes of variation from data sets that can comprise multiple omic layers and/or groups of samples. Additional time or space information on the samples can be incorporated using the MEFISTO framework, which is part of MOFA2. Downstream analysis functions to inspect molecular features underlying each factor, visualisation, imputation etc are available. Package: r-bioc-monocle Architecture: amd64 Version: 2.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1752 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-bioc-biobase, r-cran-ggplot2, r-cran-vgam, r-cran-ddrtree, r-cran-igraph, r-bioc-biocgenerics, r-bioc-hsmmsinglecell, r-cran-plyr, r-cran-cluster, r-cran-combinat, r-cran-fastica, r-cran-irlba, r-cran-matrixstats, r-cran-rtsne, r-cran-mass, r-cran-reshape2, r-cran-leidenbase, r-bioc-limma, r-cran-tibble, r-cran-dplyr, r-cran-pheatmap, r-cran-stringr, r-cran-proxy, r-cran-slam, r-cran-viridis, r-bioc-biocviews, r-cran-rann, r-cran-rcpp Suggests: r-bioc-destiny, r-cran-hmisc, r-cran-knitr, r-cran-seurat, r-bioc-scater, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-monocle_2.40.0-1.ca2404.1_amd64.deb Size: 1512644 MD5sum: 30c19de58306db161c9d91d3d282271a SHA1: 4196584683fbef2b47862a71fefbf0133c4d69a3 SHA256: 033794c714eba88f511a21a6fbf2ea9d764f4b86419e47912e9b54a566bb2d60 SHA512: a282a28e2d325bf4667bc258e6bec286d3e6558b04f7626a37ffec9abd31cb83e33cbe1187b14df4557db6329dc26d65739ff907398a488354f703c59ee4ccea Homepage: https://cran.r-project.org/package=monocle Description: Bioc Package 'monocle' (Clustering, differential expression, and trajectory analysis forsingle- cell RNA-Seq) Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well. Package: r-bioc-motifmatchr Architecture: amd64 Version: 1.34.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-bioc-tfbstools, r-bioc-biostrings, r-bioc-bsgenome, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-bioc-genomicranges, r-bioc-iranges, r-bioc-rsamtools, r-bioc-seqinfo, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-bsgenome.hsapiens.ucsc.hg19 Filename: pool/dists/noble/main/r-bioc-motifmatchr_1.34.0-1.ca2404.1_amd64.deb Size: 185676 MD5sum: 41c504537e944577457a5fa60af2a75c SHA1: e1e3d0cc3e0cdf5fc3ae41a2f3ead3a29a54950f SHA256: 51ccde3322cc775500675c1490af240ad4fb83606e5f83c6a0bf396a926833e3 SHA512: 44d9811fcbb528cd58760b2b6e14fdb084f514d77179221fa52eb0c9576e3da1f7ac2a27a57436018dab8637a6ac500b4d207a6d8e7b411b002c3157721f6499 Homepage: https://cran.r-project.org/package=motifmatchr Description: Bioc Package 'motifmatchr' (Fast Motif Matching in R) Quickly find motif matches for many motifs and many sequences. Wraps C++ code from the MOODS motif calling library, which was developed by Pasi Rastas, Janne Korhonen, and Petri Martinmäki. Package: r-bioc-msa Architecture: amd64 Version: 1.44.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3809 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biostrings, r-cran-rcpp, r-bioc-biocgenerics, r-bioc-iranges, r-bioc-s4vectors Suggests: r-bioc-biobase, r-cran-knitr, r-cran-seqinr, r-cran-ape, r-cran-phangorn, r-bioc-pwalign Filename: pool/dists/noble/main/r-bioc-msa_1.44.0-1.ca2404.1_amd64.deb Size: 1672866 MD5sum: 25f6776218f4738f590d6a82e6854173 SHA1: 49e5204afc8fdcb571342544f20706a518284522 SHA256: 1d443ccb9bd037fa1b4484a1cab89577ed4a604cf85874527eb457438204dda8 SHA512: f0edc432d431674682e6d7e1b0ed09dc49d260844a8b7dc48fde0d553009da404f9c3af25847674a29b10ce981b8f0a65c5059ce0632dda432fd1d64bfcdb5e9 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. 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Provides HDF5 storage based methods and functions for manipulation of flow cytometry data. 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Package: r-bioc-pcamethods Architecture: amd64 Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1753 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-biocgenerics, r-cran-rcpp, r-cran-mass Suggests: r-cran-matrixstats, r-cran-lattice, r-cran-ggplot2 Filename: pool/dists/noble/main/r-bioc-pcamethods_2.4.0-1.ca2404.1_amd64.deb Size: 1387560 MD5sum: 64af6bddab7b734abdc2f2fafc59a343 SHA1: 5dbade0ccc00a633d36b36b6c5bd76d7b3ba7611 SHA256: f569cdc8447b8cbd4ca4d766da001b4339d438ac7e6a2a38d35691c853433be9 SHA512: 2efc7e8c544bcf3eccff562b994ade6909a1c56a726a363942744d21a8cdaa31e66f37f7f6d6f9e01c26eb11eb46ca1e1c39d3cb69c6098a3e8e9f202c412040 Homepage: https://cran.r-project.org/package=pcaMethods Description: Bioc Package 'pcaMethods' (A collection of PCA methods) Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany. Package: r-bioc-pcatools Architecture: amd64 Version: 2.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8672 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-lattice, r-cran-cowplot, r-cran-reshape2, r-cran-matrix, r-bioc-delayedmatrixstats, r-bioc-delayedarray, r-bioc-beachmat, r-bioc-biocsingular, r-bioc-biocparallel, r-cran-rcpp, r-cran-dqrng, r-bioc-assorthead, r-cran-bh Suggests: r-cran-testthat, r-bioc-scran, r-bioc-biocgenerics, r-cran-knitr, r-bioc-biobase, r-bioc-geoquery, r-bioc-hgu133a.db, r-cran-ggplotify, r-cran-rmtstat, r-cran-ggforce, r-cran-concaveman, r-bioc-deseq2, r-bioc-airway, r-bioc-org.hs.eg.db, r-cran-magrittr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-pcatools_2.24.0-1.ca2404.1_amd64.deb Size: 6126386 MD5sum: 3f8366cc2ce081764b8e17e98f15fe67 SHA1: 5c011bacaccb115793d638a85ed431382faa0b4d SHA256: 371a747446b757097d16a5af2a7811ce39e8849b80a657d91ee8e3405c2b2db4 SHA512: e66c1e0d108b7dad2271fd68d3a30c24b785628f9f4ce3f69be2f258070669c4d36c9068a93ab09876c4c8c8405372b7b2f0462ff960bfd69f903525a7a693a1 Homepage: https://cran.r-project.org/package=PCAtools Description: Bioc Package 'PCAtools' (PCAtools: Everything Principal Components Analysis) Principal Component Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. It extracts the fundamental structure of the data without the need to build any model to represent it. This 'summary' of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. the 'principal components'), while at the same time being capable of easy interpretation on the original data. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular - users can also identify optimal number of principal components via different metrics, such as elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data. Package: r-bioc-plotgardener Architecture: amd64 Version: 1.18.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4202 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-data.table, r-cran-dplyr, r-bioc-genomeinfodb, r-bioc-genomicranges, r-cran-glue, r-cran-ggplotify, r-bioc-iranges, r-bioc-plyranges, r-cran-purrr, r-cran-rcpp, r-cran-rcolorbrewer, r-bioc-rhdf5, r-cran-rlang, r-cran-strawr, r-cran-withr Suggests: r-bioc-annotationdbi, r-bioc-annotationhub, r-bioc-bsgenome, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-complexheatmap, r-bioc-genomicfeatures, r-cran-ggplot2, r-bioc-interactionset, r-cran-knitr, r-bioc-org.hs.eg.db, r-bioc-rtracklayer, r-bioc-plotgardenerdata, r-cran-pdftools, r-cran-png, r-cran-rmarkdown, r-cran-scales, r-cran-showtext, r-cran-testthat, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene Filename: pool/dists/noble/main/r-bioc-plotgardener_1.18.0-1.ca2404.1_amd64.deb Size: 3574784 MD5sum: a90fec73f14bfdddfd071af996457252 SHA1: e51ed308f740674f7ad2c4f9cb29c201c362b3cb SHA256: a47fbfdc7d06958024fc12ea1738301cf6c7a6d0dd1a030c3ac4e3c9209304ef SHA512: cb4215a4da4e3eb32ce2aad93c52528fed1fb8f2713d83acb06aecf2ba1744362b688cd4b12d596337395ea056a119f3da3c1a9f03c1c3214fe562cf94fd9b07 Homepage: https://cran.r-project.org/package=plotgardener Description: Bioc Package 'plotgardener' (Coordinate-Based Genomic Visualization Package for R) Coordinate-based genomic visualization package for R. It grants users the ability to programmatically produce complex, multi-paneled figures. Tailored for genomics, plotgardener allows users to visualize large complex genomic datasets and provides exquisite control over how plots are placed and arranged on a page. Package: r-bioc-preprocesscore Architecture: amd64 Version: 1.74.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: libc6 (>= 2.38), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-bioc-preprocesscore_1.74.0-1.ca2404.1_amd64.deb Size: 151644 MD5sum: 2d077ea4a4ec31002bc0abbef11a6263 SHA1: 24b0d974c79c8f84aa1007781c1ee9f80186479a SHA256: 1fdc0d571c5f914fb489044ca516c25660bb73ad9d0b25590df6e28415ae507e SHA512: 5034b0a48d6e54cb94cb24c1e5862e7f267eeebd293b2bb160816432fa47a72b7b0a12bc86dd696d2b9d5487a36cc0fd93d2fe460ae91b5d8a1b061298142cb9 Homepage: https://cran.r-project.org/package=preprocessCore Description: Bioc Package 'preprocessCore' (A collection of pre-processing functions) A library of core preprocessing routines. Package: r-bioc-pwalign Architecture: amd64 Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1092 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-biostrings, r-bioc-xvector Suggests: r-cran-runit Filename: pool/dists/noble/main/r-bioc-pwalign_1.8.0-1.ca2404.1_amd64.deb Size: 750640 MD5sum: f96b5ac91d9479def87510aa32d57cbf SHA1: 1a998ebe2f1cf604c4978fdc1a5ae14c6b3dea24 SHA256: 6efde16cd4f079903d227ffe2dda778fabc263dac1df7195d8b268261080b849 SHA512: 1824294b669695a600b01bded4605d7e6407d43dce52d67e7dd220c1dcf9ad391d23a37fb312a6bc6534ee43b3bd793b9bcc6de136dbf25a2661db3d1aed3426 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: amd64 Version: 2.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4804 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-bioc-annotate, r-bioc-graph, r-bioc-biobase, r-bioc-s4vectors, r-bioc-biocparallel, r-bioc-annotationdbi, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomicranges, r-bioc-genomicfeatures, r-cran-mvtnorm, r-cran-qtl, r-bioc-rgraphviz Suggests: r-cran-runit, r-bioc-biocgenerics, r-bioc-biocstyle, r-bioc-genefilter, r-bioc-org.eck12.eg.db, r-cran-rlecuyer, r-cran-snow, r-bioc-category, r-bioc-gostats Filename: pool/dists/noble/main/r-bioc-qpgraph_2.46.0-1.ca2404.1_amd64.deb Size: 3915354 MD5sum: 08736719cd2b1854a6fbfe6c95c464c8 SHA1: 46b62d05aae4443433f9ac77105b9eec79182139 SHA256: e60d04b59e2558d834e8a4f7c144cdf36ad61e5844ea3f05e073e79911fede83 SHA512: d13e29b8cad6626d5da25b41f03b3e874b8ed00fb9a92357e0ae953eef4dd7233e6bd8c2f96d8a7fbda0ef86875118fc8037d97586de62981a60bab235e49e25 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: amd64 Version: 1.52.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5410 Depends: libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), libgcc-s1 (>= 3.0), liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 13.1), zlib1g (>= 1:1.2.3.3), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-bioc-rbowtie, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-biobase, r-bioc-biostrings, r-bioc-bsgenome, r-bioc-rsamtools, r-bioc-genomicfeatures, r-bioc-txdbmaker, r-bioc-shortread, r-bioc-biocparallel, r-bioc-seqinfo, r-bioc-rtracklayer, r-bioc-genomicfiles, r-bioc-annotationdbi, r-bioc-rhtslib Suggests: r-bioc-gviz, r-bioc-biocstyle, r-bioc-genomeinfodbdata, r-bioc-genomicalignments, r-bioc-rhisat2, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-quasr_1.52.0-1.ca2404.1_amd64.deb Size: 3321804 MD5sum: b17d893bdb999f0cb412d33fedbe134c SHA1: 49a219cc9f90793aa9dc7216276153e785e8bc9a SHA256: 0c15aeda2376391569077d5ddcafe684b243b393ddb226b8e2a764df0ca5bb59 SHA512: 78df5e35f09a2517a2214f93d26c974b486604740caf72415e4a3c6cfe24fa36ac8c09a0806c4faa314faf9f7edffc75e2729ea60199bc8531b14a9a0a9aadc0 Homepage: https://cran.r-project.org/package=QuasR Description: Bioc Package 'QuasR' (Quantify and Annotate Short Reads in R) This package provides a framework for the quantification and analysis of Short Reads. It covers a complete workflow starting from raw sequence reads, over creation of alignments and quality control plots, to the quantification of genomic regions of interest. Read alignments are either generated through Rbowtie (data from DNA/ChIP/ATAC/Bis-seq experiments) or Rhisat2 (data from RNA-seq experiments that require spliced alignments), or can be provided in the form of bam files. Package: r-bioc-rbgl Architecture: amd64 Version: 1.88.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6357 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-graph, r-cran-bh Suggests: r-bioc-rgraphviz, r-cran-xml, r-cran-runit, r-bioc-biocgenerics, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/noble/main/r-bioc-rbgl_1.88.0-1.ca2404.1_amd64.deb Size: 4349000 MD5sum: 9ea5612468e45b024898bf4ae1d57755 SHA1: b7a0908e09809c16b4a75495e3eae9cedb2d2b7c SHA256: c451e8d2d3b4969c15af4d1e09fcccd7c2dfe57176a7a8c202b06e28abcf9456 SHA512: 60061a841f210d1be4826107beb36735ef042f51b5eb64c30d2648050835524a7c9ddf086079da7bc3dcc2761a84e2bb7f3fc331e6dc27d15538743fdc35c136 Homepage: https://cran.r-project.org/package=RBGL Description: Bioc Package 'RBGL' (An interface to the BOOST graph library) A fairly extensive and comprehensive interface to the graph algorithms contained in the BOOST library. Package: r-bioc-rbowtie2 Architecture: amd64 Version: 2.18.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6606 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.4), libstdc++6 (>= 13.1), zlib1g (>= 1:1.2.6), r-base-core (>= 4.6.0), r-api-4.0, r-cran-magrittr, r-bioc-rsamtools Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-rbowtie2_2.18.0-1.ca2404.1_amd64.deb Size: 1980348 MD5sum: 191269d089dbad8bfdb5d12a1480b545 SHA1: 7bffb633d3ce26f7c11606c3e9ad5132f020165a SHA256: 21e27e91b54c39f23c1eec021117995084dbf638356935636683310f28926359 SHA512: ad2f70147898f73908b6fe44e119041c9c70cde154bc73d72fd59b68597ecc1ba3d57e67a30c2467f902128768b331af0baccf1fb833da4d4a795c580e321713 Homepage: https://cran.r-project.org/package=Rbowtie2 Description: Bioc Package 'Rbowtie2' (An R Wrapper for Bowtie2 and AdapterRemoval) This package provides an R wrapper of the popular bowtie2 sequencing reads aligner and AdapterRemoval, a convenient tool for rapid adapter trimming, identification, and read merging. The package contains wrapper functions that allow for genome indexing and alignment to those indexes. The package also allows for the creation of .bam files via Rsamtools. 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The package is used by the QuasR bioconductor package. We recommend to use the QuasR package instead of using Rbowtie directly. Package: r-bioc-rdisop Architecture: amd64 Version: 1.72.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 545 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-rdisop_1.72.0-1.ca2404.1_amd64.deb Size: 241796 MD5sum: 037bb315c63cfd98e7c283939d21c7f5 SHA1: 5b82cb4c1348a802709ffb670b56cf1806f88af7 SHA256: f7339e085c364fedba942ca082f0699357a6cd2e6b00c4a72e35387a0213151d SHA512: a2cc49d2cc146a6f49b33259b045c85ca33a2509bbcb7f17cc7cd7737efcfa0ccfa1a18bf67f7c4f3469ffde801e987a226a44e2a2073e665427716da399e5fd 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. 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Rfastp is an R wrapper of fastp developed in c++. fastp performs quality control for fastq files. including low quality bases trimming, polyX trimming, adapter auto-detection and trimming, paired-end reads merging, UMI sequence/id handling. Rfastp can concatenate multiple files into one file (like shell command cat) and accept multiple files as input. 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Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, Factor, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages). Package: r-bioc-sc3 Architecture: amd64 Version: 1.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5983 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-e1071, r-cran-foreach, r-cran-doparallel, r-cran-dorng, r-cran-shiny, r-cran-ggplot2, r-cran-pheatmap, r-cran-rocr, r-cran-robustbase, r-cran-rrcov, r-cran-cluster, r-cran-writexls, r-cran-rcpp, r-bioc-summarizedexperiment, r-bioc-singlecellexperiment, r-bioc-biocgenerics, r-bioc-s4vectors, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mclust, r-bioc-scater, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-sc3_1.40.0-1.ca2404.1_amd64.deb Size: 4759250 MD5sum: b93cde725caf6ff141d31fd7301272d3 SHA1: bfdb2b241e12fb64b175d83b11ff9e7837f2e284 SHA256: 62e9d036abdb8e8a2f3a4faa1ee27d1efe5dbe3891dc2056b8ac269daa784a9f SHA512: 1c0e457a5c0d4eb69811637ae30eb3f41ca34cf10458475aadfe71ee0b15d5bbe3188eeb10ae9427069350e217c0dc177a88be7036d1812bd7c2da88a9a24386 Homepage: https://cran.r-project.org/package=SC3 Description: Bioc Package 'SC3' (Single-Cell Consensus Clustering) A tool for unsupervised clustering and analysis of single cell RNA-Seq data. Package: r-bioc-scde Architecture: amd64 Version: 2.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2413 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-flexmix, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-mgcv, r-cran-rook, r-cran-rjson, r-cran-mass, r-cran-cairo, r-cran-rcolorbrewer, r-bioc-edger, r-cran-quantreg, r-cran-nnet, r-cran-rmtstat, r-cran-extremes, r-bioc-pcamethods, r-bioc-biocparallel Suggests: r-cran-knitr, r-cran-cba, r-cran-fastcluster, r-cran-wgcna, r-bioc-go.db, r-bioc-org.hs.eg.db, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-scde_2.40.0-1.ca2404.1_amd64.deb Size: 2214028 MD5sum: ca70f7b2eef6c258fdce19ddeed6e19e SHA1: 5694f496602119876b6c313d528f6f6841e5c7d4 SHA256: aefd463f1e794ddaeb725bcd88154f8428777058ea4efb0069cd06edfe92dab4 SHA512: 689f35b136703849facc0dc8cf07692d1398f2a50a304c8f512c18a774528a146c1ef1b82782284826b5024a6ea4ad733011719f4ab043ecf569ee573e539a55 Homepage: https://cran.r-project.org/package=scde Description: Bioc Package 'scde' (Single Cell Differential Expression) The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify and characterize putative cell subpopulations based on transcriptional signatures. The overall approach to the differential expression analysis is detailed in the following publication: "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi: 10.1038/nmeth.2967). The overall approach to subpopulation identification and characterization is detailed in the following pre-print: "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734). Package: r-bioc-scran Architecture: amd64 Version: 1.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2290 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-bioc-scuttle, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-bioc-biocgenerics, r-bioc-biocparallel, r-cran-rcpp, r-cran-matrix, r-bioc-edger, r-bioc-limma, r-cran-igraph, r-cran-statmod, r-bioc-matrixgenerics, r-bioc-s4arrays, r-bioc-delayedarray, r-bioc-biocsingular, r-bioc-bluster, r-bioc-metapod, r-cran-dqrng, r-bioc-beachmat, r-cran-bh Suggests: r-cran-testthat, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-delayedmatrixstats, r-bioc-hdf5array, r-bioc-scrnaseq, r-cran-dynamictreecut, r-bioc-residualmatrix, r-bioc-scaledmatrix, r-bioc-deseq2, r-cran-pheatmap, r-bioc-scater, r-bioc-scrapper Filename: pool/dists/noble/main/r-bioc-scran_1.40.0-1.ca2404.1_amd64.deb Size: 1275454 MD5sum: d488695f99d00aeba513e194fb148b0e SHA1: 991a879fe308e04f627d22738e6dc6ebba7ccbc9 SHA256: bf81b355a1bae1b0f5c18df0f608de2735f7d5e49346df82af42ada2f1c8590c SHA512: 0e1f179166e2b4e008ee2e969f8174fd633ebf8d6a032b5ab171f1cb0bf6b464ad920deb43d0f02ab83a55e32f8ccc7f9358d8125c9c94711e9368e5ccb65d5b Homepage: https://cran.r-project.org/package=scran Description: Bioc Package 'scran' (Methods for Single-Cell RNA-Seq Data Analysis) Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and other common tasks in routine single-cell analysis workflows. Package: r-bioc-scrapper Architecture: amd64 Version: 1.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6147 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.4), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-bioc-beachmat, r-bioc-s4vectors, r-bioc-sparsearray, r-bioc-delayedarray, r-bioc-biocneighbors, r-bioc-assorthead, r-bioc-rigraphlib Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-matrix, r-bioc-iranges, r-bioc-summarizedexperiment, r-bioc-singlecellexperiment, r-bioc-scrnaseq, r-bioc-org.mm.eg.db, r-bioc-scater, r-cran-igraph Filename: pool/dists/noble/main/r-bioc-scrapper_1.6.3-1.ca2404.1_amd64.deb Size: 2806662 MD5sum: a18b396c110aeace40917464f0e7901a SHA1: e56e9a771fd67fc295ff8605e6c462b091773cf4 SHA256: ae3740c16b516d662a5d3198babc2d85e665f3f39afdd7bd7e5a439b2a3d9354 SHA512: a35482199f41eaba6d050c1a6c960a8793d5eb09ab8c9cb9fe2b1360e541303d8c33fddd85f4bf8ff811d9a30976d78ed052ece2d704a229a9d054f51849e3e4 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. 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The scRepertoire framework supports use of 10x, AIRR, BD, MiXCR, TRUST4, and WAT3R single-cell formats. The functionality includes basic clonal analyses, repertoire summaries, distance-based clustering and interaction with the popular Seurat and SingleCellExperiment/Bioconductor R single-cell workflows. Package: r-bioc-scuttle Architecture: amd64 Version: 1.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1776 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-cran-matrix, r-cran-rcpp, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-biocparallel, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-s4arrays, r-bioc-matrixgenerics, r-bioc-sparsearray, r-bioc-delayedarray, r-bioc-beachmat, r-bioc-assorthead Suggests: r-bioc-biocstyle, r-cran-knitr, r-bioc-scrnaseq, r-cran-rmarkdown, r-cran-testthat, r-bioc-sparsematrixstats, r-bioc-delayedmatrixstats, r-bioc-scran Filename: pool/dists/noble/main/r-bioc-scuttle_1.22.0-1.ca2404.1_amd64.deb Size: 736906 MD5sum: f0a28ab0cc73580e1243911b5a2d7562 SHA1: fab98c3b5a013256659baa46fbce1e391854fd5a SHA256: a3e2236c8e994944a8a1aff8766d8086c5707476f8eb1aac414f26735e1c58aa SHA512: 11fe5acde530cdb3375c5a7ab58d700264e00a1d6c9260ccf69ce86b09369c926401381be29d6d4dd180e9cbb59630832c596a7751220799642539203cc6f33c 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. 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The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, and easily manipulated for a diversity of purposes. The package also contains legacy support for early single-end, ungapped alignment formats. Package: r-bioc-signaturesearch Architecture: amd64 Version: 1.26.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90462 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-bioc-summarizedexperiment, r-bioc-org.hs.eg.db, r-bioc-annotationdbi, r-cran-ggplot2, r-cran-data.table, r-bioc-experimenthub, r-bioc-hdf5array, r-cran-magrittr, r-cran-rsqlite, r-cran-dplyr, r-bioc-fgsea, r-cran-scales, r-bioc-qvalue, r-cran-reshape2, r-cran-visnetwork, r-bioc-biocparallel, r-cran-fastmatch, r-bioc-reactome.db, r-cran-matrix, r-cran-readr, r-bioc-rhdf5, r-bioc-gseabase, r-bioc-delayedarray, r-bioc-go.db, r-bioc-biocgenerics, r-cran-tibble, r-bioc-dose, r-bioc-annotationhub, r-cran-stringr Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-bioc-biocstyle, r-bioc-signaturesearchdata, r-cran-dt Filename: pool/dists/noble/main/r-bioc-signaturesearch_1.26.0-1.ca2404.1_amd64.deb Size: 87999734 MD5sum: da8742b06de8b3b7b9360923240d2194 SHA1: 411506ac2b3e333d658a4f6c0bf8f88533afd158 SHA256: 9ee6e3e3113985a1b5df57cb535e41e806a5c8ebe676e789540ccaf595b235ea SHA512: 39196a9273c9f025f3f661f3023b509fae61a35e010ab7449f6099742fba67f499d67d5824bc9f656b65c33c6b532faea6f0ba4a5852fe93865bac2771771179 Homepage: https://cran.r-project.org/package=signatureSearch Description: Bioc Package 'signatureSearch' (Environment for Gene Expression Searching Combined withFunctional Enrichment Analysis) This package implements algorithms and data structures for performing gene expression signature (GES) searches, and subsequently interpreting the results functionally with specialized enrichment methods. Package: r-bioc-simona Architecture: amd64 Version: 1.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2561 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-matrixstats, r-cran-getoptlong, r-cran-globaloptions, r-cran-igraph, r-cran-polychrome, r-bioc-s4vectors, r-cran-xml2, r-cran-circlize, r-bioc-complexheatmap, r-cran-shiny, r-cran-fastmatch Suggests: r-cran-knitr, r-cran-testthat, r-cran-biocmanager, r-bioc-go.db, r-bioc-org.hs.eg.db, r-cran-proxyc, r-bioc-annotationdbi, r-cran-matrix, r-cran-diagrammer, r-cran-ragg, r-cran-png, r-bioc-interactivecomplexheatmap, r-bioc-uniprotkeywords, r-bioc-simplifyenrichment, r-bioc-annotationhub, r-cran-jsonlite Filename: pool/dists/noble/main/r-bioc-simona_1.10.0-1.ca2404.1_amd64.deb Size: 1924474 MD5sum: 1ee8d9ab66b7afa10557908aa97bb513 SHA1: face9367779aaf42b4b9ba8849ba4049255882ee SHA256: 671a45e9da02402861dadf68ae2d6e711580fad39bf9de75767b0a8ff946b67f SHA512: abab68fe893b90103812b5ed249e37e613bc04a439702f2270c2e4bc49d254c97520dd5fc0dc70679cbb321b13368f13ac5a1c446e7a9f22bf8e4783737d56f3 Homepage: https://cran.r-project.org/package=simona Description: Bioc Package 'simona' (Semantic Similarity on Bio-Ontologies) This package implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. It provides a robust toolbox supporting over 70 methods for semantic similarity analysis. Package: r-bioc-singler Architecture: amd64 Version: 2.14.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2057 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-summarizedexperiment, r-cran-matrix, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-delayedarray, r-cran-rcpp, r-bioc-beachmat, r-bioc-assorthead Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-bioc-biocparallel, r-bioc-singlecellexperiment, r-bioc-scrapper, r-bioc-scrnaseq, r-cran-ggplot2, r-cran-pheatmap, r-cran-gridextra, r-cran-viridis, r-bioc-celldex Filename: pool/dists/noble/main/r-bioc-singler_2.14.0-1.ca2404.1_amd64.deb Size: 919708 MD5sum: 3b0e341060028e30c6f0889d85d803c2 SHA1: 8a5df0a4abf5fffdd9fb39c540a28701da13071a SHA256: a75202743b6df6b54ff5afad4092ca6db5cbd62f8698ce3abec99bd17d88b83e SHA512: 9d7095ffe21f9248a211390c5dc2e8ade5fd1d3e9a127f9e9f852ef0ab3c648ca52f8dbc88c7e164c4936c9478ff2a33a97635e278b9a46e20f87a6cc9e88d1c Homepage: https://cran.r-project.org/package=SingleR Description: Bioc Package 'SingleR' (Reference-Based Single-Cell RNA-Seq Annotation) Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently. Package: r-bioc-snprelate Architecture: amd64 Version: 1.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6411 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-gdsfmt, r-cran-rhpcblasctl Suggests: r-cran-matrix, r-cran-runit, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-mass, r-bioc-biocgenerics Filename: pool/dists/noble/main/r-bioc-snprelate_1.46.0-1.ca2404.1_amd64.deb Size: 3848948 MD5sum: 120c79b046605c64ab38bbdb9569fde4 SHA1: 63c466fc0b4aa1838be18b1c8e56ec9911d6d9b3 SHA256: 9776b9cb2795d7717cd1df2c66c7c952ed9d1ef7aaa24840a20c1b65485d9a1a SHA512: a113d72f963d51afaab8c0cf2c5fb5c7702b43b1179a9c893806ec896efbcdbdfec6ef19bbd882eb357fb568b65c38fc85f94e6a0c304b37fd8f178080301bb2 Homepage: https://cran.r-project.org/package=SNPRelate Description: Bioc Package 'SNPRelate' (Parallel Computing Toolset for Relatedness and PrincipalComponent Analysis of SNP Data) Genome-wide association studies (GWAS) are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed an R package SNPRelate to provide a binary format for single-nucleotide polymorphism (SNP) data in GWAS utilizing CoreArray Genomic Data Structure (GDS) data files. The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. SNPRelate is also designed to accelerate two key computations on SNP data using parallel computing for multi-core symmetric multiprocessing computer architectures: Principal Component Analysis (PCA) and relatedness analysis using Identity-By-Descent measures. The SNP GDS format is also used by the GWASTools package with the support of S4 classes and generic functions. The extended GDS format is implemented in the SeqArray package to support the storage of single nucleotide variations (SNVs), insertion/deletion polymorphism (indel) and structural variation calls in whole-genome and whole-exome variant data. Package: r-bioc-snpstats Architecture: amd64 Version: 1.62.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9262 Depends: libc6 (>= 2.38), zlib1g (>= 1:1.1.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-matrix, r-bioc-biocgenerics Suggests: r-cran-hexbin Filename: pool/dists/noble/main/r-bioc-snpstats_1.62.0-1.ca2404.1_amd64.deb Size: 8463888 MD5sum: d8febdb84f9ce98154f9453a697fa4a0 SHA1: df7ac7c230ae9cb0b0c7aca8b90ade3cf0b3f897 SHA256: 19d33b8a2d7b47e4b6d6c55b1bc0802df24b9292b1191d25dedc1c91a7bf7c52 SHA512: 6e750404409aec9a86816509ae9393e7b59cf623049d1a51b8775c973bb7cfc3cf2d6689d8c57e45aab84bebdd486502f65779b92bb5aa6da189ebb80010afa0 Homepage: https://cran.r-project.org/package=snpStats Description: Bioc Package 'snpStats' (SnpMatrix and XSnpMatrix classes and methods) Classes and statistical methods for large SNP association studies. This extends the earlier snpMatrix package, allowing for uncertainty in genotypes. Package: r-bioc-sparsearray Architecture: amd64 Version: 1.12.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3007 Depends: libc6 (>= 2.14), libgomp1 (>= 9), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-bioc-biocgenerics, r-bioc-matrixgenerics, r-bioc-s4vectors, r-bioc-s4arrays, r-cran-matrixstats, r-bioc-iranges, r-bioc-xvector Suggests: r-bioc-hdf5array, r-bioc-experimenthub, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-sparsearray_1.12.2-1.ca2404.1_amd64.deb Size: 1610506 MD5sum: c20c8c7bcd98a58ce35a61e91f1ca972 SHA1: dbab31c4155f8a11d8743b6553f8b2ff3a8e6501 SHA256: 4d6cc6b4a19ec7d384b88f8a3d70c80fdb43dd60d779df89719f1439b1dc15c2 SHA512: 6e7ee437a583e6fcafcb5679eeb185c19bee1028084f08e93a2e3d3390e98958563fa07616adad90f6bdb3edabbb58b8da1599cbcba2d687fa2cc18e54d76eff Homepage: https://cran.r-project.org/package=SparseArray Description: Bioc Package 'SparseArray' (High-performance sparse data representation and manipulation inR) The SparseArray package provides array-like containers for efficient in-memory representation of multidimensional sparse data in R (arrays and matrices). The package defines the SparseArray virtual class and two concrete subclasses: COO_SparseArray and SVT_SparseArray. Each subclass uses its own internal representation of the nonzero multidimensional data: the "COO layout" and the "SVT layout", respectively. SVT_SparseArray objects mimic as much as possible the behavior of ordinary matrix and array objects in base R. In particular, they suppport most of the "standard matrix and array API" defined in base R and in the matrixStats package from CRAN. Package: r-bioc-sparsematrixstats Architecture: amd64 Version: 1.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2053 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-matrixgenerics, r-cran-rcpp, r-cran-matrix, r-cran-matrixstats Suggests: r-cran-testthat, r-cran-knitr, r-cran-bench, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-sparsematrixstats_1.24.0-1.ca2404.1_amd64.deb Size: 1147938 MD5sum: c54e9833ed4b565fb6ed0396910f268b SHA1: 409f50dbb117ac30cab86420728f69a07153daaa SHA256: 1ede6a0b0f47b1c1dafae10add5e2f5a1089ff5e7e36d3c7b03912cecc713e25 SHA512: ae8273c7a969d78ffe680c5c94bbedc1dcd7e548505708f638e8559bd2de6fe67d8099aa9a06e27c13cf6ce2ff129b4f7672449bb4724674d027d5aac1c6d259 Homepage: https://cran.r-project.org/package=sparseMatrixStats Description: Bioc Package 'sparseMatrixStats' (Summary Statistics for Rows and Columns of Sparse Matrices) High performance functions for row and column operations on sparse matrices. For example: col / rowMeans2, col / rowMedians, col / rowVars etc. Currently, the optimizations are limited to data in the column sparse format. This package is inspired by the matrixStats package by Henrik Bengtsson. Package: r-bioc-survcomp Architecture: amd64 Version: 1.62.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1003 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-prodlim, r-cran-ipred, r-cran-suppdists, r-cran-kernsmooth, r-cran-survivalroc, r-cran-bootstrap, r-cran-rmeta Suggests: r-cran-hmisc, r-cran-clinfun, r-cran-xtable, r-bioc-biobase, r-cran-biocmanager Filename: pool/dists/noble/main/r-bioc-survcomp_1.62.0-1.ca2404.1_amd64.deb Size: 839112 MD5sum: cd2f7d9a238ce9918f2c6215e9c9bb77 SHA1: 0b4cf92dc5279cb0d4cb25aa685ee27fe011e708 SHA256: 154fb3b3099e053f248bb5730a18c76a08e5d1b9266de3022b19028bc4f36a1e SHA512: 4a2fd9f3072440466e2a34a67373a938be1b4501a65e3ba447a2b4ec3a5d9ab00f36e5658352ec62921b2d852aa930eb2be063437df1f0d2710384a69bdbd96b Homepage: https://cran.r-project.org/package=survcomp Description: Bioc Package 'survcomp' (Performance Assessment and Comparison for Survival Analysis) Assessment and Comparison for Performance of Risk Prediction (Survival) Models. Package: r-bioc-sva Architecture: amd64 Version: 3.60.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 959 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mgcv, r-bioc-genefilter, r-bioc-biocparallel, r-cran-matrixstats, r-bioc-limma, r-bioc-edger Suggests: r-cran-pamr, r-bioc-bladderbatch, r-bioc-biocstyle, r-bioc-zebrafishrnaseq, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-sva_3.60.0-1.ca2404.1_amd64.deb Size: 463292 MD5sum: f7ce8a09d42a456fa1dabe9164693fde SHA1: 443a238146554b0fe818d41c3efbda1ad986026d SHA256: d0451ca462669c76d8fcf4c9ae64d7e0273b234ef10838c3ac7da73289129278 SHA512: 01d09fd8332484bb4930a5ec7fbe87e0df645da3f8bf5519ba5e6d07b2b010a2d8736131871e16b08a426fbbf14ef629487e725ada77ed66d40b67b49de025d7 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: amd64 Version: 1.50.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2744 Depends: libc6 (>= 2.4), 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/noble/main/r-bioc-tfbstools_1.50.0-1.ca2404.1_amd64.deb Size: 1384702 MD5sum: c92177ab36a65aa760bc065b6fc79566 SHA1: 602d53bb0c3ca03f827e093c4b3c303ddc8a3558 SHA256: 7cf5db257cf7928ce9a72273e23453eda7a71682e9e5531cc6a78b5d2d5dd998 SHA512: 8cd41daf95349b01f8d068229b3be52d8f792c9c58e7e75b7e6b41d51e46d6b3973a1fe8a588a2124c538ba0d4842a951fb24bfe3964542e394e68062d9ec00b 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: amd64 Version: 1.58.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-bioc-limma, r-bioc-edger, r-bioc-cqn Suggests: r-bioc-tweedeseqcountdata, r-cran-xtable Filename: pool/dists/noble/main/r-bioc-tweedeseq_1.58.0-1.ca2404.1_amd64.deb Size: 355538 MD5sum: 481174e6e7cc20b31f0d84d7c912f193 SHA1: fe64dda7a2c13df696a39170e3516d5fe03ecb16 SHA256: 8bb4b956427c69240628dacec34d45575b7689d1a79276338e36fe51efc0cb3c SHA512: 42d87953511a7a1aa45aa0ed53ec261ef910ab68e41aad0dcc8f8dd62ce1e15084563f6e80e17ad48ac843919883b235cb0448e899b1e6a6197f143c24ce5a8d 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: amd64 Version: 1.30.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6874 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-universalmotif_1.30.1-1.ca2404.1_amd64.deb Size: 5383968 MD5sum: 080f394fd232f39d1fff24feb62b4118 SHA1: e8716a654ee80f42f979a2e97e59f39da06aa343 SHA256: 18cd07c3e27edd7ca5e4464aa874e377e0134e79dd63498523bd287cf63b8ff8 SHA512: 05fde7271dfe768de0fe6206196ff54f89ba209d1b09eb0288691af0fed78b570c125d466e32c77015a5fe56ef90927eb47bf11402f43d14fc44a5bc869628e9 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: amd64 Version: 1.58.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6896 Depends: libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), liblzma5 (>= 5.1.1alpha+20120614), zlib1g (>= 1:1.2.3.3), 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/noble/main/r-bioc-variantannotation_1.58.0-1.ca2404.1_amd64.deb Size: 3650480 MD5sum: 7a38e028716d80ffc25b066fc53f87fb SHA1: 5b793c1a0ae8ab275ee449bfcace8c45d5f95e42 SHA256: 51c2e6c8387c1a4638e43df5f75352afecd505fb404ccd4ca14577a2d34aeecf SHA512: e7cc578d9e3b1030baf4f488200d0a8070d4ef3fb1053b7e07ea689989d7ee9b7a65c1d912ffdc1fb989b0ea5f03657ca35bcbd24a055a3c513224af6edec1cc Homepage: https://cran.r-project.org/package=VariantAnnotation Description: Bioc Package 'VariantAnnotation' (Annotation of Genetic Variants) Annotate variants, compute amino acid coding changes, predict coding outcomes. Package: r-bioc-vsn Architecture: amd64 Version: 3.80.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4469 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-affy, r-bioc-limma, r-cran-lattice, r-cran-ggplot2 Suggests: r-bioc-affydata, r-bioc-hgu95av2cdf, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-testthat, r-cran-hexbin Filename: pool/dists/noble/main/r-bioc-vsn_3.80.0-1.ca2404.1_amd64.deb Size: 2006458 MD5sum: 02854ae8341aa64efbed4d198e96a4a7 SHA1: d826d40133faba90753db785977db7ec8724c8e1 SHA256: c88803406602f6332ed8e2798a6f8655c9eaf2225fa44868c2760b108d7a6a51 SHA512: c5ca9991978aefe8531b3d981d93ae8c277f5696b14ad25de44eecad73c5ff4b992f84048155684ce505f7468cfca4cd6f354ed9cc80662d997f932cd58cab4d 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: amd64 Version: 4.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 19088 Depends: libc6 (>= 2.29), 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/noble/main/r-bioc-xcms_4.10.0-1.ca2404.1_amd64.deb Size: 11210086 MD5sum: 29cdce010dcf6eb95a59dfb12dacaf92 SHA1: d19a855f714a71f412279d3730333f4250b28ad0 SHA256: 96f89cd14050145ead95c4e25269a678091d058dd4fef6f17b75dc319e6682bc SHA512: 3cc9b819d63f3320dabf66f680c10b985b560d0bb095b36be7fbead22c6ebb484902467fe29da769627d95ee1e28017bc4a0ed2fc154fedaf7750f51a41c1363 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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See the vignette for instructions on use. 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See Zhang and Liu (2014) for details. Package: r-cran-abcoptim Architecture: amd64 Version: 0.15.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-abcoptim_0.15.0-1.ca2404.1_amd64.deb Size: 72952 MD5sum: 97c16f5ac58dfc125b3482592cd52a47 SHA1: f24936237742d853316c7afb94bdae154f3bab22 SHA256: 6716b13cd69a8e7835589d60df56f3e225d03dae5a31b744475acf2587b8e479 SHA512: f08c29c84b29ef3a4cf6fa5da5c92b2fffec58908fca421c58c3846cb841dc90fc9e7d37fac5afa095970523b71f7fa9a1f540357d65406a7420e148aa3179a0 Homepage: https://cran.r-project.org/package=ABCoptim Description: CRAN Package 'ABCoptim' (Implementation of Artificial Bee Colony (ABC) Optimization) An implementation of Karaboga (2005) Artificial Bee Colony Optimization algorithm . 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Pudlo P., Marin J.-M., Estoup A., Cornuet J.-M., Gautier M. and Robert C. P. (2016) . Raynal L., Marin J.-M., Pudlo P., Ribatet M., Robert C. P. and Estoup A. (2019) . Package: r-cran-abctools Architecture: amd64 Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2780 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abc, r-cran-abind, r-cran-plyr, r-cran-hmisc Suggests: r-cran-ggplot2, r-cran-abc.data Filename: pool/dists/noble/main/r-cran-abctools_1.1.8-1.ca2404.1_amd64.deb Size: 2796964 MD5sum: f576edba5430aeeda54cad99c7553298 SHA1: 0693c8ac6a084bde783d773b9d28dc07a2c5ea96 SHA256: 177613faba0c697805b684cd8e43f9907ff7f90e3497d2fe44c76422dcef8536 SHA512: e9e7eb590716b54af58b373e244296b904abe2601bb451245388b63aa7c72a51572ee8a4902a4dc52f04ce503c9a9b7a018bc184b1d518c90a2a205c729fe8f5 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. 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This package is its R interface. The package implements and generalizes algorithms designed in that exploits a novel sequencing-and-splicing technique to guarantee exact support recovery and globally optimal solution in polynomial times for linear model. It also supports best subset selection for logistic regression, Poisson regression, Cox proportional hazard model, Gamma regression, multiple-response regression, multinomial logistic regression, ordinal regression, Ising model reconstruction , (sequential) principal component analysis, and robust principal component analysis. The other valuable features such as the best subset of group selection and sure independence screening are also provided. Package: r-cran-abm Architecture: amd64 Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 772 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-abm_0.4.3-1.ca2404.1_amd64.deb Size: 396136 MD5sum: aa9462c46ca70244bb8727c0a6fce1a1 SHA1: a5b3e5874e8726896e636c5eb23445a43d9c27f7 SHA256: a87a70e66a7222c67c18f8d619b897d3ea3f1eb89597c1beca162f858e1c69f1 SHA512: a585135bff160590c327930ada09febbdaa677b24d80b6a92ee79e7f879adb0c81ceab76d3bab28bb9ca3bf2b09f86e58633031edccd336048bc9c5aa69876c0 Homepage: https://cran.r-project.org/package=ABM Description: CRAN Package 'ABM' (Agent Based Model Simulation Framework) A high-performance, flexible and extensible framework to develop continuous-time agent based models. Its high performance allows it to simulate millions of agents efficiently. Agents are defined by their states (arbitrary R lists). The events are handled in chronological order. This avoids the multi-event interaction problem in a time step of discrete-time simulations, and gives precise outcomes. The states are modified by provided or user-defined events. The framework provides a flexible and customizable implementation of state transitions (either spontaneous or caused by agent interactions), making the framework suitable to apply to epidemiology and ecology, e.g., to model life history stages, competition and cooperation, and disease and information spread. The agent interactions are flexible and extensible. The framework provides random mixing and network interactions, and supports multi-level mixing patterns. It can be easily extended to other interactions such as inter- and intra-households (or workplaces and schools) by subclassing an R6 class. It can be used to study the effect of age-specific, group-specific, and contact- specific intervention strategies, and complex interactions between individual behavior and population dynamics. This modeling concept can also be used in business, economical and political models. As a generic event based framework, it can be applied to many other fields. More information about the implementation and examples can be found at . Package: r-cran-abn Architecture: amd64 Version: 3.1.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5538 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl27 (>= 2.7.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-glmmtmb, r-bioc-graph, r-cran-jsonlite, r-cran-lme4, r-cran-mclogit, r-cran-nnet, r-cran-rcpp, r-bioc-rgraphviz, r-cran-rjags, r-cran-stringi, r-cran-rcpparmadillo Suggests: r-cran-bookdown, r-cran-boot, r-cran-brglm, r-cran-devtools, r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-matrix, r-cran-matrixmodels, r-cran-microbenchmark, r-cran-r.rsp, r-cran-rhpcblasctl, r-cran-rmarkdown, r-cran-testthat, r-cran-entropy, r-cran-moments, r-cran-r6 Filename: pool/dists/noble/main/r-cran-abn_3.1.13-1.ca2404.1_amd64.deb Size: 4030372 MD5sum: 3ef32460f1e1037af52a0e49a09cadda SHA1: f4b119d2774122b1fb48d18c08b5d68eba5b045f SHA256: e5c562483014d63f74a3c9904d7b7d3c3dc39633210ad2f999152f831c53bbd6 SHA512: 992ea952ca0b22a890dacf8b17e356eec705f4bb45fd5e47cfa1350dd6c30cacdd0ed47989c6bcbc524c15a7cd151cf607996ea17351dfdf5e806cdaf29a60f9 Homepage: https://cran.r-project.org/package=abn Description: CRAN Package 'abn' (Modelling Multivariate Data with Additive Bayesian Networks) The 'abn' R package facilitates Bayesian network analysis, a probabilistic graphical model that derives from empirical data a directed acyclic graph (DAG). This DAG describes the dependency structure between random variables. The R package 'abn' provides routines to help determine optimal Bayesian network models for a given data set. These models are used to identify statistical dependencies in messy, complex data. Their additive formulation is equivalent to multivariate generalised linear modelling, including mixed models with independent and identically distributed (iid) random effects. The core functionality of the 'abn' package revolves around model selection, also known as structure discovery. It supports both exact and heuristic structure learning algorithms and does not restrict the data distribution of parent-child combinations, providing flexibility in model creation and analysis. The 'abn' package uses Laplace approximations for metric estimation and includes wrappers to the 'INLA' package. It also employs 'JAGS' for data simulation purposes. For more resources and information, visit the 'abn' website. 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Gronau, Raj K. N., & Wagenmakers (2021) . Package: r-cran-abundant Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glasso Filename: pool/dists/noble/main/r-cran-abundant_1.2-1.ca2404.1_amd64.deb Size: 41864 MD5sum: 5d3304276838bf760b8ede2b3c795385 SHA1: 91e3c350e50d1bc7fa4f277932a407dc693750c3 SHA256: e1b9839a4a23032aa4a7999b33b8bce22648debae9914541cea92672695080a3 SHA512: a660205cf4312703560fe60d5ce6d4de7952390493dd662429f37e53c336e383db032a11227c804442b8ec0c18f39d3b0ee3cc3352c60924c944ad273aaaa058 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. 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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). 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Functions to create trade, price, or volume durations from transaction data, perform diurnal adjustments, fit various ACD models, and test them. Package: r-cran-acebayes Architecture: amd64 Version: 1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1762 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lhs, r-cran-rcpp, r-cran-compare, r-cran-randtoolbox, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-acebayes_1.11-1.ca2404.1_amd64.deb Size: 1610526 MD5sum: 561af54992cc31ba6bc4d605177a5fb1 SHA1: 687afd2acc1725372b6cb179e6ec3080a1428588 SHA256: 598ab01d3103d0d5a5b0106e2f411f89f944eecd7c2945446d2a322875bdaf74 SHA512: 0869cf1c5b214322c91854c006b4a22dbdc70e1af4a7b1cb7d0735ba5439db835dee7868bb44a359fa4d598d7de492752b8a98231dc1b2fb4c8572dad3f75851 Homepage: https://cran.r-project.org/package=acebayes Description: CRAN Package 'acebayes' (Optimal Bayesian Experimental Design using the ACE Algorithm) Optimal Bayesian experimental design using the approximate coordinate exchange (ACE) algorithm. Package: r-cran-acepack Architecture: amd64 Version: 1.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-acepack_1.6.3-1.ca2404.1_amd64.deb Size: 84232 MD5sum: 9d8eeacf8f9b2edb17011959d52deb97 SHA1: 79953516ed109083bd2e505b82794835d3380df2 SHA256: e2573573c36c8570e4c8e0cb4a412e16c4eb34691753e99178966c923562c804 SHA512: 8f3fd3b17d0ac601f63743e47dfe27521d72255a606a0bd85aa832322756e080883de89d85642189ac7a6ad8a28f91b87c49395c3ec972d94319b1c8b5d8210d 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 ]. 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All of the three algorithms are used to fit high dimensional data set with either sparse structure, or dense structure with smaller contributions from all predictors. The state-of-the-art GLP algorithm is in Giannone, D., Lenza, M., & Primiceri, G. E. (2021, ISBN:978-92-899-4542-4) "Economic predictions with big data: The illusion of sparsity". The two new algorithms, ACSS algorithm and INSS algorithm, and the discussion on their performance can be seen in Yang, Z., Khare, K., & Michailidis, G. (2024, submitted to Journal of Business & Economic Statistics) "Bayesian methodology for adaptive sparsity and shrinkage in regression". 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Given the sample statistic of sum-scores, cluster analysis techniques can be used to classify examinees into latent classes based on their attribute patterns. In addition to the algorithms used to classify data, three labeling approaches are proposed to label clusters so that examinees' attribute profiles can be obtained. 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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. 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Support for many additional probability distributions to model insurance loss size and frequency: 23 continuous heavy tailed distributions; the Poisson-inverse Gaussian discrete distribution; zero-truncated and zero-modified extensions of the standard discrete distributions. Support for phase-type distributions commonly used to compute ruin probabilities. Main reference: . Implementation of the Feller-Pareto family of distributions: . Package: r-cran-adahuber Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-adahuber_1.1-1.ca2404.1_amd64.deb Size: 130494 MD5sum: 50c31622c04150ed8d4f565d8abe1fd4 SHA1: 8b8d6523c5f1992c2919773be1a059d6f5e31421 SHA256: 6afbb3436edd75c856bfabc3fcf9de6369ecac062d612642c59c67b20dbadc5a SHA512: 6f0de71577491804451edaefb77a4f8a9dbba0de6b7ae6348b8762fabce380ed4b78f0437ea6df1b6eb24f09d734426af177ccc82b905a0ea13a170d90b5db07 Homepage: https://cran.r-project.org/package=adaHuber Description: CRAN Package 'adaHuber' (Adaptive Huber Estimation and Regression) Huber-type estimation for mean, covariance and (regularized) regression. For all the methods, the robustification parameter tau is chosen by a tuning-free principle. 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Package: r-cran-adaptivetau Architecture: amd64 Version: 2.3-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-adaptivetau_2.3-2-1.ca2404.1_amd64.deb Size: 213320 MD5sum: 1ea11b7830ce92e409ac1c1980f57960 SHA1: a0a0726ee26d53d5171bca938d3d58fb89eab94d SHA256: 1335ddee3dd58e91db8c19b88d085d20ff7eabf9515fa5c6768da55567a89c06 SHA512: cc18a2c0cc5e7b0fed56ab79a42b8cf89167b690e11f4bef79c18029168892da7fa3e551f1ac7aa731dfa02b19554d5387e1536ab3ceed90acce6d78981b00ef 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. 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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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The package contains functions to represent comparative data, compute phylogenetic proximities, perform multivariate analysis with phylogenetic constraints and test for the presence of phylogenetic autocorrelation. The package is described in Jombart et al (2010) . 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Package: r-cran-adestr Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1470 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-adoptr, r-cran-cubature, r-cran-ggplot2, r-cran-ggpubr, r-cran-scales, r-cran-latex2exp, r-cran-forcats, r-cran-future.apply, r-cran-progressr, r-cran-rdpack Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-adestr_1.0.0-1.ca2404.1_amd64.deb Size: 805780 MD5sum: 3b7c5fe103214da4a1016af1f959a6c5 SHA1: c89638b2e0ac81434e2f35c1eafaa31a087ae756 SHA256: 68a4aa523a4d5042d058ba06011ac2395729a3c029a62f4e79660366a679005b SHA512: a7f188e5c5968833eb65c778748a63643b825dbbd1a7745d345bd3b0c46dec2b56de417f3398513046730fb634ebc519e8c56bfaf09f65606e3dba80276405b3 Homepage: https://cran.r-project.org/package=adestr Description: CRAN Package 'adestr' (Estimation in Optimal Adaptive Two-Stage Designs) Methods to evaluate the performance characteristics of various point and interval estimators for optimal adaptive two-stage designs as described in Meis et al. 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Package: r-cran-adherencerx Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 488 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-anytime, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-lubridate, r-cran-rlang Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-adherencerx_1.0.0-1.ca2404.1_amd64.deb Size: 402604 MD5sum: d93be0ef8904deb8d5c46e6c90521111 SHA1: 0d264f05097892d3d3300f33f2b6b446304f5713 SHA256: b73ba860c3e22dc54a2c6586d788ae61344448a6827c055eae942d3a081da4d8 SHA512: 348bbf328f8a7ba42f21ed5958d1dcd09f98d7799cd1e5a0b22ac307f6c323745130a63b27f3d3afcc9d740018f8165b7eb72bb92f334e8c6ccba39d662af765 Homepage: https://cran.r-project.org/package=adheRenceRX Description: CRAN Package 'adheRenceRX' (Assess Medication Adherence from Pharmaceutical Claims Data) A (mildly) opinionated set of functions to help assess medication adherence for researchers working with medication claims data. Medication adherence analyses have several complex steps that are often convoluted and can be time-intensive. The focus is to create a set of functions using "tidy principles" geared towards transparency, speed, and flexibility while working with adherence metrics. All functions perform exactly one task with an intuitive name so that a researcher can handle details (often achieved with vectorized solutions) while we handle non-vectorized tasks common to most adherence calculations such as adjusting fill dates and determining episodes of care. The methodologies in referenced in this package come from Canfield SL, et al (2019) "Navigating the Wild West of Medication Adherence Reporting in Specialty Pharmacy" . 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Typical application fields in bioinformatics include Genome-Wide Association Studies or Hi-C data analysis, where the similarity between items is a decreasing function of their genomic distance. Taking advantage of this feature, the implemented algorithm is time and memory efficient. This algorithm is described in Ambroise et al (2019) . 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Supports nearest-neighbor graphs, heat-kernel weights, graph Laplacians, diffusion operators, and bilateral smoothers for graph-based data analysis, following spectral graph methods in von Luxburg (2007) , diffusion maps in Coifman and Lafon (2006) , and bilateral filtering in Tomasi and Manduchi (1998) . Package: r-cran-adjsurvci Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 616 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-adjsurvci_1.0-1.ca2404.1_amd64.deb Size: 276988 MD5sum: 406b3c252926da64aa13cc77b9f08b6d SHA1: eba0233f122000534f0f035554373ae49766c383 SHA256: 1cb49eb016cd45c8fa67f74f0b3d3ed51375a9ff8b26248b696fa88cdb8bb828 SHA512: e1a5f0f4761b5b7fbbe1b95aa26ce0777b38bff5cae842658ccb985e216ad418dfa7f56950ae9f47c1fc5dd83f9861ff61566c3f04abe684280d56addbad673b Homepage: https://cran.r-project.org/package=adjSURVCI Description: CRAN Package 'adjSURVCI' (Parameter and Adjusted Probability Estimation for Right-CensoredData) Functions in this package fit a stratified Cox proportional hazards and a proportional subdistribution hazards model by extending Zhang et al., (2007) and Zhang et al., (2011) respectively to clustered right-censored data. The functions also provide the estimates of the cumulative baseline hazard along with their standard errors. Furthermore, the adjusted survival and cumulative incidence probabilities are also provided along with their standard errors. Finally, the estimate of cumulative incidence and survival probabilities given a vector of covariates along with their standard errors are also provided. Package: r-cran-adlift Architecture: amd64 Version: 1.4-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-ebayesthresh Filename: pool/dists/noble/main/r-cran-adlift_1.4-6-1.ca2404.1_amd64.deb Size: 323828 MD5sum: f169c8c317303f9ceaf9e4f36db04023 SHA1: a1c48cb6a3663a8b5d210bed83b2236b133c44ec SHA256: f9aa2d804c3994f9b1afedd8d8c57031d42d65601ccfe776525c9eb47b433806 SHA512: 325ecca64d515845741e7b37d8919e8e4398e460dd4a2186973866546abd4399245d32861ef1504b0d3dd4e9de6a8918444c33133bf82994461f088d72f54a27 Homepage: https://cran.r-project.org/package=adlift Description: CRAN Package 'adlift' (An Adaptive Lifting Scheme Algorithm) Adaptive wavelet lifting transforms for signal denoising using optimal local neighbourhood regression, from Nunes et al. (2006) . Package: r-cran-admisc Architecture: amd64 Version: 0.40-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: libc6 (>= 2.14), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-qca Filename: pool/dists/noble/main/r-cran-admisc_0.40-1.ca2404.1_amd64.deb Size: 375686 MD5sum: 1aaec73015b8fff0b334ce4c6872c1fe SHA1: 91f06321ea371f7a69af72589261e8289b872ef6 SHA256: f0d8d3ea00a5a25341e3ded7c11ece8a3f4fe16d262fa3c104e61a7e145f2873 SHA512: b8c2720d590537d733de0aa4395c72969e2a5e479c1334643bf85e88dd6c226369d7eebe8d4be028b92be29dcc46be8675ee6ef2045de8bc728da1caa1ddc364 Homepage: https://cran.r-project.org/package=admisc Description: CRAN Package 'admisc' (Adrian Dusa's Miscellaneous) Contains functions used across packages 'DDIwR', 'QCA' and 'venn'. Interprets and translates, factorizes and negates SOP - Sum of Products expressions, for both binary and multi-value crisp sets, and extracts information (set names, set values) from those expressions. Other functions perform various other checks if possibly numeric (even if all numbers reside in a character vector) and coerce to numeric, or check if the numbers are whole. It also offers, among many others, a highly versatile recoding routine and some more flexible alternatives to the base functions 'with()' and 'within()'. SOP simplification functions in this package use related minimization from package 'QCA', which is recommended to be installed despite not being listed in the Imports field, due to circular dependency issues. Package: r-cran-admit Architecture: amd64 Version: 2.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-coda Filename: pool/dists/noble/main/r-cran-admit_2.1.9-1.ca2404.1_amd64.deb Size: 93058 MD5sum: aaeff0d68017a6ba8f0e36017048cdf5 SHA1: b092bb36cb793e9e6405143390c5c02df70ceb7e SHA256: e681a016e020527524d4ed39146f1ccbf1b33352747973580296c482d266f944 SHA512: 4ccc6e785fb016b800d6b7c2a81859c39bdba2b98ae2e82a785b1bd6a12cf6dbc8fd008fc0c7a1824947ab79dc392754e752c0d0f6c44bebf3609b52533785e6 Homepage: https://cran.r-project.org/package=AdMit Description: CRAN Package 'AdMit' (Adaptive Mixture of Student-t Distributions) Provides functions to perform the fitting of an adaptive mixture of Student-t distributions to a target density through its kernel function as described in Ardia et al. (2009) . The mixture approximation can then be used as the importance density in importance sampling or as the candidate density in the Metropolis-Hastings algorithm to obtain quantities of interest for the target density itself. Package: r-cran-admix Architecture: amd64 Version: 2.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2802 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cubature, r-cran-envstats, r-cran-fdrtool, r-cran-iso, r-cran-mass, r-cran-orthopolynom, r-cran-pracma, r-cran-rcpp, r-cran-rdpack Suggests: r-cran-doparallel, r-cran-dorng, r-cran-evd, r-cran-flexsurv, r-cran-foreach, r-cran-gridextra, r-cran-knitr, r-cran-lattice, r-cran-logitnorm, r-cran-plyr, r-cran-reshape2, r-cran-rmarkdown, r-cran-rmutil, r-cran-testthat Filename: pool/dists/noble/main/r-cran-admix_2.5.2-1.ca2404.1_amd64.deb Size: 2591638 MD5sum: 6a4d345f0088039e7bc26623b301f65b SHA1: f23e969cbe563e6f969d7595eb253ddb023aecd0 SHA256: 301d0ff7b99eb31214d3363ad387f013711282ddcbba6406b5e29e8ee12440f0 SHA512: 05d3caf3ab518dbd3546e38a50d09aa3b44af8fb22c2016876a0a8174a6da44c4bf4eb54df969d431c42df5191e9d18f438c8710b28bd1a2d2b6139a7688c957 Homepage: https://cran.r-project.org/package=admix Description: CRAN Package 'admix' (Package Admix for Admixture (aka Contamination) Models) Implements techniques to estimate the unknown quantities related to two-component admixture models, where the two components can belong to any distribution (note that in the case of multinomial mixtures, the two components must belong to the same family). 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Package: r-cran-aftpencda Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 562 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aftpencda_0.1.1-1.ca2404.1_amd64.deb Size: 228112 MD5sum: 89bc08cedc263218bbc87147cc26a347 SHA1: 91a86e1f4107caa69b450a52153430381ce9d7f9 SHA256: d52198433bb7c86081ca29f6b7883495288ffd8e0b702bc77ec5c77694daa4c8 SHA512: c4d902fc874856b752fcae986d58422d086515f7d97804bd9af0240d3a86575a973d5a039486a6dcdf506a02bd2daf546b3cc5390a78452211a953dc134d6411 Homepage: https://cran.r-project.org/package=aftPenCDA Description: CRAN Package 'aftPenCDA' (Estimating Penalized AFT Models via Coordinate Descent) Provides penalized accelerated failure time (AFT) model estimation for right-censored and partly interval-censored survival data using induced smoothing and coordinate descent algorithms. 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The main methods are: Jin rank-based method (Jin (2003) ), Heller’s estimating method (Heller (2012) ), Polynomial smoothed Gehan function method (Chung (2013) ), Buckley-James method (Buckley (1979) ) and Jin`s improved least squares method (Jin (2006) ). This package can be used for modeling right-censored data and for comparing different estimation algorithms. Package: r-cran-afttest Architecture: amd64 Version: 4.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 534 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-aftgee, r-cran-ggplot2, r-cran-gridextra, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-afttest_4.5.3-1.ca2404.1_amd64.deb Size: 240348 MD5sum: 927c0b772d4f48309be64756f28bab75 SHA1: 7c72fae782d529f229f095e1ce89db76786caa50 SHA256: 5a82083fe5796f5817ae27cb4d585d2a2daff813e94480d6ee88d31b6d35c276 SHA512: 5c31b4c49f33bba15d0b9847381a4783d784aa8e222a60856f352d2f7951364737bb0f679aee992b01ff9ee47a87177e5bbbb5ebe0c72d6e810aa1e09add403f Homepage: https://cran.r-project.org/package=afttest Description: CRAN Package 'afttest' (Model Diagnostics for Accelerated Failure Time Models) A collection of model checking methods for semiparametric accelerated failure time (AFT) models under the rank-based approach. 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Package: r-cran-ahw Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-timereg, r-cran-plyr, r-cran-data.table Suggests: r-cran-testthat, r-cran-survival Filename: pool/dists/noble/main/r-cran-ahw_0.1.0-1.ca2404.1_amd64.deb Size: 63080 MD5sum: c2dc8ba31760804d6d225955f18dd2d7 SHA1: cef520da5b818290db319b5c4a650ded8fe6b820 SHA256: 3a6454c621e27dae4f6f405e268b7f85843558c2f599a16724dc6b29118b5fa7 SHA512: 73fbd8918aaada1b67f52c9a870538ef4d0f19b61c2f79e8f2e5c8dd55f7fe216ce2d5996bb68accd0b0b02d8d4c711118edd970f9869f9390bf7cfd2f3473fc Homepage: https://cran.r-project.org/package=ahw Description: CRAN Package 'ahw' (Calculates Continuous Time Likelihood Ratio Weights AssumingMultiplicative Intensity Models and Additive Hazard Models) Estimates continuous time weights for performing causal survival analysis. For instance, weighted Nelson-Aalen or Kaplan-Meier estimates can be given a causal interpretation. See Ryalen, Stensrud, and Røysland (2019) and Ryalen (2019) for theory and examples. Package: r-cran-aifeducation Architecture: amd64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3189 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-iotarelr, r-cran-rcpp, r-cran-reshape2, r-cran-reticulate, r-cran-rlang, r-cran-stringi, r-cran-rcpparmadillo Suggests: r-cran-bslib, r-cran-dt, r-cran-fs, r-cran-future, r-cran-ggplot2, r-cran-knitr, r-cran-pkgdown, r-cran-promises, r-cran-readtext, r-cran-readxl, r-cran-rmarkdown, r-cran-shiny, r-cran-shinyfiles, r-cran-shinywidgets, r-cran-shinycssloaders, r-cran-sortable, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aifeducation_1.1.5-1.ca2404.1_amd64.deb Size: 2535538 MD5sum: 99330b04df6e660e134533e31868b5fd SHA1: a5dfcf095c78b629105f2aa042d962c77f746995 SHA256: a7b94219e281d4582345b3e09bfefd3c24da33e0efb637923158af341454f834 SHA512: 4a468ada25d51cc2c9e41addfeadbd80de8f43dac4d67f248a21f24c9bffd7a349b28200e3c4b128952ae2182f0bcd84866920a83fad2ac588f25237d3f25773 Homepage: https://cran.r-project.org/package=aifeducation Description: CRAN Package 'aifeducation' (Artificial Intelligence for Education) In social and educational settings, the use of Artificial Intelligence (AI) is a challenging task. Relevant data is often only available in handwritten forms, or the use of data is restricted by privacy policies. This often leads to small data sets. Furthermore, in the educational and social sciences, data is often unbalanced in terms of frequencies. To support educators as well as educational and social researchers in using the potentials of AI for their work, this package provides a unified interface for neural nets in 'PyTorch' to deal with natural language problems. In addition, the package ships with a shiny app, providing a graphical user interface. This allows the usage of AI for people without skills in writing python/R scripts. The tools integrate existing mathematical and statistical methods for dealing with small data sets via pseudo-labeling (e.g. Cascante-Bonilla et al. (2020) ) and imbalanced data via the creation of synthetic cases (e.g. Islam et al. (2012) ). Performance evaluation of AI is connected to measures from content analysis which educational and social researchers are generally more familiar with (e.g. Berding & Pargmann (2022) , Gwet (2014) , Krippendorff (2019) ). Estimation of energy consumption and CO2 emissions during model training is done with the 'python' library 'codecarbon'. Finally, all objects created with this package allow to share trained AI models with other people. Package: r-cran-aihuman Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2633 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-coda, r-cran-magrittr, r-cran-purrr, r-cran-abind, r-cran-foreach, r-cran-doparallel, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-metr, r-cran-mass, r-cran-glmmadaptive, r-cran-gbm, r-cran-tidyselect, r-cran-stringr, r-cran-forcats, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aihuman_1.0.1-1.ca2404.1_amd64.deb Size: 1519582 MD5sum: 384713a79fc70fab278525974a93bd45 SHA1: dedfefa9d6f8dd40b5a8893a6a0812076c0b2956 SHA256: e2c01f9b86eb1ed1eef9419c2f104be223d4d4d010bba8b6a5e982b8ff382b12 SHA512: dab9870a78c3d039c616e5d0c673b3c3fe1bf3a64c8f269152cdd844dc816350f54384f8c312a280e414092bf99720c998c48218d8c2d5ca1e1437b5e4157e8a Homepage: https://cran.r-project.org/package=aihuman Description: CRAN Package 'aihuman' (Experimental Evaluation of Algorithm-Assisted HumanDecision-Making) Provides statistical methods for analyzing experimental evaluation of the causal impacts of algorithmic recommendations on human decisions developed by Imai, Jiang, Greiner, Halen, and Shin (2023) and Ben-Michael, Greiner, Huang, Imai, Jiang, and Shin (2024) . The data used for this paper, and made available here, are interim, based on only half of the observations in the study and (for those observations) only half of the study follow-up period. We use them only to illustrate methods, not to draw substantive conclusions. Package: r-cran-airgr Architecture: amd64 Version: 1.7.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3765 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-caramel, r-cran-coda, r-cran-deoptim, r-cran-fme, r-cran-ggmcmc, r-cran-rmalschains, r-cran-ggally, r-cran-ggplot2, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-airgr_1.7.8-1.ca2404.1_amd64.deb Size: 3184866 MD5sum: 78f636a1429e05cc9e7a94018bb61653 SHA1: 97a50904f445fd0c57bc7122d7c4665f6986cf64 SHA256: af7bdd5c0c1cf137561e93e35119b92dcc64d4b926a77d227eb7fdfa198f04f6 SHA512: 8ab88bcd05f88654c2b58fc1e0778b270cc482a7b453a20e1757075668b4ebe81749f07db850330672948ab8295aa8c6a66a59cc003a29ce4486d41c828ea38e 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). 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Akima for irregular and regular gridded data are available through this package, both for the bivariate case (irregular data: ACM 761, regular data: ACM 760) and univariate case (ACM 433 and ACM 697). Linear interpolation of irregular gridded data is also covered by reusing D. J. Renkas triangulation code which is part of Akimas Fortran code. A bilinear interpolator for regular grids was also added for comparison with the bicubic interpolator on regular grids. Please note that most of the functions are now also covered in package interp, which is a re-implementation from scratch under a free license. Package: r-cran-alakazam Architecture: amd64 Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2337 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-alakazam_1.4.3-1.ca2404.1_amd64.deb Size: 1952232 MD5sum: 1ebb8eece629858f9797c0ab8a8f7d21 SHA1: 3f48a45ba2462372cbfd287a86dfe91824415bcd SHA256: 4fa4d4ed93f3886b1f5fe9311286d030e08fe1b7997dc8de9aa9417b8943cdf0 SHA512: 7f3ba1c15023092dcda5ecbf957045ca0629da973b8fd0df745f39b8e69371f4b4f45a6f62ab6907410ec39ad6b6d21657732777756948858d4ccf84f27e0c36 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. 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Package: r-cran-alassosurvic Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-alassosurvic_0.1.1-1.ca2404.1_amd64.deb Size: 124076 MD5sum: 691d33a2d8c59b4a2bb6b4703976f8d0 SHA1: df3fefc2a6f9d8964ce4c6ab39806e9379c065d7 SHA256: c97ba5d9ab58b2398aeade77639e1a1516652b227708086c091762aa30ad5ff1 SHA512: b40b91c747e80ef4774cff72824137447e86179a8f42d88ceedd41542a8744d15027a51e5bab5fcaa413eab9e98bdc14c8fe25d4b3eb299667a197f8e7876fc3 Homepage: https://cran.r-project.org/package=ALassoSurvIC Description: CRAN Package 'ALassoSurvIC' (Adaptive Lasso for the Cox Regression with Interval Censored andPossibly Left Truncated Data) Penalized variable selection tools for the Cox proportional hazards model with interval censored and possibly left truncated data. It performs variable selection via penalized nonparametric maximum likelihood estimation with an adaptive lasso penalty. The optimal thresholding parameter can be searched by the package based on the profile Bayesian information criterion (BIC). The asymptotic validity of the methodology is established in Li et al. (2019 ). The unpenalized nonparametric maximum likelihood estimation for interval censored and possibly left truncated data is also available. Package: r-cran-alcyon Architecture: amd64 Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3450 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), 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/noble/main/r-cran-alcyon_0.8.1-1.ca2404.1_amd64.deb Size: 1387008 MD5sum: a9abf506e37a7e9da473c5fb5e422566 SHA1: 08416844e113aa9bb29ff594d4d56df846a3a60b SHA256: 14e52ef9afdc445309f5fc9f5a426cd48fc180b2e793f2ecd3b5d16b003609df SHA512: bcd365a639fc718cf8a68a85739af36a98d61a76178452c6f58e62065e590f8b088891db8b941b3fa631e1769344333b68657dd13a9570f086ac2c7e5ce044d0 Homepage: https://cran.r-project.org/package=alcyon Description: CRAN Package 'alcyon' (Spatial Network Analysis) Interface package for 'sala', the spatial network analysis library from the 'depthmapX' software application. The R parts of the code are based on the 'rdepthmap' package. Allows for the analysis of urban and building-scale networks and provides metrics and methods usually found within the Space Syntax domain. Methods in this package are described by K. Al-Sayed, A. Turner, B. Hillier, S. Iida and A. Penn (2014) "Space Syntax methodology", and also by A. Turner (2004) "Depthmap 4: a researcher's handbook". Package: r-cran-alfam2 Architecture: amd64 Version: 4.2.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 818 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-alfam2_4.2.14-1.ca2404.1_amd64.deb Size: 425538 MD5sum: 6a3da918231cc50ef8e872b101623896 SHA1: d31fa91265669e2cc3587518b36c197c999afd61 SHA256: 521a5a60870c8de9fccd6f6cb39b3400b2ce63d3dc0195f7371dab456cfb24e7 SHA512: 18fc586fd67c245c5455c891aa3cf4b4edb428babfacc9eb383186d7999d06c68f015a133c89ce95bcdf62cd1cdba2ede2363a17e80ccb6d7919db6e485437e8 Homepage: https://cran.r-project.org/package=ALFAM2 Description: CRAN Package 'ALFAM2' (Dynamic Model of Ammonia Emission from Field-Applied Manure) An implementation of the ALFAM2 dynamic emission model for ammonia volatilization from field-applied animal slurry (manure with dry matter below about 15%). The model can be used to predict cumulative emission and emission rate of ammonia following field application of slurry. Predictions may be useful for emission inventory calculations, fertilizer management, assessment of mitigation strategies, or research aimed at understanding ammonia emission. Default parameter sets include effects of application method, slurry composition, and weather. The model structure is based on a simplified representation of the physical-chemical slurry-soil-atmosphere system. More information is available via citation("ALFAM2"). Package: r-cran-algdesign Architecture: amd64 Version: 1.2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 776 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-algdesign_1.2.1.2-1.ca2404.1_amd64.deb Size: 565394 MD5sum: 325e9c39978c3c7127d98954f69cc1e8 SHA1: 55b1ebadc10ffa161b7e6a556034d41d367bb488 SHA256: dbdcba56bc7cda62754bf2aacfe04e844207248664057c3a71e20bdb47e75d5f SHA512: 29fb14ef6d75d215b9b37839d4dc9465eb62a625c0e23bcc69111c39debfe522b331a007f1b1efaedb34f42c9b9dac109b86985d826ce3b65732c297addf9a83 Homepage: https://cran.r-project.org/package=AlgDesign Description: CRAN Package 'AlgDesign' (Algorithmic Experimental Design) Algorithmic experimental designs. Calculates exact and approximate theory experimental designs for D,A, and I criteria. Very large designs may be created. Experimental designs may be blocked or blocked designs created from a candidate list, using several criteria. The blocking can be done when whole and within plot factors interact. Package: r-cran-allelicseries Architecture: amd64 Version: 0.1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 842 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-compquadform, r-cran-glue, r-cran-rcpp, r-cran-rnomni, r-cran-skat, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-allelicseries_0.1.1.5-1.ca2404.1_amd64.deb Size: 400840 MD5sum: 2be7905e6f98e406845b0e544b989f6e SHA1: 66e29da203e58af34964c1e8e5868e21e314551b SHA256: ae97381601f48e362b93c404a786d7bc8ddb42b787b41263c835577398eaff8c SHA512: a914e5c08e7f08b4a4ba34f7da1b5b7b3820660f65e84fc2eb8306d13e082bc66af8cf582fb34d38df9377137493b9bb914f90b531c9df2e28d233425ddb606b Homepage: https://cran.r-project.org/package=AllelicSeries Description: CRAN Package 'AllelicSeries' (Allelic Series Test) Implementation of gene-level rare variant association tests targeting allelic series: genes where increasingly deleterious mutations have increasingly large phenotypic effects. The COding-variant Allelic Series Test (COAST) operates on the benign missense variants (BMVs), deleterious missense variants (DMVs), and protein truncating variants (PTVs) within a gene. COAST uses a set of adjustable weights that tailor the test towards rejecting the null hypothesis for genes where the average magnitude of effect increases monotonically from BMVs to DMVs to PTVs. See McCaw ZR, O’Dushlaine C, Somineni H, Bereket M, Klein C, Karaletsos T, Casale FP, Koller D, Soare TW. (2023) "An allelic series rare variant association test for candidate gene discovery" . Package: r-cran-almanac Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 819 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-r6, r-cran-rlang, r-cran-v8, r-cran-vctrs Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-slider, r-cran-testthat Filename: pool/dists/noble/main/r-cran-almanac_1.0.0-1.ca2404.1_amd64.deb Size: 427688 MD5sum: 10e0a43e9079641253bb21198248de19 SHA1: 76dc8f112f86d2c8fc476289377bf19441e81364 SHA256: 5317bcdbb8580350f6568239bc52716781f59a9799a2850b632cac1e49720363 SHA512: de0bf220358c9c067d9e0104c8e5af50f60cf20f24356669568d80810fb9b2dc5d44ca07f370766b1be6904ec6371aa43844095610aa42c2c217ee9882455f5b Homepage: https://cran.r-project.org/package=almanac Description: CRAN Package 'almanac' (Tools for Working with Recurrence Rules) Provides tools for defining recurrence rules and recurrence sets. Recurrence rules are a programmatic way to define a recurring event, like the first Monday of December. Multiple recurrence rules can be combined into larger recurrence sets. A full holiday and calendar interface is also provided that can generate holidays within a particular year, can detect if a date is a holiday, can respect holiday observance rules, and allows for custom holidays. Package: r-cran-alpaca Architecture: amd64 Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-formula, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-bife, r-cran-car, r-cran-knitr, r-cran-lfe, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-alpaca_0.3.5-1.ca2404.1_amd64.deb Size: 206706 MD5sum: 18ec5acee91480818af1b8fa12331a21 SHA1: a1859aa1ee84317e57beffc799bbef66660b7602 SHA256: f9cf236975d2956003d173de35bfe79b7163fed8fcf5bce4c1a03596f7b93ada SHA512: cf22b8a71a50352834ccb535aac7e441a0c9f2d9de076b31d28663d3317c12e0c66e476b2855e68c2fa85a20d4f217cfd3a136bbd00d3b53248f771b65cfb1de Homepage: https://cran.r-project.org/package=alpaca Description: CRAN Package 'alpaca' (Fit GLM's with High-Dimensional k-Way Fixed Effects) Provides a routine to partial out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is based on the algorithm described in Stammann (2018) and is restricted to glm's that are based on maximum likelihood estimation and nonlinear. It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides analytical bias corrections for binary choice models derived by Fernandez-Val and Weidner (2016) and Hinz, Stammann, and Wanner (2020) . Package: r-cran-alphabetr Architecture: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-clue, r-cran-dplyr, r-cran-multicool Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-alphabetr_0.2.2-1.ca2404.1_amd64.deb Size: 173556 MD5sum: 1af906069cce677df614d0af1abd5438 SHA1: 0ab96e4f28d09379329b9f6b5f4d05210bdfa63b SHA256: fef440b28f71d0d2641f78b21b5da3369dc2ff8b3f5b4be8ebceb1842df0fc46 SHA512: 456d78765ac20f4c2e03ed813904e7d26d544f2f20a394d0a3dede847bb339c8419e4fcf1896245d51b0deae32a99f21a10c0d944c3b308457fd4de788684014 Homepage: https://cran.r-project.org/package=alphabetr Description: CRAN Package 'alphabetr' (Algorithms for High-Throughput Sequencing of Antigen-Specific TCells) Provides algorithms for frequency-based pairing of alpha-beta T cell receptors. Package: r-cran-alphapart Architecture: amd64 Version: 0.9.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2667 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-directlabels, r-cran-ggplot2, r-cran-pedigree, r-cran-rcpp, r-cran-reshape, r-cran-dplyr, r-cran-magrittr, r-cran-tibble Suggests: r-cran-rcolorbrewer, r-cran-truncnorm, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-ggridges Filename: pool/dists/noble/main/r-cran-alphapart_0.9.8-1.ca2404.1_amd64.deb Size: 2107084 MD5sum: 25a5d533032e2af1afc9f178a513cf0b SHA1: 4bf33371a74c5412cf00ebb911a6a3851bed5473 SHA256: 0baa7c51e7872b8a6a6a36773fa0d8937494d085b8f23e2b6f5bb1a5d04f5d3d SHA512: 8f59ae9cd1d584299a88aa94c93264251f490f319936928e5cb4894897e67c5ab24c3a9acc3d0cd2a81d344af518b807eb59eb7068f37de0b3745abcfb60aa60 Homepage: https://cran.r-project.org/package=AlphaPart Description: CRAN Package 'AlphaPart' (Partition/Decomposition of Breeding Values by Paths ofInformation) A software that implements a method for partitioning genetic trends to quantify the sources of genetic gain in breeding programmes. The partitioning method is described in Garcia-Cortes et al. (2008) . The package includes the main function AlphaPart for partitioning breeding values and auxiliary functions for manipulating data and summarizing, visualizing, and saving results. Package: r-cran-alphashape3d Architecture: amd64 Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-geometry, r-cran-rgl, r-cran-rann Suggests: r-cran-alphahull Filename: pool/dists/noble/main/r-cran-alphashape3d_1.3.3-1.ca2404.1_amd64.deb Size: 89630 MD5sum: c7cad92da94a3e74fce0496fcbe465bd SHA1: 09bb2b98cc3803e616b43a41e5ea8108df796c6c SHA256: b97c2f58e0b8d0af3952c6ab416da7926fbc8f0b6c783e4c90422e982ccddc37 SHA512: ad7deaf9621b11eaae243290eca626d35f5215d51b5d0b5eb7e9d01fd5bd440d7099855904463bd5f79e6fce638b8f76a59742bb5ae8480a2c002e28ac8581f6 Homepage: https://cran.r-project.org/package=alphashape3d Description: CRAN Package 'alphashape3d' (Implementation of the 3D Alpha-Shape for the Reconstruction of3D Sets from a Point Cloud) Implementation in R of the alpha-shape of a finite set of points in the three-dimensional space. The alpha-shape generalizes the convex hull and allows to recover the shape of non-convex and even non-connected sets in 3D, given a random sample of points taken into it. Besides the computation of the alpha-shape, this package provides users with functions to compute the volume of the alpha-shape, identify the connected components and facilitate the three-dimensional graphical visualization of the estimated set. Package: r-cran-alphasimr Architecture: amd64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2737 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-r6, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-alphasimr_2.1.0-1.ca2404.1_amd64.deb Size: 1660214 MD5sum: cb334dc07394534b3fbea6207da73808 SHA1: f1ff6d3ce627c5219e1b53835fdf55fed6d77fa3 SHA256: 67be60c447983f6f7791eac8b53f2d2acc3805164d55305f57f4a8be202a2a1d SHA512: cbf9b1ea1d4c80394d61695ddcde792947d399b1df770ef0344bda1b2e312362c9352eea665807aecf2c3754efef11927d4ff679c8adc804b1b8b32e5da25f1f Homepage: https://cran.r-project.org/package=AlphaSimR Description: CRAN Package 'AlphaSimR' (Breeding Program Simulations) The successor to the 'AlphaSim' software for breeding program simulation [Faux et al. (2016) ]. Used for stochastic simulations of breeding programs to the level of DNA sequence for every individual. Contained is a wide range of functions for modeling common tasks in a breeding program, such as selection and crossing. These functions allow for constructing simulations of highly complex plant and animal breeding programs via scripting in the R software environment. Such simulations can be used to evaluate overall breeding program performance and conduct research into breeding program design, such as implementation of genomic selection. Included is the 'Markovian Coalescent Simulator' ('MaCS') for fast simulation of biallelic sequences according to a population demographic history [Chen et al. (2009) ]. Package: r-cran-alqrfe Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-alqrfe_1.3-1.ca2404.1_amd64.deb Size: 95998 MD5sum: 2e511d8a8720aeb3c4ba1e48534ec5b8 SHA1: 84cb9d4fa3c8b3e8ba4a8974dc0c97978ee15ee5 SHA256: 416e6157cf737d84cf25ab590f3efbe00d47f370bc915f2f7c888000f7314e5d SHA512: d19f2e36f297eb24f4f47e5ee88eefda215ec45b7d61694fd40183f1d95d2861445f0651bfaa905d552468f21e296d1c2cf555dca4832a005ad21ad851fcde2c Homepage: https://cran.r-project.org/package=alqrfe Description: CRAN Package 'alqrfe' (Adaptive Lasso Quantile Regression with Fixed Effects) Quantile regression with fixed effects solves longitudinal data, considering the individual intercepts as fixed effects. The parametric set of this type of problem used to be huge. Thus penalized methods such as Lasso are currently applied. Adaptive Lasso presents oracle proprieties, which include Gaussianity and correct model selection. Bayesian information criteria (BIC) estimates the optimal tuning parameter lambda. Plot tools are also available. Package: r-cran-alternativeroc Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-proc, r-cran-plyr, r-cran-sn, r-cran-hmisc, r-cran-rcpp Suggests: r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-alternativeroc_1.0.4-1.ca2404.1_amd64.deb Size: 122870 MD5sum: 05ff7b3997ae251b2b29ca1fa8865ebd SHA1: 8d0c40d034228a9f5569f59028853b23eac54a40 SHA256: 37fa9b1061ef52991a9b4fc5dda96c525942d186074704885bd7181ba06cf90b SHA512: a12b675cc57096f2401359460b3d7f5476917fd0d440b5d704a3b0c96192199cde8fe75b74f95ba81413303b0d3408bb658ef7a7104f38548b37d964e9ce97a5 Homepage: https://cran.r-project.org/package=alternativeROC Description: CRAN Package 'alternativeROC' (Alternative and Fast ROC Analysis) Alternative and fast algorithms for the analysis of receiver operating characteristics curves (ROC curves) as described in Thomas et al. (2017) and Thomas et al. (2023) . Package: r-cran-alues Architecture: amd64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3171 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-markdown, r-cran-knitr, r-cran-microbenchmark, r-cran-ggmap, r-cran-raster, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-alues_0.2.1-1.ca2404.1_amd64.deb Size: 1799496 MD5sum: 1b9625c95e74988e6ac69cf5ab9b0115 SHA1: 61a9ce922058fd2a93487866056237f97fe47be5 SHA256: 90b6e149ffafa6ae958e88f742083a1fc1405141adf44d55a4bbd0ab6da0a467 SHA512: 2877d9796c8bec785a44621e64352ec1d1f93d831614147a4ddbfddeb548105d2c678b0fc973f0f0aa802c7f2712a8e1ce393e704d568265fca5a78cfbdc54e2 Homepage: https://cran.r-project.org/package=ALUES Description: CRAN Package 'ALUES' (Agricultural Land Use Evaluation System) Evaluates land suitability for different crops production. The package is based on the Food and Agriculture Organization (FAO) and the International Rice Research Institute (IRRI) methodology for land evaluation. Development of ALUES is inspired by similar tool for land evaluation, Land Use Suitability Evaluation Tool (LUSET). The package uses fuzzy logic approach to evaluate land suitability of a particular area based on inputs such as rainfall, temperature, topography, and soil properties. The membership functions used for fuzzy modeling are the following: Triangular, Trapezoidal and Gaussian. The methods for computing the overall suitability of a particular area are also included, and these are the Minimum, Maximum and Average. Finally, ALUES is a highly optimized library with core algorithms written in C++. Package: r-cran-amap Architecture: amd64 Version: 0.8-20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 395 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.1.1), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-bioc-biobase Filename: pool/dists/noble/main/r-cran-amap_0.8-20-1.ca2404.1_amd64.deb Size: 282634 MD5sum: 7197512f56196cf294be30f58a04b1e3 SHA1: c033f82c13c96bdf4923539967c99ca7066ee4bb SHA256: 66a67db8f04fd35e1abbdf9684dff3dacc094340674d0168c026d3f5636f3fc5 SHA512: e4d9f502da0e5d178f4a8652250b231f5be2e938d01488afe7fb8dc7ec35d8d3b08e20fca5ed34c41be344be1ef052a1bf624dd189f50eab05dc931eb6c5aa40 Homepage: https://cran.r-project.org/package=amap Description: CRAN Package 'amap' (Another Multidimensional Analysis Package) Tools for Clustering and Principal Component Analysis (With robust methods, and parallelized functions). Package: r-cran-ambient Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 985 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-cpp11 Suggests: r-cran-covr Filename: pool/dists/noble/main/r-cran-ambient_1.0.3-1.ca2404.1_amd64.deb Size: 846478 MD5sum: c60cd8a7cbd823c583c2bab54357956b SHA1: 005566f7ce7efaba77910debe76c65f9ddf65d73 SHA256: a64024c9f8866c47ff0875cb38100a85c3f51a1bd0a5203267ec71d25cb42995 SHA512: 657d3470cd147867663ecca758a42fc8f965fd78d2291159fda923b092063b91287da941352c4022c9b8b462626c7b4b68b2f57bbbc558ab777678d67c6387b7 Homepage: https://cran.r-project.org/package=ambient Description: CRAN Package 'ambient' (A Generator of Multidimensional Noise) Generation of natural looking noise has many application within simulation, procedural generation, and art, to name a few. The 'ambient' package provides an interface to the 'FastNoise' C++ library and allows for efficient generation of perlin, simplex, worley, cubic, value, and white noise with optional perturbation in either 2, 3, or 4 (in case of simplex and white noise) dimensions. Package: r-cran-ambit Architecture: amd64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1862 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-deoptim, r-cran-fbasics, r-cran-lsts, r-cran-nnet, r-cran-rcpp Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-latex2exp, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ambit_0.2.3-1.ca2404.1_amd64.deb Size: 536404 MD5sum: 54433715362eb334e3c29312443249a2 SHA1: 7e7f43a3fa811acb8749f51a8b20489aaa04d1d7 SHA256: 96a8297e452d226aef67ed437444d5e9cbeb8b9434c0461fe5b56baf83b7ff2c SHA512: 1fb2e4ee0f191f15efe55cf1b232139b6d1cf661a5b738c1f5f982420a41ca510e45eb6dc370332041ce674f174a9be231c3b7d3dd63efcbb9c23694cd4186e9 Homepage: https://cran.r-project.org/package=ambit Description: CRAN Package 'ambit' (Simulation and Estimation of Ambit Processes) Simulation and estimation tools for various types of ambit processes, including trawl processes and weighted trawl processes. Package: r-cran-amelia Architecture: amd64 Version: 1.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2213 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-foreign, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-broom, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-amelia_1.8.3-1.ca2404.1_amd64.deb Size: 1445862 MD5sum: fcd47d376de649a4ea31a1ee50175906 SHA1: a266d9adb874123d7f0d28a32c17a96a335a1f64 SHA256: b51015bc7e6cdedabcd8328b1a37609c53b1c7397cb5fe7ac57ad9bcd2a9bb3b SHA512: f86c880e850bd5ff1ded8b5037985a3e3c30c375ca6d66afcec5f019111455b53d84255d4bf7cb925279242b2eba0d4a749b30287da7ad5bf91c0a439b9bfc87 Homepage: https://cran.r-project.org/package=Amelia Description: CRAN Package 'Amelia' (A Program for Missing Data) A tool that "multiply imputes" missing data in a single cross-section (such as a survey), from a time series (like variables collected for each year in a country), or from a time-series-cross-sectional data set (such as collected by years for each of several countries). Amelia II implements our bootstrapping-based algorithm that gives essentially the same answers as the standard IP or EMis approaches, is usually considerably faster than existing approaches and can handle many more variables. Unlike Amelia I and other statistically rigorous imputation software, it virtually never crashes (but please let us know if you find to the contrary!). The program also generalizes existing approaches by allowing for trends in time series across observations within a cross-sectional unit, as well as priors that allow experts to incorporate beliefs they have about the values of missing cells in their data. Amelia II also includes useful diagnostics of the fit of multiple imputation models. The program works from the R command line or via a graphical user interface that does not require users to know R. Package: r-cran-ameras Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1872 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-nimble, r-cran-rcpp, r-cran-rcppeigen, r-cran-coda, r-cran-numderiv, r-cran-mvtnorm, r-cran-mcmcvis, r-cran-tidyselect, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-scales, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-ameras_0.3.0-1.ca2404.1_amd64.deb Size: 1304852 MD5sum: 8e59a6c1558527aec9e062f2d2931a85 SHA1: daa807f0e20d7884ea570b64cf348a843bffc391 SHA256: ffb235a13a262bbe702a5a8fc9fab97e8a7a2f6f3eb14d29cca875e71dda92a9 SHA512: c4e7e7e44e9aa1ef778481c638beb9569881c1033b65c92e222f53f15000178b6ad334b3b20e7ed8899b0bfb076b9a159caec5ad4d5de56c1e50ca10e4206502 Homepage: https://cran.r-project.org/package=ameras Description: CRAN Package 'ameras' (Analyze Multiple Exposure Realizations in Association Studies) Analyze association studies with multiple realizations of a noisy or uncertain exposure. These can be obtained from e.g. a two-dimensional Monte Carlo dosimetry system (Simon et al 2015 ) to characterize exposure uncertainty. The implemented methods are regression calibration (Carroll et al. 2006 ), extended regression calibration (Little et al. 2023 ), Monte Carlo maximum likelihood (Stayner et al. 2007 ), frequentist model averaging (Kwon et al. 2023 ), and Bayesian model averaging (Kwon et al. 2016 ). Supported model families are Gaussian, binomial, multinomial, Poisson, proportional hazards, and conditional logistic. Package: r-cran-amisforinfectiousdiseases Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1014 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-mclust, r-cran-mnormt, r-cran-rcpp, r-cran-weights, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-patchwork, r-cran-rmarkdown, r-cran-sf, r-cran-viridis Filename: pool/dists/noble/main/r-cran-amisforinfectiousdiseases_0.1.0-1.ca2404.1_amd64.deb Size: 563848 MD5sum: 5c86e09a1019d6f3e572dd7e316d3f7a SHA1: 71905319289e775cab054ef2d67a3fcd2721dc54 SHA256: 485c4d36b1b0f339faa6fc3e29534785a478330b87303afce992e61aa90132a8 SHA512: d2a8a19f62760d19a8da4a5af2ac4f03813148b67e2d33560e43b44f5043d5fd333543b1306e7e226b76299bcd9375c1045d600e4cea6c97719f7165ff0fba23 Homepage: https://cran.r-project.org/package=AMISforInfectiousDiseases Description: CRAN Package 'AMISforInfectiousDiseases' (Implement the AMIS Algorithm for Infectious Disease Models) Implements the Adaptive Multiple Importance Sampling (AMIS) algorithm, as described by Retkute et al. (2021, ), to estimate key epidemiological parameters by combining outputs from a geostatistical model of infectious diseases (such as prevalence, incidence, or relative risk) with a disease transmission model. Utilising the resulting posterior distributions, the package enables forward projections at the local level. Package: r-cran-ampir Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1849 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-peptides, r-cran-caret, r-cran-kernlab, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-ampir_1.1.0-1.ca2404.1_amd64.deb Size: 1729100 MD5sum: 3d06beaf8ed61d911fe108166eb14506 SHA1: 6919d659b41c17eb55c44ae4008e80e268147fed SHA256: 35ac7377cdc0f085f98f71ba8e2583637e6a30e96dd198f627e9718f29b9db93 SHA512: 045cea0972a6d96da8d6b7e5e927e27aa6b4e9c7efc57d314a853a7e79502a05966446326d3348e742f78b8929c77d71c473f060bdbb2782aeddf490babf4643 Homepage: https://cran.r-project.org/package=ampir Description: CRAN Package 'ampir' (Predict Antimicrobial Peptides) A toolkit to predict antimicrobial peptides from protein sequences on a genome-wide scale. It incorporates two support vector machine models ("precursor" and "mature") trained on publicly available antimicrobial peptide data using calculated physico-chemical and compositional sequence properties described in Meher et al. (2017) . In order to support genome-wide analyses, these models are designed to accept any type of protein as input and calculation of compositional properties has been optimised for high-throughput use. For best results it is important to select the model that accurately represents your sequence type: for full length proteins, it is recommended to use the default "precursor" model. The alternative, "mature", model is best suited for mature peptide sequences that represent the final antimicrobial peptide sequence after post-translational processing. For details see Fingerhut et al. (2020) . The 'ampir' package is also available via a Shiny based GUI at . Package: r-cran-amssim Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-amssim_0.1.0-1.ca2404.1_amd64.deb Size: 80320 MD5sum: 43e2bf245e818d3764e7164a9958eefb SHA1: 11f794d5557b6b0179b7647ef9e05c77d177a3b8 SHA256: 1491401454bcbb203d6a71669d3f29398e38b36e11b30c39c76cd65a7c3ed0bf SHA512: 793d2cb9c18c8503f0f03d98509cc32ea2cd67abc3bcd0f204d66853f9326acb084b8db939dca7ac7300c0811b3232403fc534bd68d8d7c20c93fa7204f60173 Homepage: https://cran.r-project.org/package=amsSim Description: CRAN Package 'amsSim' (Adaptive Multilevel Splitting for Option Simulation and Pricing) Simulation and pricing routines for rare-event options using Adaptive Multilevel Splitting and standard Monte Carlo under Black-Scholes and Heston models. Core routines are implemented in C++ via Rcpp and RcppArmadillo with lightweight R wrappers. Package: r-cran-anacoda Architecture: amd64 Version: 0.1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3718 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-vgam, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-hmisc, r-cran-coda, r-cran-testthat, r-cran-lmodel2, r-cran-markdown Filename: pool/dists/noble/main/r-cran-anacoda_0.1.4.4-1.ca2404.1_amd64.deb Size: 1553418 MD5sum: f45e15ae585fa17e790d6dd7d81d2963 SHA1: 74cfbdc7f178aa260599f0a66dee05359ad643e7 SHA256: 5953a179e3c7164a232dd0758254781a89e8659e171ab27a0d923026c0f54c37 SHA512: cfd3eb87dd80b4346b68f5a5fa2fe828096a789c2eadf3d9856852477ef1af59503cfd4c09c442c8b63b7c762ab40560c3189f06c75203cb4de3e846b4c7c0da Homepage: https://cran.r-project.org/package=AnaCoDa Description: CRAN Package 'AnaCoDa' (Analysis of Codon Data under Stationarity using a BayesianFramework) Is a collection of models to analyze genome scale codon data using a Bayesian framework. Provides visualization routines and checkpointing for model fittings. Currently published models to analyze gene data for selection on codon usage based on Ribosome Overhead Cost (ROC) are: ROC (Gilchrist et al. (2015) ), and ROC with phi (Wallace & Drummond (2013) ). In addition 'AnaCoDa' contains three currently unpublished models. The FONSE (First order approximation On NonSense Error) model analyzes gene data for selection on codon usage against of nonsense error rates. The PA (PAusing time) and PANSE (PAusing time + NonSense Error) models use ribosome footprinting data to analyze estimate ribosome pausing times with and without nonsense error rate from ribosome footprinting data. Package: r-cran-anacor Architecture: amd64 Version: 1.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-colorspace, r-cran-fda Filename: pool/dists/noble/main/r-cran-anacor_1.1-4-1.ca2404.1_amd64.deb Size: 339226 MD5sum: 78e259453c8619c1c6bd2b12b7918943 SHA1: d47da7153bdd5b756e5a3ce4dbe93c9becf2abfd SHA256: d585345ad523911bdf27ac4322bff94de86d7c76a599b95bad1b10072e077645 SHA512: 10df5d3d0891d6294404f302a9d4e7acee7e7a0e011a40a94bb8e86c1d7af64beaef2962861e7b1bd01e3826b76433db74e2f8cfccccde13491cf40cc178449c Homepage: https://cran.r-project.org/package=anacor Description: CRAN Package 'anacor' (Simple and Canonical Correspondence Analysis) Performs simple and canonical CA (covariates on rows/columns) on a two-way frequency table (with missings) by means of SVD. Different scaling methods (standard, centroid, Benzecri, Goodman) as well as various plots including confidence ellipsoids are provided. 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Package: r-cran-analyzefmri Architecture: amd64 Version: 1.1-25-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 993 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.matlab, r-cran-fastica Suggests: r-cran-tkrplot Filename: pool/dists/noble/main/r-cran-analyzefmri_1.1-25-1.ca2404.1_amd64.deb Size: 602966 MD5sum: 5d8fb4ec1fab566c7e440dd42172b3ee SHA1: 5caf97ac65d73fda035c4d553c624e236f034437 SHA256: 17e65d40041957ee4ab8b8e4c07ddd4d76252df2a3b2b66f79db8abfa534a532 SHA512: 3c6444adb43071c491ec31c829c65f1e1e4402c328a214cd60c7bb3f667efd43b19eac4a235809acb27a66ec8c70adf0f636b05532eb6bb70c9a8ca391a62ffe Homepage: https://cran.r-project.org/package=AnalyzeFMRI Description: CRAN Package 'AnalyzeFMRI' (Functions for Analysis of fMRI Datasets Stored in the ANALYZE or'NIFTI' Format) Functions for I/O, visualisation and analysis of functional Magnetic Resonance Imaging (fMRI) datasets stored in the ANALYZE or 'NIFTI' format. Note that the latest version of 'XQuartz' seems to be necessary under MacOS. Package: r-cran-animalsequences Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-dplyr, r-cran-tidytext, r-cran-ggplot2, r-cran-fpc, r-cran-mclust, r-cran-kernlab, r-cran-dbscan, r-cran-apcluster, r-cran-tidyr, r-cran-tibble, r-cran-rlang, r-cran-igraph, r-cran-ggraph, r-cran-magrittr, r-cran-naivebayes, r-cran-ranger Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-animalsequences_0.2.0-1.ca2404.1_amd64.deb Size: 142334 MD5sum: 4fe32df3023d310f972c68291a46b901 SHA1: 62a1c27a56647c6b5bc9ead0f11522681c2d8960 SHA256: def314c79cfae2aa6fb8dca253e6c7854b730c177392154af77d9488b2febbd6 SHA512: e8dbd720f0d201d395f44dd69409d594e5cbeaf6cfff2c440a7c2fb33028a4267303c87169e43e5fda398a9c2877b8bb76c7148b350a5e48242041515fd6f50f 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. 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Methods include pre-network data permutations, bootstrapping techniques to obtain confidence intervals for global and node-level network metrics, and correlation and regression analysis of the local network metrics. Package: r-cran-anmc Architecture: amd64 Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-dicekriging, r-cran-truncatednormal Filename: pool/dists/noble/main/r-cran-anmc_0.2.5-1.ca2404.1_amd64.deb Size: 153520 MD5sum: e7cce2d9dedaa30873e547f7716468c1 SHA1: 2309796abd9e5933639529b77ed3f6b630d9f7c7 SHA256: dc9a0c9625514e17af2df4c7b630061edfbb5528a50417c84085b8a0c17b05a8 SHA512: a44e0abe3c0b989ea13f9106d2d287c394de3cae3188438022193974f262eddf42ad00a8a3e29dfc276e19769b488f2dd30ad4ce7f69ddb1b39bfd9e263713ed Homepage: https://cran.r-project.org/package=anMC Description: CRAN Package 'anMC' (Compute High Dimensional Orthant Probabilities) Computationally efficient method to estimate orthant probabilities of high-dimensional Gaussian vectors. Further implements a function to compute conservative estimates of excursion sets under Gaussian random field priors. Package: r-cran-ann2 Architecture: amd64 Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3092 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-reshape2, r-cran-ggplot2, r-cran-viridislite, r-cran-rlang, r-cran-rcpparmadillo, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ann2_2.4.0-1.ca2404.1_amd64.deb Size: 694858 MD5sum: 9346845b0d30cbff6ae3aac3e5b6594b SHA1: 0a3834734c568acbfde1e2a3876f3a21beea1f6a SHA256: 778b718413a065db68bcbd61ce1ff5111fe26404173a6eb1d4e2541f36783158 SHA512: 6aaadd4b85e9e97e6f93fca676932dc000758551cfe05a698c0c255940a8c59edba1feffe2d2eba1b7621bc95dacf2d47d7fa3086e6764ab1e6c075f5b32b44a Homepage: https://cran.r-project.org/package=ANN2 Description: CRAN Package 'ANN2' (Artificial Neural Networks for Anomaly Detection) Training of neural networks for classification and regression tasks using mini-batch gradient descent. Special features include a function for training autoencoders, which can be used to detect anomalies, and some related plotting functions. Multiple activation functions are supported, including tanh, relu, step and ramp. For the use of the step and ramp activation functions in detecting anomalies using autoencoders, see Hawkins et al. (2002) . Furthermore, several loss functions are supported, including robust ones such as Huber and pseudo-Huber loss, as well as L1 and L2 regularization. The possible options for optimization algorithms are RMSprop, Adam and SGD with momentum. The package contains a vectorized C++ implementation that facilitates fast training through mini-batch learning. Package: r-cran-anomaly Architecture: amd64 Version: 4.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1549 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-rcpp, r-cran-xts, r-cran-zoo, r-cran-rdpack, r-cran-cowplot, r-cran-bh Suggests: r-cran-robustbase Filename: pool/dists/noble/main/r-cran-anomaly_4.3.3-1.ca2404.1_amd64.deb Size: 1300456 MD5sum: 2fddb64072c815fb2dbdba91cf532667 SHA1: 9181ec54f2f400ab4cb19ed6ffd3d09a455e30fd SHA256: 099e1fa94a40a9075f525ed2f570449912714fb243efab131ba6371ee9445beb SHA512: 26712ebcc5e952e8fa4119a5ec8a41908880151b2538ef44edd8c525de4ca08d8c5af2ff127358d7318e8a3f8cc8cd85ccbf9a49bca2b81d33fe3748b1cd2715 Homepage: https://cran.r-project.org/package=anomaly Description: CRAN Package 'anomaly' (Detecting Anomalies in Data) Implements Collective And Point Anomaly (CAPA) Fisch, Eckley, and Fearnhead (2022) , Multi-Variate Collective And Point Anomaly (MVCAPA) Fisch, Eckley, and Fearnhead (2021) , Proportion Adaptive Segment Selection (PASS) Jeng, Cai, and Li (2012) , and Bayesian Abnormal Region Detector (BARD) Bardwell and Fearnhead (2015) . These methods are for the detection of anomalies in time series data. Further information regarding the use of this package along with detailed examples can be found in Fisch, Grose, Eckley, Fearnhead, and Bardwell (2024) . Package: r-cran-anominate Architecture: amd64 Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2885 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-wnominate, r-cran-pscl, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-anominate_0.7-1.ca2404.1_amd64.deb Size: 2885816 MD5sum: 8fc3f032ddc7c14ba5aff0930ad00120 SHA1: bedb63354a790e1f21c07e38d765594f2f24e394 SHA256: afb8c78dd183c4489c59202d6f74882bd943db96ee25577e13d222647c45e18e SHA512: 465d6d35988890abb5d61f9283e607e2331977e8603dc1c7cca74af8a23042a97a76fd26b4bf869b30d6b210b2bd0a3a8bc930da3d6142323f0a7110686d7492 Homepage: https://cran.r-project.org/package=anominate Description: CRAN Package 'anominate' (Alpha-NOMINATE Ideal Point Estimator) Provides functions to estimate and interpret the alpha-NOMINATE ideal point model developed in Carroll et al. (2013, ). alpha-NOMINATE extends traditional spatial voting frameworks by allowing for a mixture of Gaussian and quadratic utility functions, providing flexibility in modeling political actors' preferences. The package uses Markov Chain Monte Carlo (MCMC) methods for parameter estimation, supporting robust inference about individuals' ideological positions and the shape of their utility functions. It also contains functions to simulate data from the model and to calculate the probability of a vote passing given the ideal points of the legislators/voters and the estimated location of the choice alternatives. Package: r-cran-anthropometry Architecture: amd64 Version: 1.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1899 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-shapes, r-cran-rgl, r-cran-archetypes, r-cran-nnls, r-cran-ddalpha, r-cran-fnn, r-cran-icge, r-cran-cluster Suggests: r-cran-knitr, r-cran-calibrate, r-cran-mvtnorm, r-cran-rcolorbrewer, r-cran-plotrix, r-cran-abind Filename: pool/dists/noble/main/r-cran-anthropometry_1.21-1.ca2404.1_amd64.deb Size: 1756084 MD5sum: 19f2e70b09c0ee0179ac9c66881c3d8e SHA1: 3d09a4416733166a8c811e0274f31c60ff8eb9a9 SHA256: 392132f6a6903355960b6cff4dec2ff9fad7809ea81025a567a00282ade67091 SHA512: c633cfad382e176e5c6097f7e0ba39e142eb287a4fccc70d047e1d88b8a4b8f67648f957484de1db9279220e472096e104c4c71d1f6f7d439c7745dd68abf4fe Homepage: https://cran.r-project.org/package=Anthropometry Description: CRAN Package 'Anthropometry' (Statistical Methods for Anthropometric Data) Statistical methodologies especially developed to analyze anthropometric data. These methods are aimed at providing effective solutions to some commons problems related to Ergonomics and Anthropometry. They are based on clustering, the statistical concept of data depth, statistical shape analysis and archetypal analysis. Please see Vinue (2017) . Package: r-cran-anticlust Architecture: amd64 Version: 0.8.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1284 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rann, r-cran-lpsolve Suggests: r-cran-knitr, r-cran-mass, r-cran-rglpk, r-cran-rmarkdown, r-cran-rsymphony, r-cran-tinytest, r-cran-tableone, r-cran-palmerpenguins Filename: pool/dists/noble/main/r-cran-anticlust_0.8.14-1.ca2404.1_amd64.deb Size: 756528 MD5sum: 6d02d7b3bcc0a8ff8651daa2c847fc10 SHA1: eb090580b5678c4cb3e14320b3828499bb6cfa44 SHA256: 341b1902e7c8c1c3e6cf1bf85728de3cfea9f1a43e2148b086ed18434142a999 SHA512: c9806da60bcc18e87b473f7d20800ba2c62efd3afd86741ee3b113712036375cfbbffab2dc1210b04fe5747a62c8362aacf26f2b05eccfa7e8b802702c66bb47 Homepage: https://cran.r-project.org/package=anticlust Description: CRAN Package 'anticlust' (Subset Partitioning via Anticlustering) The method of anticlustering partitions a pool of elements into groups (i.e., anticlusters) with the goal of maximizing between-group similarity or within-group heterogeneity. The anticlustering approach thereby reverses the logic of cluster analysis that strives for high within-group homogeneity and clear separation between groups. Computationally, anticlustering is accomplished by maximizing instead of minimizing a clustering objective function, such as the intra-cluster variance (used in k-means clustering) or the sum of pairwise distances within clusters. The main function anticlustering() gives access to optimal and heuristic anticlustering methods described in Papenberg and Klau (2021; ), Brusco et al. (2020; ), Papenberg (2024; ), Papenberg, Wang, et al. (2025; ), Papenberg, Breuer, et al. (2025; ), and Yang et al. (2022; ). The optimal algorithms require that an integer linear programming solver is installed. This package will install 'lpSolve' () as a default solver, but it is also possible to use the package 'Rglpk' (), which requires the GNU linear programming kit (), the package 'Rsymphony' (), which requires the SYMPHONY ILP solver (), or the commercial solver Gurobi, which provides its own R package that is not available via CRAN (). 'Rglpk', 'Rsymphony', 'gurobi' and their system dependencies have to be manually installed by the user because they are only suggested dependencies. Full access to the bicriterion anticlustering method proposed by Brusco et al. (2020) is given via the function bicriterion_anticlustering(), while kplus_anticlustering() implements the full functionality of the k-plus anticlustering approach proposed by Papenberg (2024). Some other functions are available to solve classical clustering problems. The function balanced_clustering() applies a cluster analysis under size constraints, i.e., creates equal-sized clusters. The function matching() can be used for (unrestricted, bipartite, or K-partite) matching. The function wce() can be used optimally solve the (weighted) cluster editing problem, also known as correlation clustering, clique partitioning problem or transitivity clustering. Package: r-cran-antiword Architecture: amd64 Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 625 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sys Filename: pool/dists/noble/main/r-cran-antiword_1.3.5-1.ca2404.1_amd64.deb Size: 128050 MD5sum: 1c17d8ae20bb56bc1086c9b31e3b3b10 SHA1: 3a879acb6b8ff092d2643a9b40249a78c06a5ad0 SHA256: 9907bfec6aebe0025871d8880e297fc5ae8be09bc280c89e951e13ecdc14ebb3 SHA512: 7aac716b1982c017e13ea8e60905ef3eb6130c4e6f251fab00e5b09959e0672388025dff18a731fc4ab63d941c7e9d7e78537af5488e5e375245b7286e8bbce4 Homepage: https://cran.r-project.org/package=antiword Description: CRAN Package 'antiword' (Extract Text from Microsoft Word Documents) Wraps the 'AntiWord' utility to extract text from Microsoft Word documents. The utility only supports the old 'doc' format, not the new xml based 'docx' format. Use the 'xml2' package to read the latter. Package: r-cran-antman Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 786 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-salso, r-cran-mvtnorm, r-cran-mcclust, r-cran-ggally, r-cran-bayesplot, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-dendextend, r-cran-ggdendro, r-cran-ggplot2, r-cran-jpeg Filename: pool/dists/noble/main/r-cran-antman_1.1.0-1.ca2404.1_amd64.deb Size: 433998 MD5sum: 81b3e554e9a3a055a9af8be0283b6e24 SHA1: 3d694082ffa6d4769cafb03f1892f0884b2e6f53 SHA256: 863316f30746155774af2baebd253a24bc8276c661864e48ce82ea61d6a7e021 SHA512: 289b6014e52b3198533c0a59fbf1d18a6dba806f16bed4c2191b3493fbb1afe09c74cf27d782c7df52cdd043fe977cbed013c4f91e14b682e8b37e78a2fe15ff Homepage: https://cran.r-project.org/package=AntMAN Description: CRAN Package 'AntMAN' (Anthology of Mixture Analysis Tools) Fits finite Bayesian mixture models with a random number of components. The MCMC algorithm implemented is based on point processes as proposed by Argiento and De Iorio (2019) and offers a more computationally efficient alternative to reversible jump. Different mixture kernels can be specified: univariate Gaussian, multivariate Gaussian, univariate Poisson, and multivariate Bernoulli (latent class analysis). For the parameters characterising the mixture kernel, we specify conjugate priors, with possibly user specified hyper-parameters. We allow for different choices for the prior on the number of components: shifted Poisson, negative binomial, and point masses (i.e. mixtures with fixed number of components). Package: r-cran-anytime Architecture: amd64 Version: 0.3.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-tinytest, r-cran-gettz Filename: pool/dists/noble/main/r-cran-anytime_0.3.13-1.ca2404.1_amd64.deb Size: 251892 MD5sum: 68a35967352a87d7e180f256ecf62151 SHA1: 21d536334a9991ac72698e0270df982141432c31 SHA256: f609edb01343c7fcb7b26607cdbe9aa5537d97d496b0dae6b2908b1779963d7e SHA512: 2697f9befd659cc8d1c939e32aabfe06074ddef6becad4370f9d07e13fece69dad3066f650e92c140ca3e8e2df30168010f6156698f66da347483f0a3cd2bee0 Homepage: https://cran.r-project.org/package=anytime Description: CRAN Package 'anytime' (Anything to 'POSIXct' or 'Date' Converter) Convert input in any one of character, integer, numeric, factor, or ordered type into 'POSIXct' (or 'Date') objects, using one of a number of predefined formats, and relying on Boost facilities for date and time parsing. Package: r-cran-aorsf Architecture: amd64 Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2109 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-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/noble/main/r-cran-aorsf_0.1.6-1.ca2404.1_amd64.deb Size: 1195880 MD5sum: f25c1e8ebd32489e6089b6a62d4b5d8f SHA1: a5a18bbdf9424a64da87357e1643f4020b14e729 SHA256: cf8ab5f2061c7fe9c4182920fa03f0fcf9528ccef2220388a5d30d9d31b13be9 SHA512: 881c0377529b9141882b7aff6528bbeed3b21cbb0a323c6483680042d61e3fe21e1b07aa117b7491ed4a469fd054a63b38c382faa7ba96f8fcee86fa787deba5 Homepage: https://cran.r-project.org/package=aorsf Description: CRAN Package 'aorsf' (Accelerated Oblique Random Forests) Fit, interpret, and compute predictions with oblique random forests. Includes support for partial dependence, variable importance, passing customized functions for variable importance and identification of linear combinations of features. Methods for the oblique random survival forest are described in Jaeger et al., (2023) . Package: r-cran-aovbay Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1487 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), 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/noble/main/r-cran-aovbay_0.1.0-1.ca2404.1_amd64.deb Size: 577612 MD5sum: 4819f19708977b118e0e1b900a89884c SHA1: 176d44c5527528f882ed0f2903e086eb05601602 SHA256: 686e4077162c2fe60e12b50ebecc234e9dd79af4379eed7ad7e4ac2496cd2c55 SHA512: d23e25a80659d07ca1e8c5ba2a0683e5b2d29be670245ee0dbb8065893580d6648912cde67d5861d1788ce036f109eac7c23fb0e68cc872b587f84800988fd63 Homepage: https://cran.r-project.org/package=AovBay Description: CRAN Package 'AovBay' (Classic, Nonparametric and Bayesian One-Way Analysis of VariancePanel) It covers various approaches to analysis of variance, provides an assumption testing section in order to provide a decision diagram that allows selecting the most appropriate technique. It provides the classical analysis of variance, the nonparametric equivalent of Kruskal Wallis, and the Bayesian approach. These results are shown in an interactive shiny panel, which allows modifying the arguments of the tests, contains interactive graphics and presents automatic conclusions depending on the tests in order to contribute to the interpretation of these analyzes. 'AovBay' uses 'Stan' and 'FactorBayes' for Bayesian analysis and 'Highcharts' for interactive charts. Package: r-cran-apackoftheclones Architecture: amd64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2439 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-ggforce, r-cran-ggplot2, r-cran-hash, r-cran-lifecycle, r-cran-magrittr, r-cran-rcpp, r-cran-rlang, r-cran-seurat, r-cran-seuratobject Suggests: r-cran-cowplot, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr, r-cran-colorspace Filename: pool/dists/noble/main/r-cran-apackoftheclones_1.3.0-1.ca2404.1_amd64.deb Size: 2011586 MD5sum: ed902b5876b910a662bb7328aa448c9a SHA1: 2a698704a69ef61bbbdb9a7ee8643cc4544d94b7 SHA256: b580569b286bdf9f52b98baf97656997cb7d31212ed43eea3d3a8f58fdf67a5f SHA512: 054b2b8d50690ef94227241c44fda4bb3ef15cc58c28268674218902d853a5c7b9af61ba223a26b1a553061be7dfd493b6d30d098be2c84ab5d3145ac4e087fe Homepage: https://cran.r-project.org/package=APackOfTheClones Description: CRAN Package 'APackOfTheClones' (Visualization of Clonal Expansion for Single Cell ImmuneProfiles) Visualize clonal expansion via circle-packing. 'APackOfTheClones' extends 'scRepertoire' to produce a publication-ready visualization of clonal expansion at a single cell resolution, by representing expanded clones as differently sized circles. The method was originally implemented by Murray Christian and Ben Murrell in the following immunology study: Ma et al. (2021) . Package: r-cran-apcf Architecture: amd64 Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 657 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgeos-c1t64 (>= 3.4.2), libstdc++6 (>= 13.1), 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/noble/main/r-cran-apcf_0.3.3-1.ca2404.1_amd64.deb Size: 437048 MD5sum: ad1971c875731aef7547bd919b51ea81 SHA1: 1b2e17de98562d603542cd397729162251ea8aee SHA256: fdf99b592df230cee83eb39a03390820baf52a68308cff960a8bca25f7fb68ef SHA512: c6eda76fe03a116e727af299233de872951e4ba5e572eba018af43b24f8cb46957ba137054132079bb1b4f9905c5d6fbd213703c46cb0e79959bc0d056138527 Homepage: https://cran.r-project.org/package=apcf Description: CRAN Package 'apcf' (Adapted Pair Correlation Function) The adapted pair correlation function transfers the concept of the pair correlation function from point patterns to patterns of objects of finite size and irregular shape (e.g. lakes within a country). The pair correlation function describes the spatial distribution of objects, e.g. random, aggregated or regularly spaced. This is a reimplementation of the method suggested by Nuske et al. (2009) using the library 'GEOS' . Package: r-cran-apcluster Architecture: amd64 Version: 1.4.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2119 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-apcluster_1.4.14-1.ca2404.1_amd64.deb Size: 1475994 MD5sum: 85a8abeb8e667aa54b47fd6f711d7982 SHA1: cf5291000d93b9bfb004a9fb371c2113c0b172c4 SHA256: 58b6570f7a5eef86902a14228a9ddf839fd59adf5ebcd8af1e6a16bf225ecd3a SHA512: 4927cd62459be48a9e8db7252113c23cf5ba7ee8711dcea5e43f93346644c89ecbb4a2b094981f030f694e0f8b699c1e947c2bac3cac3710bb467ffff1cc6e30 Homepage: https://cran.r-project.org/package=apcluster Description: CRAN Package 'apcluster' (Affinity Propagation Clustering) Implements Affinity Propagation clustering introduced by Frey and Dueck (2007) . The algorithms are largely analogous to the 'Matlab' code published by Frey and Dueck. The package further provides leveraged affinity propagation and an algorithm for exemplar-based agglomerative clustering that can also be used to join clusters obtained from affinity propagation. Various plotting functions are available for analyzing clustering results. Package: r-cran-ape Architecture: amd64 Version: 5.8-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3364 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-lattice, r-cran-rcpp, r-cran-digest Suggests: r-cran-gee, r-cran-expm, r-cran-igraph, r-cran-phangorn, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-ape_5.8-1-1.ca2404.1_amd64.deb Size: 2910258 MD5sum: 5f97db7b08df81522de8f0663c4d1909 SHA1: 9c8f1eb9e457768d81ce4194d1b40c31626db2a0 SHA256: 3cbaa80adbf50194db459fc67a2b1f1cc7d5b3b7ce89e340cf3fa2c319cfe5f3 SHA512: 946ff111d93405fef6e28e2abc0080b0f7ee66e299deee54bfee6e5f592d6f12da49486344e17deabd7cd1b35a879b905c8fb699e2d001ec0163f4c58f3ce1d3 Homepage: https://cran.r-project.org/package=ape Description: CRAN Package 'ape' (Analyses of Phylogenetics and Evolution) Functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, ancestral character analyses, analyses of diversification and macroevolution, computing distances from DNA sequences, reading and writing nucleotide sequences as well as importing from BioConductor, and several tools such as Mantel's test, generalized skyline plots, graphical exploration of phylogenetic data (alex, trex, kronoviz), estimation of absolute evolutionary rates and clock-like trees using mean path lengths and penalized likelihood, dating trees with non-contemporaneous sequences, translating DNA into AA sequences, and assessing sequence alignments. Phylogeny estimation can be done with the NJ, BIONJ, ME, MVR, SDM, and triangle methods, and several methods handling incomplete distance matrices (NJ*, BIONJ*, MVR*, and the corresponding triangle method). Some functions call external applications (PhyML, Clustal, T-Coffee, Muscle) whose results are returned into R. Package: r-cran-aphid Architecture: amd64 Version: 1.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1023 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-openssl, r-cran-kmer, r-cran-qpdf, r-cran-rcpp Suggests: r-cran-ape, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aphid_1.3.6-1.ca2404.1_amd64.deb Size: 668434 MD5sum: 51524a062abbab4133d0f22c82057a9c SHA1: 884197965509fdaccab020173b988f3b1e222bd3 SHA256: 2d31eb0f21a997b436f2ad2a7cae6b98ccf18296f5c5f79bd0711a9b53c75407 SHA512: 0758719159878b07431c822cada9e4aacef15556ea447ddce45099a3a0e2ff5ac2cc267a3b78153a0c16ae2e7d70f9f02a9c854cc08ba33067e1a06137975719 Homepage: https://cran.r-project.org/package=aphid Description: CRAN Package 'aphid' (Analysis with Profile Hidden Markov Models) Designed for the development and application of hidden Markov models and profile HMMs for biological sequence analysis. Contains functions for multiple and pairwise sequence alignment, model construction and parameter optimization, file import/export, implementation of the forward, backward and Viterbi algorithms for conditional sequence probabilities, tree-based sequence weighting, and sequence simulation. Features a wide variety of potential applications including database searching, gene-finding and annotation, phylogenetic analysis and sequence classification. Based on the models and algorithms described in Durbin et al (1998, ISBN: 9780521629713). Package: r-cran-aphylo Architecture: amd64 Version: 0.3-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2130 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-rcpp, r-cran-matrix, r-cran-coda, r-cran-fmcmc, r-cran-mass, r-cran-xml2 Suggests: r-cran-covr, r-cran-knitr, r-cran-tinytest, r-cran-auc, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aphylo_0.3-6-1.ca2404.1_amd64.deb Size: 1199228 MD5sum: a35c1b3810b177554beb6e31c371eff8 SHA1: 4f7f006422cb785703fa0ea92318daf24b9794d5 SHA256: 8731cc8b37ce995d3011ba72efbd342e276ec83ad8c57f728f13ece0f866cbca SHA512: 0e084dc71b64a6392b2161b7f82a17e883261abc0dca090df224c1429231526196a96f76fffd2c34922afc6cb2e14ece776efff3eb478a9854897cf8af07f6db 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. 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Package: r-cran-apis Architecture: amd64 Version: 2.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 613 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-shinybs, r-cran-cowplot, r-cran-data.table, r-cran-doparallel, r-cran-dplyr, r-cran-dt, r-cran-foreach, r-cran-ggplot2, r-cran-gridextra, r-cran-htmltools, r-cran-plotly, r-cran-rlang, r-cran-shiny, r-cran-shinythemes Filename: pool/dists/noble/main/r-cran-apis_2.0.8-1.ca2404.1_amd64.deb Size: 555048 MD5sum: 03063d7cb2e2da0b63f3e1a1d4db9cdc SHA1: a3d63c7fd552a3fd727ba56b0afba37ddc8b1e86 SHA256: d3db0d5eb362f0ad46e22af999cad824f404b91c214c296ccfce4e2ecad9a902 SHA512: 4a269c7511f9800f22a20fc666f136bfe9ecdf4ececb4a4ce1fb84e8eb182c16f1f69e9fbacb650ec539ee17147577966ec095cee3ddc805713509f160eef326 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-aplcms Architecture: amd64 Version: 6.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3762 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-iterators, r-cran-rocr, r-cran-rcpp, r-cran-doparallel, r-cran-rgl, r-bioc-mzr, r-cran-e1071, r-cran-gbm, r-cran-randomforest, r-cran-mass, r-cran-rocs Suggests: r-bioc-msdata Filename: pool/dists/noble/main/r-cran-aplcms_6.8.3-1.ca2404.1_amd64.deb Size: 3724006 MD5sum: 62815ce5dc389219da35718dc984ddf2 SHA1: 545c8ebf1f5ca39be59335ea0dc30a4ad342ed95 SHA256: 4e2423ec79a0b5a34625abe1cf5e506e68f3482bd689ffe46bb8b5ea1bc58725 SHA512: f5e78719b5f86a6eedbdffcb5efc17bc7617237084aece55c3be6b10f353839228bc39dcbd4f20e11ffb97f0ce7fe2c576a9f6efd67cf065f4d39889caccf736 Homepage: https://cran.r-project.org/package=apLCMS Description: CRAN Package 'apLCMS' (Adaptive Processing of LC-MS Data) Provides methods for the processing of liquid chromatography-mass spectrometry (LC/MS) based metabolomics data, including adaptive tolerance level searching, non-parametric intensity grouping, the use of run filter to preserve weak signals, model-based estimation of peak intensities, and peak detection based on existing knowledge. Related references include Yu et al. (2009) , Liu et al. (2020) , Yu et al. (2014) , Yu et al. (2013) . Package: r-cran-apoderoides Architecture: amd64 Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-rcpp, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-apoderoides_3.0.1-1.ca2404.1_amd64.deb Size: 140394 MD5sum: a3987cc51b877c63bb47a74f61dda530 SHA1: 62266792fe8713cafc14f215ddc14b0c56e6fc3f SHA256: 38ff18fa8d76650dd68438b6b14ea046eeb7ddf8495e5fad04612167ebd8d217 SHA512: 3ff7d20f3386af7fe8d7b5610531d3b457e76e8e74a8d426b118d7db3e69a4bcdaf330786c99ee63c63bdbc361d64f2d3d27904e6317b229e80dc7f8955a68f3 Homepage: https://cran.r-project.org/package=Apoderoides Description: CRAN Package 'Apoderoides' (Prioritize and Delete Erroneous Taxa in a Large PhylogeneticTree) Finds, prioritizes and deletes erroneous taxa in a phylogenetic tree. This package calculates scores for taxa in a tree. Higher score means the taxon is more erroneous. If the score is zero for a taxon, the taxon is not erroneous. This package also can remove all erroneous taxa automatically by iterating score calculation and pruning taxa with the highest score. Package: r-cran-apollo Architecture: amd64 Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2329 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-maxlik, r-cran-mnormt, r-cran-mvtnorm, r-cran-randtoolbox, r-cran-numderiv, r-cran-deriv, r-cran-matrixstats, r-cran-coda, r-cran-tibble, r-cran-stringr, r-cran-bgw, r-cran-cli, r-cran-rsolnp, r-cran-rstudioapi, r-cran-mcmcpack, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-apollo_0.3.8-1.ca2404.1_amd64.deb Size: 2048022 MD5sum: 1f9e0616c809c8f0228783d5e43b36c2 SHA1: d3bb88903b31ec389e164502082b5ea589d87d7a SHA256: a17b75f788bd4a865010ad9cc5ef28824aceaa2fd98109e23ddb88298c29ad03 SHA512: afd08d79433b675ab6e2bbc617e622b1af2bc2b6f264c34f93e510edf8e6b05a373a7046c974eb28fb20f6302b35323d5d59f866d1e6934f8afeb7152ece30a0 Homepage: https://cran.r-project.org/package=apollo Description: CRAN Package 'apollo' (Tools for Choice Model Estimation and Application) Choice models are a widely used technique across numerous scientific disciplines. The Apollo package is a very flexible tool for the estimation and application of choice models in R. Users are able to write their own model functions or use a mix of already available ones. Random heterogeneity, both continuous and discrete and at the level of individuals and choices, can be incorporated for all models. There is support for both standalone models and hybrid model structures. Both classical and Bayesian estimation is available, and multiple discrete continuous models are covered in addition to discrete choice. Multi-threading processing is supported for estimation and a large number of pre and post-estimation routines, including for computing posterior (individual-level) distributions are available. For examples, a manual, and a support forum, visit . For more information on choice models see Train, K. (2009) and Hess, S. & Daly, A.J. (2014) for an overview of the field. Package: r-cran-apollonius Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4365 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-colorsgen, r-cran-gyro, r-cran-plotrix, r-cran-polychrome, r-cran-rcpp, r-cran-bh, r-cran-rcppcgal, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-apollonius_1.0.1-1.ca2404.1_amd64.deb Size: 3562276 MD5sum: 294e93a0858733413de30ccfe50eb087 SHA1: 250d50f7e7353d984b01cc21086e05287846f5e6 SHA256: 9937f0bffe46f3badef9eb41ee3e7d41e63e9088fb1681b3473c847f33ef2139 SHA512: 09af23cfdcd7f9e60fe1b9a87e1a878d2747e2138a7bdbff153b28e5f130d238ee8286b0cbafff7ba8c37dc0633ab365c9e6652e4a54558953f1f7d7510d22e2 Homepage: https://cran.r-project.org/package=Apollonius Description: CRAN Package 'Apollonius' (2D Apollonius Graphs) Computation of the Apollonius diagram of given 2D points and its dual the Apollonius graph, also known as the additively weighted Voronoï diagram, and which is a generalization of the classical Voronoï diagram. For references, see the bibliography in the CGAL documentation at . Package: r-cran-approxot Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 702 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppcgal, r-cran-bh Suggests: r-cran-testthat, r-cran-transport Filename: pool/dists/noble/main/r-cran-approxot_1.2-1.ca2404.1_amd64.deb Size: 274982 MD5sum: 8c03db1f355c1c8c7b88bf264cb5ff1b SHA1: 6bcb1d74f66863c2512f4691a672aba316b28fcc SHA256: 88825c11be3c8f19472c9f1a8953e7a5cf4695d4824d7eb315684b6b0e967d6c SHA512: a8795f569e1091f01b1e5752b93131cc4afaf186c3b94db4c88be1ce42fe52305b31be70b5256899b2251bac9689bb978091a04349552a7d1e8a3230c2d70df3 Homepage: https://cran.r-project.org/package=approxOT Description: CRAN Package 'approxOT' (Approximate and Exact Optimal Transport Methods) R and C++ functions to perform exact and approximate optimal transport. 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Package: r-cran-arcensreg Architecture: amd64 Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-matrixcalc, r-cran-msm, r-cran-mvtnorm, r-cran-numderiv, r-cran-qqplotr, r-cran-rcpp, r-cran-rdpack, r-cran-tmvtnorm, r-cran-rcpparmadillo Suggests: r-cran-smncensreg Filename: pool/dists/noble/main/r-cran-arcensreg_3.0.2-1.ca2404.1_amd64.deb Size: 315152 MD5sum: 2d136a35d64ed72e69a0bc5146e714d1 SHA1: 5059b1a020b0497ec7742508253b139e8fb791b4 SHA256: e4d4279d31780f709a5d89523220421f16939e1346fe7e1be8fc07f8eaed8536 SHA512: 652e79dce92461b1df399ef90484d16fb6fbaedb0f3cc8f128e003c6a42472de1ab3549d4bbb6570abe30776dc45ce54f78eba33a1accaaef2deee1d5951eb30 Homepage: https://cran.r-project.org/package=ARCensReg Description: CRAN Package 'ARCensReg' (Fitting Univariate Censored Linear Regression Model withAutoregressive Errors) It fits a univariate left, right, or interval censored linear regression model with autoregressive errors, considering the normal or the Student-t distribution for the innovations. It provides estimates and standard errors of the parameters, predicts future observations, and supports missing values on the dependent variable. References used for this package: Schumacher, F. L., Lachos, V. H., & Dey, D. K. (2017). Censored regression models with autoregressive errors: A likelihood-based perspective. Canadian Journal of Statistics, 45(4), 375-392 . Schumacher, F. L., Lachos, V. H., Vilca-Labra, F. E., & Castro, L. M. (2018). Influence diagnostics for censored regression models with autoregressive errors. Australian & New Zealand Journal of Statistics, 60(2), 209-229 . Valeriano, K. A., Schumacher, F. L., Galarza, C. E., & Matos, L. A. (2024). Censored autoregressive regression models with Student‐t innovations. Canadian Journal of Statistics, 52(3), 804-828 . Package: r-cran-arcgisgeocode Architecture: amd64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1285 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-arcgisutils, r-cran-cli, r-cran-httr2, r-cran-jsonify, r-cran-rcppsimdjson, r-cran-rlang, r-cran-sf Suggests: r-cran-data.table, r-cran-dplyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-arcgisgeocode_0.4.0-1.ca2404.1_amd64.deb Size: 526012 MD5sum: 3fbf00661f2d0f1bc7268c5878201126 SHA1: 6f0de49371e1dad5035f02640dd5816e5528a9c6 SHA256: 8392a1c61c1a0fe85af7e7c76263e7457f24f82c37248b0e425a8c9cea749ae9 SHA512: 449eae8cbe073948b3e2551355110f30fdad74966320af2c509b7fc04730747931c5dd4dc90dcb22a7f6a8dbf456036744705a8ff9a002f90fbe94db51337ee3 Homepage: https://cran.r-project.org/package=arcgisgeocode Description: CRAN Package 'arcgisgeocode' (A Robust Interface to ArcGIS 'Geocoding Services') A very fast and robust interface to ArcGIS 'Geocoding Services'. 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Package: r-cran-arcgisplaces Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2363 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), libssl3t64 (>= 3.0.0), r-base-core (>= 4.5.0), r-api-4.0, r-cran-arcgisutils, r-cran-cli, r-cran-httr2, r-cran-rlang, r-cran-wk Suggests: r-cran-sf Filename: pool/dists/noble/main/r-cran-arcgisplaces_0.1.2-1.ca2404.1_amd64.deb Size: 949626 MD5sum: dfb973c08e7b17be30087e1bfbfa507f SHA1: f41b65f45a28bc40ae462c53bbd71798856b5d52 SHA256: 709253afbf30107bc872969a9a32ca6f5aefbe044379fa3455501ad24374673f SHA512: 38b81def0136f3822bca55e24022a33e029ad584675697eb630e7df1e1dbc1315bf4b64822fa0f0bfe26d2be12c55579ef9082e5ef3fb463d4c9bb443c0a15b0 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. It allows you to find, locate, and discover detailed information about each place. Query for places near a point, within a bounding box, filter based on categories, or provide search text. 'arcgisplaces' integrates with 'sf' for out of the box compatibility with other spatial libraries. Learn more in the 'Places service' API reference . Package: r-cran-arcgisutils Architecture: amd64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1367 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-r6, r-cran-rcppsimdjson, r-cran-rlang, r-cran-s7, r-cran-sf, r-cran-yyjsonr, r-cran-lifecycle Suggests: r-cran-collapse, r-cran-data.table, r-cran-jsonify, r-cran-testthat, r-cran-vctrs, r-cran-curl, r-cran-shinyoauth Filename: pool/dists/noble/main/r-cran-arcgisutils_0.5.0-1.ca2404.1_amd64.deb Size: 716272 MD5sum: 6056b1b40b57d6617b6d8513b72fbaa7 SHA1: 1bd6d1a0885633a39e139c1a7cd79788ce82843d SHA256: fa6a2009d79aa33f15fa4b5d4478ac20eae02551e7d9203526b5b37dccc8c604 SHA512: 44c97499792fbeb0c6519895a3e94a65631d0263bc84500c9f8c5dd8c0e93cc0922997681b85e336a5d03bdde696a9d7c750fb6b1c9278628705f5d62d5818a2 Homepage: https://cran.r-project.org/package=arcgisutils Description: CRAN Package 'arcgisutils' (R-ArcGIS Bridge Utility Functions) Developer oriented utility functions designed to be used as the building blocks of R packages that work with ArcGIS Location Services. It provides functionality for authorization, Esri JSON construction and parsing, as well as other utilities pertaining to geometry and Esri type conversions. To support 'ArcGIS Pro' users, authorization can be done via 'arcgisbinding'. Installation instructions for 'arcgisbinding' can be found at . Package: r-cran-archive Architecture: amd64 Version: 1.1.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: libarchive13t64 (>= 3.3.3), libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-rlang, r-cran-tibble Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-archive_1.1.13-1.ca2404.1_amd64.deb Size: 127672 MD5sum: c7308ef5d96776092054b94f3d32fe4e SHA1: a4cecdad212ee81088079765af6c8dea1808c9d1 SHA256: 5dba328062b00717d32295bd7c3df7ee8d97daa08842e24a23ac7320361a5a10 SHA512: b89375c25a9623cf52a74e4d0e553aa385a3768d4f72b363cfd0ecc46895400b41d5a69875efdbcd24543a8cd6bb2e6cc22a581a680bb44aa95598199674050b Homepage: https://cran.r-project.org/package=archive Description: CRAN Package 'archive' (Multi-Format Archive and Compression Support) Bindings to 'libarchive' the Multi-format archive and compression library. 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Package: r-cran-arcokrig Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 695 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-ggplot2, r-cran-rcpparmadillo, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-arcokrig_0.1.3-1.ca2404.1_amd64.deb Size: 361594 MD5sum: 61407616beeb67d1da043a91f750d4e2 SHA1: bfab5e48d40aad8ade44eab1a9c54d38d9e8a93d SHA256: 489e781769da16a266d77c4a15c5e9d1b55e5c9261e39ee916cd7014d966f5a6 SHA512: 0f5a9c4b8539ef65b5c56a5f9e88c36baf723ee698c5c8f9b7c157878d4c3ebd41c17a95ca83693953f1178f310310502a8357ba267d815aa17281ab2d114066 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: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-marqlevalg, r-cran-trust Filename: pool/dists/noble/main/r-cran-arcopt_0.3.0-1.ca2404.1_amd64.deb Size: 209184 MD5sum: 16dd63e5f0e252b0ee47bbd3cbf99f4e SHA1: a599c5a2dfee59da4a2d5d632de83ccad83ad2b2 SHA256: a63f0bfae94852d4d9a63b31f809629b25ac00f8797313fbe3ce2f9c72e70e26 SHA512: b759d4ec2cc5af728392ac0dd3bc6fad4f2bfc04bce83265405bf8688b563f5af4fd740be72ed002c7a61725628df04502be74f692b410a1ec504c98b6117eb5 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: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2193 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/noble/main/r-cran-arcpbf_0.2.0-1.ca2404.1_amd64.deb Size: 762538 MD5sum: 69775861b4fa9d41b947b3ceff0f6228 SHA1: 92d9cb438e06f31a9607f9f5a29f3b18433db443 SHA256: 3e4cae1b45f98d9f807eaf3835265689b29e757310427e94f67d70381532e4d8 SHA512: 9dc8cda472713c7cb4b42fad5600c7b3d188f449af8101b81643ab19003d6c503907083e7b2d07b42f08d271d994c3b3c1414699de42b5dbcdec91f0d006807b 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'. 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The shoelace formula is described at . Package: r-cran-arfima Architecture: amd64 Version: 1.8-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-ltsa Filename: pool/dists/noble/main/r-cran-arfima_1.8-2-1.ca2404.1_amd64.deb Size: 395356 MD5sum: 4c210313dc23cf69e7de35983e84cf50 SHA1: e3922c80e0b08c3d18e63c68db2faf7433d5332c SHA256: a70c832ae6ecac109f41dac7a1a08d5d3452cec75e0ac35c1719847689b7a5a9 SHA512: 74303836af74bbe6a0989c48bb7ff88ab3d6ee132c5318ebf39f0ec7848cd3aa65d7a2ed31bc6af11f4d659a278ec515e3392a3577c6cc3621ae2f35febb5095 Homepage: https://cran.r-project.org/package=arfima Description: CRAN Package 'arfima' (Fractional ARIMA (and Other Long Memory) Time Series Modeling) Simulates, fits, and predicts long-memory and anti-persistent time series, possibly mixed with ARMA, regression, transfer-function components. Exact methods (MLE, forecasting, simulation) are used. Bug reports should be done via GitHub (at ), where the development version of this package lives; it can be installed using devtools. Package: r-cran-argus Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-runuran Filename: pool/dists/noble/main/r-cran-argus_0.1.1-1.ca2404.1_amd64.deb Size: 29552 MD5sum: b8a6008f5dc62b6012f57fcf715551d4 SHA1: 810059060d411588e8dff95212903c7f64ef76e5 SHA256: 3f2468fd874d926596f5cf3e3c84d25ea176a4cdd41cbf1a195cc6bde2102e49 SHA512: a18a34982763baa08c1421cf2bd0df55790653bf2900dfff966a0b78b36c64cac28f20434fee8c768658ae1c3e49f3eff46d54ce94f9f6638a998524ceede81a Homepage: https://cran.r-project.org/package=argus Description: CRAN Package 'argus' (Random Variate Generator for the Argus Distribution) Random variate generation, density, CDF and quantile function for the Argus distribution. Especially, it includes for random variate generation a flexible inversion method that is also fast in the varying parameter case. 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Package: r-cran-aricode Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-lifecycle Suggests: r-cran-testthat, r-cran-spelling, r-cran-mclust, r-cran-ggplot2, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-aricode_1.1.0-1.ca2404.1_amd64.deb Size: 97306 MD5sum: 4063528d676385e19d5cec81d1ba4429 SHA1: ba55df46dbe59909d9b5f88db2aefa2facb34328 SHA256: e215fcc723f270979a40c89bb95437b909f6189f6b0d1eaa16973866fbfcde06 SHA512: 4c8b13fe2449fcd6901a8c73a441f31b64659a0a7e7967ff4e573a0c9bd46571ea3751d9dc6f50900f50daf7f4bbe6a7dc65607e4b02afc97893aee59be658cd 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. 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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: amd64 Version: 0.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-arkhaia_0.5.5-1.ca2404.1_amd64.deb Size: 200592 MD5sum: 9822f7eaa353945b38410cc185be480b SHA1: 713c9189c5a499e1e3ebe51c0bc1ff34febab2ed SHA256: cf10355783d7f9d65617319b2bd2c4eedaab0af763a2a5754af676d9f6dba726 SHA512: c5a20dc4faa1ee6d2e1bd5216689249abf6e7396d9b32c40b00b3e2962921ee99d06a5313095f6391ac0e19bc8eb5dfd6762c0d6edf3dccf737c0ec432d6581d 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: amd64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-armspp_0.0.3-1.ca2404.1_amd64.deb Size: 133500 MD5sum: 6ac5b3b877baddd3daaee68ad01a5b09 SHA1: 2f997763b06440bf14e86457e5e8013c2c3dfefa SHA256: 8c3e43d71298ac76ad0f5b00091a256811b6859b59c79fbc5902d525b1489bcf SHA512: ae1bdd65b273fb07608d32f6f7afd709a41cc0e457d66318e089f19fabcb2c2995dd074a748dac57312ad99cf4f0c61bf7ddb2485d79faa7f5f7a21245282ee8 Homepage: https://cran.r-project.org/package=armspp Description: CRAN Package 'armspp' (Adaptive Rejection Metropolis Sampling (ARMS) via 'Rcpp') An efficient 'Rcpp' implementation of the Adaptive Rejection Metropolis Sampling (ARMS) algorithm proposed by Gilks, W. R., Best, N. G. and Tan, K. K. C. (1995) . This allows for sampling from a univariate target probability distribution specified by its (potentially unnormalised) log density. Package: r-cran-arrangements Architecture: amd64 Version: 1.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: libc6 (>= 2.14), libgmp10 (>= 2:6.3.0+dfsg), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gmp, r-cran-r6 Suggests: r-cran-foreach, r-cran-testthat Filename: pool/dists/noble/main/r-cran-arrangements_1.1.10-1.ca2404.1_amd64.deb Size: 270348 MD5sum: 9e624fa717f6a3b179394ba6cde3b095 SHA1: 7dc4581b879638ff5224bfdcaa3ba96a0564d023 SHA256: a42b08e181ee7cc7109c30271531259a1b19309132b218f080b7161625b92caa SHA512: 3ea877fe5e2937cb96fdb27aef9c52c60e3be5f75e94afc18daa60e8decb6d4c21af601d9a29a0172d600ab7f53e89c7ee635381e7156584d6bc44c403a6e1e1 Homepage: https://cran.r-project.org/package=arrangements Description: CRAN Package 'arrangements' (Fast Generators and Iterators for Permutations, Combinations,Integer Partitions and Compositions) Fast generators and iterators for permutations, combinations, integer partitions and compositions. 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Package: r-cran-arrapply Architecture: amd64 Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-arrapply_2.2.1-1.ca2404.1_amd64.deb Size: 90976 MD5sum: 8fbaf3cb19b9b21ad9bc662371af6ea3 SHA1: 861986e361643f9f2a87b396eb4b40192af392ab SHA256: 6a897a37a560e1661dd3cf9ebf340eac6ae7472327cea9df5c42daeea0971a1e SHA512: 91d35eedea56d124525be41cb9d2a5f16dd87502360dacd7e0eb517657849e8cbb4cc74bd4ee2c893ed8948a8e2f36cda930dd48080a8657c23670467b85a1a6 Homepage: https://cran.r-project.org/package=arrApply Description: CRAN Package 'arrApply' (Apply a Function to a Margin of an Array) High performance variant of apply() for a fixed set of functions. 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Hahsler et al (2019) . Package: r-cran-arulessequences Architecture: amd64 Version: 0.2-32-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3259 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-arules Filename: pool/dists/noble/main/r-cran-arulessequences_0.2-32-1.ca2404.1_amd64.deb Size: 1103264 MD5sum: 069409d43db968aa591e53c355a69a3c SHA1: db414794e9409457bd38b2d788a90804a79db82a SHA256: e80a54f2f992a4230190b292a396dc068a000311228064654b1a2598e3933843 SHA512: 83d8ff95f0e525766f6438fdd34fa6d09fe089cc43ed0a6425967738cb291f3c5c0676eb2419787f9557cf428da0363f5949c9a3b078a46afc7f08dfd4fabdc3 Homepage: https://cran.r-project.org/package=arulesSequences Description: CRAN Package 'arulesSequences' (Mining Frequent Sequences) Add-on for arules to handle and mine frequent sequences. Provides interfaces to the C++ implementation of cSPADE by Mohammed J. Zaki. 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Package: r-cran-askpass Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sys Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-askpass_1.2.1-1.ca2404.1_amd64.deb Size: 23324 MD5sum: b6c465888f579fe9474424540d3434bd SHA1: a848fe057477fd40ea79aaaa23ea45bb7b0549c4 SHA256: 3113d47faf8a0ca25f35bb178ff327a3f2bb63fa1a520995b7b47574d60c362f SHA512: 4bd09f07e8c85cf55d78416ecf6bd6d0382d2b326f2b071692357f6c5a2d5b997b77c0aa10fa1e792af804568382f6dae9fb1ad9f9a07ab12b8a7cf86ec5f2fa Homepage: https://cran.r-project.org/package=askpass Description: CRAN Package 'askpass' (Password Entry Utilities for R, Git, and SSH) Cross-platform utilities for prompting the user for credentials or a passphrase, for example to authenticate with a server or read a protected key. Includes native programs for MacOS and Windows, hence no 'tcltk' is required. 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Package: r-cran-asmbpls Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2960 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-ggpubr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-asmbpls_1.0.0-1.ca2404.1_amd64.deb Size: 1812704 MD5sum: e49ce2babf0b5a85fb6dc106eaf3cab4 SHA1: 4fbd69557c3f2a78154ca225f2283a8caf00c3be SHA256: 5cccf218c3d3d2a0526b81fd01067fec957b58434470db61e01df46e8f69e737 SHA512: a93c23850e4d13b23b1a1118c41b61b55adf869c7a4b1a31dca4b2b2f895ff462353076ff36b89fbfe0e437131ad733c5b07e21a7798eea2674f83fad5b8f80d 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-aspect Architecture: amd64 Version: 1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-sem, r-cran-polycor Filename: pool/dists/noble/main/r-cran-aspect_1.0-6-1.ca2404.1_amd64.deb Size: 94286 MD5sum: 488754a176f4fbf65cfa44d229b50dfd SHA1: b147905ff84759b7c4b53fcb7610d270502a3a6c SHA256: af429a82831c847e14a24d750f9a2a39dfae950629fe44dc4c7b684d0a44ea8e SHA512: 783c4ce6c9848fee095619e8185855532c49c0d79475c1b4712e06ef6247d7487343213cb7b9c22eda3188ae941ef63053c3c7792a53f3b4cdc36a49eb150295 Homepage: https://cran.r-project.org/package=aspect Description: CRAN Package 'aspect' (A General Framework for Multivariate Analysis with OptimalScaling) Contains various functions for optimal scaling. One function performs optimal scaling by maximizing an aspect (i.e. a target function such as the sum of eigenvalues, sum of squared correlations, squared multiple correlations, etc.) of the corresponding correlation matrix. Another function performs implements the LINEALS approach for optimal scaling by minimization of an aspect based on pairwise correlations and correlation ratios. The resulting correlation matrix and category scores can be used for further multivariate methods such as structural equation models. Package: r-cran-aspline Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-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/noble/main/r-cran-aspline_0.2.0-1.ca2404.1_amd64.deb Size: 204066 MD5sum: 0d447c86a171d5e70c37875257d7be2b SHA1: 3f7c7b8338a58335cb7c6d044a0db2ce005f4de3 SHA256: 1e530955dc949018efe62570cc56e23768c05dda4edce161ee8427e960f2586c SHA512: 80b38d55574e18b7edad243868bdf5bac166b748ffae87363a3941e9a7a89083b2f14a4a9719f8470f83f43af048a0c37d14649c7bb6b71b219274063cd35350 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: amd64 Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-assa_2.0-1.ca2404.1_amd64.deb Size: 170370 MD5sum: e987d5ab7831e468badb96064cc273a0 SHA1: 30bf0c83cf57c9cb2d03739d5cbafd7b238af3a5 SHA256: 70b347596d4c688de18959ef7429a90bd5262e90892ff086e1ae8a002e904c50 SHA512: 0bf1b474aab8ac745b7341e10dc6ae7c1be23b36c13358873c8564533477b28074152ed778efa87c78cd64c43ec09e08a5b84584599b6a9bae21e7a3962c66a4 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: amd64 Version: 3.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1009 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-lattice Filename: pool/dists/noble/main/r-cran-assist_3.1.9-1.ca2404.1_amd64.deb Size: 800616 MD5sum: da6744d5eacc30a11ce0a17120b1f8ef SHA1: f30a05f8dbd40eebd57a20f1e9f30f1e4aa55f01 SHA256: 714c1555ea09963174c0f252263961a7e285b6532ad1fe2b9185401c31d4462c SHA512: 246f2723a3a92ca46833a7e5c893ada7951d32584cdfd5ed5913b788daf09753a5da618331ded7cf77a4327b52185debaf78ae0e4d44081f98fb3a3537ce9543 Homepage: https://cran.r-project.org/package=assist Description: CRAN Package 'assist' (A Suite of R Functions Implementing Spline Smoothing Techniques) Fit various smoothing spline models. Includes an ssr() function for smoothing spline regression, an nnr() function for nonparametric nonlinear regression, an snr() function for semiparametric nonlinear regression, an slm() function for semiparametric linear mixed-effects models, and an snm() function for semiparametric nonlinear mixed-effects models. See Wang (2011) for an overview. Package: r-cran-aster2 Architecture: amd64 Version: 0.3-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-aster Filename: pool/dists/noble/main/r-cran-aster2_0.3-2-1.ca2404.1_amd64.deb Size: 193972 MD5sum: bbc67eb0c7f11f0948dc7103daeb2a5a SHA1: ed0dd478690cf58792685a98e6125b62856be941 SHA256: 753d29c1f62837a7c4d7fe9ddc54f2465111c2b9d35331b5c0d57ff52e28bfca SHA512: d9ad8f508168a6a8e0237298ead6e66a7739310e397ebbdbcaca1ccdf77e3bbfba93b78f8017ac3c073a6582706370615a6b4e7024f02647e1f06ea1ec1513d9 Homepage: https://cran.r-project.org/package=aster2 Description: CRAN Package 'aster2' (Aster Models) Aster models are exponential family regression models for life history analysis. They are like generalized linear models except that elements of the response vector can have different families (e. g., some Bernoulli, some Poisson, some zero-truncated Poisson, some normal) and can be dependent, the dependence indicated by a graphical structure. Discrete time survival analysis, zero-inflated Poisson regression, and generalized linear models that are exponential family (e. g., logistic regression and Poisson regression with log link) are special cases. Main use is for data in which there is survival over discrete time periods and there is additional data about what happens conditional on survival (e. g., number of offspring). Uses the exponential family canonical parameterization (aster transform of usual parameterization). Unlike the aster package, this package does dependence groups (nodes of the graph need not be conditionally independent given their predecessor node), including multinomial and two-parameter normal as families. Thus this package also generalizes mark-capture-recapture analysis. Package: r-cran-aster Architecture: amd64 Version: 1.3-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3021 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-trust Suggests: r-cran-numderiv, r-cran-knitr Filename: pool/dists/noble/main/r-cran-aster_1.3-7-1.ca2404.1_amd64.deb Size: 2591588 MD5sum: 6b4af24e36b90637290f8bd53f3c88af SHA1: 5cd3d72984032c24931aa67e4a4745bf67d5386a SHA256: c08c88a988f3f0be8828efc12945ad22e13b2413e2f4bd0126ad874fe6b01e88 SHA512: b2da2c572e390aa6fb8bb72fb8dc3bcc860f7e0c046a4f97a4127a1aca7f25f9991dd5426abaccd45e944e112b6db563734efa7c3730c2ca7ccadcacd93ecb82 Homepage: https://cran.r-project.org/package=aster Description: CRAN Package 'aster' (Aster Models) Aster models (Geyer, Wagenius, and Shaw, 2007, ; Shaw, Geyer, Wagenius, Hangelbroek, and Etterson, 2008, ; Geyer, Ridley, Latta, Etterson, and Shaw, 2013, ) are exponential family regression models for life history analysis. They are like generalized linear models except that elements of the response vector can have different families (e. g., some Bernoulli, some Poisson, some zero-truncated Poisson, some normal) and can be dependent, the dependence indicated by a graphical structure. Discrete time survival analysis, life table analysis, zero-inflated Poisson regression, and generalized linear models that are exponential family (e. g., logistic regression and Poisson regression with log link) are special cases. Main use is for data in which there is survival over discrete time periods and there is additional data about what happens conditional on survival (e. g., number of offspring). Uses the exponential family canonical parameterization (aster transform of usual parameterization). There are also random effects versions of these models. Package: r-cran-asterisk Architecture: amd64 Version: 1.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3530 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-nanotime, r-cran-onion, r-cran-rcpp, r-cran-rcppparallel, r-cran-gsl, r-cran-polynom, r-cran-httr, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-formatr, r-cran-webshot, r-bioc-biocstyle, r-cran-runit, r-cran-plotly, r-cran-lazyeval, r-cran-dplyr, r-cran-ggmap, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-asterisk_1.4.5-1.ca2404.1_amd64.deb Size: 1816190 MD5sum: 5c610eeadaaf3d2ab43b9515adb2e655 SHA1: fe75c6275b7af2e786c937f900d257ebba539933 SHA256: 01ffd6e04f2b46fa1e8761af806711925e7b55bca4f663df59271a07bfd56d03 SHA512: e272bb7cdcc6a9f7b8a5de2df7abffe132d649898648193953a207771daadd8d19ed453cfbf13f2ae1bb832195b528fdb9faf20f4fa545673794a76f23da4afd Homepage: https://cran.r-project.org/package=asteRisk Description: CRAN Package 'asteRisk' (Computation of Satellite Position) Provides basic functionalities to calculate the position of satellites given a known state vector. The package includes implementations of the SGP4 and SDP4 simplified perturbation models to propagate orbital state vectors, as well as utilities to read TLE files and convert coordinates between different frames of reference. Several of the functionalities of the package (including the high-precision numerical orbit propagator) require the coefficients and data included in the 'asteRiskData' package, available in a 'drat' repository. To install this data package, run 'install.packages("asteRiskData", repos="https://rafael-ayala.github.io/drat/")'. Felix R. Hoots, Ronald L. Roehrich and T.S. Kelso (1988) . David Vallado, Paul Crawford, Richard Hujsak and T.S. Kelso (2012) . Felix R. Hoots, Paul W. Schumacher Jr. and Robert A. Glover (2014) . Package: r-cran-astgrepr Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4421 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-rrapply, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-spelling, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-astgrepr_0.1.1-1.ca2404.1_amd64.deb Size: 1205008 MD5sum: 94fa0742c1e57f4ad02a1612c0572cec SHA1: e5a6b87ab6939b321b69ad65350566988ae30990 SHA256: 1f396235bc4b2471cade5e5d79988e17718884ccba8cd56eb88176a33f264238 SHA512: 4d6b75e00c4eaafee15dc9a0751d83db38c704e9fa567fb8dcf15cf1e6cb719aa12f7c1b42b67a2ec197da6e164f46270fb1b3788472bb8797f44a707c80ecb6 Homepage: https://cran.r-project.org/package=astgrepr Description: CRAN Package 'astgrepr' (Parse and Manipulate R Code) Parsing R code is key to build tools such as linters and stylers. This package provides a binding to the 'Rust' crate 'ast-grep' so that one can parse and explore R code. Package: r-cran-astrochron Architecture: amd64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1507 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-multitaper, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-idpmisc, r-cran-fields, r-cran-viridislite, r-cran-palinsol Filename: pool/dists/noble/main/r-cran-astrochron_1.6-1.ca2404.1_amd64.deb Size: 1432672 MD5sum: 6ece2f64ca052f70c031c97bf1a49873 SHA1: bc185fb2cff5a15f461f432c824a3512412508bc SHA256: 0975d7dd94f2e20568a9ee992bc55d00ef64257e24591096e691afaa5e570623 SHA512: ff94221d59e0f7ce0b298c648a900705bb68a3d28a75187c25a51ad3d6e6de0bff88646b3a29ac3a92341edba1913325fb628e887298e997642b82973c296af4 Homepage: https://cran.r-project.org/package=astrochron Description: CRAN Package 'astrochron' (A Computational Tool for Astrochronology) Routines for astrochronologic testing, astronomical time scale construction, and time series analysis . Also included are a range of statistical analysis and modeling routines that are relevant to time scale development and paleoclimate analysis. Package: r-cran-astronomyengine Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1239 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-astronomyengine_0.1.0-1.ca2404.1_amd64.deb Size: 471686 MD5sum: 4ca42dd642ce54891f6aa7fcf3ba0de3 SHA1: 88064ac6122c1c1c068c87c5442fc1be28aa8384 SHA256: 8aabc42d8243d0180769e60b24aa84f9e9041e89f6b2a18555910bad7b0ec022 SHA512: 700c17bd0f3434ce84e30e44bf7f63c71dc9328ef359fedb38576b02d0df2dadaac93bc6e23710dd63f9e6990ea09fcdd1451df70a7868c2601bff70723f158d 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: amd64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-freqdom, r-cran-rcpparmadillo, r-cran-rcppprogress Filename: pool/dists/noble/main/r-cran-asv_1.1.4-1.ca2404.1_amd64.deb Size: 215650 MD5sum: a6051c3a4ce4f35cc4acb6357c07b361 SHA1: 0ebd48fbbe3835d688f4c7a2374bef4962ca92fb SHA256: 47c0b26211f2728f333ab1379d3cad52916ca168ee1d0339bd1d7c0c78c53880 SHA512: 4df6c6ba93ff7d91460ea528ef735ca66e45d07878c33f3e71ecc3f478b6a289fc1704ac345eb1f17b27c525bdd455b827baa5e1494c589a0112a3cb43fd6064 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. Package: r-cran-atakrig Architecture: amd64 Version: 0.9.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: libc6 (>= 2.35), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-gstat, r-cran-sf, r-cran-foreach, r-cran-dosnow, r-cran-snow, r-cran-fnn, r-cran-mass, r-cran-rcpp Suggests: r-cran-rtop, r-cran-sp Filename: pool/dists/noble/main/r-cran-atakrig_0.9.8.2-1.ca2404.1_amd64.deb Size: 226264 MD5sum: a40e2f7dd89cc1e006dd34ccadb11691 SHA1: 8e1f09876900255cd3fc4bb4ca333ce54a7523e2 SHA256: dbd725afdc074a926a5ff902c24cf630bd0888fd9df1f1ad55e7179405a9ae92 SHA512: 71bff800d31f092e909f712293d376847f9615bf3cfd4fa8bb410863a1a1b0fbe4b7d55971f2a4ca60527ad5b046dd6e31fb123ef8575c6272835c06ca116a67 Homepage: https://cran.r-project.org/package=atakrig Description: CRAN Package 'atakrig' (Area-to-Area Kriging) Point-scale variogram deconvolution from irregular/regular spatial support according to Goovaerts, P., (2008) ; ordinary area-to-area (co)Kriging and area-to-point (co)Kriging. Package: r-cran-atemevs Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-ncvreg Filename: pool/dists/noble/main/r-cran-atemevs_0.1.0-1.ca2404.1_amd64.deb Size: 48858 MD5sum: e2383e51a8061c6ae9f8d295f94889ac SHA1: cfd852d5188f0edb33451412629cf0746adb02b1 SHA256: 5d6fd93e624a9f0a1e61348c4d7f08ccad539e4d7bcc9874511cd75b37288139 SHA512: 31fdc3e4d94be73e5e43b66d4d89fbc3f9ce1ce92557b653f8bb833727e2e04b7a70b592e7a1faad1ce7373f0af3080e59b30ce15529f17b0527213a4532f49f Homepage: https://cran.r-project.org/package=AteMeVs Description: CRAN Package 'AteMeVs' (Average Treatment Effects with Measurement Error and VariableSelection for Confounders) A recent method proposed by Yi and Chen (2023) is used to estimate the average treatment effects using noisy data containing both measurement error and spurious variables. The package 'AteMeVs' contains a set of functions that provide a step-by-step estimation procedure, including the correction of the measurement error effects, variable selection for building the model used to estimate the propensity scores, and estimation of the average treatment effects. The functions contain multiple options for users to implement, including different ways to correct for the measurement error effects, distinct choices of penalty functions to do variable selection, and various regression models to characterize propensity scores. Package: r-cran-atnr Architecture: amd64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1378 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-desolve, r-cran-r.rsp, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-atnr_1.1.2-1.ca2404.1_amd64.deb Size: 609486 MD5sum: 88dafe0a3a5c0923f25d5373e9c0d893 SHA1: daca4ae14fa782d3a9d25c229a8c572ada5bd43f SHA256: 7ef99f1fb3ce4f3975aaf67b455557b863bf7a1b904ce8b221a473ac05e72ad6 SHA512: 2db3881d3ed26f1e5d249685ecd972d878776cc4c520108f0d7bb68b6899cfede80b5954827e251c6d71d5c5fbdd95698466d37fea4ce5846c149c614d2573d3 Homepage: https://cran.r-project.org/package=ATNr Description: CRAN Package 'ATNr' (Run Allometric Trophic Networks Models) Implements the differential equations associated to different versions of Allometric Trophic Models (ATN) to estimate the temporal dynamics of species biomasses in food webs. It offers several features to generate synthetic food webs and to parametrise models as well as a wrapper to the ODE solver deSolve. Package: r-cran-audio Architecture: amd64 Version: 0.1-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: libc6 (>= 2.34), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-audio_0.1-12-1.ca2404.1_amd64.deb Size: 53130 MD5sum: 2c452f08ee81b1f8c4dd89c6b85929ff SHA1: 1d4ffdc916f8eaf93811764e1cdf4cf4bf228be7 SHA256: 7c4039e941cc3440dca5c94c2d69e304eab2e123e3d15734149219aafa4136e0 SHA512: d3bda89042d409e659653c2b9cf163e39ed8293f36ff706d209da72cd907586ce8eb657e937c22d0db2752e76c1d7a3f70f71b772e0d6942954b484aa2216ae7 Homepage: https://cran.r-project.org/package=audio Description: CRAN Package 'audio' (Audio Interface for R) Interfaces to audio devices (mainly sample-based) from R to allow recording and playback of audio. Built-in devices include Windows MM, Mac OS X AudioUnits and PortAudio (the last one is very experimental). Package: r-cran-augsimex Architecture: amd64 Version: 3.7.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-formula, r-cran-nleqslv Filename: pool/dists/noble/main/r-cran-augsimex_3.7.4-1.ca2404.1_amd64.deb Size: 269062 MD5sum: 3122974c19bd28017626addd6b2b76c8 SHA1: 3317a3867edc387c99fd7ee9703e0183344c9f14 SHA256: a7b5b19ef57da3cdaee4463ba3e72c59e4fc7c20c1181864acec759a1a6b93ee SHA512: 09cd44252d3da37573df3d94e5a03c6122baa228d86b83f31abc76f4157bb88d2dab2f5763f1c015c733b6a379a2ac7063694b977d9ad3b6b431d4e1d2ef5f9f 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: amd64 Version: 2024.6.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-data.table Suggests: r-cran-testthat, r-cran-kernlab, r-cran-nc, r-cran-ggplot2, r-cran-weightedroc, r-cran-penaltylearning, r-cran-knitr, r-cran-markdown, r-cran-mlbench, r-cran-directlabels, r-cran-microbenchmark, r-cran-covr, r-cran-atime, r-cran-ggrepel Filename: pool/dists/noble/main/r-cran-aum_2024.6.19-1.ca2404.1_amd64.deb Size: 225742 MD5sum: 98f3a62ec18118dd5f96760e2f448f0e SHA1: 224d2394cc482dc05e964db2e7caef840d001b05 SHA256: eb2e525a3e60aed0464e431862d18c11aab69b8b312a3f70183460670bd7ec20 SHA512: e4bbcef2601b1de79fc3068f467eab33b368c070c843be8786c4744f7afc32016c48b8fa210d7279cf26de3db2d976e5fe1888a0b996dd8f5194ccd012bc72b6 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-autocart Architecture: amd64 Version: 1.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 575 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields, r-cran-mgcv, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-autocart_1.4.5-1.ca2404.1_amd64.deb Size: 223306 MD5sum: ddb96ace65c768568cc00039e9fcdd49 SHA1: 24385b47050c4c60cdd9c851caaed8437eb45c2a SHA256: 3118df2d18a8fecdf81ec6781bc03f7d698856dc939530fb155ea82e38b8f14e SHA512: e4ddcc67c10a3298220fc09088c447d8162caef800c024b371b668df61915f75d2340b823c4041e07334c548288bcc7de8cbd548b669831e7e6f2ddc700efeba Homepage: https://cran.r-project.org/package=autocart Description: CRAN Package 'autocart' (Autocorrelation Regression Trees) A modified version of the classification and regression tree (CART) algorithm for modelling spatial data that features coordinate information. Coordinate information can be used to evaluate measures of spatial autocorrelation and spatial compactness during the splitting phase of the tree, leading to better predictions and more physically realistic predictions on these types of datasets. These methods are described in Ancell and Bean (2021) . Package: r-cran-autofrk Architecture: amd64 Version: 1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spam, r-cran-fields, r-cran-filehashsqlite, r-cran-filehash, r-cran-mass, r-cran-mgcv, r-cran-latticekrig, r-cran-fnn, r-cran-filematrix, r-cran-rcpp, r-cran-rspectra, r-cran-rcppeigen, r-cran-rcppparallel Filename: pool/dists/noble/main/r-cran-autofrk_1.4.4-1.ca2404.1_amd64.deb Size: 288636 MD5sum: 358cd62bbd616c6a777a55f51c1b5b82 SHA1: 877f3841e6fca30ddcf341d6e03366805b5a7f52 SHA256: 4442c6a5ec611e8790484479e57489823cb81f7c36462c01a6a7e3842e9aa2cc SHA512: 4933536ca9dced0109193893c16b0c8948fd55a963ace2bd1c24b46eb4fdc602992703a3e29e782b9c14c95f4c8bcdfdaac28365caaf5a0db6aac2ca7832ff7f Homepage: https://cran.r-project.org/package=autoFRK Description: CRAN Package 'autoFRK' (Automatic Fixed Rank Kriging) Automatic fixed rank kriging for (irregularly located) spatial data using a class of basis functions with multi-resolution features and ordered in terms of their resolutions. The model parameters are estimated by maximum likelihood (ML) and the number of basis functions is determined by Akaike's information criterion (AIC). For spatial data with either one realization or independent replicates, the ML estimates and AIC are efficiently computed using their closed-form expressions when no missing value occurs. Details regarding the basis function construction, parameter estimation, and AIC calculation can be found in Tzeng and Huang (2018) . For data with missing values, the ML estimates are obtained using the expectation- maximization algorithm. Apart from the number of basis functions, there are no other tuning parameters, making the method fully automatic. Users can also include a stationary structure in the spatial covariance, which utilizes 'LatticeKrig' package. Package: r-cran-automerge Architecture: amd64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3234 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-automerge_0.4.0-1.ca2404.1_amd64.deb Size: 1197830 MD5sum: e6e5a20e0e7a04cafa8d15bc060fabcd SHA1: aeb001ec7f95828dcf081bb7f60fdc158ef77fcf SHA256: b1f68beefeeeb3d99f2ffccc0df3798bf36b206be2f5d06b6386a5182ed32b0a SHA512: eb4a5e740b514bad5b905c4d0d958f3231d488b54c366ac5ec83e6de96ba74de294576162645002d26bdce51e8b27690f86174b8e510b7aed84eba04d1048269 Homepage: https://cran.r-project.org/package=automerge Description: CRAN Package 'automerge' (R Bindings for 'Automerge' 'CRDT' Library) Provides R bindings to the 'Automerge' Conflict-free Replicated Data Type ('CRDT') library. 'Automerge' enables automatic merging of concurrent changes without conflicts, making it ideal for distributed systems, collaborative applications, and offline-first architectures. The approach of local-first software was proposed in Kleppmann, M., Wiggins, A., van Hardenberg, P., McGranaghan, M. (2019) . This package supports all 'Automerge' data types (maps, lists, text, counters) and provides both low-level and high-level synchronization protocols for seamless interoperability with 'JavaScript' and other 'Automerge' implementations. Package: r-cran-autometric Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: libc6 (>= 2.34), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ps, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-autometric_0.1.2-1.ca2404.1_amd64.deb Size: 197878 MD5sum: 844f791113995cc51baf12c923203034 SHA1: b5793da2e171117a212612f49cd33101eeb29401 SHA256: f562e426acdfba136aecc47a9dcee05761c6f46ea97d68161831708573dcd46e SHA512: fcff7c36fe28f084e757f32c953c2811336f4b8f79bf3479d65a7a11a3a2a2b9f0119740732bcc0adf29d029c3c033091ba795c9afd6085e5f07a18f08d5d27a Homepage: https://cran.r-project.org/package=autometric Description: CRAN Package 'autometric' (Background Resource Logging) Intense parallel workloads can be difficult to monitor. Packages 'crew.cluster', 'clustermq', and 'future.batchtools' distribute hundreds of worker processes over multiple computers. If a worker process exhausts its available memory, it may terminate silently, leaving the underlying problem difficult to detect or troubleshoot. Using the 'autometric' package, a worker can proactively monitor itself in a detached background thread. The worker process itself runs normally, and the thread writes to a log every few seconds. If the worker terminates unexpectedly, 'autometric' can read and visualize the log file to reveal potential resource-related reasons for the crash. The 'autometric' package borrows heavily from the methods of packages 'ps' and 'psutil'. Package: r-cran-autorasch Architecture: amd64 Version: 0.2.2-1.ca2404.1 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), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-rcpp, r-cran-lavaan, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-autorasch_0.2.2-1.ca2404.1_amd64.deb Size: 411004 MD5sum: 6f0832c1891e1fbdbd4870f50e1d37c6 SHA1: 50d33ea612f86b2a9005039bfadf500df52a83ad SHA256: 658388ec7ec10b419d28ad667f19579578599cf5d80bfe705a5960581529da60 SHA512: ea2eefc443053684bc84c51affba192ff92c092c0d1fd38f903977e202262fec0cc4be165a4c69aa393a10329074e213127952fd780d269f6eefbab50502ed86 Homepage: https://cran.r-project.org/package=autoRasch Description: CRAN Package 'autoRasch' (Semi-Automated Rasch Analysis) Performs Rasch analysis (semi-)automatically, which has been shown to be comparable with the standard Rasch analysis (Feri Wijayanto et al. (2021) , Feri Wijayanto et al. (2022) , Feri Wijayanto et al. (2022) ). Package: r-cran-autothresholdr Architecture: amd64 Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1531 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-ijtiff, r-cran-magrittr, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-strex, r-cran-stringr Suggests: r-cran-covr, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-autothresholdr_1.4.3-1.ca2404.1_amd64.deb Size: 893950 MD5sum: aa48a1281b10520c3f77ba45772b0a45 SHA1: 420f3c47481238e163273f3285675f10ded52dfe SHA256: dc3aa9aef144660e7ca3d9cb3e4c386c4bfa6d3c5c333d78e958ae81fca0f637 SHA512: 204d57b444aa7bcb8d1e3ca44cacb4be4d2a028f14af4eaf8632c7c55c588990abbdfd868f5761ee386d87392cd8fe97c6bde95d354b4e3e617462a71fb0e03f Homepage: https://cran.r-project.org/package=autothresholdr Description: CRAN Package 'autothresholdr' (An R Port of the 'ImageJ' Plugin 'Auto Threshold') Algorithms for automatically finding appropriate thresholds for numerical data, with special functions for thresholding images. Provides the 'ImageJ' 'Auto Threshold' plugin functionality to R users. See and Landini et al. (2017) . Package: r-cran-av1r Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-magick, r-cran-testthat Filename: pool/dists/noble/main/r-cran-av1r_0.1.3-1.ca2404.1_amd64.deb Size: 100894 MD5sum: ea3b83e30bc138315dca85dbb4728ff3 SHA1: f4e11dd268b8a3c8d0b9c2addb7e99532e2681ca SHA256: 0651703d53497de1e5e35288d682f641e8f44e6a6a459e1767c914fba4356664 SHA512: d9f9192d9a49114949b44253a36d799de6453cb0c48e24b0b4be5abeb73759c191f82c96419d515a86d8ba4e7750d91eb5facfed385f9a22a5c0dd98f231754a Homepage: https://cran.r-project.org/package=AV1R Description: CRAN Package 'AV1R' ('AV1' Video Encoding for Biological Microscopy Data) Converts legacy microscopy video formats (H.264/H.265, AVI/MJPEG, TIFF stacks) to the modern 'AV1' codec with minimal quality loss. Typical use cases include compressing large TIFF stacks from confocal microscopy and time-lapse experiments from hundreds of gigabytes to manageable sizes, re-encoding MP4 files exported from 'CellProfiler', 'ImageJ'/'Fiji', and microscope software with approximately 2x better compression at the same visual quality, and converting legacy AVI (MJPEG) and H.265 recordings to a single patent-free format suited for long-term archival. Automatically selects the best available backend: GPU hardware acceleration via 'Vulkan' 'VK_KHR_VIDEO_ENCODE_AV1' or 'VAAPI' (tested on AMD RDNA4; bundled headers, builds with any 'Vulkan' SDK >= 1.3.275), with automatic fallback to CPU encoding through 'FFmpeg' and 'SVT-AV1'. User controls quality via a single CRF parameter; each backend adapts automatically (CPU and Vulkan use CRF directly, VAAPI targets 55 percent of input bitrate). TIFF stacks use near-lossless CRF 5 by default, with optional proportional scaling via tiff_scale (multiplier or bounding box, aspect ratio always preserved). Small frames are automatically scaled up to meet hardware encoder minimums. Audio tracks are preserved automatically. Provides a simple R API for batch conversion of entire experiment folders. Package: r-cran-av Architecture: amd64 Version: 0.9.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 861 Depends: libavfilter9 (>= 7:6.0), libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-curl, r-cran-testthat, r-cran-ps, r-cran-ggplot2, r-cran-gapminder Filename: pool/dists/noble/main/r-cran-av_0.9.6-1.ca2404.1_amd64.deb Size: 803084 MD5sum: e686b89a84dee6c210e6129f1c0f3328 SHA1: 82c36c95d88588b482f6fd71b1d0c8998a83ea83 SHA256: eb8474a533612eb12131d6fb985404a812da50dffa05c612da8b93f70a3bfefe SHA512: f88db7b6aae78533853fbe370764820b39c5ad48ca8cd1f14b152026c446c225fb54ae6bfbfe6d87434a617b904e6b827b18f549ef02c81234ff2aaeff3d5c2b 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. Also offers high performance tools for reading raw audio, creating 'spectrograms', and converting between countless audio / video formats. This package interfaces directly to the C API and does not require any command line utilities. Package: r-cran-avar Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-simts, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-avar_0.1.3-1.ca2404.1_amd64.deb Size: 442330 MD5sum: 690897c8fb48197828e4aae312bfc666 SHA1: c634ade4fb47cd9457f283557fe9126ef1a3fe0d SHA256: b4a978e48e50a8494d3e99684f4d79ba33068eb95c73c1bc0e267464bcc72a2a SHA512: d9a4abae633c2ef4a6d6ea15e307a232656c9f4989574edf2892d43a00cd082ab7f51510da1bd21ba39f5c70e4242f0f46a3b49a6326c9a82b4bd25fe79fbc7d Homepage: https://cran.r-project.org/package=avar Description: CRAN Package 'avar' (Allan Variance) Implements the allan variance and allan variance linear regression estimator for latent time series models. More details about the method can be found, for example, in Guerrier, S., Molinari, R., & Stebler, Y. (2016) . Package: r-cran-awdb Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4787 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-rlang, r-cran-sf Filename: pool/dists/noble/main/r-cran-awdb_0.1.3-1.ca2404.1_amd64.deb Size: 2709714 MD5sum: dbbad555bfc36e2316f48682d38f92e4 SHA1: 72ec25ac06d31071ba8babfdf69a5bda5c2f0948 SHA256: 74e5f306026865c2ba2b403d61a48c0e6df92c63dc617e81f056b74f345c3948 SHA512: c3b20d857bf43f2640470902695ec765e0d61f69ffabb5a97a4e9c36cbe8f412c29f0687b9da783f4050351d3973925986e2b2ed84f8d405de0079d918aa8bc9 Homepage: https://cran.r-project.org/package=awdb Description: CRAN Package 'awdb' (Query the USDA NWCC Air and Water Database REST API) Query the four endpoints of the 'Air and Water Database (AWDB) REST API' maintained by the National Water and Climate Center (NWCC) at the United States Department of Agriculture (USDA). Endpoints include data, forecast, reference-data, and metadata. The package is extremely light weight, with 'Rust' via 'extendr' doing most of the heavy lifting to deserialize and flatten deeply nested 'JSON' responses. The AWDB can be found at . 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Package: r-cran-baggr Architecture: amd64 Version: 0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6950 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bayesplot, r-cran-crayon, r-cran-forestplot, r-cran-ggplot2, r-cran-ggplotify, r-cran-ggrepel, r-cran-gridextra, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-covr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-baggr_0.8-1.ca2404.1_amd64.deb Size: 2489178 MD5sum: 4a0c1525b8579b360af2071533a5cc4f SHA1: a64fb35a8dd0cd675784c3473bea8a652c2a6c01 SHA256: b502bcaca443f7601466d0817f56d49c02470eeae5020760bbf9887392fd0613 SHA512: 75516280eddc98df5c5760c2f660eab3210f8379dc7cff921b16a0f994cc355300e98b8df3c8228e71e16515e9edee4e650e4f9c8d84dc8be8db68f4b5a8801b Homepage: https://cran.r-project.org/package=baggr Description: CRAN Package 'baggr' (Bayesian Aggregate Treatment Effects) Running and comparing meta-analyses of data with hierarchical Bayesian models in Stan, including convenience functions for formatting data, plotting and pooling measures specific to meta-analysis. This implements many models from Meager (2019) . Package: r-cran-bain Architecture: amd64 Version: 0.2.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 914 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan Suggests: r-cran-mass, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bain_0.2.11-1.ca2404.1_amd64.deb Size: 622296 MD5sum: 17ce49f185ea00282be287232ca93658 SHA1: fe13d626972e99e800b3a3dbc443d599bc658ed7 SHA256: bdd5c85405876ff9b7d13f2cbfbfd03293c421c9a163aed4bab7d3bdf5738edf SHA512: 131e4b4ab4067138b88f4c4337feabd24c5efe621dbe285ed96b44eca2210762bd0577fd56e059936914f8125e41cb120f8b002e7ba52c5b6319be13bf6bcaf9 Homepage: https://cran.r-project.org/package=bain Description: CRAN Package 'bain' (Bayes Factors for Informative Hypotheses) Computes approximated adjusted fractional Bayes factors for equality, inequality, and about equality constrained hypotheses. For a tutorial on this method, see Hoijtink, Mulder, van Lissa, & Gu, (2019) . For applications in structural equation modeling, see: Van Lissa, Gu, Mulder, Rosseel, Van Zundert, & Hoijtink, (2021) . For the statistical underpinnings, see Gu, Mulder, and Hoijtink (2018) ; Hoijtink, Gu, & Mulder, J. (2019) ; Hoijtink, Gu, Mulder, & Rosseel, (2019) . Package: r-cran-bakr Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6879 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-hmisc, r-cran-ggplot2, r-cran-data.table, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rmarkdown, r-cran-knitr, r-bioc-deseq2, r-cran-pheatmap, r-cran-ckmeans.1d.dp, r-cran-corrplot Filename: pool/dists/noble/main/r-cran-bakr_1.0.1-1.ca2404.1_amd64.deb Size: 2066472 MD5sum: bdc12935ad005aa287c83620eb7f9896 SHA1: 96faf2fba60372a7a86bbcf0916449070cc0ca6a SHA256: f25718bc17a2118c86010680896ef47c3cff9856f07f44805699857863ef5df3 SHA512: 483d0c6e0f8d710b8c4ce26c54004f09421ed0bf1bf851f4a109e7c1e9c30295bbc18378290a586b6027b1c5a665f98ce6f812b13ec659c5a7ec11a3cce1a473 Homepage: https://cran.r-project.org/package=bakR Description: CRAN Package 'bakR' (Analyze and Compare Nucleotide Recoding RNA Sequencing Datasets) Several implementations of a novel Bayesian hierarchical statistical model of nucleotide recoding RNA-seq experiments (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.) for analyzing and comparing NR-seq datasets (see 'Vock and Simon' (2023) ). NR-seq is a powerful extension of RNA-seq that provides information about the kinetics of RNA metabolism (e.g., RNA degradation rate constants), which is notably lacking in standard RNA-seq data. The statistical model makes maximal use of these high-throughput datasets by sharing information across transcripts to significantly improve uncertainty quantification and increase statistical power. 'bakR' includes a maximally efficient implementation of this model for conservative initial investigations of datasets. 'bakR' also provides more highly powered implementations using the probabilistic programming language 'Stan' to sample from the full posterior distribution. 'bakR' performs multiple-test adjusted statistical inference with the output of these model implementations to help biologists separate signal from background. Methods to automatically visualize key results and detect batch effects are also provided. Package: r-cran-balancedsampling Architecture: amd64 Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-balancedsampling_2.1.1-1.ca2404.1_amd64.deb Size: 173438 MD5sum: 7800f5128136001f4d79edc5dbc2e4e2 SHA1: 7cd0135e70689cc6d7a0f037a8b955a248039bb6 SHA256: 68651d3de31251e29da4396f110391881f5aee9696ff572ea3c83a706d615d2d SHA512: 5c990edd6683fcc0ab86e81a85d9c171722077d7c4fece5956ea11e09c6a6fd77edd4a9c7fe29b9abf75e16c86788a3a6ad06417a7815a91c5981155c4b69c47 Homepage: https://cran.r-project.org/package=BalancedSampling Description: CRAN Package 'BalancedSampling' (Balanced and Spatially Balanced Sampling) Select balanced and spatially balanced probability samples in multi-dimensional spaces with any prescribed inclusion probabilities. It contains fast (C++ via Rcpp) implementations of the included sampling methods. The local pivotal method by Grafström, Lundström and Schelin (2012) and spatially correlated Poisson sampling by Grafström (2012) are included. Also the cube method (for balanced sampling) and the local cube method (for doubly balanced sampling) are included, see Grafström and Tillé (2013) . Package: r-cran-baldur Architecture: amd64 Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4696 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-stringr, r-cran-tidyr, r-cran-rlang, r-cran-rdpack, r-cran-multidplyr, r-cran-ggplot2, r-cran-tibble, r-cran-viridislite, r-cran-lifecycle, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-baldur_0.0.4-1.ca2404.1_amd64.deb Size: 2303182 MD5sum: a766c6ec5db09170cbcb652b3e217320 SHA1: f4a46445c67b73ef8773c7cec2f758e98fe7b6e9 SHA256: dda65ab49c6ca5cc4cd71765f3722226c3de985ed17bc4976f4161cbe3f78721 SHA512: 792d388502e4b9c49442a3b93b6b7245c2d62ac3f33b5af6a820230cbcdaf2a2d4069bf3d985088ee74d710c215008903891c8339ac409dc479735b620faa46d Homepage: https://cran.r-project.org/package=baldur Description: CRAN Package 'baldur' (Bayesian Hierarchical Modeling for Label-Free Proteomics) Statistical decision in proteomics data using a hierarchical Bayesian model. There are two regression models for describing the mean-variance trend, a gamma regression or a latent gamma mixture regression. The regression model is then used as an Empirical Bayes estimator for the prior on the variance in a peptide. Further, it assumes that each measurement has an uncertainty (increased variance) associated with it that is also inferred. Finally, it tries to estimate the posterior distribution (by Hamiltonian Monte Carlo) for the differences in means for each peptide in the data. Once the posterior is inferred, it integrates the tails to estimate the probability of error from which a statistical decision can be made. See Berg and Popescu for details (). Package: r-cran-ball Architecture: amd64 Version: 1.3.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3025 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), 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/noble/main/r-cran-ball_1.3.13-1.ca2404.1_amd64.deb Size: 2427242 MD5sum: d1fa99b71f7a13f13f5e67749e9f19bd SHA1: 1ab7d2f488436f612aff16c3ce6871dc813c3102 SHA256: cd18262e74ba276adc6f523e8593f068771d761711658694032257829f310deb SHA512: 9d91fa4927876aad7053d82717fd57d700330b0a33d3c791f14089e42d43a4cd103a1b55c0d3f53ab3d9f8630d5b1d5ae408bc778038b7bb08c200787142842e Homepage: https://cran.r-project.org/package=Ball Description: CRAN Package 'Ball' (Statistical Inference and Sure Independence Screening via BallStatistics) Hypothesis tests and sure independence screening (SIS) procedure based on ball statistics, including ball divergence , ball covariance , and ball correlation , are developed to analyze complex data in metric spaces, e.g, shape, directional, compositional and symmetric positive definite matrix data. The ball divergence and ball covariance based distribution-free tests are implemented to detecting distribution difference and association in metric spaces . Furthermore, several generic non-parametric feature selection procedures based on ball correlation, BCor-SIS and all of its variants, are implemented to tackle the challenge in the context of ultra high dimensional data. A fast implementation for large-scale multiple K-sample testing with ball divergence is supported, which is particularly helpful for genome-wide association study. Package: r-cran-balnet Architecture: amd64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1781 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-balnet_0.0.2-1.ca2404.1_amd64.deb Size: 525328 MD5sum: 976ff3ed000d567ae2891ef9e7b43ccf SHA1: 3e66554274ec432757bcfbcf83a6e872cc202893 SHA256: d89485325cb124d95861a90514cc630b3de5935615098e0a0b63bdf61d62f984 SHA512: 19b621d1ecec27147f3aa8495e6fb89d7180f9a79e4a77ef20aa29d12fffdf78337977101e167261bfa576f22a9e029fc65b2ab790f96af0bc91f490b71865db Homepage: https://cran.r-project.org/package=balnet Description: CRAN Package 'balnet' (Pathwise Estimation of Covariate Balancing Propensity Scores) Provides pathwise estimation of regularized logistic propensity score models using covariate balancing loss functions rather than maximum likelihood. Regularization paths are fit via the 'adelie' elastic-net solver with a 'glmnet'-like interface, yielding balancing weights that target covariate balance for the ATE and ATT. For details, see Sverdrup & Hastie (2026) . Package: r-cran-bama Architecture: amd64 Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1126 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppdist, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bama_1.3.1-1.ca2404.1_amd64.deb Size: 932588 MD5sum: 502b9b62235922673b5f78bf84761896 SHA1: e62bca55d0ccb9c67e80c952042c859532c410a1 SHA256: 4dd40e77bf4a108e4a248acafe58187032df8a8e2a57e681591cb9e331d64ddf SHA512: b8772a70a02c0b353eedbce2d6b743c40efd3a19de82fea5e46f70c8772f9c9dc245572eba5aa6375f386b3a739703761d987708a699210d3baa8e75d1383229 Homepage: https://cran.r-project.org/package=bama Description: CRAN Package 'bama' (High Dimensional Bayesian Mediation Analysis) Perform mediation analysis in the presence of high-dimensional mediators based on the potential outcome framework. Bayesian Mediation Analysis (BAMA), developed by Song et al (2019) and Song et al (2020) , relies on two Bayesian sparse linear mixed models to simultaneously analyze a relatively large number of mediators for a continuous exposure and outcome assuming a small number of mediators are truly active. This sparsity assumption also allows the extension of univariate mediator analysis by casting the identification of active mediators as a variable selection problem and applying Bayesian methods with continuous shrinkage priors on the effects. Package: r-cran-bambi Architecture: amd64 Version: 2.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 989 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-lattice, r-cran-rcpp, r-cran-qrng, r-cran-mvtnorm, r-cran-gtools, r-cran-label.switching, r-cran-coda, r-cran-future.apply, r-cran-loo, r-cran-rcolorbrewer, r-cran-bridgesampling, r-cran-scales, r-cran-numderiv, r-cran-rcpparmadillo Suggests: r-cran-future, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-bambi_2.3.7-1.ca2404.1_amd64.deb Size: 628614 MD5sum: 415dc188c064f37381347ac8125f6c31 SHA1: baf3a2072dac3b735f864d6565087367e1ca9a80 SHA256: 6d1b8b2212048a01ff1e7391a75154cd3212373a3f03e3a942d3643d1d046538 SHA512: 798b5b60b0d5872bc5468926947812c77f11dc01ddb7d429a41581f60756162fafc8de1c811b7af38dd325201478d20cd3b7786f70a44da0c7394fa0df67bd46 Homepage: https://cran.r-project.org/package=BAMBI Description: CRAN Package 'BAMBI' (Bivariate Angular Mixture Models) Fit (using Bayesian methods) and simulate mixtures of univariate and bivariate angular distributions. Chakraborty and Wong (2021) . Package: r-cran-bamlss Architecture: amd64 Version: 1.2-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4536 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-colorspace, r-cran-distributions3, r-cran-mgcv, r-cran-formula, r-cran-mba, r-cran-mvtnorm, r-cran-sp, r-cran-matrix, r-cran-survival Suggests: r-cran-bit, r-cran-ff, r-cran-fields, r-cran-gamlss, r-cran-gamlss.dist, r-cran-interp, r-cran-rjags, r-cran-bayesx, r-cran-mapdata, r-cran-maps, r-cran-sf, r-cran-nnet, r-cran-spatstat, r-cran-spdep, r-cran-zoo, r-cran-keras, r-cran-splines2, r-cran-sdprior, r-cran-statmod, r-cran-glogis, r-cran-glmnet, r-cran-scoringrules, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-tensorflow Filename: pool/dists/noble/main/r-cran-bamlss_1.2-5-1.ca2404.1_amd64.deb Size: 4030908 MD5sum: 21000b7dbb9d302223043c880713c8c0 SHA1: afc27d8e5be3725563b566c0efddd19b6998e049 SHA256: d8fd759661ec4a9acb0624dc2d18615442a1db0aea60ae520f238465f4fd5149 SHA512: a7ee2585f22c5e6cdc1791924b5b2143b05937da191e491934cb3b6e485ae103cb83bdb0235dc28219b39f9297f1cfea8e0ae57a49d249922cc05acb3ce466b8 Homepage: https://cran.r-project.org/package=bamlss Description: CRAN Package 'bamlss' (Bayesian Additive Models for Location, Scale, and Shape (andBeyond)) Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019) and the R package in Umlauf, Klein, Simon, Zeileis (2021) . Package: r-cran-bamm Architecture: amd64 Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1699 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-animation, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-igraph, r-cran-leaflet, r-cran-magrittr, r-cran-matrix, r-cran-purrr, r-cran-raster, r-cran-rcpp, r-cran-rdpack, r-cran-rspectra, r-cran-sp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-crosstalk, r-cran-plotly, r-cran-spelling Filename: pool/dists/noble/main/r-cran-bamm_0.6.2-1.ca2404.1_amd64.deb Size: 1080346 MD5sum: 587cb583c21afb68db9096dd1a606659 SHA1: 8abda55e38fe8a506115176de039d050d3084358 SHA256: 5c5ebf96404a6d91df97c26c96dc55dffba2b241635ff90731bda547fe2b2b34 SHA512: b96ec076a1e04369ece912a685b5515ae615abb783712f2353fcd2601749034a30443e7dc9e06312ff88afc307c5a661604fa8e02e03f013c88ed1b3e5e4548b Homepage: https://cran.r-project.org/package=bamm Description: CRAN Package 'bamm' (Species Distribution Models as a Function of Biotic, Abiotic andMovement Factors (BAM)) Species Distribution Modeling (SDM) is a practical methodology that aims to estimate the area of distribution of a species. However, most of the work has focused on estimating static expressions of the correlation between environmental variables. The outputs of correlative species distribution models can be interpreted as maps of the suitable environment for a species but not generally as maps of its actual distribution. Soberón and Peterson (2005) presented the BAM scheme, a heuristic framework that states that the occupied area of a species occurs on sites that have been accessible through dispersal (M) and have both favorable biotic (B) and abiotic conditions (A). The 'bamm' package implements classes and functions to operate on each element of the BAM and by using a cellular automata model where the occupied area of a species at time t is estimated by the multiplication of three binary matrices: one matrix represents movements (M), another abiotic -niche- tolerances (A), and a third, biotic interactions (B). The theoretical background of the package can be found in Soberón and Osorio-Olvera (2023) . Package: r-cran-bammtools Architecture: amd64 Version: 2.1.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1123 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-rcpp, r-cran-gplots Filename: pool/dists/noble/main/r-cran-bammtools_2.1.12-1.ca2404.1_amd64.deb Size: 1079994 MD5sum: 5a443bd752530ef2c6b21ab99e81bc38 SHA1: 83b0aad084ec4d66f74aa9fa10b073c9f90d1d7f SHA256: 101d9a4d41c3fca7c41b8232eeebfbc264d06d42c54022e2f24642efb62c84a1 SHA512: 5b7596e0d60ebfd86dfb9929fe7cd1ffe43be4c339e44d8691b0207332cd7203dd32d7922b3dfd24364345c63c8d4082d9cb8648d669821146f758b14053e609 Homepage: https://cran.r-project.org/package=BAMMtools Description: CRAN Package 'BAMMtools' (Analysis and Visualization of Macroevolutionary Dynamics onPhylogenetic Trees) Provides functions for analyzing and visualizing complex macroevolutionary dynamics on phylogenetic trees. It is a companion package to the command line program BAMM (Bayesian Analysis of Macroevolutionary Mixtures) and is entirely oriented towards the analysis, interpretation, and visualization of evolutionary rates. Functionality includes visualization of rate shifts on phylogenies, estimating evolutionary rates through time, comparing posterior distributions of evolutionary rates across clades, comparing diversification models using Bayes factors, and more. Package: r-cran-bamp Architecture: amd64 Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 951 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-abind Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-bamp_2.1.3-1.ca2404.1_amd64.deb Size: 667282 MD5sum: e9cf9469e4fdbb2a4e790d234e0e1374 SHA1: 473c87dc798d68f53b3f000a312cce969645f32d SHA256: 1920ef05c18f5098c651a3befc63a751cded90623717d026ab4f0b5b26ab2b2c SHA512: 9ffff6316828d5fcbfafc8aba86e71684a46cabfa92c361e6aef1b095ea8ba2e76c62274e1562e3c97c25605c4e3745ade2d93946c8972fd7b3d3525a5d9ec22 Homepage: https://cran.r-project.org/package=bamp Description: CRAN Package 'bamp' (Bayesian Age-Period-Cohort Modeling and Prediction) Bayesian Age-Period-Cohort Modeling and Prediction using efficient Markov Chain Monte Carlo Methods. This is the R version of the previous BAMP software as described in Volker Schmid and Leonhard Held (2007) Bayesian Age-Period-Cohort Modeling and Prediction - BAMP, Journal of Statistical Software 21:8. This package includes checks of convergence using Gelman's R. Package: r-cran-banditpam Architecture: amd64 Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 736 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-banditpam_1.0-2-1.ca2404.1_amd64.deb Size: 340008 MD5sum: afbdcd52626692890f93d9fcc6a0e277 SHA1: f20ef96faf6a7b05a512e98c96a6b5fec92078d6 SHA256: ba7bd9e95e90e993a53c545c4a36e4159a9c1912450c34adcc1929db0431a384 SHA512: 242814c374955404278a93c2c4e3d7d9a27c698d0bd7de86626e089134118f829e6fc1282d59253cacbd103435596da7b4b2a8b63d3cea91915fba332eda9dde Homepage: https://cran.r-project.org/package=banditpam Description: CRAN Package 'banditpam' (Almost Linear-Time k-Medoids Clustering) Interface to a high-performance implementation of k-medoids clustering described in Tiwari, Zhang, Mayclin, Thrun, Piech and Shomorony (2020) "BanditPAM: Almost Linear Time k-medoids Clustering via Multi-Armed Bandits" . Package: r-cran-banova Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 816 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags, r-cran-runjags, r-cran-coda, r-cran-rstan Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-banova_1.2.1-1.ca2404.1_amd64.deb Size: 651134 MD5sum: 6490eb5ba9ddbb78312086f19435911c SHA1: 2fe80ad5eeb1e35e89782ffe22f67315726a90a4 SHA256: fc3d7a643c36dd06ddd01e5d5d3e12592caa4900f6e352eb6c3c663840bdb663 SHA512: 81399d5a6a6b63cce8740fe16889f89447dc69e232326e2ac5d3b8a73c72087925aacaa57c3ea2307a08984d771f3c52106ed75afde936844dfe4b706442c216 Homepage: https://cran.r-project.org/package=BANOVA Description: CRAN Package 'BANOVA' (Hierarchical Bayesian ANOVA Models) It covers several Bayesian Analysis of Variance (BANOVA) models used in analysis of experimental designs in which both within- and between- subjects factors are manipulated. They can be applied to data that are common in the behavioral and social sciences. The package includes: Hierarchical Bayes ANOVA models with normal response, t response, Binomial (Bernoulli) response, Poisson response, ordered multinomial response and multinomial response variables. All models accommodate unobserved heterogeneity by including a normal distribution of the parameters across individuals. Outputs of the package include tables of sums of squares, effect sizes and p-values, and tables of predictions, which are easily interpretable for behavioral and social researchers. The floodlight analysis and mediation analysis based on these models are also provided. BANOVA uses 'Stan' and 'JAGS' as the computational platform. References: Dong and Wedel (2017) ; Wedel and Dong (2020) . Package: r-cran-bareb Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 944 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-bareb_0.1.2-1.ca2404.1_amd64.deb Size: 604522 MD5sum: 27e5846357c60f5a331f4df1e66f9f17 SHA1: 84a7e987b00525043dfc1e14b64c0371e66c808f SHA256: 493965da7510cadc2e2bfe2c6f54dc81547caa1d0b8bec3f316e5f9609109f3e SHA512: b2bb031da83122a9fb1e2116d6eba25bbaf19fe3257a90675d8639bb92e573e765cf58383332e4d7c87fb2fed8be2e8c0a84ed6e0781e3bc12501b655ca9aab7 Homepage: https://cran.r-project.org/package=BAREB Description: CRAN Package 'BAREB' (A Bayesian Repulsive Biclustering Model for Periodontal Data) Simultaneously clusters the Periodontal diseases (PD) patients and their tooth sites after taking the patient- and site-level covariates into consideration. 'BAREB' uses the determinantal point process (DPP) prior to induce diversity among different biclusters to facilitate parsimony and interpretability. Essentially, 'BAREB' is a cluster-wise linear model based on Yuliang (2020) . Package: r-cran-bark Architecture: amd64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-bart, r-cran-e1071, r-cran-fdm2id, r-cran-rmarkdown, r-cran-knitr, r-cran-roxygen2, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-bark_1.0.5-1.ca2404.1_amd64.deb Size: 271158 MD5sum: 8175520a1d58e3777aca552f957c3f4a SHA1: 274979f8547a81b889873a7ec7f2483fa3daab8a SHA256: 1719b5b2b63c54e2d0445435c7d6d5f58cc84a077f12db3c6798d79363004b38 SHA512: 2d0fce5e9d3f34e7a2cd05d0de9e5759b4fe3abf1a6a9063777d10da712d7cc7263a4cf84c20bde3c7b4d5306e8a4919413e6719432e762c8bbc0e63c92e7ef8 Homepage: https://cran.r-project.org/package=bark Description: CRAN Package 'bark' (Bayesian Additive Regression Kernels) Bayesian Additive Regression Kernels (BARK) provides an implementation for non-parametric function estimation using Levy Random Field priors for functions that may be represented as a sum of additive multivariate kernels. Kernels are located at every data point as in Support Vector Machines, however, coefficients may be heavily shrunk to zero under the Cauchy process prior, or even, set to zero. The number of active features is controlled by priors on precision parameters within the kernels, permitting feature selection. For more details see Ouyang, Z (2008) "Bayesian Additive Regression Kernels", Duke University. PhD dissertation, Chapter 3 and Wolpert, R. L, Clyde, M.A, and Tu, C. (2011) "Stochastic Expansions with Continuous Dictionaries Levy Adaptive Regression Kernels, Annals of Statistics Vol (39) pages 1916-1962 . Package: r-cran-barnard Architecture: amd64 Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-barnard_1.8-1.ca2404.1_amd64.deb Size: 23098 MD5sum: 2523040c4f08e3a7a1ac2df213cb9740 SHA1: 7756b5e7c5a9e7960f39865295c5431aa742a508 SHA256: 0bee3bef36bc75127e17a01958925e778b9e987186153327d658f36be6b5dd8a SHA512: b03500b7a1d7e0abc2332da2f9cc39fdf189343f898fa011bf2b5472c9642eb3783798aa9cbafab5d135883a86d261f525ae28c14d2e2964a1ba3f1870357d25 Homepage: https://cran.r-project.org/package=Barnard Description: CRAN Package 'Barnard' (Barnard's Unconditional Test) Barnard's unconditional test for 2x2 contingency tables. Package: r-cran-bart Architecture: amd64 Version: 2.9.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4795 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-survival, r-cran-rcpp Suggests: r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-rpart, r-cran-rpart.plot Filename: pool/dists/noble/main/r-cran-bart_2.9.10-1.ca2404.1_amd64.deb Size: 4316822 MD5sum: 2185566479837246f74473588cf74515 SHA1: cf371e708b86cdebf5cca9b06c1b2b1e386d0bd9 SHA256: eef49ac92ff1cafa5433f453c70af529ea913d3d3de553508bd561ad16c3024f SHA512: 8badc7dd96bb8ff1fb67cf57c079c3fbc6ecad73d9afaceb71aaaeed713e041d1962859728b9166b7a9c1aa473b51a0acd3cd571eec1d188c4b1c7d41dce5181 Homepage: https://cran.r-project.org/package=BART Description: CRAN Package 'BART' (Bayesian Additive Regression Trees) Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. For more information see Sparapani, Spanbauer and McCulloch . Package: r-cran-bartcs Architecture: amd64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 535 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-ggcharts, r-cran-ggplot2, r-cran-invgamma, r-cran-mcmcpack, r-cran-rcpp, r-cran-rlang, r-cran-rootsolve Suggests: r-cran-knitr, r-cran-microbenchmark, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bartcs_1.3.0-1.ca2404.1_amd64.deb Size: 283082 MD5sum: d1874346110dbea2d0855b552f2a80bc SHA1: d7c9c8a9e737ce6a059c6eb5824dee57ee97fc10 SHA256: e53704c700f957c6f9f8d9ab68218b8ff74d1d4a7a831d9b064d2197320bb5b5 SHA512: 4f942988a6f866bd5b4f0e0985967fc2b9e66401ea428b29898bb0d4aa08cf999ea2ba036266dd229bdba0e8a9c5c8308e6cb1f58d677ca20d77bfa0c8ebdd3a Homepage: https://cran.r-project.org/package=bartcs Description: CRAN Package 'bartcs' (Bayesian Additive Regression Trees for Confounder Selection) Fit Bayesian Regression Additive Trees (BART) models to select true confounders from a large set of potential confounders and to estimate average treatment effect. For more information, see Kim et al. (2023) . Package: r-cran-bartxviz Architecture: amd64 Version: 1.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-superlearner, r-cran-bartmachine, r-cran-bart, r-cran-ggplot2, r-cran-ggforce, r-cran-data.table, r-cran-ggfittext, r-cran-ggpubr, r-cran-foreach, r-cran-gggenes, r-cran-rcpp, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-abind, r-cran-dbarts, r-cran-forcats, r-cran-gridextra, r-cran-reshape2, r-cran-missforest, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-bartxviz_1.0.11-1.ca2404.1_amd64.deb Size: 489348 MD5sum: c3d52b4a131b990e0772fac1adc7362d SHA1: 64eaf260a71e9d5d8d6a73229ed88539effc3dd1 SHA256: c346e8447a1d45febf54bce7402e77c83097706bb4aa1ac42406ab91997a7735 SHA512: 79c26be501ca0fcade7e4763cdd1d271550e1f3768389d424a7cab33abf2d33338ac974d4e63b686d9aed8fedcb6b26c6e1e91aa3952c94bf9c52f7ce693d2fd Homepage: https://cran.r-project.org/package=bartXViz Description: CRAN Package 'bartXViz' (Visualization of BART and BARP using SHAP) Complex machine learning models are often difficult to interpret. Shapley values serve as a powerful tool to understand and explain why a model makes a particular prediction. This package computes variable contributions using permutation-based Shapley values for Bayesian Additive Regression Trees (BART) and its extension with Post-Stratification (BARP). The permutation-based SHAP method proposed by Strumbel and Kononenko (2014) is grounded in data obtained via MCMC sampling. Similar to the BART model introduced by Chipman, George, and McCulloch (2010) , this package leverages Bayesian posterior samples generated during model estimation, allowing variable contributions to be computed without requiring additional sampling. The BART model is designed to work with the following R packages: 'BART' , 'bartMachine' , and 'dbarts' . For XGBoost and baseline adjustments, the approach by Lundberg et al. (2020) is also considered. The BARP model proposed by Bisbee (2019) was implemented with reference to and is designed to work with modified functions based on that implementation. BARP extends post-stratification by computing variable contributions within each stratum defined by stratifying variables. The resulting Shapley values are visualized through both global and local explanation methods. Package: r-cran-bas Architecture: amd64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2153 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass, r-cran-knitr, r-cran-ggplot2, r-cran-ggally, r-cran-rmarkdown, r-cran-roxygen2, r-cran-dplyr, r-cran-glmbb, r-cran-testthat, r-cran-covr, r-cran-faraway Filename: pool/dists/noble/main/r-cran-bas_2.0.2-1.ca2404.1_amd64.deb Size: 1171094 MD5sum: 40bbf7c8b4e51e4adda7af24491fb1eb SHA1: 855552966a5f7923d98f25b1ec787cc4b5d36062 SHA256: 245dfe5002b72a9e6ad8c029ddb4ee5d60a2425b39f54b75111c4dbf00877f5d SHA512: a839ecffaa48a934c352be44540b9a2ee41ec151014f8b2f2f24849a37049e963b09d9541e910da12aaff1c74f7bdbb7dd9cacaf8b3e0200fafd91e70cc72481 Homepage: https://cran.r-project.org/package=BAS Description: CRAN Package 'BAS' (Bayesian Variable Selection and Model Averaging using BayesianAdaptive Sampling) Package for Bayesian Variable Selection and Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner's g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008) for linear models or mixtures of g-priors from Li and Clyde (2019) in generalized linear models. Other model selection criteria include AIC, BIC and Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models using sampling w/out replacement or an efficient MCMC algorithm which samples models using a tree structure of the model space as an efficient hash table. See Clyde, Ghosh and Littman (2010) for details on the sampling algorithms. Uniform priors over all models or beta-binomial prior distributions on model size are allowed, and for large p truncated priors on the model space may be used to enforce sampling models that are full rank. The user may force variables to always be included in addition to imposing constraints that higher order interactions are included only if their parents are included in the model. This material is based upon work supported by the National Science Foundation under Division of Mathematical Sciences grant 1106891. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. 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Typical use of the package is for removing background effects from spectra originating from various types of spectroscopy and spectrometry, possibly optimizing this with regard to regression or classification results. Correction methods include polynomial fitting, weighted local smoothers and many more. Package: r-cran-bases Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-cpp11 Suggests: r-cran-mgcv, r-cran-recipes, r-cran-tibble, r-cran-adj, r-cran-rspectra, r-cran-igraph, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bases_0.2.0-1.ca2404.1_amd64.deb Size: 303076 MD5sum: d2b52d1572a13a2da7a7ba2be70ed6c7 SHA1: d24f2ee09425671750dee0b1ecf7a897dcc5e9b5 SHA256: 8d746bd32d932991484ad76ca93d35ed98e7992c7e828d8352e8a84da5258ecf SHA512: 77b7eb4167addbb973cc99a70ae16a16b7f3ec6516713aeda4bb54f739bc54bf48bf3781daaa26ca9e98b6aa6387d78f55e4d203e035e7e52e0c26e419f97268 Homepage: https://cran.r-project.org/package=bases Description: CRAN Package 'bases' (Basis Expansions for Regression Modeling) Provides various basis expansions for flexible regression modeling, including random Fourier features (Rahimi & Recht, 2007) , exact kernel / Gaussian process feature maps, prior features for Bayesian Additive Regression Trees (BART) (Chipman et al., 2010) , and a helpful interface for n-way interactions. The provided functions may be used within any modeling formula, allowing the use of kernel methods and other basis expansions in modeling functions that do not otherwise support them. Along with the basis expansions, a number of kernel functions are also provided, which support kernel arithmetic to form new kernels. Basic ridge regression functionality is included as well. Package: r-cran-basicspace Architecture: amd64 Version: 0.25-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2230 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-basicspace_0.25-1.ca2404.1_amd64.deb Size: 2036038 MD5sum: 89d453c0b19db53e2b0c412db4f46e8d SHA1: 77e4640e8ef1fb7b5e60865328c9ad21c96b00b2 SHA256: f5d02701763fc512a6de73da53b22b14b6dab684e9f1a99d98d6b13fec8aa5a0 SHA512: c2334f9300d073ca347c109e3bb5f0d0494c5f214ee9d62866423616f557999f04121eafa63863e23439ff98b36885e592cb492238d92934e381dee8cec52e7f Homepage: https://cran.r-project.org/package=basicspace Description: CRAN Package 'basicspace' (Recovering a Basic Space from Issue Scales) Provides functions to estimate latent dimensions of choice and judgment using Aldrich-McKelvey and Blackbox scaling methods, as described in Poole et al. (2016, ). These techniques allow researchers (particularly those analyzing political attitudes, public opinion, and legislative behavior) to recover spatial estimates of political actors' ideal points and stimuli from issue scale data, accounting for perceptual bias, multidimensional spaces, and missing data. The package uses singular value decomposition and alternating least squares (ALS) procedures to scale self-placement and perceptual data into a common latent space for the analysis of ideological or evaluative dimensions. Functionality also include tools for assessing model fit, handling complex survey data structures, and reproducing simulated datasets for methodological validation. Package: r-cran-baskexact Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 489 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-baskexact_1.0.1-1.ca2404.1_amd64.deb Size: 262084 MD5sum: 797cd46241e2664a2ccc9259628af791 SHA1: 659ba1fdfc06d52c37dc52e2a57b8202ed9883a5 SHA256: 59b0f8ec49383eb43042cf264cf010e48407ed4b387e384b77b9d87097755514 SHA512: 6f322c84a07d20d54219f7f095786e3f2b9045df1ec7b1c30998b1b6b807f8ba833a48ee260373d7de659d5acf825b5d738fb87119d154bcb85972f5edef9d2d Homepage: https://cran.r-project.org/package=baskexact Description: CRAN Package 'baskexact' (Analytical Calculation of Basket Trial Operating Characteristics) Analytically calculates the operating characteristics of single-stage and two-stage basket trials with equal sample sizes using the power prior design by Baumann et al. (2024) and the design by Fujikawa et al. (2020) . Package: r-cran-batchmix Architecture: amd64 Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 886 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-tidyr, r-cran-ggplot2, r-cran-salso, r-cran-rcpparmadillo Suggests: r-cran-xml2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-batchmix_2.2.1-1.ca2404.1_amd64.deb Size: 450502 MD5sum: 2be1052f91aa5233e14bb1819f964790 SHA1: 525064adb1078355fb5eaaece2e6f7ab2277e89b SHA256: 3314d097f0fe59296a2082154c1d3152951737a36a1163fdfe20d2b8f6bd4fce SHA512: 75e36e31538ae96ed167e169129fc7a90acde4b5b389cb48065833395f5bc2b1c7e1f3425a441c0ca92cbbd0d3be1fc15c338851ea9b8e908f789f1218efb182 Homepage: https://cran.r-project.org/package=batchmix Description: CRAN Package 'batchmix' (Semi-Supervised Bayesian Mixture Models Incorporating BatchCorrection) Semi-supervised and unsupervised Bayesian mixture models that simultaneously infer the cluster/class structure and a batch correction. Densities available are the multivariate normal and the multivariate t. The model sampler is implemented in C++. This package is aimed at analysis of low-dimensional data generated across several batches. See Coleman et al. (2022) for details of the model. Package: r-cran-batchtools Architecture: amd64 Version: 0.9.18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1649 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-base64url, r-cran-brew, r-cran-checkmate, r-cran-data.table, r-cran-digest, r-cran-fs, r-cran-progress, r-cran-r6, r-cran-rappdirs, r-cran-stringi, r-cran-withr Suggests: r-cran-debugme, r-cran-dompi, r-cran-doparallel, r-cran-e1071, r-cran-foreach, r-cran-future, r-cran-future.batchtools, r-cran-knitr, r-cran-parallelmap, r-cran-ranger, r-cran-rmarkdown, r-cran-rpart, r-cran-snow, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-batchtools_0.9.18-1.ca2404.1_amd64.deb Size: 1028314 MD5sum: 514e762aaa31cd52fd249ffdd13d3d5b SHA1: c0790ced0dcbcf0bc04ca4417367f95f4020c315 SHA256: 7a307bbe7be048fcdda29c8a94fbf49e5e913dbe2d160c789fd9cd18b5b44a85 SHA512: dfe7ac4ce8c6aeebc1e7b5beb0dbb15cdccb92d3444d5377413c0207fd446c227c14c60d463ebb66d2d2b2b64b22cd77f4c9149cd629ec865a5b5f36f3308e62 Homepage: https://cran.r-project.org/package=batchtools Description: CRAN Package 'batchtools' (Tools for Computation on Batch Systems) As a successor of the packages 'BatchJobs' and 'BatchExperiments', this package provides a parallel implementation of the Map function for high performance computing systems managed by schedulers 'IBM Spectrum LSF' (), 'Univa Grid Engine'/'Oracle Grid Engine' (), 'Slurm' (), 'TORQUE/PBS' (), or 'Docker Swarm' (). A multicore and socket mode allow the parallelization on a local machines, and multiple machines can be hooked up via SSH to create a makeshift cluster. Moreover, the package provides an abstraction mechanism to define large-scale computer experiments in a well-organized and reproducible way. Package: r-cran-batman Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-batman_0.1.0-1.ca2404.1_amd64.deb Size: 42488 MD5sum: bea2cac1590a11bcf051dd54a0ac0eaf SHA1: e8e5685046c1ea6b63d772138864f12af9ee9f86 SHA256: 81591b7ece5866d3a69a1b613c3cec83e97dcd3c6b22f542c46d038969d8cf00 SHA512: b912e18545c4db8907498f90fed6adef51f4d422878ac54f0f377635953e5bbc30c2966fe6d72b199236044ceef923261af39d33a1dc71ea946b34ee2bfec9db 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. 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Package: r-cran-bayenet Architecture: amd64 Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mcmcpack, r-cran-gsl, r-cran-vgam, r-cran-mass, r-cran-hbmem, r-cran-suppdists, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-bayenet_0.3-1.ca2404.1_amd64.deb Size: 163272 MD5sum: 59b01cea45960c58c36d5ef10fa03077 SHA1: 8b28cb031bddc93b1d2bf2c0ab9ac54f36e6842e SHA256: d13012c346ef9cd18528e3b02f8459f1386960bb91e03056d86b07de35d64dfe SHA512: a367f47b3c20050b308dbf525a59a010ec020465ca01a425a908472ecb098a2711aa57cf277827ad1dec72e9f36c7148e514352de4448f91915631d8db9748e0 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. 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Package: r-cran-bayes4psy Architecture: amd64 Version: 1.2.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7940 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-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/noble/main/r-cran-bayes4psy_1.2.13-1.ca2404.1_amd64.deb Size: 3208336 MD5sum: df70c493e35cd35cf2f91728e2321498 SHA1: 8af67bf43727f10c129c04f5a1ac536d26945b0c SHA256: 7bcd731a9f4e68040f3048acb3ab3c3deb7d067b9f23bbeeaeb1bc9d8c11d1da SHA512: a6371fc0dafafbcfece3262e36a27bec160f730a8620bf98712bf2f41e39fe6a59ec050a2145aea3620779897ed7fd22d912ceaef9b1a2874cf50e8260bc0be6 Homepage: https://cran.r-project.org/package=bayes4psy Description: CRAN Package 'bayes4psy' (User Friendly Bayesian Data Analysis for Psychology) Contains several Bayesian models for data analysis of psychological tests. 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Package: r-cran-bayescomm Architecture: amd64 Version: 0.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-abind, r-cran-coda, r-cran-mvtnorm, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-bayescomm_0.1-2-1.ca2404.1_amd64.deb Size: 103072 MD5sum: 5e142d291a365bcad9d4285d977826de SHA1: c294a189dc55430862cc5e318fdc736f5fb1315f SHA256: 3aa2c52a7a69873a05686347662c0147294f55507e8c81b5cbbc3def16fe0f99 SHA512: 5e400f6b7736cd1cf2538d18f8ea1e18f25ee4ff51dae868160b278c6281532095406074bd504a4d916a736cebab54e109c52207a769a4d51bd2563f30712e6e Homepage: https://cran.r-project.org/package=BayesComm Description: CRAN Package 'BayesComm' (Bayesian Community Ecology Analysis) Bayesian multivariate binary (probit) regression models for analysis of ecological communities. 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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. Package: r-cran-bayescureratemodel Architecture: amd64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-doparallel, r-cran-foreach, r-cran-mclust, r-cran-coda, r-cran-hdinterval, r-cran-vgam, r-cran-calculus, r-cran-flexsurv, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-bayescureratemodel_1.6-1.ca2404.1_amd64.deb Size: 329012 MD5sum: a5001dcb68185bc6861c8ac0e228fdf0 SHA1: ab6afa9d6987e9a45cfd2e5a77e73b9c32bb8e2e SHA256: 359693fe3dc08016cfe5df5a41484fb9606a891f5219592b98f5317c0f46c719 SHA512: dd663081216fb266fc301e5e979efe1f43c1a9dbfda4872713276896fe73e78d04b31472e6e20f6e4637e47a628d6b9d8aa9f591c02acbe7681e619c0189e28c Homepage: https://cran.r-project.org/package=bayesCureRateModel Description: CRAN Package 'bayesCureRateModel' (Bayesian Cure Rate Modeling for Time-to-Event Data) A fully Bayesian approach in order to estimate a general family of cure rate models under the presence of covariates, see Papastamoulis and Milienos (2024) and Papastamoulis and Milienos (2024b) . The promotion time can be modelled (a) parametrically using typical distributional assumptions for time to event data (including the Weibull, Exponential, Gompertz, log-Logistic distributions), or (b) semiparametrically using finite mixtures of distributions. In both cases, user-defined families of distributions are allowed under some specific requirements. Posterior inference is carried out by constructing a Metropolis-coupled Markov chain Monte Carlo (MCMC) sampler, which combines Gibbs sampling for the latent cure indicators and Metropolis-Hastings steps with Langevin diffusion dynamics for parameter updates. The main MCMC algorithm is embedded within a parallel tempering scheme by considering heated versions of the target posterior distribution. Package: r-cran-bayesdccgarch Architecture: amd64 Version: 3.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv, r-cran-coda Filename: pool/dists/noble/main/r-cran-bayesdccgarch_3.0.4-1.ca2404.1_amd64.deb Size: 144924 MD5sum: f2bc190efa95c06a16140f57c65d07f9 SHA1: dd1d510649f5182e7dc7ad5596f86bc43b7f9a49 SHA256: 238f1a69655b58425d67f43aa9bc35fa4322ae31c42024066d012ad33bc532ab SHA512: d9b679bd8205c865d2e1f57c5098346a5c83d79b8af01d36d7306fbe934bbadf4e8e38ec855161de6843c514210765a453a479d32f731dda7ba267a9122a302d Homepage: https://cran.r-project.org/package=bayesDccGarch Description: CRAN Package 'bayesDccGarch' (Methods and Tools for Bayesian Dynamic Conditional CorrelationGARCH(1,1) Model) Bayesian estimation of dynamic conditional correlation GARCH model for multivariate time series volatility (Fioruci, J.A., Ehlers, R.S. and Andrade-Filho, M.G., (2014). . Package: r-cran-bayesdecon Architecture: amd64 Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1274 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-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/noble/main/r-cran-bayesdecon_0.1.6-1.ca2404.1_amd64.deb Size: 629622 MD5sum: 0bbaec87e948308d740fb075eaa14715 SHA1: 10ab83ca56eeafb9f90dfe7bb72655ecc5b96ac4 SHA256: d19a890b91f2376659a08af2efc8c7bc8f191383412ebc4a35a7cc6e8857ec71 SHA512: 0da29ad948f1644f3865ede37d3cece85bfe07fe63c0c943dc0ac719509ef00d44ff2793b1f3fd256a04eb58d8c39b57de0f196cc4be5101f4d2c56c46cf4de1 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. For more details see Sarkar (2021) . Package: r-cran-bayesdfa Architecture: amd64 Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6434 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-loo, r-cran-mgcv, r-cran-rcpp, r-cran-reshape2, r-cran-rlang, r-cran-rstan, r-cran-viridislite, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bayesdfa_1.3.4-1.ca2404.1_amd64.deb Size: 2120338 MD5sum: 4c5c39fbb08feba2dddd459c855c5666 SHA1: 77f7d31e2fac2acbc61dbe0e8f742c8568a46fbf SHA256: 46fd83c7db341bed2df92f0302b15242d229b6de49f2445c38d8b2dbc05fabc1 SHA512: 796a36af73745e83afebb0e3f47660d606113765de30e42d8032af6734ac13b426190aac4cc08f23521fdb688e9df9fc2cea62d31c71c33002b4fe606f37ae01 Homepage: https://cran.r-project.org/package=bayesdfa Description: CRAN Package 'bayesdfa' (Bayesian Dynamic Factor Analysis (DFA) with 'Stan') Implements Bayesian dynamic factor analysis with 'Stan'. Dynamic factor analysis is a dimension reduction tool for multivariate time series. 'bayesdfa' extends conventional dynamic factor models in several ways. First, extreme events may be estimated in the latent trend by modeling process error with a student-t distribution. Second, alternative constraints (including proportions are allowed). Third, the estimated dynamic factors can be analyzed with hidden Markov models to evaluate support for latent regimes. Package: r-cran-bayesdlmfmri Architecture: amd64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2062 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-oro.nifti, r-cran-neurobase, r-cran-pbapply, r-cran-rcpp, r-cran-rdpack, r-cran-mathjaxr, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesdlmfmri_0.0.3-1.ca2404.1_amd64.deb Size: 1015692 MD5sum: 546cc114302d30dcf4b2830e50b4d8d1 SHA1: b7315478b7ae6107a503c1299d4b67784389c596 SHA256: 14206af7de5cd91e957d7bd62d9d18ee73a0ce0bf0c3743594c62527e5c9d3f8 SHA512: 4916a98c927b1d93aa536d8473e263dd6975c94392597918cc951022b66874a1f33ad639b202e2c3169bb9601b43cbde08e507da7566449cee2cf7cf07a1da89 Homepage: https://cran.r-project.org/package=BayesDLMfMRI Description: CRAN Package 'BayesDLMfMRI' (Statistical Analysis for Task-Based Fmri Data) The 'BayesDLMfMRI' package performs statistical analysis for task-based functional magnetic resonance imaging (fMRI) data at both individual and group levels. The analysis to detect brain activation at the individual level is based on modeling the fMRI signal using Matrix-Variate Dynamic Linear Models (MDLM). The analysis for the group stage is based on posterior distributions of the state parameter obtained from the modeling at the individual level. In this way, this package offers several R functions with different algorithms to perform inference on the state parameter to assess brain activation for both individual and group stages. Those functions allow for parallel computation when the analysis is performed for the entire brain as well as analysis at specific voxels when it is required. References: Cardona-Jiménez (2021) ; Cardona-Jiménez (2021) . Package: r-cran-bayesdp Architecture: amd64 Version: 1.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3061 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-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/noble/main/r-cran-bayesdp_1.3.7-1.ca2404.1_amd64.deb Size: 1513618 MD5sum: 7a778679cf75365939a9bb6d82f71a50 SHA1: 985403a4077deb9f832404b683492c2937adf475 SHA256: 9a4e1dd74ee54c38ef9fc68658d1189c88a4864ff2405df7c18193af66a342da SHA512: 71f932f008cab1dce24530dc2b3f83c730b8373dc80360a7d44e854b9c40d02954bf7b398a071cedc9ccbaaf424577dcf80fe87d745c05c1f00f25ffd71f0a36 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: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4466 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-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/noble/main/r-cran-bayeseo_0.2.2-1.ca2404.1_amd64.deb Size: 2301336 MD5sum: b7052145360a730faa8ee4862e8ece35 SHA1: c239f62e12a4c1e0d370b231401e6d7698e91172 SHA256: 5c5b58ba529e2a512e536d3ca9e08f6f9839f25249edc43d8d6a497038671386 SHA512: 762d3e00d315db94670520861489760bf25656e36d43c7a323ac4e4f24ebc554d5c405d2d78f7905109539a0d30b05191916e192fd146506f969ad5aa8739471 Homepage: https://cran.r-project.org/package=bayesEO Description: CRAN Package 'bayesEO' (Bayesian Smoothing of Remote Sensing Image Classification) A Bayesian smoothing method for post-processing of remote sensing image classification which refines the labelling in a classified image in order to enhance its classification accuracy. Combines pixel-based classification methods with a spatial post-processing method to remove outliers and misclassified pixels. Package: r-cran-bayesess Architecture: amd64 Version: 0.1.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-bayesess_0.1.19-1.ca2404.1_amd64.deb Size: 80396 MD5sum: 5f590ec1ab8eb947831e6c3be876935e SHA1: 65bb9ba4c14d9836c4c8b96575dd47e1d27c22a4 SHA256: 0656409f5defb379bbf092e7bd71070e35b11452a8843b9a0ecc54c83ddde7bf SHA512: 6ea1acb7c5b093e4ad5a80f9153dc1e34458631d571e51ce957cbf8658d39999580f048652a299a2fc0a51001fe90585ef9da90dc010a306082af6cbf8ea97d6 Homepage: https://cran.r-project.org/package=BayesESS Description: CRAN Package 'BayesESS' (Determining Effective Sample Size) Determines effective sample size of a parametric prior distribution in Bayesian models. For a web-based Shiny application related to this package, see . Package: r-cran-bayesfactor Architecture: amd64 Version: 0.9.12-4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12661 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-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/noble/main/r-cran-bayesfactor_0.9.12-4.8-1.ca2404.1_amd64.deb Size: 6662496 MD5sum: ad800afc146f94ba500d25ddf0688959 SHA1: fc39c527f75df0573f1a7c4da758806368195101 SHA256: 6c1702648ee63fe3a8dc201c6d061c7dd839e119b47e0516fef47ad6b28cfaa4 SHA512: 98ae222c9317dbaab2e1fd1e519936d95c6675ae9ef3b9f67ed53b341d3d03ac98114c5182b258fa45cdeb7c9e6676dfbfba873a6bbad1ab5ef7a2062e494047 Homepage: https://cran.r-project.org/package=BayesFactor Description: CRAN Package 'BayesFactor' (Computation of Bayes Factors for Common Designs) A suite of functions for computing various Bayes factors for simple designs, including contingency tables, one- and two-sample designs, one-way designs, general ANOVA designs, and linear regression. Package: r-cran-bayesfm Architecture: amd64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-coda, r-cran-ggplot2, r-cran-gridextra, r-cran-plyr Filename: pool/dists/noble/main/r-cran-bayesfm_0.1.7-1.ca2404.1_amd64.deb Size: 202210 MD5sum: 036112f3928561d2c5bd6b9b28e2f8ec SHA1: de7d290a7edbb5b854672926af536234e26fc514 SHA256: ca678fcf07dc29eace908d04b59e3e5d6bbc33af34fa0ff2d07a177622065111 SHA512: 3842c544d00aecacf97ba9e2444815e6dfefb966021597ceaf378fdb595abe83c67ba3d360b993ffd171b1f2275bf0b1b31972903c64cf7ce63bfdbb75d522be Homepage: https://cran.r-project.org/package=BayesFM Description: CRAN Package 'BayesFM' (Bayesian Inference for Factor Modeling) Collection of procedures to perform Bayesian analysis on a variety of factor models. Currently, it includes: "Bayesian Exploratory Factor Analysis" (befa) from G. Conti, S. Frühwirth-Schnatter, J.J. Heckman, R. Piatek (2014) , an approach to dedicated factor analysis with stochastic search on the structure of the factor loading matrix. The number of latent factors, as well as the allocation of the manifest variables to the factors, are not fixed a priori but determined during MCMC sampling. Package: r-cran-bayesfmri Architecture: amd64 Version: 0.11.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 979 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-ciftitools, r-cran-excursions, r-cran-foreach, r-cran-fmritools, r-cran-mass, r-cran-matrix, r-cran-matrixstats, r-cran-rcpp, r-cran-sp, r-cran-viridislite, r-cran-rcppeigen Suggests: r-cran-covr, r-cran-abind, r-cran-hrf, r-cran-knitr, r-cran-matrixmodels, r-cran-purrr, r-cran-rmarkdown, r-cran-squarem, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-bayesfmri_0.11.0-1.ca2404.1_amd64.deb Size: 708614 MD5sum: 7b4bdd459f675eeaf49e043d28bd7a38 SHA1: 7dfc62c9ec574937238942f98ddf6242fbd76633 SHA256: 14fc8243744fc2467a0770362ad31a7c5ef80efed600010f0ec22e975b2c913a SHA512: e0dafc0423546dc4b24ea4c320d0fb8a686943e9a4ee2205787e0113dc3dc81f8651e3abb08e5abb8524e998dbd49edc354590d1402d664b11aa76ff13724a53 Homepage: https://cran.r-project.org/package=BayesfMRI Description: CRAN Package 'BayesfMRI' (Spatial Bayesian Methods for Task Functional MRI Studies) Performs a spatial Bayesian general linear model (GLM) for task functional magnetic resonance imaging (fMRI) data on the cortical surface. Additional models include group analysis and inference to detect thresholded areas of activation. Includes direct support for the 'CIFTI' neuroimaging file format. For more information see A. F. Mejia, Y. R. Yue, D. Bolin, F. Lindgren, M. A. Lindquist (2020) and D. Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022) . Package: r-cran-bayesforecast Architecture: amd64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8575 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayesplot, r-cran-bridgesampling, r-cran-forecast, r-cran-ggplot2, r-cran-gridextra, r-cran-loo, r-cran-lubridate, r-cran-mass, r-cran-prophet, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-zoo, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggfortify Filename: pool/dists/noble/main/r-cran-bayesforecast_1.0.5-1.ca2404.1_amd64.deb Size: 3460804 MD5sum: 92a0ae5b0af6a28b3a08368fa1e6c235 SHA1: 5d223ee5ce9497821871acb89d1a49fe14903c35 SHA256: ebf440f666e14cfadd6d9807be40844cd99e239f39f835ffeb4fea0aba2a459a SHA512: 1bf55e1210d3d1b1c4fd962e54d278e29f9668b36ea7fde29fd06a6132f4548d8570d5832f030a885291f94ac618c82d243cf09f74fb61859552eee8cccc9c8a Homepage: https://cran.r-project.org/package=bayesforecast Description: CRAN Package 'bayesforecast' (Bayesian Time Series Modeling with Stan) Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) ; Carpenter et al. (2017) . Package: r-cran-bayesgam Architecture: amd64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6251 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bayesplot, r-cran-boot, r-cran-cluster, r-cran-corrplot, r-cran-ggplot2, r-cran-gridextra, r-cran-loo, r-cran-mlbench, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-semipar, r-cran-geometry, r-cran-mass, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesgam_0.0.2-1.ca2404.1_amd64.deb Size: 1810880 MD5sum: 743e1a08dea649bbcac13c27bfc1eae7 SHA1: 51d8cbb3619bdea6450591de6a61cbfc51e2454d SHA256: 0332a0bb306cc223849a5742ac665c4e5a59d85d4194be33ccb2f858ef2fe4f8 SHA512: ceb3e64a0f7f27495232d14fb0f656d55d8e184646c6a7aa90e279317dd4f236da924693fa20b26f5b2540a22147f8bef50e47336aee03b401feb4872e7ae8ec Homepage: https://cran.r-project.org/package=bayesGAM Description: CRAN Package 'bayesGAM' (Fit Multivariate Response Generalized Additive Models usingHamiltonian Monte Carlo) The 'bayesGAM' package is designed to provide a user friendly option to fit univariate and multivariate response Generalized Additive Models (GAM) using Hamiltonian Monte Carlo (HMC) with few technical burdens. The functions in this package use 'rstan' (Stan Development Team 2020) to call 'Stan' routines that run the HMC simulations. The 'Stan' code for these models is already pre-compiled for the user. The programming formulation for models in 'bayesGAM' is designed to be familiar to analysts who fit statistical models in 'R'. Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., ... & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of statistical software, 76(1). Stan Development Team. 2018. RStan: the R interface to Stan. R package version 2.17.3. Neal, Radford (2011) "Handbook of Markov Chain Monte Carlo" ISBN: 978-1420079418. Betancourt, Michael, and Mark Girolami. "Hamiltonian Monte Carlo for hierarchical models." Current trends in Bayesian methodology with applications 79.30 (2015): 2-4. Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" , Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) "Bayesian Data Analysis" ISBN: 978-1439840955, Agresti, Alan (2015) "Foundations of Linear and Generalized Linear Models ISBN: 978-1118730034, Pinheiro, J., Bates, D. (2006) "Mixed-effects Models in S and S-Plus" ISBN: 978-1441903174. Ruppert, D., Wand, M. P., & Carroll, R. J. (2003). Semiparametric regression (No. 12). Cambridge university press. ISBN: 978-0521785167. Package: r-cran-bayesgarch Architecture: amd64 Version: 2.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-coda Filename: pool/dists/noble/main/r-cran-bayesgarch_2.1.10-1.ca2404.1_amd64.deb Size: 73384 MD5sum: 092e9060d7277650cd09d7af2296dceb SHA1: df6d080cca65d0a16d1cf6b1c47e4412e10086eb SHA256: f7d7331675f98451571066cccd81f8ab377ec2947a6193631c616a4b4d77eb39 SHA512: 8b9b39633973a24b6542206086f2d7bcc2e2ed4b349e2603994ce1597b18931b5c14fdfddc030fabce5425fa60fe683286bd6e6678d2cedbd22a752cc4997174 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) . 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The methodology is based on Comment (2018) and a demonstration of its application can be found at Yimer et al. (2022) . 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Computations leverage finite element approximations and adaptive quadrature for efficient inference. Methods are detailed in Zhang, Stringer, Brown, and Stafford (2023) ; Zhang, Stringer, Brown, and Stafford (2024) ; Zhang, Brown, and Stafford (2023) ; and Stringer, Brown, and Stafford (2021) . Package: r-cran-bayesgpfit Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Filename: pool/dists/noble/main/r-cran-bayesgpfit_1.1.0-1.ca2404.1_amd64.deb Size: 96774 MD5sum: 7032ba63ba0b07a4070996afcfb640b0 SHA1: 3bf3fc6915ae7b5ad99c6eaa28b8e8bbcd551f30 SHA256: e1d0c7f9a8678f2af32c2369947f2fb8502db95b10daf2887a220737bbc85b38 SHA512: c1f813917c5423df5ab47ae4a5df284da10b6c328c0c5c9ac27c6fccc7dff01fd5d384ef204d53672a41b1780a30ddba433e2542e9203b96c43fff77fc16fe82 Homepage: https://cran.r-project.org/package=BayesGPfit Description: CRAN Package 'BayesGPfit' (Fast Bayesian Gaussian Process Regression Fitting) Bayesian inferences on nonparametric regression via Gaussian Processes with a modified exponential square kernel using a basis expansion approach. Package: r-cran-bayesgrowth Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4124 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-aquaticlifehistory, r-cran-bayesplot, r-cran-dplyr, r-cran-ggplot2, r-cran-loo, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-tibble, r-cran-tidybayes, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bayesgrowth_1.0.0-1.ca2404.1_amd64.deb Size: 1764042 MD5sum: 364490027b160bc530168aad178d56c6 SHA1: b4883a6690120a784707c65f2b892a2c57e092f9 SHA256: 70c9bffc6281eb02a5912496fc02379716644699ce81cb1cd2721ecdc7514478 SHA512: 42abc7b68e65a5cb1db4f30ac3cfdf08c631bbfcdf089173cc7f95d237ca75ae31646ed818bbeac0d338b151ec5a90ce617d973cf84bfd2b50b87d311311a6a2 Homepage: https://cran.r-project.org/package=BayesGrowth Description: CRAN Package 'BayesGrowth' (Estimate Fish Growth Using MCMC Analysis) Estimate fish length-at-age models using MCMC analysis with 'rstan' models. This package allows a multimodel approach to growth fitting to be applied to length-at-age data and is supported by further analyses to determine model selection and result presentation. The core methods of this package are presented in Smart and Grammer (2021) "Modernising fish and shark growth curves with Bayesian length-at-age models". PLOS ONE 16(2): e0246734 . Package: r-cran-bayesianetas Architecture: amd64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesianetas_2.0.0-1.ca2404.1_amd64.deb Size: 114638 MD5sum: cd4f2d6fc95521878cba03fc8f628a48 SHA1: 6ff87ff42265001ac728e35e9ffe918d6560b63c SHA256: 69100aec476e8b08e6e491bccf1670a4e6cbbe814d5a523b26ddcddd7b669cc7 SHA512: 732ab4e0d8283a4c883247dd2e14a073d2b3db31832283b63bbed4374a2203002a9c28088c153a625f7692cdc8167fd4e7ca6f70ac1654e9a053aff63a6f710d 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. 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This package supports fixed ratio and Bayesian adaptive randomization approaches for randomization. Additionally, it allows for the study of time trend problems in platform trials. There are demos available for a multi-arm multi-stage trial with two different null scenarios, as well as for Bayesian trial cutoff screening. The Bayesian adaptive randomisation approaches are described in: Trippa et al. (2012) and Wathen et al. (2017) . The randomisation algorithm is described in: Zhao W . The analysis methods of time trend effect in platform trial are described in: Saville et al. (2022) and Bofill Roig et al. (2022) . Package: r-cran-bayesiantools Architecture: amd64 Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1347 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-emulator, r-cran-mvtnorm, r-cran-tmvtnorm, r-cran-idpmisc, r-cran-rcpp, r-cran-ellipse, r-cran-numderiv, r-cran-msm, r-cran-mass, r-cran-matrix, r-cran-dharma, r-cran-gap, r-cran-bridgesampling Suggests: r-cran-deoptim, r-cran-sensitivity, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesiantools_0.1.9-1.ca2404.1_amd64.deb Size: 927712 MD5sum: 11ff186a3ba729e35cdb70874016bb9f SHA1: 882e28984dd6ca08232a885676249f40d455d8a8 SHA256: cc2c27bffb97f306f31877b4504586d26a43a0087bfd2236097988f1379ae327 SHA512: 3c8f99946b1a0b84bf932e2d35ac9bc0034b0e8127f7f01b99d6956dbeeba9cff1f3b62d262d6f622b408df0f689027d10d5f8e8bb35b8c741ec163e55598586 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. 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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. 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It includes functionalities to estimate different types of list experiment models with varying prior information. See Lu and Traunmüller (2026) for examples and details of estimation. 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In fact, under the common priors for the variance, useful quantities in the original data scale (like mean and quantiles) do not have posterior moments that are finite (Fabrizi et al. 2012 ). This package allows to easily carry out a proper Bayesian inferential procedure by fixing a suitable distribution (the generalized inverse Gaussian) as prior for the variance. Functions to estimate several kind of means (unconditional, conditional and conditional under a mixed model) and quantiles (unconditional and conditional) are provided. 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Package: r-cran-bayesm Architecture: amd64 Version: 3.1-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5867 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bayesm_3.1-7-1.ca2404.1_amd64.deb Size: 2635578 MD5sum: c85ded8a6f530482312c76a15edfbb14 SHA1: aed7b1ec75fa606b8fd73385cc64647ba2a5fc4c SHA256: 44a8b64abd239335b1c9ef71a98d1b274eb35380a4f4cf8ba3fdc6099366a504 SHA512: d9ba19199465050075280c2eb5cececc1d48dbb997da044734886dffe9884b90d2ff3c6d16278eaa40585a01d39b69a9a000e0abb74c14b6fb77089374cae0d3 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). 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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-bayesppd Architecture: amd64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 946 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-rcppnumerical Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-ggplot2, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-bayesppd_1.1.3-1.ca2404.1_amd64.deb Size: 410390 MD5sum: 4ba1c8cea1522edef32be847e1653f32 SHA1: 476a377d446dfdf287fb693b36ac855d09ba6054 SHA256: c87be91ec0cb48be6b7ae423cf4fc4d994240c64123c2ea786b0f053d58d69de SHA512: 20c013f8ba5303b14341597343991a9f4cfb92349558c2e391c52a1ca01ba740380db2e7350b1c0e36ab951d6d947d07f574e4bdcfcac83f794d72f9baa4006c Homepage: https://cran.r-project.org/package=BayesPPD Description: CRAN Package 'BayesPPD' (Bayesian Power Prior Design) Bayesian power/type I error calculation and model fitting using the power prior and the normalized power prior for generalized linear models. Detailed examples of applying the package are available at . Models for time-to-event outcomes are implemented in the R package 'BayesPPDSurv'. The Bayesian clinical trial design methodology is described in Chen et al. (2011) , and Psioda and Ibrahim (2019) . The normalized power prior is described in Duan et al. (2006) and Ibrahim et al. (2015) . Package: r-cran-bayesppdsurv Architecture: amd64 Version: 1.0.3-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-tidyr, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesppdsurv_1.0.3-1.ca2404.1_amd64.deb Size: 193460 MD5sum: 13b0c3cce05a852f558e57a0253051a5 SHA1: 8adcf34a2687ecf96775938e4174e1a7bf7d42f6 SHA256: 525e37ac656f76118fbad30cf1b8ef580410e3749575ecf3c8230c268a38ff6f SHA512: a8509c9cfd1846174271a028dc15f9aadf29a1f32102b6a5ace79251208dc0c56f2265a1c443d9f2dd15ad865e5f79dd5be434c0fb4751a6d27367509651fb15 Homepage: https://cran.r-project.org/package=BayesPPDSurv Description: CRAN Package 'BayesPPDSurv' (Bayesian Power Prior Design for Survival Data) Bayesian power/type I error calculation and model fitting using the power prior and the normalized power prior for proportional hazards models with piecewise constant hazard. The methodology and examples of applying the package are detailed in . The Bayesian clinical trial design methodology is described in Chen et al. (2011) , and Psioda and Ibrahim (2019) . The proportional hazards model with piecewise constant hazard is detailed in Ibrahim et al. (2001) . Package: r-cran-bayesproject Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-bayesproject_1.0-1.ca2404.1_amd64.deb Size: 117256 MD5sum: 0386b7db3bcde41a1700894d90d50743 SHA1: d768160d6db1a154f4e28eb0da08f8db2ef2c746 SHA256: e36dceaaf88d233a62344e052aa51afd6fa5b3671b7dd97d1cdce45faa1eef54 SHA512: af47a16be1f7b0bb2648b2e2ba95991572f9bd687ea9c1950ba64299f045ccc02c339ff3e51ac1f389a9f566e706667ef447ce3c44b68c300ad91a5a79154eea Homepage: https://cran.r-project.org/package=BayesProject Description: CRAN Package 'BayesProject' (Fast Projection Direction for Multivariate Changepoint Detection) Implementations in 'cpp' of the BayesProject algorithm (see G. Hahn, P. Fearnhead, I.A. Eckley (2020) ) which implements a fast approach to compute a projection direction for multivariate changepoint detection, as well as the sum-cusum and max-cusum methods, and a wild binary segmentation wrapper for all algorithms. Package: r-cran-bayesqr Architecture: amd64 Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bayesqr_2.4-1.ca2404.1_amd64.deb Size: 99154 MD5sum: 5ae2ac53caafc7f67e89a71567711922 SHA1: d17663e7a0263a39eeae36f5570854ad790893d3 SHA256: 92e90fcd44de1c214362aa13abbc36de4d3ca13980304b7fda45d072b86111a5 SHA512: 90cc92445fff34858b337c3e48f5a950156c70ab14710b731fc68812230db8212771c40979d9abb6f04486fe747bcf77367cca0990e5d6ae11d31529df106f93 Homepage: https://cran.r-project.org/package=bayesQR Description: CRAN Package 'bayesQR' (Bayesian Quantile Regression) Bayesian quantile regression using the asymmetric Laplace distribution, both continuous as well as binary dependent variables are supported. The package consists of implementations of the methods of Yu & Moyeed (2001) , Benoit & Van den Poel (2012) and Al-Hamzawi, Yu & Benoit (2012) . To speed up the calculations, the Markov Chain Monte Carlo core of all algorithms is programmed in Fortran and called from R. Package: r-cran-bayesqrsurvey Architecture: amd64 Version: 0.2.2-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-pracma, r-cran-ggplot2, r-cran-rlang, r-cran-posterior, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesqrsurvey_0.2.2-1.ca2404.1_amd64.deb Size: 351368 MD5sum: 5e64f124e31a41429725dfddcd50a89d SHA1: 6052e9c9b0f4576e44155d7b65e8d87aee4fb84f SHA256: 0de1ea5938a4bc4fd3ddd28b26a44bd9fed3091530e378b3ebd2a2767c124d9e SHA512: 569cc7ce30ca10284206a18c82a8ae154c4c4d4297745013d821da8249183557a810ad6a5ecd7927e9b1e451fb558212c19a2e9a9a209ef42f2b3e5265c78af1 Homepage: https://cran.r-project.org/package=bayesQRsurvey Description: CRAN Package 'bayesQRsurvey' (Bayesian Quantile Regression Models for Complex Survey DataAnalysis) Provides Bayesian quantile regression models for complex survey data under informative sampling using survey-weighted estimators. Both single- and multiple-output models are supported. To accelerate computation, all algorithms are implemented in 'C++' using 'Rcpp', 'RcppArmadillo', and 'RcppEigen', and are called from 'R'. See Nascimento and Gonçalves (2024) and Nascimento and Gonçalves (2025, in press) . Package: r-cran-bayesregdtr Architecture: amd64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dorng, r-cran-rcpp, r-cran-mvtnorm, r-cran-foreach, r-cran-progressr, r-cran-future, r-cran-rcpparmadillo Suggests: r-cran-cli, r-cran-testthat, r-cran-dofuture Filename: pool/dists/noble/main/r-cran-bayesregdtr_1.1.2-1.ca2404.1_amd64.deb Size: 278096 MD5sum: 86041c5b3ea601273a131147de383b90 SHA1: 770ac389249d3199734a2d178f039957ff2f8526 SHA256: a943c1d29d9441c61052099834d83a0c986b5b48373bfeba51c2ed3b087412d3 SHA512: eb9ed3921660446c366e08f1dfa7f969173b776d9cf563594c35d77743ccfc7717d8b3859e7c0e30cd2076f76990b15f2ae4852fac77c1fa3b2061dad57cfd9c Homepage: https://cran.r-project.org/package=BayesRegDTR Description: CRAN Package 'BayesRegDTR' (Bayesian Regression for Dynamic Treatment Regimes) Methods to estimate optimal dynamic treatment regimes using Bayesian likelihood-based regression approach as described in Yu, W., & Bondell, H. D. (2023) Uses backward induction and dynamic programming theory for computing expected values. Offers options for future parallel computing. Package: r-cran-bayesrel Architecture: amd64 Version: 0.7.8-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 445 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-laplacesdemon, r-cran-mass, r-cran-lavaan, r-cran-coda, r-cran-rdpack, r-cran-rcpp, r-cran-psych, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-bayesrel_0.7.8-1.ca2404.2_amd64.deb Size: 328214 MD5sum: 868b21a39ccda8cb780b9ede665f7063 SHA1: 1f952286899959904faf91d26639183ea72021ae SHA256: afa76a48a0ebc330adfaba1eecdd0ed17734b9097d7a3d57e9720e068fc8195e SHA512: 9d776d77fa59fa0bc6eaf6710db055c9dfa3d02c5b5cd9e84b1e4f81523f546d8f07b9b5b60a15870ade46e0b6ca45ab1a6a2c91914bbdab77d2ee07cdcacb51 Homepage: https://cran.r-project.org/package=Bayesrel Description: CRAN Package 'Bayesrel' (Bayesian Reliability Estimation) Functionality for reliability estimates. For 'unidimensional' tests: Coefficient alpha, 'Guttman's' lambda-2/-4/-6, the Greatest lower bound and coefficient omega_u ('unidimensional') in a Bayesian and a frequentist version. For multidimensional tests: omega_t (total) and omega_h (hierarchical). The results include confidence and credible intervals, the probability of a coefficient being larger than a cutoff, and a check for the factor models, necessary for the omega coefficients. The method for the Bayesian 'unidimensional' estimates, except for omega_u, is sampling from the posterior inverse 'Wishart' for the covariance matrix based measures (see 'Murphy', 2007, . The Bayesian omegas (u, t, and h) are obtained by 'Gibbs' sampling from the conditional posterior distributions of (1) the single factor model, (2) the second-order factor model, (3) the bi-factor model, (4) the correlated factor model ('Lee', 2007, ). Package: r-cran-bayesreversepllh Architecture: amd64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-bayesreversepllh_1.5-1.ca2404.1_amd64.deb Size: 131462 MD5sum: 0f54e51ae5ef9df6d578e954dcb220e8 SHA1: f31d6eae1763a34505af72c9182b9ccd0c8678be SHA256: 2d844a3ee46cf49e1aa130e485e5184733795e3668d7dbf876805503c4022802 SHA512: bd9dc954f92578d86d5199ab8b4006af54f78ab52f5142b4ea252249606f0c1ae819764fe3900f5f3c967507a73066ca62f2712def1f09682bf1ac4758d68497 Homepage: https://cran.r-project.org/package=BayesReversePLLH Description: CRAN Package 'BayesReversePLLH' (Fits the Bayesian Piecewise Linear Log-Hazard Model) Contains posterior samplers for the Bayesian piecewise linear log-hazard and piecewise exponential hazard models, including Cox models. Posterior mean restricted survival times are also computed for non-Cox an Cox models with only treatment indicators. The ApproxMean() function can be used to estimate restricted posterior mean survival times given a vector of patient covariates in the Cox model. Functions included to return the posterior mean hazard and survival functions for the piecewise exponential and piecewise linear log-hazard models. Chapple, AG, Peak, T, Hemal, A (2020). Under Revision. Package: r-cran-bayesrgmm Architecture: amd64 Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1133 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-batchmeans, r-cran-abind, r-cran-reshape, r-cran-msm, r-cran-mvtnorm, r-cran-plyr, r-cran-rdpack, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesrgmm_2.2-1.ca2404.1_amd64.deb Size: 649956 MD5sum: 48980fb2a1b61a99b459a797d74bbf60 SHA1: 5afe08dbd3d64876622351f6c16f75cf37ccf45c SHA256: c276cfad820cde9f851b650e6a134f6d17ca3ff575bbfbf89a28e91f40d597e8 SHA512: ebddfcb8d1b45b9264887ba0e23cc835b27cfe00a29277a03d7f1e00a91ae42f58547b8e22b6678b34b28859b9d577c0e534ed528f97d4a68b00b2422a99f89d Homepage: https://cran.r-project.org/package=BayesRGMM Description: CRAN Package 'BayesRGMM' (Bayesian Robust Generalized Mixed Models for Longitudinal Data) To perform model estimation using MCMC algorithms with Bayesian methods for incomplete longitudinal studies on binary and ordinal outcomes that are measured repeatedly on subjects over time with drop-outs. Details about the method can be found in the vignette or . 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It also provides estimations for the specifications of the models using diagnostics of exposure status with a non-linear mixed effects model. It implements the methods that are first proposed in and . 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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) . 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Those can be not only right-censored but also interval-censored, doubly-interval-censored or misclassified interval-censored. The methods implemented in the package have been published in Komárek and Lesaffre (2006, Stat. Modelling) , Komárek, Lesaffre and Legrand (2007, Stat. in Medicine) , Komárek and Lesaffre (2007, Stat. Sinica) , Komárek and Lesaffre (2008, JASA) , García-Zattera, Jara and Komárek (2016, Biometrics) . 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See Moriña D, Puig P, Navarro A. (2021) . 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Enables classification of data into multiple response groups based on a large number of discrete predictors that can take factor values of heterogeneous levels. Either pseudo-likelihood or mean field inference can be used with L2 regularization, cross-validation, and prediction on new data. . Package: r-cran-bbmisc Architecture: amd64 Version: 1.13.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table Suggests: r-cran-codetools, r-cran-microbenchmark, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bbmisc_1.13.1-1.ca2404.1_amd64.deb Size: 309876 MD5sum: 46783b7ea27f9822c791c5a86e1e3e23 SHA1: a6319f17a9d4c51f303374e29982ec51cc00cb55 SHA256: 03b03c8d357c8dcb4c5eb0a0284a070b8a596fdc896980939df4b5135f52b407 SHA512: 91d318e9d10e50f7dfeb20da899c10ddc20ca38a485afa4cde8abbb355d99f542852ff73a1368af4e8fc4a0bbf4d443a332f0228dbc2631683719ab810555fb7 Homepage: https://cran.r-project.org/package=BBmisc Description: CRAN Package 'BBmisc' (Miscellaneous Helper Functions for B. Bischl) Miscellaneous helper functions for and from B. Bischl and some other guys, mainly for package development. Package: r-cran-bbmix Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1942 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-r.utils, r-cran-data.table, r-cran-rmutil, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bbmix_1.0.0-1.ca2404.1_amd64.deb Size: 813266 MD5sum: 632f2648f282e7c6d5f9df3ebe283d5a SHA1: 194c8fe50ee08330f79a496d7393fce9b0dca486 SHA256: 79ae2de69b95918da973f65caf8dab508e0d87e6f5cce4b672d77736ec80778e SHA512: 295b1b23896080d4d625157e19f4bc8f8362413e41f8d921c6a9ed9abf84b980267af652b8637c87c8c85c2785a670375d436a12322c03e834b0cf8268d399ca Homepage: https://cran.r-project.org/package=bbmix Description: CRAN Package 'bbmix' (Bayesian Model for Genotyping using RNA-Seq) The method models RNA-seq reads using a mixture of 3 beta-binomial distributions to generate posterior probabilities for genotyping bi-allelic single nucleotide polymorphisms. Elena Vigorito, Anne Barton, Costantino Pitzalis, Myles J. Lewis and Chris Wallace (2023) "BBmix: a Bayesian beta-binomial mixture model for accurate genotyping from RNA-sequencing." Package: r-cran-bbotk Architecture: amd64 Version: 1.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1822 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-paradox, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-r6 Suggests: r-cran-adagio, r-cran-emoa, r-cran-gensa, r-cran-irace, r-cran-knitr, r-cran-mirai, r-cran-nloptr, r-cran-processx, r-cran-progressr, r-cran-redux, r-cran-rhpcblasctl, r-cran-rush, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bbotk_1.10.0-1.ca2404.1_amd64.deb Size: 1148092 MD5sum: 0931694c336f56d6dab3f295072f887b SHA1: 383a9d3a717c9d92c819c948cc79ffb8ba3d826b SHA256: 5af12c1bf7f3fe8f26044285e41e44f571d27e470795cc5cc949e64bee00e644 SHA512: f67db53a0227e3729923b3be19a5cbe619df019a067b99b3ed978400b2d12f62b8e0c6a44413879820a648b3b89590e148894a8efc0ec56d6ac80f2f06afb1a0 Homepage: https://cran.r-project.org/package=bbotk Description: CRAN Package 'bbotk' (Black-Box Optimization Toolkit) Features highly configurable search spaces via the 'paradox' package and optimizes every user-defined objective function. The package includes several optimization algorithms e.g. Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband (in 'mlr3hyperband'). bbotk is the base package of 'mlr3tuning', 'mlr3fselect' and 'miesmuschel'. Package: r-cran-bbssl Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-svmisc, r-cran-mvnfast, r-cran-rmutil, r-cran-greybox, r-cran-statmod, r-cran-matrix, r-cran-glmnet, r-cran-truncnorm Filename: pool/dists/noble/main/r-cran-bbssl_0.1.0-1.ca2404.1_amd64.deb Size: 71318 MD5sum: 923f6bc9321261a9928f5303402ad4a3 SHA1: 092b5c7300cee23de0e353299c2364585a0576b3 SHA256: 91d2d1391a81dbc032db368629c5a9fc61292a4275cbd82f3089d8eea3b1d262 SHA512: ef2d7437d5c9f425d0ba5b362a8b2eea1f92951adc75247c081a7f34c4748bff30180ce8bbc3b9ed527c4ed1c37a756a03d07eff446ffb2c9f50014cace4227b Homepage: https://cran.r-project.org/package=BBSSL Description: CRAN Package 'BBSSL' (Bayesian Bootstrap Spike-and-Slab LASSO) Posterior sampling for Spike-and-Slab LASSO prior in linear models from Nie and Rockova . Package: r-cran-bcbcsf Architecture: amd64 Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 749 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind Filename: pool/dists/noble/main/r-cran-bcbcsf_1.0-2-1.ca2404.1_amd64.deb Size: 720916 MD5sum: c2df3dcab2b59f4e9026930af05d05c5 SHA1: b55dc06c4aae590a2332d507f3d07382f5703fe7 SHA256: 3002721896024b1673397403bf7f55c403a58fa64545d3baf659e27db06072cd SHA512: 01b47adfeda252cdd6d70969d46362b410143ba01d55b26fcefd6a96a02e6679b93f92d604e165ef923e4a1c1d43264e91011142a55960df674e5800e528327d Homepage: https://cran.r-project.org/package=BCBCSF Description: CRAN Package 'BCBCSF' (Bias-Corrected Bayesian Classification with Selected Features) Fully Bayesian Classification with a subset of high-dimensional features, such as expression levels of genes. The data are modeled with a hierarchical Bayesian models using heavy-tailed t distributions as priors. When a large number of features are available, one may like to select only a subset of features to use, typically those features strongly correlated with the response in training cases. Such a feature selection procedure is however invalid since the relationship between the response and the features has be exaggerated by feature selection. This package provides a way to avoid this bias and yield better-calibrated predictions for future cases when one uses F-statistic to select features. Package: r-cran-bcclong Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5026 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-coda, r-cran-ggplot2, r-cran-label.switching, r-cran-laplacesdemon, r-cran-lme4, r-cran-mass, r-cran-mclust, r-cran-mcmcpack, r-cran-mixak, r-cran-mvtnorm, r-cran-nnet, r-cran-rcpp, r-cran-rmpfr, r-cran-truncdist, r-cran-abind, r-cran-gridextra, r-cran-rcpparmadillo Suggests: r-cran-cowplot, r-cran-joinerml, r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-survminer, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bcclong_1.0.3-1.ca2404.1_amd64.deb Size: 4375124 MD5sum: 92df6ce1d95f24556cdd8039dcebf200 SHA1: 12609fc477e2ac1d9272bb9c334a4af10e0688fa SHA256: 56ec23228b67e56a4a6c75aa72c1b9fe459c113638311c3f262097722458cefc SHA512: 43f0c347bdddec056b4f0916911a17b942d223a64ea3c0cfa559eef7aa0f4d030a3e357c04a583c3c0d872267826fb478e1a1e5ab7b2c48e1fadad08554cf6be Homepage: https://cran.r-project.org/package=BCClong Description: CRAN Package 'BCClong' (Bayesian Consensus Clustering for Multiple Longitudinal Features) It is very common nowadays for a study to collect multiple features and appropriately integrating multiple longitudinal features simultaneously for defining individual clusters becomes increasingly crucial to understanding population heterogeneity and predicting future outcomes. 'BCClong' implements a Bayesian consensus clustering (BCC) model for multiple longitudinal features via a generalized linear mixed model. Compared to existing packages, several key features make the 'BCClong' package appealing: (a) it allows simultaneous clustering of mixed-type (e.g., continuous, discrete and categorical) longitudinal features, (b) it allows each longitudinal feature to be collected from different sources with measurements taken at distinct sets of time points (known as irregularly sampled longitudinal data), (c) it relaxes the assumption that all features have the same clustering structure by estimating the feature-specific (local) clusterings and consensus (global) clustering. Package: r-cran-bcee Architecture: amd64 Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bma, r-cran-leaps, r-cran-boot, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-bcee_1.3.2-1.ca2404.1_amd64.deb Size: 154122 MD5sum: d71f3bc16674e7b7204aa476469b58de SHA1: 37e2bfda543e7aeb605a3df2c57fa0e7b4dd67a6 SHA256: f1e68b47ded1602d2d21b4a761d1c44bb981f459f147451021d344f01785ff28 SHA512: bf562b69425dfc8b8d9eb5c238262b7f29dc70d0db7ea9d6ee6c3e1417e90275100d5b4296ec6d203d798643e55510b710f1b6cdac5dd045a28b2815cee32db6 Homepage: https://cran.r-project.org/package=BCEE Description: CRAN Package 'BCEE' (The Bayesian Causal Effect Estimation Algorithm) A Bayesian model averaging approach to causal effect estimation based on the BCEE algorithm. Currently supports binary or continuous exposures and outcomes. For more details, see Talbot et al. (2015) Talbot and Beaudoin (2022) . Package: r-cran-bcf Architecture: amd64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1512 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-coda, r-cran-hmisc, r-cran-doparallel, r-cran-foreach, r-cran-matrixstats, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-latex2exp, r-cran-ggplot2, r-cran-rpart, r-cran-rpart.plot, r-cran-partykit Filename: pool/dists/noble/main/r-cran-bcf_2.0.2-1.ca2404.1_amd64.deb Size: 868416 MD5sum: 1faf32dc0f6ae90d56e655bc19e24cb9 SHA1: 2cf4d801799d17b882b8b1aa67c439e888847400 SHA256: 80f1f0950e5cbf2e76c4a5e9794df0e30ed01dcd0bd956fc9e602108d13259ab SHA512: 3d4420e5161d7a95fb06e91826e5b28468be0fdde2f3df888d69afb45c1cc6423f97605e2b43305c6f46b9c717226c4b9694633ecd69fd2eadfecc34be74ad7c Homepage: https://cran.r-project.org/package=bcf Description: CRAN Package 'bcf' (Causal Inference using Bayesian Causal Forests) Causal inference for a binary treatment and continuous outcome using Bayesian Causal Forests. See Hahn, Murray and Carvalho (2020) for additional information. This implementation relies on code originally accompanying Pratola et. al. (2013) . Package: r-cran-bcfm Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1053 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-dplyr, r-cran-fastmatrix, r-cran-ggplot2, r-cran-gridextra, r-cran-laplacesdemon, r-cran-mvtnorm, r-cran-psych, r-cran-rcolorbrewer, r-cran-tidyr, r-cran-ggpubr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bcfm_1.0.0-1.ca2404.1_amd64.deb Size: 590576 MD5sum: 58ee3bee4fee94c918d02df6dc3a27fe SHA1: cf997cbaf7b18b0879940820762fbdd78f69045f SHA256: 7da3bba6839a48cf2da1cb29154953020cd57fe462261a55ba9eb6c5e32e8591 SHA512: 493f5af074d02103f324e839956d2e9ffb240feabe7667d2c95493aa9e1938e8f84daaa038dd77bb106c82b316632996d19e79273fcb1e1f4e61ce00190290bb Homepage: https://cran.r-project.org/package=BCFM Description: CRAN Package 'BCFM' (Bayesian Clustering Factor Models) Implements the Bayesian Clustering Factor Models (BCFM) for simultaneous clustering and latent factor analysis of multivariate longitudinal data. The model accounts for within-cluster dependence through shared latent factors while allowing heterogeneity across clusters, enabling flexible covariance modeling in high-dimensional settings. Inference is performed using Markov chain Monte Carlo (MCMC) methods with computationally intensive steps implemented via 'Rcpp'. Model selection and visualization tools are provided. The methodology is described in Shin, Ferreira, and Tegge (2018) . Package: r-cran-bcgam Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nimble, r-cran-igraph, r-cran-coda Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bcgam_1.0-1.ca2404.1_amd64.deb Size: 147306 MD5sum: c089ab60b3303d17f692265ef4bd8126 SHA1: 5694d7de1bf2e7958ad81b2af29bba9911cb5db1 SHA256: 2e9c96c00bd6c2f1b3aee1eb9b051b29c4a2b3c08ec2bc2aa42c45ecbbf1b351 SHA512: a1b036d71a8c02a690a6e8b7bedad4de167414635aa76aacdf4b4830de0345ab2b3bbbf33f07bf15084345c38b687fbcee4a44d6e4055fa92e923a6cc21bbc00 Homepage: https://cran.r-project.org/package=bcgam Description: CRAN Package 'bcgam' (Bayesian Constrained Generalised Linear Models) Fits generalised partial linear regression models using a Bayesian approach, where shape and smoothness constraints are imposed on nonparametrically modelled predictors through shape-restricted splines, and no constraints are imposed on optional parametrically modelled covariates. See Meyer et al. (2011) for more details. IMPORTANT: before installing 'bcgam', you need to install 'Rtools' (Windows) or 'Xcode' (Mac OS X). These are required for the correct installation of 'nimble' (). Package: r-cran-bchron Architecture: amd64 Version: 4.7.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1659 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-coda, r-cran-mclust, r-cran-ggplot2, r-cran-ggridges, r-cran-magrittr, r-cran-purrr, r-cran-ggforce, r-cran-dplyr, r-cran-scales, r-cran-stringr, r-cran-checkmate Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-bchron_4.7.8-1.ca2404.1_amd64.deb Size: 1184630 MD5sum: a9738b77a55919910502e62e1bcd37a0 SHA1: f2a80ccfbb2c30cdfcc28bf0f476f6407be91dfa SHA256: 5916a7d3c333c8d276ba1333a82fed950c3cdc9f6e5206ad7d033b9d0502ca87 SHA512: 9b22de6bba0a66c1230204162441717d3dc3791220ee05c4db6570545e03c5e9393aec340c3c386c1d5a20d1d38959a2fea3d060f454fa0016028ba103e7f6eb Homepage: https://cran.r-project.org/package=Bchron Description: CRAN Package 'Bchron' (Age-Depth Radiocarbon Modelling) Enables quick calibration of radiocarbon dates under various calibration curves (including user generated ones); age-depth modelling as per the algorithm of Haslett and Parnell (2008) ; Relative sea level rate estimation incorporating time uncertainty in polynomial regression models (Parnell and Gehrels 2015) ; non-parametric phase modelling via Gaussian mixtures as a means to determine the activity of a site (and as an alternative to the 'Oxcal' function SUM(); currently unpublished), and reverse calibration of dates from calibrated into 14C years (also unpublished). Package: r-cran-bclogit Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3587 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-checkmate, r-cran-coda, r-cran-fastlogisticregressionwrap, r-cran-geepack, r-cran-glmmtmb, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-survival, r-cran-ggdist, r-cran-data.table Filename: pool/dists/noble/main/r-cran-bclogit_1.1-1.ca2404.1_amd64.deb Size: 987392 MD5sum: ec8d07d1fbdc70ee86307017e9ba4c56 SHA1: d548223922314f895f4450d36d3265730fb421ac SHA256: 9e27b9ddeab6986157ef062996b8910ddaa822ec8e05c56e275e2c0924fdc8b0 SHA512: 6f39d5ea281d7412e2b63458e27d8fd8a6c51c5a28af1e5bd8d4a09961dfaa62608819518d2a4663ccfac5a033aa7ef9c1230afd5728a4341904453d4842c1ac Homepage: https://cran.r-project.org/package=bclogit Description: CRAN Package 'bclogit' (Conditional Logistic Regression) Performs inference for Bayesian conditional logistic regression with informative priors built from the concordant pair data. We include many options to build the priors. And we include many options during the inference step for estimation, testing and confidence set creation. For details, see Kapelner and Tennenbaum (2026) "Improved Conditional Logistic Regression using Information in Concordant Pairs with Software" . Package: r-cran-bclustlong Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 596 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-lme4, r-cran-mcclust, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-lattice Filename: pool/dists/noble/main/r-cran-bclustlong_0.1.3-1.ca2404.1_amd64.deb Size: 433274 MD5sum: 6907dbf121afd8d4ff985fb0cab454d9 SHA1: 92180ece50b08e7d502c1718573ef3c7b6a89dcf SHA256: 6f19578aecfdbaf869ec1f22cc69e1f0d28d36b56ea23491e351a485707dec59 SHA512: b65d6dc841fbd0fd372ea422fbd15d208d6a334d096d7fb9bc9eca27991c8936ea97f9ba5c679490ab18f339c322c697725c348a54a3752240dbc4702c77fb8e Homepage: https://cran.r-project.org/package=BClustLonG Description: CRAN Package 'BClustLonG' (A Dirichlet Process Mixture Model for Clustering LongitudinalGene Expression Data) Many clustering methods have been proposed, but most of them cannot work for longitudinal gene expression data. 'BClustLonG' is a package that allows us to perform clustering analysis for longitudinal gene expression data. It adopts a linear-mixed effects framework to model the trajectory of genes over time, while clustering is jointly conducted based on the regression coefficients obtained from all genes. To account for the correlations among genes and alleviate the high dimensionality challenges, factor analysis models are adopted for the regression coefficients. The Dirichlet process prior distribution is utilized for the means of the regression coefficients to induce clustering. This package allows users to specify which variables to use for clustering (intercepts or slopes or both) and whether a factor analysis model is desired. More details about this method can be found in Jiehuan Sun, et al. (2017) . Package: r-cran-bcp Architecture: amd64 Version: 4.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 589 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-bioc-dnacopy, r-cran-coda, r-cran-strucchange, r-cran-vegan, r-cran-ggplot2, r-cran-igraph Filename: pool/dists/noble/main/r-cran-bcp_4.0.4-1.ca2404.1_amd64.deb Size: 307096 MD5sum: c7971244bce4f3704ce0374116646a4c SHA1: 028dab6c800b34138c04e0d1fbf2149859a77243 SHA256: faa000844208424d9efbab520d65f4ed45fdf8f7cc8e256db2b7afea2303e9c1 SHA512: fd2d29fe0c7089cc37c5739f4bcec482f341b537e384c56e0ee5e7b8e7cf5e37ce834583b59355da969fdebc1de3f2d95f56045e920ac043ad39346444e56589 Homepage: https://cran.r-project.org/package=bcp Description: CRAN Package 'bcp' (Bayesian Analysis of Change Point Problems) Provides an implementation of the product partition model described in Barry and Hartigan (2019) for the normal errors change point problem using Markov Chain Monte Carlo (MCMC). It also extends the methodology to regression models on a connected graph as reported in Wang and Emerson (2015) , allowing estimation of change point models with multivariate responses. Parallel MCMC, previously available in 'bcp' v.3.0.0, is currently not implemented. Package: r-cran-bcpa Architecture: amd64 Version: 1.3.2-1.ca2404.1 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 (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-rcpp Suggests: r-cran-knitr, r-cran-lubridate, r-cran-magrittr, r-cran-circular, r-cran-digest, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bcpa_1.3.2-1.ca2404.1_amd64.deb Size: 579602 MD5sum: aa1d0aeb230b12f4be5e86a80c8dec53 SHA1: 47b683debbafde7401c0b906b4014457b51a3874 SHA256: 0f6dab116a0a95d5bbd1d5c68eca2b73ec71bde24cf4d6f94a86c6b8ead96861 SHA512: de372b0f2185f0e944b21c2d6948edc9a5bd1225669fe6c3d4266d1f938f3485bcc20a7d2bfa8b0b92424234524712e0de337034d45a36a67c86885c537e2180 Homepage: https://cran.r-project.org/package=bcpa Description: CRAN Package 'bcpa' (Behavioral Change Point Analysis of Animal Movement) The Behavioral Change Point Analysis (BCPA) is a method of identifying hidden shifts in the underlying parameters of a time series, developed specifically to be applied to animal movement data which is irregularly sampled. The method is based on: E. Gurarie, R. Andrews and K. Laidre A novel method for identifying behavioural changes in animal movement data (2009) Ecology Letters 12:5 395-408. A development version is on . NOTE: the BCPA method may be useful for any univariate, irregularly sampled Gaussian time-series, but animal movement analysts are encouraged to apply correlated velocity change point analysis as implemented in the smoove package, as of this writing on GitHub at . An example of a univariate analysis is provided in the UnivariateBCPA vignette. Package: r-cran-bcrocsurface Architecture: amd64 Version: 1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2010 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnet, r-cran-rgl, r-cran-boot, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-bcrocsurface_1.0-6-1.ca2404.1_amd64.deb Size: 675350 MD5sum: 274698e0e00b74499a5b227f916f87f2 SHA1: 4635d8ffae120640de52338d114b1ab0b289c9f7 SHA256: 990571827a2119bb5991771dc012ed42b6fd0a9d8a52c3efe1ea061c9635ba6a SHA512: 40913f6c7f99517545f299c77209a7fd1d05787fddb3c8d62b49736a0ed40e2a9081039b7f2916d030d6dc58ed23c0dbcd40327f1141888dbd143253a2b5419e Homepage: https://cran.r-project.org/package=bcROCsurface Description: CRAN Package 'bcROCsurface' (Bias-Corrected Methods for Estimating the ROC Surface ofContinuous Diagnostic Tests) The bias-corrected estimation methods for the receiver operating characteristics ROC surface and the volume under ROC surfaces (VUS) under missing at random (MAR) assumption. Package: r-cran-bcrypt Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-openssl Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-bcrypt_1.2.1-1.ca2404.1_amd64.deb Size: 28102 MD5sum: a3535e2d3536cb0ed88c97dd2b2ef3bb SHA1: 040ec2f330c409c46fd89293941b47ad643bc488 SHA256: e1f78cc4ca107bd8b7e49778b6f8458323c87c25ac32b2fa82264db87c72e8c9 SHA512: e8a754a204910e476416320112e11f1120494751b2aeb17d9847cc56efc38871748be8ad0b8dbc7ff24c7cee85903357592d7f9c25df66dd1db1fcd5d2b2c6cf Homepage: https://cran.r-project.org/package=bcrypt Description: CRAN Package 'bcrypt' ('Blowfish' Key Derivation and Password Hashing) Bindings to the 'blowfish' password hashing algorithm derived from the 'OpenBSD' implementation. Package: r-cran-bcsub Architecture: amd64 Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 528 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mcclust, r-cran-nfactors, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-bcsub_0.5-1.ca2404.1_amd64.deb Size: 351604 MD5sum: d4c3e4282ae96d48a1d6f8137a474579 SHA1: 740d09ed2fd18a5b59a845797e59f8ea29f8a7bd SHA256: 529c755267c180d78e6c0d56a6d0f4d6d52d93dace391809bb33af1e2381d406 SHA512: 57bccf3f78e2a9aa31ddcb5d7e5ea58629a89b4d58488ea12c4cd546aa130174c5804dd9e7fd0578473743d78959e6c6dfa6d96522277903855ed0c454d379d6 Homepage: https://cran.r-project.org/package=BCSub Description: CRAN Package 'BCSub' (A Bayesian Semiparametric Factor Analysis Model for SubtypeIdentification (Clustering)) Gene expression profiles are commonly utilized to infer disease subtypes and many clustering methods can be adopted for this task. However, existing clustering methods may not perform well when genes are highly correlated and many uninformative genes are included for clustering. To deal with these challenges, we develop a novel clustering method in the Bayesian setting. This method, called BCSub, adopts an innovative semiparametric Bayesian factor analysis model to reduce the dimension of the data to a few factor scores for clustering. Specifically, the factor scores are assumed to follow the Dirichlet process mixture model in order to induce clustering. Package: r-cran-bct Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 402 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-stringr, r-cran-igraph Filename: pool/dists/noble/main/r-cran-bct_1.2-1.ca2404.1_amd64.deb Size: 218150 MD5sum: 598001c3c681e79d31fee2efc6519a85 SHA1: d5d67e5d83d40bd586dd8af72cb1f9cf3c68e855 SHA256: 627a3eb2f2133edb26545ae75f60a2bbf45f4376b7d6c94e047150308bc153d6 SHA512: 86a5a6ed874ffd0ebb58747cb9db51ad7296bc59f57474e0817c2e5131f547cccb406cb57bc7b778e9aa5d31aaa87905a0cf64f1ff4b312ee17ac2cd7b786ad2 Homepage: https://cran.r-project.org/package=BCT Description: CRAN Package 'BCT' (Bayesian Context Trees for Discrete Time Series) An implementation of a collection of tools for exact Bayesian inference with discrete times series. This package contains functions that can be used for prediction, model selection, estimation, segmentation/change-point detection and other statistical tasks. Specifically, the functions provided can be used for the exact computation of the prior predictive likelihood of the data, for the identification of the a posteriori most likely (MAP) variable-memory Markov models, for calculating the exact posterior probabilities and the AIC and BIC scores of these models, for prediction with respect to log-loss and 0-1 loss and segmentation/change-point detection. Example data sets from finance, genetics, animal communication and meteorology are also provided. Detailed descriptions of the underlying theory and algorithms can be found in [Kontoyiannis et al. 'Bayesian Context Trees: Modelling and exact inference for discrete time series.' Journal of the Royal Statistical Society: Series B (Statistical Methodology), April 2022. Available at: [stat.ME], July 2020] and [Lungu et al. 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Leday and Richardson (2019), Biometrics, . Package: r-cran-beanz Architecture: amd64 Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7060 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-survival, r-cran-loo, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-shiny, r-cran-rmarkdown, r-cran-pander, r-cran-shinythemes, r-cran-dt, r-cran-testthat Filename: pool/dists/noble/main/r-cran-beanz_3.1-1.ca2404.1_amd64.deb Size: 2059106 MD5sum: 64f4a8c22b2c5649e4d4b6efc0961e47 SHA1: 587a9922dabc3726f2721f629cb62f5e8271a15f SHA256: bc2e39159fe4d01c3687073141289cca54049d5728fe22b2e5939c4a2d37ef08 SHA512: ba056d2fa88cef9837796ea4a4f57e75ad6c31af47fc8a767ea4ed8660a76d489387aafc992bcecb9de97326de204f23e16f0eee2e9ff500353523a9337dae37 Homepage: https://cran.r-project.org/package=beanz Description: CRAN Package 'beanz' (Bayesian Analysis of Heterogeneous Treatment Effect) It is vital to assess the heterogeneity of treatment effects (HTE) when making health care decisions for an individual patient or a group of patients. Nevertheless, it remains challenging to evaluate HTE based on information collected from clinical studies that are often designed and conducted to evaluate the efficacy of a treatment for the overall population. The Bayesian framework offers a principled and flexible approach to estimate and compare treatment effects across subgroups of patients defined by their characteristics. This package allows users to explore a wide range of Bayesian HTE analysis models, and produce posterior inferences about HTE. See Wang et al. (2018) for further details. Package: r-cran-beastt Architecture: amd64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3275 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-cobalt, r-cran-distributional, r-cran-dplyr, r-cran-generics, r-cran-ggdist, r-cran-ggplot2, r-cran-mixtools, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-stringr, r-cran-tidyr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-mvtnorm, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-vdiffr, r-cran-survival Filename: pool/dists/noble/main/r-cran-beastt_0.0.3-1.ca2404.1_amd64.deb Size: 1340960 MD5sum: 923987189ca9f34ea09b6ce3a689a6e3 SHA1: 4ff487c308ede41d28d5a67782a32f38806c685a SHA256: acbf25f881a1581aa37df2c8f25d25528ee35b5d43cce6e18a92c5e790cebc45 SHA512: 696908a99a367589094a3084d801cdd77e4389ddcaa153a929e1dea4e570dd81b2845b8f2f4814670737eec796d291dbd0ccf698b1b5c91148928617b7220532 Homepage: https://cran.r-project.org/package=beastt Description: CRAN Package 'beastt' (Bayesian Evaluation, Analysis, and Simulation Software Tools forTrials) Bayesian dynamic borrowing with covariate adjustment via inverse probability weighting for simulations and data analyses in clinical trials. This makes it easy to use propensity score methods to balance covariate distributions between external and internal data. This methodology based on Psioda et al (2025) . Package: r-cran-bed Architecture: amd64 Version: 1.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3302 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-stringr, r-cran-shiny, r-cran-htmltools, r-cran-dt, r-cran-miniui, r-cran-rstudioapi Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biomart, r-bioc-geoquery, r-cran-base64enc, r-cran-webshot2, r-cran-rcurl Filename: pool/dists/noble/main/r-cran-bed_1.6.2-1.ca2404.1_amd64.deb Size: 1619426 MD5sum: fc8e3a50a8c4517344f1c4acb218029f SHA1: cd3a2f03f75e7dfcc9cdfdaba7071c82b6d89f0b SHA256: b347ec8f477f802a26bf67af8638bd312ef1541a9c5b2fd65db9c06a3fb045ed SHA512: 3f15a85dcb9fb78d06e7289f3f73b49e7881ca1af3398fdab5f9364488300af0623e68e2ac93b2039e984468332b91bbba9a6c9bbde4df5f1ed3d32417c2f4d1 Homepage: https://cran.r-project.org/package=BED Description: CRAN Package 'BED' (Biological Entity Dictionary (BED)) An interface for the 'Neo4j' database providing mapping between different identifiers of biological entities. 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. The third challenge is related to the automation of the mapping process according to the relationships between the biological entities of interest. Indeed, mapping between gene and protein ID scopes should not be done the same way than between two scopes regarding gene ID. Also, converting identifiers from different organisms should be possible using gene orthologs information. The method has been published by Godard and van Eyll (2018) . Package: r-cran-bedmatrix Architecture: amd64 Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 361 Depends: libc6 (>= 2.33), r-base-core (>= 4.4.0), r-api-4.0, r-cran-crochet Suggests: r-cran-data.table, r-cran-linkedmatrix, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-bedmatrix_2.0.4-1.ca2404.1_amd64.deb Size: 179016 MD5sum: c31ae4893f6d444bdd8c5b20c987db43 SHA1: 1dc415ab9cd5ff9e0579ff8136636d8a90426e56 SHA256: 9f4a418882d19572ffb13380a73a349a3f0ab7647c4135cb964ebec66b227e6c SHA512: af361cc00c08689893cd47526c8ddf2071c6b9f49a5c4ded72560c385bf350148f5fd6dd114240486be178067aaff295c7302cc505a0b18d98d2863a22c803ba Homepage: https://cran.r-project.org/package=BEDMatrix Description: CRAN Package 'BEDMatrix' (Extract Genotypes from a PLINK .bed File) A matrix-like data structure that allows for efficient, convenient, and scalable subsetting of binary genotype/phenotype files generated by PLINK (), the whole genome association analysis toolset, without loading the entire file into memory. Package: r-cran-beeguts Architecture: amd64 Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7801 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-data.table, r-cran-tidyr, r-cran-ggplot2, r-cran-cowplot, r-cran-dplyr, r-cran-magrittr, r-cran-gridextra, r-cran-odeguts, r-cran-doparallel, r-cran-foreach, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-beeguts_1.5.0-1.ca2404.1_amd64.deb Size: 3268570 MD5sum: e8c5e30546b616f60fdbd6b71b28283a SHA1: 5738bb5b17afb635da58c9cd0fcdd140de4683b9 SHA256: cdee9e2723f97b4cdd1bdf2ac87ac1e24ee886e1595ae8663447350869ece867 SHA512: e3db895a927fb8cb638f101481d4e088b1a38f5d74fe677ecc4ed35859702ff28c97ef2badc4ee1e88d182444ac8247d8615745c93b403aa3a11ca09a5a622a3 Homepage: https://cran.r-project.org/package=BeeGUTS Description: CRAN Package 'BeeGUTS' (General Unified Threshold Model of Survival for Bees usingBayesian Inference) Tools to calibrate, validate, and make predictions with the General Unified Threshold model of Survival adapted for Bee species. The model is presented in the publication from Baas, J., Goussen, B., Miles, M., Preuss, T.G., Roessing, I. (2022) and Baas, J., Goussen, B., Taenzler, V., Roeben, V., Miles, M., Preuss, T.G., van den Berg, S., Roessink, I. (2024) , and is based on the GUTS framework Jager, T., Albert, C., Preuss, T.G. and Ashauer, R. (2011) . The authors are grateful to Bayer A.G. for its financial support. Package: r-cran-beeswarm Architecture: amd64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-beeswarm_0.4.0-1.ca2404.1_amd64.deb Size: 78628 MD5sum: 97f06a07b8174c1250c845eb42d76dbc SHA1: 9d814a5d3b7d1815abadc6ac1cdbaf5c3c37147e SHA256: 1dabdf59997257c073a3ba1a558230852ca6995b99efa0d28ebcc8525b2604f8 SHA512: 1cf59fc55daea85ac6a87c9bf7e18e992e97698030afb30fe1eec4c94ecdb508149d718d1f099055b82a01e0cddec77db746fdd906a21dc96c796f905c8bfb50 Homepage: https://cran.r-project.org/package=beeswarm Description: CRAN Package 'beeswarm' (The Bee Swarm Plot, an Alternative to Stripchart) The bee swarm plot is a one-dimensional scatter plot like "stripchart", but with closely-packed, non-overlapping points. Package: r-cran-beezdemand Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10156 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlsr, r-cran-nlstools, r-cran-nls2, r-cran-ggplot2, r-cran-optimx, r-cran-broom, r-cran-lme4, r-cran-emmeans, r-cran-minpack.lm, r-cran-nls.multstart, r-cran-performance, r-cran-scales, r-cran-tibble, r-cran-lifecycle, r-cran-dplyr, r-cran-tidyr, r-cran-nlme, r-cran-rlang, r-cran-tmb, r-cran-rcppeigen Suggests: r-cran-broom.mixed, r-cran-ggally, r-cran-knitr, r-cran-tidyverse, r-cran-rmarkdown, r-cran-purrr, r-cran-conflicted, r-cran-devtools, r-cran-here, r-cran-readr, r-cran-patchwork, r-cran-testthat Filename: pool/dists/noble/main/r-cran-beezdemand_0.2.0-1.ca2404.1_amd64.deb Size: 5735850 MD5sum: c357762ba71782e9b45ecf3ae7f642f7 SHA1: 3c44782566a68491d5e05229ccbf3b1842f81c13 SHA256: c5e4c4870a07463a439c0d2abd66b21bae9488d72075a580d3f9d690d52fb791 SHA512: add872931394d527e85d10d3feac8a96d18b40feba1ae3a34fb696f610afb3900e3ac7ab88979801afd4456edca2720dadeb6b7cf411c12825d64ce7dde73a15 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: amd64 Version: 1.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1854 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-reshape2, r-cran-ggplot2, r-cran-mathjaxr, r-cran-gridextra, r-cran-ggfortify, r-cran-xts, r-cran-future, r-cran-future.apply, r-cran-ks, r-cran-lubridate, r-cran-pbapply, r-cran-numderiv, r-cran-moments, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bekks_1.4.7-1.ca2404.1_amd64.deb Size: 1192466 MD5sum: 08d1dd65d684996dfa4d0e2675f55d4b SHA1: c4581bfab1e3c75a8a9cbfa9cb693c86c30cd867 SHA256: 8e78f3aeeb2de0a44326e2d4017f437a341be7678146635822337eb6be467485 SHA512: ac5c5690b874d3bbc765894bd98b63f163d00afe25eee4776b3735983e8f684ed3c1c476637bc72d2a9f887664a7b7782d36870ca9c5cf8ca5be907344e935c5 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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Peers and slacks are available, partial price information can be included, and optimal cost, revenue and profit can be calculated. Evaluation of mergers is also supported. Methods for graphing the technology sets are also included. There is also support for comparative methods based on Stochastic Frontier Analyses (SFA) and for convex nonparametric least squares of convex functions (STONED). In general, the methods can be used to solve not only standard models, but also many other model variants. It complements the book, Bogetoft and Otto, Benchmarking with DEA, SFA, and R, Springer-Verlag, 2011, but can of course also be used as a stand-alone package. 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Described in Bouranis, L., Demiris, N., Kalogeropoulos, K., and Ntzoufras, I. (2022) . 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Package: r-cran-bess Architecture: amd64 Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1356 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-glmnet, r-cran-survival, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-bess_2.0.4-1.ca2404.1_amd64.deb Size: 1109260 MD5sum: aae9eaf278a6c374b8af6bb5180f7344 SHA1: f14d977cacd2483668027ea01256a47c6ad3edba SHA256: 0057be4773eee1a78e128d707923d4f95c90358b8daae791533f84bc08ac28f2 SHA512: a78e08e3fd9e81ad23bf635fad58d7a534a4092a1fa955010ca6ff2844622d32ed0f0fa772d2d8f020e1791dba583e4349d069a8051468fe912477734080d2e6 Homepage: https://cran.r-project.org/package=BeSS Description: CRAN Package 'BeSS' (Best Subset Selection in Linear, Logistic and CoxPH Models) An implementation of best subset selection in generalized linear model and Cox proportional hazard model via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. 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Implements PCR and PLS using AIC/BIC. Implements one-standard deviation rule for use with the 'caret' package. Package: r-cran-bestie Architecture: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bidag, r-cran-rcpp, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-bestie_0.1.5-1.ca2404.1_amd64.deb Size: 76026 MD5sum: 7d4f6f951ec5b45658291e70253da12d SHA1: c2aeded2c687f44c6cd73dfc03b4d2c1e94d1c9a SHA256: 7ce9c616c3129dd37cc427b3aa93717d172a49ce6176946fe8fd35187c022519 SHA512: abeb2a44453c206edadc16fc838ea77ca6c3e89db0ec75897de911636a867a90de063798d8772d18f34675d9b6356c18718b6340535a9d75fac7961bdc38a87a Homepage: https://cran.r-project.org/package=Bestie Description: CRAN Package 'Bestie' (Bayesian Estimation of Intervention Effects) An implementation of intervention effect estimation for DAGs (directed acyclic graphs) learned from binary or continuous data. 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Package: r-cran-bestridge Architecture: amd64 Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3966 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-mass, r-cran-pheatmap, r-cran-survival, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bestridge_1.0.7-1.ca2404.1_amd64.deb Size: 3366142 MD5sum: 965ff1f199a4d33f7c448c445d291891 SHA1: 8b49d9b64ffd9a6f18acfb544c138897647874bd SHA256: 168ad37228591e1446df79f815c7df9fb23bab8b3d7421bf2843043f203e4121 SHA512: 88e005a2895b191e695bf54850d00f671ac4c28475a6307a44cbca5a8567e6445c288261f5f55397ebf332922ca61e708d99e07f556f69d2ae5e406c27962f13 Homepage: https://cran.r-project.org/package=bestridge Description: CRAN Package 'bestridge' (A Comprehensive R Package for Best Subset Selection) The bestridge package is designed to provide a one-stand service for users to successfully carry out best ridge regression in various complex situations via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. (2020) . This package allows users to perform the regression, classification, count regression and censored regression for (ultra) high dimensional data, and it also supports advanced usages like group variable selection and nuisance variable selection. Package: r-cran-bet Architecture: amd64 Version: 0.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-bet_0.5.4-1.ca2404.1_amd64.deb Size: 154318 MD5sum: 7b2bdb0f1c555515dfb0979206c5c72f SHA1: c68c80977095ad7b505227411ae579e771236fd8 SHA256: b28dbfc321f64ec8c5f6e473bb084c69e99f951583b82c92430d9e0f6dda6066 SHA512: 917baabd7cc4153b5d4c424f6307d366e6078891f9b6b4f4eac035634ccae41e96ff13b9dc640ff351a31834ba3514111e08c28ff758e504d1f199c31d07d24f Homepage: https://cran.r-project.org/package=BET Description: CRAN Package 'BET' (Binary Expansion Testing) Nonparametric detection of nonuniformity and dependence with Binary Expansion Testing (BET). See Kai Zhang (2019) BET on Independence, Journal of the American Statistical Association, 114:528, 1620-1637, , Kai Zhang, Wan Zhang, Zhigen Zhao, Wen Zhou. (2023). BEAUTY Powered BEAST, and Wan Zhang, Zhigen Zhao, Michael Baiocchi, Yao Li, Kai Zhang. (2023) SorBET: A Fast and Powerful Algorithm to Test Dependence of Variables, Techinical report. Package: r-cran-betabayes Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-betareg, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-betabayes_1.0.1-1.ca2404.1_amd64.deb Size: 187560 MD5sum: b13bdbcd6452e16a47ff150bac6f0fa9 SHA1: cd181716e38d4641dc326672ec38e5d193127433 SHA256: 7a2e4795a24d21e0a7925e663000d0d603b6c14bfaa00c5fd4c5c3a086a4f149 SHA512: 9b3f5f2cb10d11ee6e8085b42da3193df654fa06e5814cecec17d8d8aca353630c273ca5dca97f8ccd81cee622eaf9052027cb98db3f21c83310c31d0c351112 Homepage: https://cran.r-project.org/package=betaBayes Description: CRAN Package 'betaBayes' (Bayesian Beta Regression) Provides a class of Bayesian beta regression models for the analysis of continuous data with support restricted to an unknown finite support. The response variable is modeled using a four-parameter beta distribution with the mean or mode parameter depending linearly on covariates through a link function. When the response support is known to be (0,1), the above class of models reduce to traditional (0,1) supported beta regression models. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou and Huang (2022) . Package: r-cran-betaclust Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 765 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-ggplot2, r-cran-plotly, r-cran-scales, r-cran-proc Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-betaclust_1.0.4-1.ca2404.1_amd64.deb Size: 649880 MD5sum: e2490b70022061e0bdf96de6fd40ac80 SHA1: 4d11230bb36e96d6aaa2216da8a1107777f9882e SHA256: e4222b9857655e663b53e1aaa5a44ce92b4d760a5463e44b225ebf16bdf705c3 SHA512: dc6bc80c7a7d0ce847977661ffd0b1fbd20d776eac7221bfadf145d3d7fe34f5fb8fd7bb7d416c1546617d0894ba2b4880db2a53d967c5aec3f48a27cdc5a7fd Homepage: https://cran.r-project.org/package=betaclust Description: CRAN Package 'betaclust' (A Family of Beta Mixture Models for Clustering Beta-Valued DNAMethylation Data) A family of novel beta mixture models (BMMs) has been developed by Majumdar et al. (2022) to appositely model the beta-valued cytosine-guanine dinucleotide (CpG) sites, to objectively identify methylation state thresholds and to identify the differentially methylated CpG (DMC) sites using a model-based clustering approach. The family of beta mixture models employs different parameter constraints applicable to different study settings. The EM algorithm is used for parameter estimation, with a novel approximation during the M-step providing tractability and ensuring computational feasibility. Package: r-cran-betareg Architecture: amd64 Version: 3.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2870 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flexmix, r-cran-formula, r-cran-lmtest, r-cran-modeltools, r-cran-sandwich Suggests: r-cran-bamlss, r-cran-car, r-cran-distributions3, r-cran-knitr, r-cran-lattice, r-cran-numderiv, r-cran-partykit, r-cran-quarto, r-cran-statmod, r-cran-strucchange Filename: pool/dists/noble/main/r-cran-betareg_3.2-4-1.ca2404.1_amd64.deb Size: 1677664 MD5sum: d1d7d82d49c67ce923cba58d50bb75cd SHA1: a3efa1e5a227d5678da0c0dfe7c4c2441dcb38ee SHA256: 42da18bf3fbcdc79276d1356bcb78bf02faa56fd116ed0308ef3bba4311eaab9 SHA512: 74d6b7fe5178835b70c499ded83071410aac4bf6ba3bf42593ea759927974ee48c67af413f059c8a8bf64b7ff1a0f359ac80dda0e1fe7c9190f80e02c086773e Homepage: https://cran.r-project.org/package=betareg Description: CRAN Package 'betareg' (Beta Regression) Beta regression for modeling beta-distributed dependent variables on the open unit interval (0, 1), e.g., rates and proportions, see Cribari-Neto and Zeileis (2010) . Moreover, extended-support beta regression models can accommodate dependent variables with boundary observations at 0 and/or 1, see Kosmidis and Zeileis (2025) . For the classical beta regression model, alternative specifications are provided: Bias-corrected and bias-reduced estimation, finite mixture models, and recursive partitioning for beta regression, see Grün, Kosmidis, and Zeileis (2012) . Package: r-cran-betaregscale Architecture: amd64 Version: 2.6.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2658 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-ggplot2, r-cran-numderiv, r-cran-rcpp, r-cran-rlang, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-betareg, r-cran-gridextra, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-betaregscale_2.6.9-1.ca2404.1_amd64.deb Size: 1758874 MD5sum: 3533f4b13f734e3adad4d200641ece5f SHA1: 3868df7850e98028ad20a63580f10c0330a62171 SHA256: 5bddaf411f7da60b614cde02a1f523eae56b9c9825e1716e0d39602362a8ca90 SHA512: d9455fccc5cb5b5bd6fae5ebdf667939f576f8f84fae57946f524020264a8b6afe4b018fd74135db50d6623b7e35c0ea1c2b330007d3529db999a70ad7a3376b Homepage: https://cran.r-project.org/package=betaregscale Description: CRAN Package 'betaregscale' (Beta Regression for Interval-Censored Scale-Derived Outcomes) Maximum-likelihood estimation of beta regression models for responses derived from bounded rating scales. 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Package: r-cran-betategarch Architecture: amd64 Version: 3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo Filename: pool/dists/noble/main/r-cran-betategarch_3.4-1.ca2404.1_amd64.deb Size: 125440 MD5sum: e74676820f4bb9d6f1d1874b0b80da6b SHA1: 6fcc78c96fbdacd0965d35f793a705e9a163b2ec SHA256: 805d6be390bfa46ee769b41b9c71a04bdf180ede6735d8369110677f292ccc35 SHA512: fe669a2bb0d08973e73b837fa65e2ca9d237e1c92edc67a73e51d89377c406f6742e4d42f56a3f15c3843a2ce2c66a25bb003d7281c974ad5816ca274564b0b3 Homepage: https://cran.r-project.org/package=betategarch Description: CRAN Package 'betategarch' (Simulation, Estimation and Forecasting of Beta-Skew-t-EGARCHModels) Simulation, estimation and forecasting of first-order Beta-Skew-t-EGARCH models with leverage (one-component, two-component, skewed versions). Package: r-cran-bevimed Architecture: amd64 Version: 7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-bevimed_7.0-1.ca2404.1_amd64.deb Size: 384592 MD5sum: b8cebca278d08ccacf2fc29b7a6dd6f7 SHA1: 50820a94f882363ab2bf258c6ef5415b51ed3bcb SHA256: b90a880c91ce288cc2c26131f24517c57a8744f4216f7393984322a19aa8da87 SHA512: 8fa91f72b15cac5bba4dfaf7cf9cc634f23f3252065299d6804b5f02f741e8db972c6dd9c44a765dfb349748c04136aefa23424b2cd8a91d51f1bc16e932b742 Homepage: https://cran.r-project.org/package=BeviMed Description: CRAN Package 'BeviMed' (Bayesian Evaluation of Variant Involvement in Mendelian Disease) A fast integrative genetic association test for rare diseases based on a model for disease status given allele counts at rare variant sites. Probability of association, mode of inheritance and probability of pathogenicity for individual variants are all inferred in a Bayesian framework - 'A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases', Greene et al 2017 . Package: r-cran-beyondwhittle Architecture: amd64 Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ltsa, r-cran-rcpp, r-cran-mass, r-cran-forecast, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-beyondwhittle_1.3.1-1.ca2404.1_amd64.deb Size: 562902 MD5sum: 0ac8902a8ace5c30f643eb8c73e3d9e1 SHA1: 5b6faffa780ba61f54ec994d701748268438bb72 SHA256: 0f8af8e1311809ece32675781018d15099bc20fd82bdaa5faf7cc8a727b179e4 SHA512: 5429ed360cfe2a926b57f019414658d5a09f5ebae7daf36b1ea4c7c35e77fdda27d4817fceda53d1cc2b1fbc57533d4e0715a4280dcb852c6f971ec6dafd2ee9 Homepage: https://cran.r-project.org/package=beyondWhittle Description: CRAN Package 'beyondWhittle' (Bayesian Spectral Inference for Time Series) Implementations of Bayesian parametric, nonparametric and semiparametric procedures for univariate and multivariate time series. The package is based on the methods presented in C. Kirch et al (2018) , A. Meier (2018) and Y. Tang et al (2025) . It was supported by DFG grants KI 1443/3-1 and KI 1443/3-2. Package: r-cran-bfast Architecture: amd64 Version: 1.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 726 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-strucchangercpp, r-cran-zoo, r-cran-forecast, r-cran-rcpp, r-cran-rdpack Suggests: r-cran-mass, r-cran-sfsmisc, r-cran-stlplus, r-cran-terra Filename: pool/dists/noble/main/r-cran-bfast_1.7.2-1.ca2404.1_amd64.deb Size: 258580 MD5sum: da5248b06b660f48022b1aec80af77fa SHA1: 0ba5087a2d156cf1ea2cbb187a201d69ef2aa130 SHA256: 022a2b2f6e5b50254987884dffad553e1d6e3d3eee9fd29f30bb4c1673d03fc0 SHA512: 203a4f252bd1a4a73e768de9475ccf7ac2555d181c31767e7610c7157dcaa79633ee0de0d8a9a7b7e3ec538fa759e8ca74e5a8e1f179b832a9c4afe6499ae78c Homepage: https://cran.r-project.org/package=bfast Description: CRAN Package 'bfast' (Breaks for Additive Season and Trend) Decomposition of time series into trend, seasonal, and remainder components with methods for detecting and characterizing abrupt changes within the trend and seasonal components. 'BFAST' can be used to analyze different types of satellite image time series and can be applied to other disciplines dealing with seasonal or non-seasonal time series, such as hydrology, climatology, and econometrics. The algorithm can be extended to label detected changes with information on the parameters of the fitted piecewise linear models. 'BFAST' monitoring functionality is described in Verbesselt et al. (2010) . 'BFAST monitor' provides functionality to detect disturbance in near real-time based on 'BFAST'- type models, and is described in Verbesselt et al. (2012) . 'BFAST Lite' approach is a flexible approach that handles missing data without interpolation, and will be described in an upcoming paper. Furthermore, different models can now be used to fit the time series data and detect structural changes (breaks). Package: r-cran-bfp Architecture: amd64 Version: 0.0-50-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 762 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-doby, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-bfp_0.0-50-1.ca2404.1_amd64.deb Size: 363954 MD5sum: 082d77de92f45b61f0dcaf3b3dd19c37 SHA1: 354d9fd6536e27cdf08cb8f3aec8d9c1b1536d64 SHA256: 924de9fc469e9929e6463982cc385865dc9b3ad952067ce796f427837f5cef3f SHA512: 5f9a7fd933495d396e834c0a03bdc76a9103e78f7aac171d59a24ca99fa76668a3904f8681fecd0bc0de9d5a17acd3e2cdf8dcb9b97ddc74a95245e4a5c324e8 Homepage: https://cran.r-project.org/package=bfp Description: CRAN Package 'bfp' (Bayesian Fractional Polynomials) Implements the Bayesian paradigm for fractional polynomial models under the assumption of normally distributed error terms, see Sabanes Bove, D. and Held, L. (2011) . Package: r-cran-bfpack Architecture: amd64 Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1002 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-bain, r-cran-mass, r-cran-mvtnorm, r-cran-pracma, r-cran-lme4, r-cran-sandwich, r-cran-qrm, r-cran-coda, r-cran-metabma, r-cran-berryfunctions, r-cran-ergm, r-cran-bergm Suggests: r-cran-testthat, r-cran-polycor, r-cran-survival, r-cran-pscl, r-cran-metafor, r-cran-knitr, r-cran-rmarkdown, r-cran-lmtest Filename: pool/dists/noble/main/r-cran-bfpack_1.6.0-1.ca2404.1_amd64.deb Size: 792930 MD5sum: e2b67a5f0c83db15258e15ae708e334b SHA1: 3fe0ef9ef2b4b45d43608b5e42eb2acf272d220f SHA256: 3b02fe6f6f56bfbba0f7898a9be519d181ce0bce6d3666364c74e7a90db3959a SHA512: 180a7e6d775c31bca2803c71edde8c6c5c8b5a6873588c8d85ddd0aa2df3916565c90b0e6517e12928224552ee11a53fea61a15ebb53b880358d4a4d6f5972e1 Homepage: https://cran.r-project.org/package=BFpack Description: CRAN Package 'BFpack' (Flexible Bayes Factor Testing of Scientific Expectations) Implementation of default Bayes factors for testing statistical hypotheses under various statistical models. The package is intended for applied quantitative researchers in the social and behavioral sciences, medical research, and related fields. The Bayes factor tests can be executed for statistical models such as univariate and multivariate normal linear models, correlation analysis, generalized linear models, special cases of linear mixed models, survival models, relational event models. Parameters that can be tested are location parameters (e.g., group means, regression coefficients), variances (e.g., group variances), and measures of association (e.g,. polychoric/polyserial/biserial/tetrachoric/product moments correlations), among others. Relevant references on the methodology The statistical underpinnings are described in O'Hagan (1995) , Mulder and Xin (2022) , Mulder and Gelissen (2019) , Mulder and Fox (2019) , Boeing-Messing, van Assen, Hofman, Hoijtink, and Mulder (2017) , Hoijtink, Mulder, van Lissa, and Gu (2018) , Gu, Mulder, and Hoijtink (2018) , Hoijtink, Gu, and Mulder (2018) , and Hoijtink, Gu, Mulder, and Rosseel (2018) . When using the packages, please refer to the package Mulder et al. (2021) and the relevant methodological papers. 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Package: r-cran-bgvar Architecture: amd64 Version: 2.5.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4810 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-bayesm, r-cran-coda, r-cran-gigrvg, r-cran-knitr, r-cran-mass, r-cran-matrix, r-cran-rcpp, r-cran-rcppparallel, r-cran-readxl, r-cran-stochvol, r-cran-xts, r-cran-zoo, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bgvar_2.5.9-1.ca2404.1_amd64.deb Size: 3248604 MD5sum: 0ee6e3bfd7f397cc1c92362e7a0d8f71 SHA1: 351f4904c22231e44f58fef46d2a967862ddf450 SHA256: 91ad6cb44f3a3b6c10f538cf8db3b063ce559576ea21a22521cc7bfe0efb1525 SHA512: 9a20702fcc0644eff2cb0e9113d91f338d4974eeef4c650d70ae2821422485d57178a0ddcda9a5abf5b66ae23edb2ae8d6b6fb4a9365a3ff74e07a65712c9952 Homepage: https://cran.r-project.org/package=BGVAR Description: CRAN Package 'BGVAR' (Bayesian Global Vector Autoregressions) Estimation of Bayesian Global Vector Autoregressions (BGVAR) with different prior setups and the possibility to introduce stochastic volatility. Built-in priors include the Minnesota, the stochastic search variable selection and Normal-Gamma (NG) prior. For a reference see also Crespo Cuaresma, J., Feldkircher, M. and F. Huber (2016) "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach", Journal of Applied Econometrics, Vol. 31(7), pp. 1371-1391 . Post-processing functions allow for doing predictions, structurally identify the model with short-run or sign-restrictions and compute impulse response functions, historical decompositions and forecast error variance decompositions. Plotting functions are also available. The package has a companion paper: Boeck, M., Feldkircher, M. and F. Huber (2022) "BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R", Journal of Statistical Software, Vol. 104(9), pp. 1-28 . Package: r-cran-bgw Architecture: amd64 Version: 0.1.4-1.ca2404.1 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/noble/main/r-cran-bgw_0.1.4-1.ca2404.1_amd64.deb Size: 137786 MD5sum: 4d94b67f27f63e5b7dd7a9ec05cba231 SHA1: 6c046bac15eb2094f3e618f31342a7db5ce0ca91 SHA256: 25dd094f81d1f2b4d278eab95de381ff7c7e4d111f9b5b45b3b18916405f750c SHA512: 63a2331d07454c0ef9c6e5a5f8eb3b6781f8325d45c73b6740229d4557ccaedb82d9be664013e87300be827d11af15a5b9de07fdaa13c88641557faf6a8f1e6a Homepage: https://cran.r-project.org/package=bgw Description: CRAN Package 'bgw' (Bunch-Gay-Welsch Statistical Estimation) Performs statistical estimation and inference-related computations by accessing and executing modified versions of 'Fortran' subroutines originally published in the Association for Computing Machinery (ACM) journal Transactions on Mathematical Software (TOMS) by Bunch, Gay and Welsch (1993) . The acronym 'BGW' (from the authors' last names) will be used when making reference to technical content (e.g., algorithm, methodology) that originally appeared in ACM TOMS. A key feature of BGW is that it exploits the special structure of statistical estimation problems within a trust-region-based optimization approach to produce an estimation algorithm that is much more effective than the usual practice of using optimization methods and codes originally developed for general optimization. The 'bgw' package bundles 'R' wrapper (and related) functions with modified 'Fortran' source code so that it can be compiled and linked in the 'R' environment for fast execution. This version implements a function ('bgw_mle.R') that performs maximum likelihood estimation (MLE) for a user-provided model object that computes probabilities (a.k.a. probability densities). The original motivation for producing this package was to provide fast, efficient, and reliable MLE for discrete choice models that can be called from the 'Apollo' choice modelling 'R' package ( see ). Starting with the release of Apollo 3.0, BGW is the default estimation package. However, estimation can also be performed using BGW in a stand-alone fashion without using 'Apollo' (as shown in simple examples included in the package). Note also that BGW capabilities are not limited to MLE, and future extension to other estimators (e.g., nonlinear least squares, generalized method of moments, etc.) is possible. The 'Fortran' code included in 'bgw' was modified by one of the original BGW authors (Bunch) under his rights as confirmed by direct consultation with the ACM Intellectual Property and Rights Manager. See . The main requirement is clear citation of the original publication (see above). Package: r-cran-bhetgp Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 676 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-gpgp, r-cran-gpvecchia, r-cran-matrix, r-cran-rcpp, r-cran-mvtnorm, r-cran-fnn, r-cran-hetgp, r-cran-lagp, r-cran-rcpparmadillo Suggests: r-cran-interp Filename: pool/dists/noble/main/r-cran-bhetgp_1.0.2-1.ca2404.1_amd64.deb Size: 497666 MD5sum: e6939af0d95dc033e6fb4ff072e9ad52 SHA1: c73ff7cab1e2c2a6b75c6fa9cfbf816a23b64bbe SHA256: c2d1e86d0b7397ac9757a33344d291a7bac845af856c12701bb396be71c9e905 SHA512: 915cd1764a850ed7381235a95cd52eb14b45b57a4300779f7ba4bc8f0521e923e690f423e636e52368ea3766288894ff59cc158edc766fbb20dc6decb7f9b12b Homepage: https://cran.r-project.org/package=bhetGP Description: CRAN Package 'bhetGP' (Bayesian Heteroskedastic Gaussian Processes) Performs Bayesian posterior inference for heteroskedastic Gaussian processes. Models are trained through MCMC including elliptical slice sampling (ESS) of latent noise processes and Metropolis-Hastings sampling of kernel hyperparameters. Replicates are handled efficientyly through a Woodbury formulation of the joint likelihood for the mean and noise process (Binois, M., Gramacy, R., Ludkovski, M. (2018) ) For large data, Vecchia-approximation for faster computation is leveraged (Sauer, A., Cooper, A., and Gramacy, R., (2023), ). Incorporates 'OpenMP' and SNOW parallelization and utilizes 'C'/'C++' under the hood. Package: r-cran-bhmsmafmri Architecture: amd64 Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 868 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-oro.nifti, r-cran-wavethresh, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-bhmsmafmri_2.3-1.ca2404.1_amd64.deb Size: 603080 MD5sum: 676fe9c77ac963aa98e2030b38e02b4d SHA1: 863048ea7c5c18f4754b2c85a3dc9dc5d6799c41 SHA256: 95bcc1081bb212d6b090b17fda0745e5d6f79bc2aa5543d39448a2d2016e677c SHA512: 39578e4557684fce5543c22fd9ee4d7166e1a0f3139e778a0fc5d633f50a19f71a1780c401ce121e3b1db4e3ac4c802f0e413ad8f76fd1e70c83ffda56743622 Homepage: https://cran.r-project.org/package=BHMSMAfMRI Description: CRAN Package 'BHMSMAfMRI' (Bayesian Hierarchical Multi-Subject Multiscale Analysis ofFunctional MRI (fMRI) Data) Package BHMSMAfMRI performs Bayesian hierarchical multi-subject multiscale analysis of fMRI data as described in Sanyal & Ferreira (2012) , or other multiscale data, using wavelet-based prior that borrows strength across subjects and provides posterior smoothed images of the effect sizes and samples from the posterior distribution. Package: r-cran-bhpm Architecture: amd64 Version: 1.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1109 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda Filename: pool/dists/noble/main/r-cran-bhpm_1.8.1-1.ca2404.1_amd64.deb Size: 819952 MD5sum: 02202540e0991712e0e0dc15b980b685 SHA1: ad9587474d57cd10b3d77b009570e6b41c5053ae SHA256: ce381c980042cdd0c5b5f351b5a550070a85dc9c0be9a892260cf6947b248b78 SHA512: 46c9264e27720b5609ceab377e0e6c3e5deb332d364a79de186403be7f91014e4e1dea289260e91d3464d24336e7b96fc8a452d50420f63766b83a6e92309eba Homepage: https://cran.r-project.org/package=bhpm Description: CRAN Package 'bhpm' (Bayesian Hierarchical Poisson Models for Multiple GroupedOutcomes with Clustering) Bayesian hierarchical methods for the detection of differences in rates of related outcomes for multiple treatments for clustered observations (Carragher et al. (2020) ). This software was developed for the Precision Drug Theraputics: Risk Prediction in Pharmacoepidemiology project as part of a Rutherford Fund Fellowship at Health Data Research (UK), Medical Research Council (UK) award reference MR/S003967/1 (). Principal Investigator: Raymond Carragher. Package: r-cran-bhsbvar Architecture: amd64 Version: 3.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 677 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-bhsbvar_3.1.3-1.ca2404.1_amd64.deb Size: 329920 MD5sum: ef6ae51501397a1848714865e04a699f SHA1: 3520886d18e7c12f99418cd16b972257d12bf0c3 SHA256: be4cca30dbc74964e802607449f78e784bdf4164ef8839bf54a4c7a97d511c21 SHA512: 85e5854cfdd523bea04a16d362c89b59057d59d840be98b8b143578a68214677b6021c77ad24209b85df837a4887989243286f8fe9ea4eda86606c9bd491aa7c Homepage: https://cran.r-project.org/package=BHSBVAR Description: CRAN Package 'BHSBVAR' (Structural Bayesian Vector Autoregression Models) Provides a function for estimating the parameters of Structural Bayesian Vector Autoregression models with the method developed by Baumeister and Hamilton (2015) , Baumeister and Hamilton (2017) , and Baumeister and Hamilton (2018) . Functions for plotting impulse responses, historical decompositions, and posterior distributions of model parameters are also provided. Package: r-cran-biasedurn Architecture: amd64 Version: 2.0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 430 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.1.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-biasedurn_2.0.12-1.ca2404.1_amd64.deb Size: 280586 MD5sum: 40b26bbccc7e24e8319d1ab11323d5b6 SHA1: 119839f1df134408f9e7dab57916e15da991fcaa SHA256: a70519ad66d1bddebf5a6401572bf5f3623073d3a230453301d89c1ae5afee3d SHA512: ba26c2d6c9e3cbfa37def5a4cd2986ac80fb7b967c284a3d1256d8e4ca6c50ee4e689b9826b38f5e2c6a0cc3eeea28df3196cb6229cbff11d1e2bcaf600eff92 Homepage: https://cran.r-project.org/package=BiasedUrn Description: CRAN Package 'BiasedUrn' (Biased Urn Model Distributions) Statistical models of biased sampling in the form of univariate and multivariate noncentral hypergeometric distributions, including Wallenius' noncentral hypergeometric distribution and Fisher's noncentral hypergeometric distribution. See vignette("UrnTheory") for explanation of these distributions. Literature: Fog, A. (2008a). Calculation Methods for Wallenius' Noncentral Hypergeometric Distribution, Communications in Statistics, Simulation and Computation, 37(2) . Fog, A. (2008b). Sampling methods for Wallenius’ and Fisher’s noncentral hypergeometric distributions, Communications in Statistics—Simulation and Computation, 37(2) . Package: r-cran-biclassify Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2233 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-fields, r-cran-mass, r-cran-mvtnorm, r-cran-expm, r-cran-daag, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-biclassify_1.3-1.ca2404.1_amd64.deb Size: 2105056 MD5sum: a2a2d330b61e2693be615c679e4cc8ac SHA1: e439c823bcf3548c22e5b3537b9be03e0fc2567a SHA256: 5adf3b8b0c48a476c31bfcfd31759bb68bfaa7ab005cb2c9934ee4c46477d071 SHA512: 6ea45dbcd75e88e9ba7a205f7d5e3fd8f5c84e17202a223fefde8e4581b920915e45d2b3c7fa0aebc12e68874a2624270da049e6127f385aae07ff12ec579802 Homepage: https://cran.r-project.org/package=biClassify Description: CRAN Package 'biClassify' (Binary Classification Using Extensions of Discriminant Analysis) Implements methods for sample size reduction within Linear and Quadratic Discriminant Analysis in Lapanowski and Gaynanova (2020) . Also includes methods for non-linear discriminant analysis with simultaneous sparse feature selection in Lapanowski and Gaynanova (2019) PMLR 89:1704-1713. Package: r-cran-biclust Architecture: amd64 Version: 2.0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1372 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-colorspace, r-cran-lattice, r-cran-flexclust, r-cran-additivitytests, r-cran-tidyr, r-cran-ggplot2 Suggests: r-cran-isa2 Filename: pool/dists/noble/main/r-cran-biclust_2.0.3.1-1.ca2404.1_amd64.deb Size: 1311624 MD5sum: 55901dc858b3cae3babae1a22283e3ff SHA1: 48f7172d08af5bc44d488e6202733ccafe31a7ec SHA256: 5f8260c292c628569dc98e583b8605ff5dd97ac0af49e2117fc218742cc35524 SHA512: cc0397abfdc3a354f991d191a5fd8354de1f5a0159468c632911402eeda6da25f241c0119cb737532a0fb430e50c28acdcb23e9d1636268f767d90e502b341f8 Homepage: https://cran.r-project.org/package=biclust Description: CRAN Package 'biclust' (BiCluster Algorithms) The main function biclust() provides several algorithms to find biclusters in two-dimensional data: Cheng and Church (2000, ISBN:1-57735-115-0), spectral (2003) , plaid model (2005) , xmotifs (2003) and bimax (2006) . In addition, the package provides methods for data preprocessing (normalization and discretisation), visualisation, and validation of bicluster solutions. Package: r-cran-bidag Architecture: amd64 Version: 2.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1689 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-bioc-graph, r-bioc-rgraphviz, r-bioc-rbgl, r-cran-pcalg, r-cran-matrix, r-cran-coda Filename: pool/dists/noble/main/r-cran-bidag_2.1.4-1.ca2404.1_amd64.deb Size: 1609152 MD5sum: f9ad5d6a1b91c4dfa0df2f5599c09922 SHA1: 3ef3dba3159584ab0ffc62b6677151a7cafa9f0a SHA256: bc8a2a6e86c6b2ac0d43f1cd89cc1c2d0a7fac2c87d58ea63f411a23564ca5d3 SHA512: 3095df5ea51b34af69b81e18415b04dab197cb55c56d20b9cc011cecba259b665cfc9900fc6ecdf62659fae44daa72ec2aa269d62c80a009559a31d2f3719a90 Homepage: https://cran.r-project.org/package=BiDAG Description: CRAN Package 'BiDAG' (Bayesian Inference for Directed Acyclic Graphs) Implementation of a collection of MCMC methods for Bayesian structure learning of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient inference on larger DAGs, the space of DAGs is pruned according to the data. To filter the search space, the algorithm employs a hybrid approach, combining constraint-based learning with search and score. A reduced search space is initially defined on the basis of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with search and score. Search and score is then performed following two approaches: Order MCMC, or Partition MCMC. The BGe score is implemented for continuous data and the BDe score is implemented for binary data or categorical data. The algorithms may provide the maximum a posteriori (MAP) graph or a sample (a collection of DAGs) from the posterior distribution given the data. All algorithms are also applicable for structure learning and sampling for dynamic Bayesian networks. References: J. Kuipers, P. Suter, G. Moffa (2022) , N. Friedman and D. Koller (2003) , J. Kuipers and G. Moffa (2017) , M. Kalisch et al. (2012) , D. Geiger and D. Heckerman (2002) , P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) . Package: r-cran-bidistances Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-paralleldist, r-cran-datavisualizations, r-cran-diptest, r-cran-e1071, r-cran-vegan, r-cran-pracma, r-cran-ggplot2, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-remotes, r-cran-sphet, r-cran-transport, r-cran-ineq Filename: pool/dists/noble/main/r-cran-bidistances_0.1.3-1.ca2404.1_amd64.deb Size: 153744 MD5sum: 5848d5036ae397de077079725a4a8c6e SHA1: d4b8c793a07fe97b407cd068cda34e7713bc5e95 SHA256: 495738cb2d0cfd32eaac88fa34be81c8f65d9a1d7d95343732de3319b7633c25 SHA512: 695e2a81c00481ed20a5f6fdaca435e851ced474a4e99fef6eeccb4ae98365856863e65a5f4d77298c18a9fc955b8b925ace69a97bc930b1bc6fffd4c9f08dac Homepage: https://cran.r-project.org/package=BIDistances Description: CRAN Package 'BIDistances' (Bioinformatic Distances) A selection of distances measures for bioinformatics data. Other important distance measures for bioinformatics data are selected from the R package 'parallelDist'. A special distance measure for the Gene Ontology is available. Package: r-cran-bife Architecture: amd64 Version: 0.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-formula, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-alpaca, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bife_0.7.3-1.ca2404.1_amd64.deb Size: 236104 MD5sum: 1fbc5a38e8e904751273f116f121bcfe SHA1: 4d135ee447bbd84b8ff33559ed322b125bad50e6 SHA256: 119543a6bb4b39b1e2a9dffb3d21c7d5056c5ab9927fc3f2a6dbf3f5f7496d96 SHA512: 89997c4e996a06f3abc03353c83d52a594c41ca32d4edc065e7e568821886b7993231d1531b5597c8eec0d38864655c9f9320c8b78de4b0a6c4507504b95a595 Homepage: https://cran.r-project.org/package=bife Description: CRAN Package 'bife' (Binary Choice Models with Fixed Effects) Estimates fixed effects binary choice models (logit and probit) with potentially many individual fixed effects and computes average partial effects. Incidental parameter bias can be reduced with an asymptotic bias correction proposed by Fernandez-Val (2009) . Package: r-cran-bifiesurvey Architecture: amd64 Version: 3.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2691 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-miceadds, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-lavaan, r-cran-mitools, r-cran-survey, r-cran-tam Filename: pool/dists/noble/main/r-cran-bifiesurvey_3.8.0-1.ca2404.1_amd64.deb Size: 2179102 MD5sum: 540daae743fee00f401bdff5170f93d5 SHA1: 9855940b132c604b5fe3ca4995fa9d510b3d0b04 SHA256: 893a4436023e302e9c5e72e76d350ae1705141188203ac16b0ceb07b0dce74b7 SHA512: dba37363f2851ecce974641412e1d4f0cb7b74ef05626b267145b6c5b284ed89cdf211f5fb229830a3d4988c6ebf10105b3a6a4400d19bcd9c6e46b9a1cbf4d8 Homepage: https://cran.r-project.org/package=BIFIEsurvey Description: CRAN Package 'BIFIEsurvey' (Tools for Survey Statistics in Educational Assessment) Contains tools for survey statistics (especially in educational assessment) for datasets with replication designs (jackknife, bootstrap, replicate weights; see Kolenikov, 2010; Pfefferman & Rao, 2009a, 2009b, , ); Shao, 1996, ). Descriptive statistics, linear and logistic regression, path models for manifest variables with measurement error correction and two-level hierarchical regressions for weighted samples are included. Statistical inference can be conducted for multiply imputed datasets and nested multiply imputed datasets and is in particularly suited for the analysis of plausible values (for details see George, Oberwimmer & Itzlinger-Bruneforth, 2016; Bruneforth, Oberwimmer & Robitzsch, 2016; Robitzsch, Pham & Yanagida, 2016). The package development was supported by BIFIE (Federal Institute for Educational Research, Innovation and Development of the Austrian School System; Salzburg, Austria). Package: r-cran-bigalgebra Architecture: amd64 Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigmemory, r-cran-bh, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bigalgebra_3.1.0-1.ca2404.1_amd64.deb Size: 134144 MD5sum: 314f61eddc336b21920e4edc1fa87092 SHA1: 2d04da2285163c124ec570d826c5385a10248454 SHA256: c48f95a08ad9ecc2c220a929c794df4cf0e1617ffefcdec201f7f35b2586f998 SHA512: b84bb6b107b154428aa5b87f9e16e430bdcab1d4f5b7fc8feadb075b93bb68f267f2b5d43ab75975a1b71ece671d525210cf35e16f57175f6a819552b16868b6 Homepage: https://cran.r-project.org/package=bigalgebra Description: CRAN Package 'bigalgebra' ('BLAS' and 'LAPACK' Routines for Native R Matrices and'big.matrix' Objects) Provides arithmetic functions for R matrix and 'big.matrix' objects as well as functions for QR factorization, Cholesky factorization, General eigenvalue, and Singular value decomposition (SVD). A method matrix multiplication and an arithmetic method -for matrix addition, matrix difference- allows for mixed type operation -a matrix class object and a big.matrix class object- and pure type operation for two big.matrix class objects. Package: r-cran-biganalytics Architecture: amd64 Version: 1.1.22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bigmemory, r-cran-foreach, r-cran-biglm, r-cran-rcpp, r-cran-bh Filename: pool/dists/noble/main/r-cran-biganalytics_1.1.22-1.ca2404.1_amd64.deb Size: 136106 MD5sum: 51bbdea6b830f2b9a46a2be042a00e57 SHA1: 6a28af85ebafc6f0c413a23b8c06fac889f56af5 SHA256: 6bfd29d71707d9579fb8e05824e3164881d7d012164235f9cae3f7a7cb8ab360 SHA512: 9edf43f887ad208bd60d286b4709395bdab1a2e9f278eb57b73338fbf63b38823d9dc2391b9d528f4ce0d5d47356e38d493ed331e04535cb7e5cc5c5ba70056c Homepage: https://cran.r-project.org/package=biganalytics Description: CRAN Package 'biganalytics' (Utilities for 'big.matrix' Objects from Package 'bigmemory') Extend the 'bigmemory' package with various analytics. Functions 'bigkmeans' and 'binit' may also be used with native R objects. For 'tapply'-like functions, the bigtabulate package may also be helpful. For linear algebra support, see 'bigalgebra'. For mutex (locking) support for advanced shared-memory usage, see 'synchronicity'. Package: r-cran-bigannoy Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 655 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppannoy, r-cran-bh, r-cran-bigmemory Suggests: r-cran-knitr, r-cran-litedown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bigannoy_0.3.0-1.ca2404.1_amd64.deb Size: 257970 MD5sum: 069b2b80633da05ddeb393bff931e477 SHA1: fd7e271f821abda31ea4d83cc2398cbb1abd05d3 SHA256: 7aba75e89b0bd928180e0f450da2d60d750ccd59022d8275cea0e30d2946f71b SHA512: 22f6665f1938cc1ddfff38457c819abae4e842703e338c34581910b3628cd3558b12340c3b91617de6d25d0a3cd8701841e5d448bc97f1f58ce036d52317acda Homepage: https://cran.r-project.org/package=bigANNOY Description: CRAN Package 'bigANNOY' (Approximate k-Nearest Neighbour Search for 'bigmemory' Matriceswith Annoy) Approximate Euclidean k-nearest neighbour search routines that operate on 'bigmemory::big.matrix' data through Annoy indexes created with 'RcppAnnoy'. The package builds persistent on-disk indexes plus sidecar metadata from streamed 'big.matrix' rows, supports euclidean, angular, Manhattan, and dot-product Annoy metrics, and can either return in-memory results or stream neighbour indices and distances into destination 'bigmemory' matrices. Explicit index life cycle helpers, stronger metadata validation, descriptor-aware file-backed workflows, and benchmark helpers are also included. Package: r-cran-bigdatadist Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-fnn, r-cran-rrcov, r-cran-pdist Filename: pool/dists/noble/main/r-cran-bigdatadist_1.1-1.ca2404.1_amd64.deb Size: 218430 MD5sum: d7612c4cae22c15d22add11016aba841 SHA1: 5fd5c0f5a74290e9a8561f37be65782bf56f8e03 SHA256: 3fe2a9709a1f3a104b730a1400ef5dd431ef4ca8a0402891b95393c2acfc3729 SHA512: 1c3531daeb19ea1d8438f87495a88a67ae5c030eb717763e4826019f8ed644eefd31428ce3ff6fe93f5b290d7a40539a7c611d6e59a65bd4ea05d90232ea835c Homepage: https://cran.r-project.org/package=bigdatadist Description: CRAN Package 'bigdatadist' (Distances for Machine Learning and Statistics in the Context ofBig Data) Functions to compute distances between probability measures or any other data object than can be posed in this way, entropy measures for samples of curves, distances and depth measures for functional data, and the Generalized Mahalanobis Kernel distance for high dimensional data. For further details about the metrics please refer to Martos et al (2014) ; Martos et al (2018) ; Hernandez et al (2018, submitted); Martos et al (2018, submitted). Package: r-cran-bigdatastatmeth Architecture: amd64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12120 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libcurl4t64 (>= 7.16.2), libgcc-s1 (>= 12), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libssl3t64 (>= 3.0.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bigdatastatmeth_2.0.1-1.ca2404.1_amd64.deb Size: 4068822 MD5sum: 91a32d2c07cc6ad53731afd0e2569010 SHA1: 36daabb7dd07e0e4cff038d7d77eaaa4c8554953 SHA256: d718cd621e3e1f298e88906b3b2333fe587168afe4aad93190d0bca5ee495858 SHA512: 9d3b3cae965f0190c1f5118b4dcda9f0d9599242caa2392c7612a2eee756eb89f388d397ee1f869689054e1273e75b6b1b8d481cce6f98ecceaae873362536ec Homepage: https://cran.r-project.org/package=BigDataStatMeth Description: CRAN Package 'BigDataStatMeth' (Title: Scalable Statistical Computing with HDF5-Backed Matrices) A framework for 'scalable' statistical computing on large on-disk matrices stored in 'HDF5' files. It provides efficient block-wise implementations of core linear-algebra operations (matrix multiplication, SVD, PCA, QR decomposition, and canonical correlation analysis) written in C++ and R. These building blocks are designed not only for direct use, but also as foundational components for developing new statistical methods that must operate on datasets too large to fit in memory. The package supports data provided either as 'HDF5' files or standard R objects, and is intended for high-dimensional applications such as 'omics' and precision-medicine research. Package: r-cran-bigergm Architecture: amd64 Version: 1.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2693 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ergm, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-network, r-cran-matrix, r-cran-cachem, r-cran-tidyr, r-cran-statnet.common, r-cran-stringr, r-cran-intergraph, r-cran-igraph, r-cran-magrittr, r-cran-purrr, r-cran-dplyr, r-cran-glue, r-cran-readr, r-cran-foreach, r-cran-rlang, r-cran-memoise, r-cran-reticulate, r-cran-ergm.multi Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-sna, r-cran-tibble Filename: pool/dists/noble/main/r-cran-bigergm_1.2.6-1.ca2404.1_amd64.deb Size: 1923764 MD5sum: c129c863a3a02188e9c53e5300ca6d7e SHA1: 94997aa44a947a5d0e9e7d295722b43d390e76b7 SHA256: 1398e4361ab9fc103fb077540fa3fcb55ab1131cf17f578cc43d38064240f6e4 SHA512: fbbf4bb4cb06d7855733ddfba8a548cdb44df9840b0b9bb506bcca3cd9a6535ac9ee01b6cb4926337cdc7d199837d421ce1c8fc9dcc6b7e08147cff34046af4e Homepage: https://cran.r-project.org/package=bigergm Description: CRAN Package 'bigergm' (Fit, Simulate, and Diagnose Hierarchical Exponential-FamilyModels for Big Networks) A toolbox for analyzing and simulating large networks based on hierarchical exponential-family random graph models (HERGMs).'bigergm' implements the estimation for large networks efficiently building on the 'lighthergm' and 'hergm' packages. Moreover, the package contains tools for simulating networks with local dependence to assess the goodness-of-fit. Package: r-cran-biggp Architecture: amd64 Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1487 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), liblapack3 | liblapack.so.3, libopenmpi3t64 (>= 4.1.6), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmpi Suggests: r-cran-rlecuyer, r-cran-fields Filename: pool/dists/noble/main/r-cran-biggp_0.1.9-1.ca2404.1_amd64.deb Size: 1404366 MD5sum: 8eb686b773ee43382d95461b6e39739d SHA1: 9a030cdab01534e20d54fb79633bc9763873cf6a SHA256: 418eb5618fbed5d87f15a14bff18a4da9b274d0874404dcb0c2dbcd15efde9e4 SHA512: db73420582bfe1a8c7219cc515c43b6b9432919a41d9dae2fe476c58394d38863fc15eb798ba40b36c34a433a28a55d97406dcb1cd93bfc1b177ef50a303fad2 Homepage: https://cran.r-project.org/package=bigGP Description: CRAN Package 'bigGP' (Distributed Gaussian Process Calculations) Distributes Gaussian process calculations across nodes in a distributed memory setting, using Rmpi. 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Package: r-cran-bigknn Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 727 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-bigmemory, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bigknn_0.3.0-1.ca2404.1_amd64.deb Size: 274602 MD5sum: 18fd0176d9cbba0aa7b7d1561deddb6c SHA1: 979b917d96de63b4ba593016d15e3d3cf808ed98 SHA256: d55bc2dacddf1f661fa69824c0100997199c9d5ac721d6ebe9ec2d9a0d12e32a SHA512: 50f48c3049d5dee8d19b7a3add75365fb8abb02953af0a6e6f09da74535134e546478f2644de3316c0ae19411273998ed93989cfe3950afde9a1e085b007fa69 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: amd64 Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1469 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bigmemory, r-cran-matrix, r-cran-ncvreg, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-glmnet, r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-biglasso_1.6.1-1.ca2404.1_amd64.deb Size: 971768 MD5sum: 546fbfa7d476432673db0b7864a8a6e5 SHA1: a9c634010f557c7315160a7ce2b654ac0d78be0b SHA256: e67a3012a5dee16dad1efd1cce528f79db88542d1718dc22b49f5a1699ed05b6 SHA512: a4ae9aec75fd3f6ea27d895c38601905fd3327b951f5726a91e736b975d66f13ef0f682eeda3f8c2973c736c5ee0f0c4ee4df85f24686048227e656a7be3c0ba Homepage: https://cran.r-project.org/package=biglasso Description: CRAN Package 'biglasso' (Extending Lasso Model Fitting to Big Data) Extend lasso and elastic-net model fitting for large data sets that cannot be loaded into memory. Designed to be more memory- and computation-efficient than existing lasso-fitting packages like 'glmnet' and 'ncvreg', thus allowing the user to analyze big data with limited RAM . Package: r-cran-biglm Architecture: amd64 Version: 0.9-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi Suggests: r-cran-rsqlite, r-cran-rodbc Filename: pool/dists/noble/main/r-cran-biglm_0.9-3-1.ca2404.1_amd64.deb Size: 67144 MD5sum: e956937f7136d5f112caf0afdbc653fa SHA1: 7e8cefb689f71f12e07d4ed6718d3dc9513ab020 SHA256: f1df6b3fb9deedffcc25beb7ad724fa5b0717f801697948df2b69c8facda9286 SHA512: e2eb1a72795832424e046bfcb6b853c142ad683afc24ea431d3b56ece7e20b3127bf3dcf65c09931acfdfb6b9c3c99954dd62b8f4fb90d2878aa6f58e7b7587e Homepage: https://cran.r-project.org/package=biglm Description: CRAN Package 'biglm' (Bounded Memory Linear and Generalized Linear Models) Regression for data too large to fit in memory. Package: r-cran-biglmm Architecture: amd64 Version: 0.9-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi Suggests: r-cran-rsqlite, r-cran-rodbc Filename: pool/dists/noble/main/r-cran-biglmm_0.9-3-1.ca2404.1_amd64.deb Size: 67836 MD5sum: c9e8ef5bd28cdca8133daf7f53734a6c SHA1: 777a473dc5f129509083b81ade80fe2d4145741c SHA256: 9f963a0be7eb82efb4bb978b6ffecdc5a79f033e52ea02ed01c7aaead8b50a1f SHA512: bed37ec8a7c339e94e93b6890e0e4941d1a3365e791d678e06685ec71480a6ee08f8e1d51020775c00fac6287855f24ec4980fabcd1adb4534a6eb7183fede0d Homepage: https://cran.r-project.org/package=biglmm Description: CRAN Package 'biglmm' (Bounded Memory Linear and Generalized Linear Models) Regression for data too large to fit in memory. This package functions exactly like the 'biglm' package, but works with later versions of R. 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Package: r-cran-bignum Architecture: amd64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1174 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-vctrs, r-cran-bh, r-cran-cpp11 Suggests: r-cran-knitr, r-cran-pillar, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bignum_0.3.2-1.ca2404.1_amd64.deb Size: 401308 MD5sum: 490d32defbdfcf6b445fa0536e1d63e1 SHA1: 21d4e3861064f46cd07d757c64feec2586cd4952 SHA256: 56558ff47af9338db52b5f1ec7f3b7d8b4738af27d3f546ba31e4ac102018c31 SHA512: ff9007d52bff00cf952d09c4547319f11c547f6e03c89d72a2e3276d3af3a19417b47c40dea5c1fe29f0b31cab2954292d7631f8c2603454979385d170fe2586 Homepage: https://cran.r-project.org/package=bignum Description: CRAN Package 'bignum' (Arbitrary-Precision Integer and Floating-Point Mathematics) Classes for storing and manipulating arbitrary-precision integer vectors and high-precision floating-point vectors. These extend the range and precision of the 'integer' and 'double' data types found in R. This package utilizes the 'Boost.Multiprecision' C++ library. It is specifically designed to work well with the 'tidyverse' collection of R packages. Package: r-cran-bigpcacpp Architecture: amd64 Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2341 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-withr, r-cran-bigmemory, r-cran-bh Suggests: r-cran-bench, r-cran-ggplot2, r-cran-irlba, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bigpcacpp_0.9.1-1.ca2404.1_amd64.deb Size: 1425248 MD5sum: 3b374ee429ae0ff6fbdd96f139007e3a SHA1: 7cd6e0a7119c729b5831d7a4bd1f37696480112b SHA256: 28d3d5c9c7a14c8aa49f8787d716cb6292494423dd49dfeb0da4abe72f81efe4 SHA512: 08fbb93cd6cb0316ab0b0129f5f0d63f4af2e7002f654efdaa6fe64187390b0e74207d6c333a237993753085349eb155d026a6192306e7a30edf501b4be2ead6 Homepage: https://cran.r-project.org/package=bigPCAcpp Description: CRAN Package 'bigPCAcpp' (Principal Component Analysis for 'bigmemory' Matrices) High performance principal component analysis routines that operate directly on bigmemory::big.matrix() objects. The package avoids materialising large matrices in memory by streaming data through 'BLAS' and 'LAPACK' kernels and provides helpers to derive scores, loadings, correlations, and contribution diagnostics, including utilities that stream results into 'bigmemory'-backed matrices for file-based workflows. Additional interfaces expose 'scalable' singular value decomposition, robust PCA, and robust SVD algorithms so that users can explore large matrices while tempering the influence of outliers. 'Scalable' principal component analysis is also implemented, Elgamal, Yabandeh, Aboulnaga, Mustafa, and Hefeeda (2015) . Package: r-cran-bigplscox Architecture: amd64 Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2262 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigmemory, r-cran-bigalgebra, r-cran-bigsurvsgd, r-cran-caret, r-cran-doparallel, r-cran-foreach, r-cran-kernlab, r-cran-rcpp, r-cran-risksetroc, r-cran-rms, r-cran-sgpls, r-cran-survauc, r-bioc-survcomp, r-cran-survival, r-cran-bh, r-cran-rcpparmadillo Suggests: r-cran-bench, r-cran-knitr, r-cran-plsrcox, r-cran-mvtnorm, r-cran-readr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bigplscox_0.8.1-1.ca2404.1_amd64.deb Size: 1450936 MD5sum: 041babc862af048307d1982183d747ab SHA1: 61a1ddfcf7ccd7205ebe996590517261e63646c8 SHA256: 4ad5aa2b087820039b9311c659f893a44622dae21d6253365b7ab7a2553c05fd SHA512: 239ec26331f048e44da65e05b40e8802d70279c726f262296bebcffa51226335a26b0f75803423b1eadfe9939f4c74509a7525ce64d26a79aa9a8ff482668e45 Homepage: https://cran.r-project.org/package=bigPLScox Description: CRAN Package 'bigPLScox' (Partial Least Squares for Cox Models with Big Matrices) Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models for big data. Provides a Partial Least Squares (PLS) algorithm adapted to Cox proportional hazards models that works with 'bigmemory' matrices without loading the entire dataset in memory. Also implements a gradient-descent based solver for Cox proportional hazards models that works directly on 'bigmemory' matrices. Bertrand and Maumy (2023) , and highlighted fitting and cross-validating PLS-based Cox models to censored big data. Package: r-cran-bigplsr Architecture: amd64 Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4337 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bigmemory, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-bench, r-cran-dplyr, r-cran-forcats, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-knitr, r-cran-pls, r-cran-plsrglm, r-cran-rmarkdown, r-cran-rhpcblasctl, r-cran-svglite, r-cran-testthat, r-cran-tidyr, r-cran-withr Filename: pool/dists/noble/main/r-cran-bigplsr_0.7.2-1.ca2404.1_amd64.deb Size: 2412986 MD5sum: b4279589e76a89be6698f91f79d7561c SHA1: f0a1d09b84f8ea586105e4d49bd69940d9db2924 SHA256: 847011ee3b1feeb39e71bbf138d816860b0570cedfd0346871e954dfecaf4450 SHA512: 6fdd7cf905994350d2340546fb43b872dd8b8628c74502103220c5786a14422d8b4c26add19200f2ed673605e8db6b06f75c8c35143cf74c5f1e7ea96e3a0295 Homepage: https://cran.r-project.org/package=bigPLSR Description: CRAN Package 'bigPLSR' (Partial Least Squares Regression Models with Big Matrices) Fast partial least squares (PLS) for dense and out-of-core data. Provides SIMPLS (straightforward implementation of a statistically inspired modification of the PLS method) and NIPALS (non-linear iterative partial least-squares) solvers, plus kernel-style PLS variants ('kernelpls' and 'widekernelpls') with parity to 'pls'. Optimized for 'bigmemory'-backed matrices with streamed cross-products and chunked BLAS (Basic Linear Algebra Subprograms) (XtX/XtY and XXt/YX), optional file-backed score sinks, and deterministic testing helpers. Includes an auto-selection strategy that chooses between XtX SIMPLS, XXt (wide) SIMPLS, and NIPALS based on (n, p) and a configurable memory budget. About the package, Bertrand and Maumy (2023) , and highlighted fitting and cross-validating PLS regression models to big data. For more details about some of the techniques featured in the package, Dayal and MacGregor (1997) , Rosipal & Trejo (2001) , Tenenhaus, Viennet, and Saporta (2007) , Rosipal (2004) , Rosipal (2019) , Song, Wang, and Bai (2024) . Includes kernel logistic PLS with 'C++'-accelerated alternating iteratively reweighted least squares (IRLS) updates, streamed reproducing kernel Hilbert space (RKHS) solvers with reusable centering statistics, and bootstrap diagnostics with graphical summaries for coefficients, scores, and cross-validation workflows, alongside dedicated plotting utilities for individuals, variables, ellipses, and biplots. The streaming backend uses far less memory and keeps memory bounded across data sizes. For PLS1, streaming is often fast enough while preserving a small memory footprint; for PLS2 it remains competitive with a bounded footprint. On small problems that fit comfortably in RAM (random-access memory), dense in-memory solvers are slightly faster; the crossover occurs as n or p grow and the Gram/cross-product cost dominates. Package: r-cran-bigqf Architecture: amd64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 569 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-svd, r-cran-compquadform, r-cran-matrix, r-cran-coxme Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-skat Filename: pool/dists/noble/main/r-cran-bigqf_1.6-1.ca2404.1_amd64.deb Size: 474730 MD5sum: 2538b0de27912b52ffc5716eb85fa0a1 SHA1: 956b4eca59f2fbbf6eecca9a7a8caf82f0a9a9b5 SHA256: 56ffaf4664a12d4e6d289832366f61f824b58899649415587756854acc79985d SHA512: d343b25d6148040df87931c2b25095245e2df64e2ba7c1a258addf73e831555d99006ca236df86f07722db6d3979c198efa87274b42e53abbf0327fb0b60229f Homepage: https://cran.r-project.org/package=bigQF Description: CRAN Package 'bigQF' (Quadratic Forms in Large Matrices) A computationally-efficient leading-eigenvalue approximation to tail probabilities and quantiles of large quadratic forms, in particular for the Sequence Kernel Association Test (SKAT) used in genomics . Also provides stochastic singular value decomposition for dense or sparse matrices. Package: r-cran-bigquic Architecture: amd64 Version: 1.1-13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 716 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-scalreg Filename: pool/dists/noble/main/r-cran-bigquic_1.1-13-1.ca2404.1_amd64.deb Size: 347098 MD5sum: 9d8f8df552acb375db699a809892721a SHA1: a612e07ed630ad918f18bdcb657a55b84d2ef567 SHA256: 9156334c24ddbdaac60c52e3e4a40986039c067272c7395a97085049d8de29bf SHA512: a97207c10056e8a78bea76ea3fb779e932a0e17f9dbe669edc4757733d027e8078f0e36c66818119d4b2220bd157051ba9bb3a657ded6d387632fa9c12b34881 Homepage: https://cran.r-project.org/package=BigQuic Description: CRAN Package 'BigQuic' (Big Quadratic Inverse Covariance Estimation) Use Newton's method, coordinate descent, and METIS clustering to solve the L1 regularized Gaussian MLE inverse covariance matrix estimation problem. Package: r-cran-bigreadr Architecture: amd64 Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-bigreadr_0.2.5-1.ca2404.1_amd64.deb Size: 177862 MD5sum: 0e265ac4c1a24473d528b530cd84541b SHA1: 50da57e8cd6d6e60872c694fb9fcf63d02242369 SHA256: e87905365f68404504cf79c55804ebd886645d292edb95889b6f18150cbebf2e SHA512: 654f1f03b872e236a958db4fe873058c95f8054f893e2843701dec3696c01a2a8481d95064e5bcb35685d93325c1cfeba84017ed186d5f95db6f014bf1575261 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: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-uuid, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-bigreg_0.1.5-1.ca2404.1_amd64.deb Size: 251634 MD5sum: aec89cc6122f00e898c9ea9caf2f77ad SHA1: a901bad76809fe854db8b11926652254063644f3 SHA256: dab5c16f14693c4f2fab84f82b922fae6cf574b24d4f9abc6b69f9a1759f5e1b SHA512: b383fa6ba21e3d327ca808da52af952f54600f1d1a98ceb4807298e4363b765479a239c67acdf7ce4e3787536a04785795bcebd88473be30cd790d2806a00c7d Homepage: https://cran.r-project.org/package=bigReg Description: CRAN Package 'bigReg' (Generalized Linear Models (GLM) for Large Data Sets) Allows the user to carry out GLM on very large data sets. Data can be created using the data_frame() function and appended to the object with object$append(data); data_frame and data_matrix objects are available that allow the user to store large data on disk. The data is stored as doubles in binary format and any character columns are transformed to factors and then stored as numeric (binary) data while a look-up table is stored in a separate .meta_data file in the same folder. The data is stored in blocks and GLM regression algorithm is modified and carries out a MapReduce- like algorithm to fit the model. The functions bglm(), and summary() and bglm_predict() are available for creating and post-processing of models. The library requires Armadillo installed on your system. It may not function on windows since multi-core processing is done using mclapply() which forks R on Unix/Linux type operating systems. Package: r-cran-bigrquery Architecture: amd64 Version: 1.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 758 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/noble/main/r-cran-bigrquery_1.6.2-1.ca2404.1_amd64.deb Size: 519806 MD5sum: b292a10dc5fa1c90ad32b5096d25af0b SHA1: e5b494c3f49d2152496f3646ec74af20d5f702e8 SHA256: fd61ede4a1230a9b1f450d67a925347e75b320bcf2f9b65e79baf11c919a2c72 SHA512: 2d2322721281e043d8b27e9cf2683d06b443cd2e2b1fc5e32f30ca7e7f40eea4c30e8e193a02f437bdbcf66a104b14d7cd6ac7546e4c95ea6720dfb428a03681 Homepage: https://cran.r-project.org/package=bigrquery Description: CRAN Package 'bigrquery' (An Interface to Google's 'BigQuery' 'API') Easily talk to Google's 'BigQuery' database from R. 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Package: r-cran-bigsnpr Architecture: amd64 Version: 1.12.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2063 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigstatsr, r-cran-bigassertr, r-cran-bigparallelr, r-cran-bigsparser, r-cran-bigreadr, r-cran-bigutilsr, r-cran-data.table, r-cran-dorng, r-cran-foreach, r-cran-ggplot2, r-cran-magrittr, r-cran-matrix, r-cran-rcpp, r-cran-runonce, r-cran-vctrs, r-cran-rcpparmadillo, r-cran-rmio, r-cran-roptim Suggests: r-cran-bindata, r-cran-covr, r-cran-dbplyr, r-cran-dplyr, r-cran-gaston, r-cran-glue, r-cran-hmisc, r-cran-microbenchmark, r-cran-pcadapt, r-cran-quadprog, r-cran-rhpcblasctl, r-cran-rmutil, r-cran-rspectra, r-cran-rsqlite, r-cran-r.utils, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-bigsnpr_1.12.21-1.ca2404.1_amd64.deb Size: 1259942 MD5sum: ebbcf00b0b181e06de267e1e91f074ce SHA1: f58514a4ecade59e7856ac6dd0a1d0e12dcf01c9 SHA256: 446bcb80f9e7dfb231659c479c05a21f0bba9c24964f104f5d5817edfe0116b3 SHA512: 178ad9ec06439616e208833ceb5f79a5e5221576bbc40594ace3e4ee8e71a15c3f9241399b6926a83edcc9feac33962bfe69435266a9ab7235ea5e5b62a45ecf Homepage: https://cran.r-project.org/package=bigsnpr Description: CRAN Package 'bigsnpr' (Analysis of Massive SNP Arrays) Easy-to-use, efficient, flexible and scalable tools for analyzing massive SNP arrays. Privé et al. (2018) . Package: r-cran-bigsparser Architecture: amd64 Version: 0.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 540 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bigassertr, r-cran-matrix, r-cran-rmio, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bigsparser_0.7.3-1.ca2404.1_amd64.deb Size: 295212 MD5sum: d63da69baad5741ff734bc144c68e87f SHA1: 27266795b5623edcce0a4ed41207d8f65507d941 SHA256: b794f2b8456ec807b47432b8cf205a78e1fb378b291ef511d2273b62694a95f7 SHA512: 04aab801452bf4e12bfefc79e2b5a68f0751e2c6a699a06fed4d3fb816c17937c928018213b9071b9c4a46b1745cd45f1df8d55f634b813ac354bd02dbdac0bd Homepage: https://cran.r-project.org/package=bigsparser Description: CRAN Package 'bigsparser' (Sparse Matrix Format with Data on Disk) Provide a sparse matrix format with data stored on disk, to be used in both R and C++. This is intended for more efficient use of sparse data in C++ and also when parallelizing, since data on disk does not need copying. Only a limited number of features will be implemented. For now, conversion can be performed from a 'dgCMatrix' or a 'dsCMatrix' from R package 'Matrix'. A new compact format is also now available. Package: r-cran-bigsplines Architecture: amd64 Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-bigsplines_1.1-1-1.ca2404.1_amd64.deb Size: 580986 MD5sum: 01153eba7ade13d5427bab0ee0f87828 SHA1: 09fc16730dcfcbafb376efedeae5691d7e55bfdd SHA256: 02eeb5ee15d8ba0fe817305ddd74512132cccb6f235acf93e5679ee234933be4 SHA512: f749f84230762d54bfd3413a59aee08901edc8f6c20cfba97604cead7a51bcc27dd25bb356ea855c6b48e70e811f9f4d806be8042cee7a93a7d9ec2bd0b13f03 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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See "The Art of Computer Programming Vol. 1" by Donald E. Knuth (1997, ISBN: 0201896834) for more details. 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Documentation is provided by several vignettes included in this package; also see Lochocki et al. (2022) . 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This package aims to provide users with a standardized way to automate genome, proteome, 'RNA', coding sequence ('CDS'), 'GFF', and metagenome retrieval from 'NCBI RefSeq', 'NCBI Genbank', 'ENSEMBL', and 'UniProt' databases. Furthermore, an interface to the 'BioMart' database (Smedley et al. (2009) ) allows users to retrieve functional annotation for genomic loci. In addition, users can download entire databases such as 'NCBI RefSeq' (Pruitt et al. (2007) ), 'NCBI nr', 'NCBI nt', 'NCBI Genbank' (Benson et al. (2013) ), etc. with only one command. Package: r-cran-biopn Architecture: amd64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-biopn_1.2.0-1.ca2404.1_amd64.deb Size: 63694 MD5sum: feda06ffc21cd55efee58bb7937489bd SHA1: 45088ccb12a09d32ff426dbd0b34273e402d7d24 SHA256: 32afaa53cf9d6f02d52c9443fcfc6700dc33717ed3360bde7d7d1db62f0b0079 SHA512: 0049084b8f1c3e01807d67062fee02dcd5d214e5591f73bf64ef969c7bf6e88271ce10e9841ed857d00a227c073b0b47ae1b57e310c652e27b0f75a7a76e636a Homepage: https://cran.r-project.org/package=bioPN Description: CRAN Package 'bioPN' (Simulation of deterministic and stochastic biochemical reactionnetworks using Petri Nets) bioPN is a package suited to perform simulation of deterministic and stochastic systems of biochemical reaction networks. Models are defined using a subset of Petri Nets, in a way that is close at how chemical reactions are defined. For deterministic solutions, bioPN creates the associated system of differential equations "on the fly", and solves it with a Runge Kutta Dormand Prince 45 explicit algorithm. For stochastic solutions, bioPN offers variants of Gillespie algorithm, or SSA. For hybrid deterministic/stochastic, it employs the Haseltine and Rawlings algorithm, that partitions the system in fast and slow reactions. bioPN algorithms are developed in C to achieve adequate performance. Package: r-cran-bioregion Architecture: amd64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7697 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-apcluster, r-cran-bipartite, r-cran-cluster, r-cran-data.table, r-cran-dbscan, r-cran-dynamictreecut, r-cran-fastcluster, r-cran-fastkmedoids, r-cran-ggplot2, r-cran-httr, r-cran-igraph, r-cran-mathjaxr, r-cran-matrix, r-cran-phangorn, r-cran-rcartocolor, r-cran-rdpack, r-cran-rlang, r-cran-rmarkdown, r-cran-segmented, r-cran-sf, r-cran-tidyr, r-cran-rcpp Suggests: r-cran-ade4, r-cran-adespatial, r-cran-betapart, r-cran-dplyr, r-cran-ecodist, r-cran-knitr, r-cran-microbenchmark, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-terra, r-cran-testthat, r-cran-vegan Filename: pool/dists/noble/main/r-cran-bioregion_1.4.0-1.ca2404.1_amd64.deb Size: 6230954 MD5sum: e1aa2bdb8ed3729166d9b612b5e98dcd SHA1: 587f461ce25f960d900adb016d7c3512979bc69d SHA256: 74f942ac22a3d1e2398c5ba5fbfc2d996810d49acd0be227e201a590bda44aa2 SHA512: e42a5ed7731ad2c46b9e365a7a89ccdd15be306a05828bc38e1e3e8ce316b4db944a4e38f8ca883b9502d551cc6847e61acc5da98964996f557d203057b0cb8d Homepage: https://cran.r-project.org/package=bioregion Description: CRAN Package 'bioregion' (Comparison of Bioregionalization Methods) The main purpose of this package is to propose a transparent methodological framework to compare bioregionalization methods based on hierarchical and non-hierarchical clustering algorithms (Kreft & Jetz (2010) ) and network algorithms (Lenormand et al. (2019) and Leroy et al. (2019) ). Package: r-cran-biosensors.usc Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5696 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-energy, r-cran-fda.usc, r-cran-paralleldist, r-cran-osqp, r-cran-truncnorm, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-biosensors.usc_1.0-1.ca2404.1_amd64.deb Size: 896606 MD5sum: a0fee482bc9e1384ea3cc32d293d33c7 SHA1: 3672f1b488b84cc828dbee6aef80caba73fb2538 SHA256: ea5e24d72831d740844dd78ad7784fcfbb8259592bd8f9dac160c0b98f60e049 SHA512: 42e7d0742bdfb18f12ec9a937ee779bd419965ef055f8d6116d2549845dfcb053d144677cd6edf7212c9045ebf08690d8061b6652d611c0d135d7caaf091dc1e Homepage: https://cran.r-project.org/package=biosensors.usc Description: CRAN Package 'biosensors.usc' (Distributional Data Analysis Techniques for Biosensor Data) Unified and user-friendly framework for using new distributional representations of biosensors data in different statistical modeling tasks: regression models, hypothesis testing, cluster analysis, visualization, and descriptive analysis. Distributional representations are a functional extension of compositional time-range metrics and we have used them successfully so far in modeling glucose profiles and accelerometer data. However, these functional representations can be used to represent any biosensor data such as ECG or medical imaging such as fMRI. Matabuena M, Petersen A, Vidal JC, Gude F. "Glucodensities: A new representation of glucose profiles using distributional data analysis" (2021) . Package: r-cran-bipartite Architecture: amd64 Version: 2.24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3944 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sna, r-cran-vegan, r-cran-corpcor, r-cran-fields, r-cran-igraph, r-cran-mass, r-cran-permute Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-bipartite_2.24-1.ca2404.1_amd64.deb Size: 2924978 MD5sum: cf4df91846c6c4ecfaaeb796aca39aec SHA1: f16150f50ef89dd3f9f1ba28011aa077615fef61 SHA256: 42c6c3d4ad8a81e79a798c4bb78a6487f457e9593038e6685d9b244f8558c73a SHA512: 60dd18f939785df1df9033b1e7a43592f5e149b6d3cf232fb56e42082be119146f3215471fdcc34353d6174071f69646274913d511096505be471d595bef409f Homepage: https://cran.r-project.org/package=bipartite Description: CRAN Package 'bipartite' (Visualising Bipartite Networks and Calculating Some (Ecological)Indices) Functions to visualise webs and calculate a series of indices commonly used to describe pattern in (ecological) webs. It focuses on webs consisting of only two levels (bipartite), e.g. pollination webs or predator-prey-webs. Visualisation is important to get an idea of what we are actually looking at, while the indices summarise different aspects of the web's topology. Package: r-cran-bipartitemodularitymaximization Architecture: amd64 Version: 1.23.120.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bipartitemodularitymaximization_1.23.120.1-1.ca2404.1_amd64.deb Size: 51460 MD5sum: 64093b4c5c2e821f17f8392f3a2733c5 SHA1: 497deacf05890d222b9cd38ec892233595705e1a SHA256: 0cbfb45e7a4f6ca3b41fff5d7c513757cfe2d62132de5e68a7f297b29dac44c5 SHA512: 295f267ce3faac627e7681d3fbf0bae05012b6eee82922f2b825d35af7000dbf5aa4c5300e2d97514736a36e8993a18fd50d9eddf42eee4f83f2286108da68ad Homepage: https://cran.r-project.org/package=BipartiteModularityMaximization Description: CRAN Package 'BipartiteModularityMaximization' (Partition Bipartite Network into Non-Overlapping Biclusters byOptimizing Bipartite Modularity) Function bipmod() that partitions a bipartite network into non-overlapping biclusters by maximizing bipartite modularity defined in Barber (2007) using the bipartite version of the algorithm described in Treviño (2015) . Package: r-cran-biplotez Architecture: amd64 Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4416 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-plotrix, r-cran-withr Suggests: r-cran-caret, r-cran-cluster, r-cran-geometry, r-cran-ggplot2, r-cran-ggrepel, r-cran-knitr, r-cran-mass, r-cran-r.devices, r-cran-rgl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-biplotez_2.2-1.ca2404.1_amd64.deb Size: 2211284 MD5sum: 53500fc76a672f40cd63427f46008294 SHA1: f92b55381bf34cd3d8c903d89cf0f321fa09fe5f SHA256: 5b47d5bd8c22b93cf53a16fa95705b37a9263a2ace0f5eb83dcf3a121a712ad8 SHA512: 8bb33f93ff5482e5fd9a9f7bca2172a8ea1b43727ee37e3510ca675b01c17147a206dd054cd59e5e15bfea2033c03bf415cfbb65c8c90c6dfd97b481542f934f Homepage: https://cran.r-project.org/package=biplotEZ Description: CRAN Package 'biplotEZ' (EZ-to-Use Biplots) Provides users with an EZ-to-use platform for representing data with biplots. Currently principal component analysis (PCA), canonical variate analysis (CVA) and simple correspondence analysis (CA) biplots are included. This is accompanied by various formatting options for the samples and axes. Alpha-bags and concentration ellipses are included for visual enhancements and interpretation. For an extensive discussion on the topic, see Gower, J.C., Lubbe, S. and le Roux, N.J. (2011, ISBN: 978-0-470-01255-0) Understanding Biplots. Wiley: Chichester. Package: r-cran-birdie Architecture: amd64 Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3285 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-rcpp, r-cran-cli, r-cran-vctrs, r-cran-generics, r-cran-dplyr, r-cran-stringi, r-cran-stringr, r-cran-squarem, r-cran-bh, r-cran-rcppeigen, r-cran-rcppthread, r-cran-stanheaders Suggests: r-cran-daarem, r-cran-easycensus, r-cran-wru, r-cran-knitr, r-cran-roxygen2, r-cran-rmarkdown, r-cran-rstan, r-cran-testthat Filename: pool/dists/noble/main/r-cran-birdie_0.7.1-1.ca2404.1_amd64.deb Size: 2389402 MD5sum: cd05fbe84574f25248153e7473f1f2ae SHA1: 122126e25d32498ed07f1ffc48a5b46c3ad48d75 SHA256: c0cb4d46a8208f91fb796bf81f5d73face804cc03c9dfff75acd29b4868302f0 SHA512: 32d15a48a4316020e825b0e9a9d79a02eeae83e1594d93723c112f2c674fef885a742b6860cf0c1546f00f795c3f5b7e08e7a7034aa1b6e3065e15d8536e0d99 Homepage: https://cran.r-project.org/package=birdie Description: CRAN Package 'birdie' (Bayesian Instrumental Regression for Disparity Estimation) Bayesian models for accurately estimating conditional distributions by race, using Bayesian Improved Surname Geocoding (BISG) probability estimates of individual race. 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Package: r-cran-birp Architecture: amd64 Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3739 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-birp_0.0.5-1.ca2404.1_amd64.deb Size: 1168818 MD5sum: d7aacfc88c63c74acc382143101d0975 SHA1: 1495fd3b452a0c2147335ab52176eb93ae7eb927 SHA256: 7c76a5be67f3745aacc57046b6d1febcd4e36279a782d8296ffda24f940ce4f2 SHA512: 511069a0286cc1d41010e9613fd95e22ee27eeebb9410e77c69f7fb7123ea3c95fc4ec5d50880c857e618e731fd2e8aa801aad5cc6eef7fe96ecb6534b2fc08c Homepage: https://cran.r-project.org/package=birp Description: CRAN Package 'birp' (Testing for Population Trends Using Low-Cost Ecological CountData) A Bayesian tool to test for population trends and changes in trends under arbitrary designs, including before-after (BA), control-intervention (CI) and before-after-control-intervention (BACI) designs commonly used to assess conservation impact. It infers changes in trends jointly from data obtained with multiple survey methods, as well as from limited and noisy data not necessarily collected in standardized ecological surveys. Observed counts can be modeled as following either a Poisson or a negative binomial model, and both deterministic and stochastic trend models are available. For more details on the model see Singer et al. (2025) , and the file 'AUTHORS' for a list of copyright holders and contributors. Package: r-cran-bisque Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvquad, r-cran-rcpp, r-cran-foreach, r-cran-itertools, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-fields Filename: pool/dists/noble/main/r-cran-bisque_1.0.2-1.ca2404.1_amd64.deb Size: 175228 MD5sum: b7e56d0d7b31e2e7f3f4ba654a04fbf2 SHA1: 575f1a10df1a12fdc6d6afa7005b884c58357065 SHA256: 4632ec2b2ac843dd8b270f6023c9621bc7247c84d435cac20a7f37007cf0dc8a SHA512: 135f48aa428ab62ec688dfe95dc4f2d290159370c5603a6b4dc0884ce9192738451a19a0275d7b96d41ca8cc6962a2f5e0ed1f473b9e49c09c23a1aa729bcfda Homepage: https://cran.r-project.org/package=bisque Description: CRAN Package 'bisque' (Approximate Bayesian Inference via Sparse Grid QuadratureEvaluation (BISQuE) for Hierarchical Models) Implementation of the 'bisque' strategy for approximate Bayesian posterior inference. See Hewitt and Hoeting (2019) for complete details. 'bisque' combines conditioning with sparse grid quadrature rules to approximate marginal posterior quantities of hierarchical Bayesian models. The resulting approximations are computationally efficient for many hierarchical Bayesian models. The 'bisque' package allows approximate posterior inference for custom models; users only need to specify the conditional densities required for the approximation. Package: r-cran-bistablehistory Architecture: amd64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2637 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-loo, r-cran-rlang, r-cran-rstantools, r-cran-rcpp, r-cran-rstan, r-cran-dplyr, r-cran-tibble, r-cran-glue, r-cran-boot, r-cran-purrr, r-cran-tidyr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-bistablehistory_1.1.4-1.ca2404.1_amd64.deb Size: 1251670 MD5sum: 71180237b5ae1d3380cfab294ac86e3c SHA1: 2fd3e89a1215a7bef97ae4ca0aa7a7ace58665da SHA256: e7b03c1ff3da9018b51b3b95adc690217233fdd20768bf0d407034d32547369f SHA512: d38702832ad1b356f387655937fd4c2926e8abbd64a6bbbd99137e9fe95906558a4b504d1bc43647ed84d6e74fafe4ac230feb505020bec0e038ceaf4ff2b19f Homepage: https://cran.r-project.org/package=bistablehistory Description: CRAN Package 'bistablehistory' (Cumulative History Analysis for Bistable Perception Time Series) Estimates cumulative history for time-series for continuously viewed bistable perceptual rivalry displays. 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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. Package: r-cran-bivrec Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-mass, r-cran-stringr, r-cran-dplyr, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bivrec_1.2.1-1.ca2404.1_amd64.deb Size: 239088 MD5sum: 7d06a78bd6dc44c2e24ccb9e97536586 SHA1: dc470ec21b8c77ee71e57dee7c9c3987451a1776 SHA256: 89afcac224e31fa8ff381fa1eaaab8328a85b8447567f65d3a794e90d233876a SHA512: 0f14b9570a468a369f7c03b6d2b11aa9e1e773b3d0ea4e186cf42eb454c6c2bccd7eb4dbf0900dd596e97af8144e8494c600165aa9844122e832c6bcbf9b5c4b Homepage: https://cran.r-project.org/package=BivRec Description: CRAN Package 'BivRec' (Bivariate Alternating Recurrent Event Data Analysis) A collection of models for bivariate alternating recurrent event data analysis. Includes non-parametric and semi-parametric methods. 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Includes a simple user interface for such applications, as well as more specialized functions designed to be called by the Migraine software (Rousset and Leblois, 2012 ; Leblois et al., 2014 ; and see URL). The latter functions are used for prediction of likelihood surfaces and implied likelihood ratio confidence intervals, and for exploration of predictor space of the surface. Prediction of the response is based on ordinary Kriging (with residual error) of the input. Estimation of smoothing parameters is performed by generalized cross-validation. 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Package: r-cran-blatent Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 783 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-mnormt, r-cran-r6, r-cran-truncnorm, r-cran-matrix, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-blatent_0.1.3-1.ca2404.1_amd64.deb Size: 538420 MD5sum: 4c2c2d319e786092a251e053e047fdff SHA1: 8fbf310f37cf239cb739f7ab7a007fb8b88daae9 SHA256: d14766de4c11f316d243137affe8f4ca29b42eac977445d06ae1907a88b5ad00 SHA512: d38c4086a703fe02aada62ef3fd7f508541cc9f048f151d4780203c5f71dd9ca05a1220562148aeba24058417c1842a3e11b655ef0911843edd6d65324befd2b Homepage: https://cran.r-project.org/package=blatent Description: CRAN Package 'blatent' (Bayesian Latent Variable Models) Estimation of latent variable models using Bayesian methods. 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References: Merkle & Rosseel (2018) ; Merkle et al. (2021) . Package: r-cran-blend Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 698 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-blend_0.1.2-1.ca2404.1_amd64.deb Size: 256024 MD5sum: 5150235a38869b3033dd5c392653acb5 SHA1: f817a5a4c4227a703ca9a9d7cbdf4db3a670ad70 SHA256: e2f092e27619a5091368c8889f81d30b80ec4c062aa739ee92e1974f6a7879fd SHA512: 1c96497a8ac3c3d4cac515e6f305c8c35de2fd4f82404a056ec306d460e2097100bc06b5e572a5af016710f0e796c7eb7a6ffe3b5487168675722f3b399ff8b9 Homepage: https://cran.r-project.org/package=Blend Description: CRAN Package 'Blend' (Robust Bayesian Longitudinal Regularized Semiparametric MixedModels) Our recently developed fully robust Bayesian semiparametric mixed-effect model for high-dimensional longitudinal studies with heterogeneous observations can be implemented through this package. This model can distinguish between time-varying interactions and constant-effect-only cases to avoid model misspecifications. Facilitated by spike-and-slab priors, this model leads to superior performance in estimation, identification and statistical inference. In particular, robust Bayesian inferences in terms of valid Bayesian credible intervals on both parametric and nonparametric effects can be validated on finite samples. The Markov chain Monte Carlo algorithms of the proposed and alternative models are efficiently implemented in 'C++'. Package: r-cran-blindrecalc Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-blindrecalc_1.1.1-1.ca2404.1_amd64.deb Size: 189240 MD5sum: 1d0200334644969a90191a70e6b50140 SHA1: ffffb76eef8b99ec518e7d53003f1ce01217360f SHA256: db11647529bc6ea7bb7486a162181ee8b6d4e9f924c4c715432e0d636c34931b SHA512: e33d87eb4d3d78e4d2735b1fb0f5251c7838f0d67b7506741f9fc093073cdec65a85cb564ceb1c7dcd96b929a6aec8f52c46fd39b2caf524ccf7d4360ad26f20 Homepage: https://cran.r-project.org/package=blindrecalc Description: CRAN Package 'blindrecalc' (Blinded Sample Size Recalculation) Computation of key characteristics and plots for blinded sample size recalculation. Continuous as well as binary endpoints are supported in superiority and non-inferiority trials. See Baumann, Pilz, Kieser (2022) for a detailed description. The implemented methods include the approaches by Lu, K. (2016) , Kieser, M. and Friede, T. (2000) , Friede, T. and Kieser, M. (2004) , Friede, T., Mitchell, C., Mueller-Veltern, G. (2007) , and Friede, T. and Kieser, M. (2011) . Package: r-cran-bliss Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4191 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-ggplot2, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-bliss_1.1.1-1.ca2404.1_amd64.deb Size: 3526936 MD5sum: 82ccb448486d10f3d190ebb9e27056c5 SHA1: 1363731a94459670d7240f746c2ec6bd7a8b7cd0 SHA256: 0344af400dda464f726341b8e44e88739c170a65ed8be7257f3b28cbcc645cf3 SHA512: 0b69d4ea7c86a4b1278c378ea1d9f9cb34f4dd3f701dff97837bf13a71a27140a37dec1b914bc3a7cd0bd650c739e71ff1b47c995c6a64db49455f083f4b388c Homepage: https://cran.r-project.org/package=bliss Description: CRAN Package 'bliss' (Bayesian Functional Linear Regression with Sparse Step Functions) A method for the Bayesian functional linear regression model (scalar-on-function), including two estimators of the coefficient function and an estimator of its support. A representation of the posterior distribution is also available. Grollemund P-M., Abraham C., Baragatti M., Pudlo P. (2019) . Package: r-cran-blmengineinr Architecture: amd64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1317 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-openxlsx, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-blmengineinr_0.1.7-1.ca2404.1_amd64.deb Size: 628622 MD5sum: a00cad53ba4d3372dd7995c1fb59d8a4 SHA1: fac0803919332592107148ee18f6bfd6119acb9d SHA256: 868ba5e9d333e6912f440093e7ebea8dcb7befe83aa77c383ecc07f0d830313c SHA512: 397bcfd008d8184346cadf333305f157825a4863b7fc86fc62cf66ba0aad08e21ec4a1c321ab94f4297e45ca961901e599f7a9d24320820a679600d06554487f Homepage: https://cran.r-project.org/package=BLMEngineInR Description: CRAN Package 'BLMEngineInR' (Biotic Ligand Model Engine) A chemical speciation and toxicity prediction model for the toxicity of metals to aquatic organisms. The Biotic Ligand Model (BLM) engine was originally programmed in 'PowerBasic' by Robert Santore and others. The main way the BLM can be used is to predict the toxicity of a metal to an organism with a known sensitivity (i.e., it is known how much of that metal must accumulate on that organism's biotic ligand to cause a physiological effect in a certain percentage of the population, such as a 20% loss in reproduction or a 50% mortality rate). The second way the BLM can be used is to estimate the chemical speciation of the metal and other constituents in water, including estimating the amount of metal accumulated to an organism's biotic ligand during a toxicity test. In the first application of the BLM, the amount of metal associated with a toxicity endpoint, or regulatory limit will be predicted, while in the second application, the amount of metal is known and the portions of that metal that exist in various forms will be determined. This version of the engine has been re-structured to perform the calculations in a different way that will make it more efficient in R, while also making it more flexible and easier to maintain in the future. Because of this, it does not currently match the desktop model exactly, but we hope to improve this comparability in the future. Package: r-cran-blockcluster Architecture: amd64 Version: 4.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2805 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rtkore, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-blockcluster_4.5.5-1.ca2404.1_amd64.deb Size: 1519386 MD5sum: 521f273a14c1a997fa7c541dd287cc12 SHA1: 3ba3480ff19111036d478a641cad1a8299306c25 SHA256: 0d7399479a5996e4486b22122c84cb68a676a29d075955c2b2ca4cb89ef2e223 SHA512: cc23de5a46dcc387151575428bd294f369d6af5093ab97b1231c8ef71ad83d4b6404811e3d53941fd9fc6db3ae4659f80a0fd40ff05cb4ae01f6cb20fb8fbecf Homepage: https://cran.r-project.org/package=blockcluster Description: CRAN Package 'blockcluster' (Co-Clustering Package for Binary, Categorical, Contingency andContinuous Data-Sets) Simultaneous clustering of rows and columns, usually designated by biclustering, co-clustering or block clustering, is an important technique in two way data analysis. It consists of estimating a mixture model which takes into account the block clustering problem on both the individual and variables sets. The 'blockcluster' package provides a bridge between the C++ core library build on top of the 'STK++' library, and the R statistical computing environment. This package allows to co-cluster binary , contingency , continuous and categorical data-sets . It also provides utility functions to visualize the results. This package may be useful for various applications in fields of Data mining, Information retrieval, Biology, computer vision and many more. More information about the project and comprehensive tutorial can be found on the link mentioned in URL. Package: r-cran-blockcv Architecture: amd64 Version: 3.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3033 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-sp, r-cran-terra, r-cran-ggplot2, r-cran-cowplot, r-cran-automap, r-cran-rcpp Suggests: r-cran-shiny, r-cran-tmap, r-cran-biomod2, r-cran-gstat, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-blockcv_3.2-0-1.ca2404.1_amd64.deb Size: 2488940 MD5sum: 9c1c27e8e47f7010ab1ef811c9f1cc2e SHA1: 605b21bc9078ad33ebe2dd30eeea829bf6cf5322 SHA256: f6cf12925632feacbe063f720e4e063f99e2764683f97ebf7bac8b0075c224cf SHA512: 98793a6c4cff58a56b308aa733236c809fc13b15462b6ce46c265640174faf19d4bdde33c8aa5928b739d5329099af24677b8b3c26fbb6e5e0fdeb354f29d718 Homepage: https://cran.r-project.org/package=blockCV Description: CRAN Package 'blockCV' (Spatial and Environmental Blocking for K-Fold and LOOCross-Validation) Creating spatially or environmentally separated folds for cross-validation to provide a robust error estimation in spatially structured environments; Investigating and visualising the effective range of spatial autocorrelation in continuous raster covariates and point samples to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019) . Package: r-cran-blockforest Architecture: amd64 Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 846 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-survival, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-blockforest_0.2.7-1.ca2404.1_amd64.deb Size: 470134 MD5sum: 37d6ee5216865003400805d5158f21c2 SHA1: b31a1815aa423bd0a07a621d9afea9e2fc3264b4 SHA256: 5e869f8db9f7fc9b95c3d4b6fc0ba1f3577cd732bbc95d50a4d9e0215bf794d6 SHA512: 9d697fdd112855546f591688689e290a0ee327a919411751220b7bd7230cc59a65449dfcba7092b61d5e44f0c155166ef0565d6bc565c32872bb9a0f39ffe111 Homepage: https://cran.r-project.org/package=blockForest Description: CRAN Package 'blockForest' (Block Forests: Random Forests for Blocks of Clinical and OmicsCovariate Data) A random forest variant 'block forest' ('BlockForest') tailored to the prediction of binary, survival and continuous outcomes using block-structured covariate data, for example, clinical covariates plus measurements of a certain omics data type or multi-omics data, that is, data for which measurements of different types of omics data and/or clinical data for each patient exist. Examples of different omics data types include gene expression measurements, mutation data and copy number variation measurements. Block forest are presented in Hornung & Wright (2019). The package includes four other random forest variants for multi-omics data: 'RandomBlock', 'BlockVarSel', 'VarProb', and 'SplitWeights'. These were also considered in Hornung & Wright (2019), but performed worse than block forest in their comparison study based on 20 real multi-omics data sets. Therefore, we recommend to use block forest ('BlockForest') in applications. The other random forest variants can, however, be consulted for academic purposes, for example, in the context of further methodological developments. Reference: Hornung, R. & Wright, M. N. (2019) Block Forests: random forests for blocks of clinical and omics covariate data. BMC Bioinformatics 20:358. . 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In addition, measures of similarity or dissimilarity based on structural equivalence and regular equivalence (REGE algorithms) can be computed and partitioned matrices can be plotted: Žiberna (2007), Žiberna (2008), Žiberna (2014). 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Package: r-cran-blocktools Architecture: amd64 Version: 0.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-mass, r-cran-tibble Suggests: r-cran-nbpmatching, r-cran-ritools, r-cran-testthat, r-cran-xtable Filename: pool/dists/noble/main/r-cran-blocktools_0.6.6-1.ca2404.1_amd64.deb Size: 190126 MD5sum: 482feda83875fc32fc2af716e7ac644f SHA1: b8b673c6aa2090061960a33f96d5434b4931b865 SHA256: 470f444b4d94c9151b6f8d873379c005c7ed6ff9d6c3d85657931372b288ec0e SHA512: b57180ebc42ac9a9c33b4ac0651b45c1ba8670dbf3b7bdfcee3f1f10e2206eb8d5a043abad477685579b5bcb06338d30ba328686200064ab16be9e5b70483089 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. 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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. Package: r-cran-bmggum Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6280 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-edstan, r-cran-ggplot2, r-cran-ggum, r-cran-loo, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bmggum_0.1.0-1.ca2404.1_amd64.deb Size: 1437948 MD5sum: e059a9f75a037d73784012ab362308d4 SHA1: 62fecd131cf9c7cf1206f17af3c5385baac48135 SHA256: c91fd2280d8cd676681d9f639aa5ad101f082594add9db701d30f8ce67ca6b11 SHA512: 4e33abdfb3cfdecde5113eb731d1151a39b43613fecb82e83dc3cce45d921717fb877eed9dbdaa422d52f353d61981fd397835f9b71eff2a0c9f646515876438 Homepage: https://cran.r-project.org/package=bmggum Description: CRAN Package 'bmggum' (Bayesian Multidimensional Generalized Graded Unfolding Model) Full Bayesian estimation of Multidimensional Generalized Graded Unfolding Model (MGGUM) using 'rstan' (See Stan Development Team (2020) ). Functions are provided for estimation, result extraction, model fit statistics, and plottings. 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Package: r-cran-bmstdr Architecture: amd64 Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7226 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-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/noble/main/r-cran-bmstdr_0.8.2-1.ca2404.1_amd64.deb Size: 3564860 MD5sum: e329e8b3ed1e2e3d4405a94afcc5dcb1 SHA1: 59ccff9e642c60c970c25a5a4b5c16d1daa42800 SHA256: 4e85cf7f30b225b5df0f014c6db5cc01356ab4b7d10db5553706be937d8d6da0 SHA512: 52d512834fc34ebaac679ed4dc381908668ea01924d511232d4c978de98c3723fd11bd25946277c699a74ed746802491bea63dd9678d4bb5542b47d68ddb6b1a 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. 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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: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bnstruct Filename: pool/dists/noble/main/r-cran-boltzmm_0.1.5-1.ca2404.1_amd64.deb Size: 125230 MD5sum: f7578f82d43b5e6581ff0320769224d2 SHA1: 55b6341eac90a63c52223462ef00a017f06068c1 SHA256: b56ac2490c6845dbfcab841b4724763087c6a4fe0722a8e9934bf5f7911ec4ec SHA512: c98467b74cfc6bdef9e8b5f52f5fea4bf3c78d8098a7fd7846a168c177a5a7e9da7763b56fded92d02544a0a7bf4a1131374729ab37518f6ba48a8a794b705cb 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. It follows the methods described in Nguyen and Wood (2016a) and Nguyen and Wood (2016b) . Package: r-cran-bondvaluation Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 754 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-timedate Filename: pool/dists/noble/main/r-cran-bondvaluation_0.1.1-1.ca2404.1_amd64.deb Size: 496842 MD5sum: 5918dcdc93551d7b2fedea9d30e627a2 SHA1: 6919bffdee0a732cf06186612a3f9e2dbb57783a SHA256: 91298ca1b4f959c04e3d4a1653f6e6bd8ff4b0fd10e063418ee21872017f6cda SHA512: 5c29f7233b7b1ca19dc4033a49ca76f314ac4a0e25680b194a6bee694500022b3f270cb2e5a25f0281de51b72694534ea2e3280a876c8abde8ddf65f0bdb2104 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: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8564 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-brms, r-cran-broom, r-cran-checkmate, r-cran-dplyr, r-cran-forcats, r-cran-gbm, r-cran-ggplot2, r-cran-glmnet, r-cran-mass, r-cran-rcpp, r-cran-splines2, r-cran-survival, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-vdiffr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bonsaiforest_0.1.1-1.ca2404.1_amd64.deb Size: 8061168 MD5sum: 4cbd42221b63f382b50148ed35f5279b SHA1: a157dee5cb2b94a7c2d12651e2d454e38ecde052 SHA256: 29303f1b3adfd326e05e4171fbbf28432b81c9c81dc14767d52467685b064f66 SHA512: f0ba0e74373f2711544fed1c671d32b67bc5349315c4938be9887755a8b5537a6ff3b945e98a8b7f2be69b4fba64982884db29c7fff2f28491cb6a19c1e180b1 Homepage: https://cran.r-project.org/package=bonsaiforest Description: CRAN Package 'bonsaiforest' (Shrinkage Based Forest Plots) Subgroup analyses are routinely performed in clinical trial analyses. From a methodological perspective, two key issues of subgroup analyses are multiplicity (even if only predefined subgroups are investigated) and the low sample sizes of subgroups which lead to highly variable estimates, see e.g. Yusuf et al (1991) . This package implements subgroup estimates based on Bayesian shrinkage priors, see Carvalho et al (2019) . In addition, estimates based on penalized likelihood inference are available, based on Simon et al (2011) . The corresponding shrinkage based forest plots address the aforementioned issues and can complement standard forest plots in practical clinical trial analyses. Package: r-cran-boodd Architecture: amd64 Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 523 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tseries, r-cran-fgarch, r-cran-timeseries, r-cran-timedate, r-cran-fbasics, r-cran-geor Filename: pool/dists/noble/main/r-cran-boodd_0.1-1.ca2404.1_amd64.deb Size: 484160 MD5sum: cdb60b38ac23f88a83cc6f40234080fc SHA1: 80260ca918641dc4a28e4b2ba803a2629308ab4f SHA256: 9507d25a0093890b2e90788958f567598e3a90b9e01596adb5bb6215ecefa711 SHA512: c77d95310d466246c6518c62e1d37a9ad66e4a3a449361a8ac90686b16c5520fe77716b6b3f3c2d957f94cb456b8c90ae80c3558203e65c2ada26804c8e31c12 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 methodology is based on Frangakis and Rubin (2002) and Imbens and Rubin (1997) , and here adapted to a specific time-to-event setting. Package: r-cran-bpvars Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2381 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bsvars, r-cran-r6, r-cran-rcpp, r-cran-rcppprogress, r-cran-rcpptn, r-cran-tmvtnsim, r-cran-generics, r-cran-rcpparmadillo Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-bpvars_1.0-1.ca2404.1_amd64.deb Size: 1393378 MD5sum: 657198722dfb43116c7ac21c158a7c00 SHA1: e448a25eca79150fe6c905180675aa552013b09a SHA256: 55999f1d2b40dfd305adf5cccf1970fdafc19fbb1ee0469a62f909678ced1c3b SHA512: 22202e54ad21b56b63abe8d2b5c5e26f82defe7afa60011986fba809171d32b82ee545b0f12bd496353b5be6d658c6bd49c4046cce593d1a8ef9bfe339d4d345 Homepage: https://cran.r-project.org/package=bpvars Description: CRAN Package 'bpvars' (Forecasting with Bayesian Panel Vector Autoregressions) Provides Bayesian estimation and forecasting of dynamic panel data using Bayesian Panel Vector Autoregressions with hierarchical prior distributions. The models include country-specific VARs that share a global prior distribution that extend the model by Jarociński (2010) . Under this prior expected value, each country's system follows a global VAR with country-invariant parameters. Further flexibility is provided by the hierarchical prior structure that retains the Minnesota prior interpretation for the global VAR and features estimated prior covariance matrices, shrinkage, and persistence levels. Bayesian forecasting is developed for models including exogenous variables, allowing conditional forecasts given the future trajectories of some variables and restricted forecasts assuring that rates are forecasted to stay positive and less than 100. The package implements the model specification, estimation, and forecasting routines, facilitating coherent workflows and reproducibility. It also includes automated pseudo-out-of-sample forecasting and computation of forecasting performance measures. Beautiful plots, informative summary functions, and extensive documentation complement all this. An extraordinary computational speed is achieved thanks to employing frontier econometric and numerical techniques and algorithms written in 'C++'. The 'bpvars' package is aligned regarding objects, workflows, and code structure with the 'R' packages 'bsvars' by Woźniak (2024) and 'bsvarSIGNs' by Wang & Woźniak (2025) , and they constitute an integrated toolset. Copyright: 2025 International Labour Organization. Package: r-cran-bqtl Architecture: amd64 Version: 1.0-39-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 580 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bqtl_1.0-39-1.ca2404.1_amd64.deb Size: 504280 MD5sum: 8ecca6f0a36e0c1a6b95b0972b2ef014 SHA1: 52572793de3493abde0f1a176460dd02dff400ae SHA256: a6bac960925cadbc01b91ed0871207813f6d591dcf458f40369a02a4a793b77c SHA512: 04d136361a6dba58893dd5daa241ad0c219b263b5c236b36c66deed3b2bc01b48cfc664c22cc5d4da4382dcd8771f967e365ca9186bbc51a0ddb50ff4cdad591 Homepage: https://cran.r-project.org/package=bqtl Description: CRAN Package 'bqtl' (Bayesian QTL Mapping Toolkit) QTL mapping toolkit for inbred crosses and recombinant inbred lines. Includes maximum likelihood and Bayesian tools. Package: r-cran-braggr Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-braggr_0.1.1-1.ca2404.1_amd64.deb Size: 48920 MD5sum: d9da7940105f9459ed8764d9b80a94c0 SHA1: a19168b0ce417fbdaa4cb32b893d77759425921f SHA256: 27c3f685a837f8f4d8bddd90f6c3f313c4fbbd540870cf116b58d6520ff36c53 SHA512: e69ae0df4a924b56173093ded1c680cabdb50d127b31c722c8ee456d0c665aedb10d4567504e4d6423041d66e895e6aa5a2940dc2bdd84740fdcdde6f9ecee85 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: amd64 Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1487 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-branchglm_3.0.1-1.ca2404.1_amd64.deb Size: 685196 MD5sum: 0a461c3c19fc005d157ea5b4cb4387dc SHA1: 35c5f070c93c4f87937160b5bee6d6860afbe90e SHA256: 6d6045daeaa488a8097de925128d08db01ccf901b5c19510ec08ff5bff2d1946 SHA512: da2d4d2e48d769224861803c59f55d49f3c141fc9f30a3b8c49931a6363950d9cc05b73fac9acf8ae737c805a388e24cf3f440fad1cc091efd99860bcacc5b2d 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: amd64 Version: 0.9.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: libc6 (>= 2.3.4), libstdc++6 (>= 4.3), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-branching_0.9.7-1.ca2404.1_amd64.deb Size: 99716 MD5sum: 7452a8636f6b90cb368673e5027183a5 SHA1: be897eada97b560cec17ecb51647153355470064 SHA256: 86688b06a8569c9705b5b84c218aafb74906873534fe973f25805b74c3fc969e SHA512: 2944ebe1d80d38d08d9d8df71ef7696f64b381712bb4fcffec77c954eaccdc7d2aef2a834c67226701f38f285f7480f178e13ffb4898dff2c9740bb977ca88bc 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: amd64 Version: 3.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Filename: pool/dists/noble/main/r-cran-bravo_3.2.2-1.ca2404.1_amd64.deb Size: 156412 MD5sum: f9d3aa120a34fdc3b73c8ed8d35bb727 SHA1: 1cf84dabfd6ea39f80d94fdd46e41d1b674885ce SHA256: e8e8d228fa07993cdf6cb935c8dcf0aceecb7ebc492824416b6c59bb856061f5 SHA512: f6a245c2931f0c19c42c05314443807ce936fdf2b39ad74c0c29aec2a0a3ab1c19102ef94a0aa041b48fc61c24e302d2286495e44f98d91e47bdd575b2fb65cf 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: amd64 Version: 2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1159 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-rcpp, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-breakfast_2.5-1.ca2404.1_amd64.deb Size: 345754 MD5sum: c87fecc2ae204df4ad43beecfcc09eec SHA1: 8aa26f57b8e2757ecdc02e715642b61578a6de2e SHA256: a7d853ffdee3a4cf40dda74e9a5b171f30e55e9e4c769a0c76a304a0e48f54ee SHA512: 4a67fc386277da0d8dbdb413585a22e6c85deb6a3074c294531685d07bd2b723175eecc48cf42ae48b1c6505ad9b92fe431a90fd5b512ad3ad3230fbd21d4f8e 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: amd64 Version: 0.8.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1624 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-breathtestcore, r-cran-dplyr, r-cran-purrr, r-cran-rstan, r-cran-rstantools, r-cran-stringr, r-cran-tidyr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-ggplot2, r-cran-shinystan, r-cran-igraph, r-cran-bayesplot, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-parallelly, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-breathteststan_0.8.9-1.ca2404.1_amd64.deb Size: 631274 MD5sum: 276df755f861985863516232ce84be69 SHA1: ca6b4da26abc11e02df757e3c8f3934c58b90226 SHA256: d08f80be7eb9ca45671c747bc111ad407d3b30ce7725516f8e5a6dff0476e845 SHA512: e9eaa7dda20e2cbc40ee0f3c35420928d796a98268ffa069acd6d1b8081eb49bb3c68ee5011b31f0087864d619dfd5b2ef8a0a8fb3fe1a48ca66917810e4317f 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: amd64 Version: 0.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-profilemodel Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-brglm_0.7.3-1.ca2404.1_amd64.deb Size: 126502 MD5sum: 364d459c228811fb8e876278dbbff8f6 SHA1: 7683068ca634e4fa0241808b332ec997a0115425 SHA256: 2e938d8054de46c7bc0bd4f10749455ea0ee838ee2c53a3a36a8e44e429ddf4a SHA512: 222bf382c0582c28ee388cb842aa677d972633cd53757f607037cffdb01ea14043fc03809233bbe26e977594f46f788281206ac8898e5a37a2327777e6c97709 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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Package: r-cran-bsamgp Architecture: amd64 Version: 1.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1460 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-bsamgp_1.2.7-1.ca2404.1_amd64.deb Size: 865614 MD5sum: 72f87cee6241ff183b774c92c97acd95 SHA1: f1d30bca29ecfda21107ad96fe29af74c9d7032d SHA256: 235bcc54b78d3f9bd7ca44c54730a727d53a6955c0eff374a1f66fc6d0345284 SHA512: 10c781560f25283533608ec70eaad44f7a0a367a1e2fce18bd53519a793024ebec518441db033a38e365e616ac4e7d3195607582ea025bde6c678b8f795f6efc Homepage: https://cran.r-project.org/package=bsamGP Description: CRAN Package 'bsamGP' (Bayesian Spectral Analysis Models using Gaussian Process Priors) Contains functions to perform Bayesian inference using a spectral analysis of Gaussian process priors. 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(2018) ) is an alternative to standard, non-parametric approximate Bayesian computation (ABC). BSL assumes a multivariate normal distribution for the summary statistic likelihood and it is suitable when the distribution of the model summary statistics is sufficiently regular. This package provides a Metropolis Hastings Markov chain Monte Carlo implementation of four methods (BSL, uBSL, semiBSL and BSLmisspec) and two shrinkage estimators (graphical lasso and Warton's estimator). uBSL (Price et al. (2018) ) uses an unbiased estimator to the normal density. A semi-parametric version of BSL (semiBSL, An et al. (2018) ) is more robust to non-normal summary statistics. BSLmisspec (Frazier et al. 2019 ) estimates the Gaussian synthetic likelihood whilst acknowledging that there may be incompatibility between the model and the observed summary statistic. 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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. 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In case of regression, equality constraints as well as monotonicity and/or positivity of B-spline weights can be imposed. Moreover, knot positions can be on regular grid or be part of optimized parameters too (in addition to the spline weights). For this end, 'bspline' is able to calculate Jacobian of basis vectors as function of knot positions. User is provided with functions calculating spline values at arbitrary points. These functions can be differentiated and integrated to obtain B-splines calculating derivatives/integrals at any point. B-splines of this package can simultaneously operate on a series of curves sharing the same set of knots. 'bspline' is written with concern about computing performance that's why the basis and Jacobian calculation is implemented in C++. The rest is implemented in R but without notable impact on computing speed. Package: r-cran-bsplinepsd Architecture: amd64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-bsplinepsd_0.6.0-1.ca2404.1_amd64.deb Size: 102080 MD5sum: 634d798f199ac3c6732fbfa4407cb4de SHA1: 0b01939e460ba3e0a883f155fc4ca6eba22e3bc8 SHA256: 6ab0a4631fa4627de57dc86cab80e32a1cbfa46e73bf0fa5b3ae9107257d6052 SHA512: 91a59d908b08ceff0444c16e9fcefd1f723bf704477fb6cc6fae5707da62c099b8adac170d821931e91fd2559da3cb0dfc48ede45e6240ae3e8067923908cc9a Homepage: https://cran.r-project.org/package=bsplinePsd Description: CRAN Package 'bsplinePsd' (Bayesian Nonparametric Spectral Density Estimation UsingB-Spline Priors) Implementation of a Metropolis-within-Gibbs MCMC algorithm to flexibly estimate the spectral density of a stationary time series. The algorithm updates a nonparametric B-spline prior using the Whittle likelihood to produce pseudo-posterior samples and is based on the work presented in Edwards, M.C., Meyer, R. and Christensen, N., Statistics and Computing (2018). . Package: r-cran-bssasymp Architecture: amd64 Version: 1.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.3), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fica, r-cran-jade Filename: pool/dists/noble/main/r-cran-bssasymp_1.2-4-1.ca2404.1_amd64.deb Size: 185530 MD5sum: b40309cc41fa410de420e5cb980c3fac SHA1: 062f5527802236fe0ee1318240f6ad9b6cbc4afd SHA256: 13be52ff2856e2d681366b1cba8d5b24d1306224c9df572a4bd1de26ec71e52b SHA512: 5b8b3e0d8979f279b9ec5758a2e392286bc85cb73b0e76ef1dc1e3de717d094d75d51e77eb02309d68381b661c307732d4b5f1ed0b95e5b1aaf52883c2a8f1eb Homepage: https://cran.r-project.org/package=BSSasymp Description: CRAN Package 'BSSasymp' (Asymptotic Covariance Matrices of Some BSS Mixing and UnmixingMatrix Estimates) Functions to compute the asymptotic covariance matrices of mixing and unmixing matrix estimates of the following blind source separation (BSS) methods: symmetric and squared symmetric FastICA, regular and adaptive deflation-based FastICA, FOBI, JADE, AMUSE and deflation-based and symmetric SOBI. Also functions to estimate these covariances based on data are available. Package: r-cran-bssm Architecture: amd64 Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6963 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayesplot, r-cran-checkmate, r-cran-coda, r-cran-diagis, r-cran-dplyr, r-cran-posterior, r-cran-rcpp, r-cran-rlang, r-cran-tidyr, r-cran-ramcmc, r-cran-rcpparmadillo, r-cran-sitmo Suggests: r-cran-covr, r-cran-ggplot2, r-cran-kfas, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-sde, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bssm_2.0.3-1.ca2404.1_amd64.deb Size: 2794652 MD5sum: 1901179bd7a4fdd9a6c5f691e97d59d5 SHA1: 134ab33c5390d42015cd39e282469b1750780e08 SHA256: bdad5534bfa2d6937080109e8b6ab7e75761ab2dd250c8254d9d10e458168ebb SHA512: 1a8a56132f0bd491fd312c890207e08dc370cb01daaed652edb3223003de8d89c74dd939a63c0ddb48ff1bc3e078ae87d627a947b87d01a724035b4f1bcd9da7 Homepage: https://cran.r-project.org/package=bssm Description: CRAN Package 'bssm' (Bayesian Inference of Non-Linear and Non-Gaussian State SpaceModels) Efficient methods for Bayesian inference of state space models via Markov chain Monte Carlo (MCMC) based on parallel importance sampling type weighted estimators (Vihola, Helske, and Franks, 2020, ), particle MCMC, and its delayed acceptance version. Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and basic stochastic volatility models with linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretised diffusion models are supported. See Helske and Vihola (2021, ) for details. Package: r-cran-bssprep Architecture: amd64 Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-bssprep_0.1-1.ca2404.1_amd64.deb Size: 43336 MD5sum: 675670b1678460b5f87c52352d4b1b9f SHA1: 1d2255c51c3ab28401181cb7d70e328c5f2a0d40 SHA256: 8ac799df8a6e0fdc8dd9171fd8cc1ac168e9cbe7bd22273384285d8d7b0ce06c SHA512: fb09428ae50bf172cc284ee449cadb0f9153c728b5ef5eea835077364fcd7f7d6c50bbac0d2b969eabe5f819c61a460271a48ba886b9ab6ec6e1dd37485e6b2f Homepage: https://cran.r-project.org/package=BSSprep Description: CRAN Package 'BSSprep' (Whitening Data as Preparation for Blind Source Separation) Whitening is the first step of almost all blind source separation (BSS) methods. A fast implementation of whitening for BSS is implemented to serve as a lightweight dependency for packages providing BSS methods. Package: r-cran-bstfa Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2797 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rcolorbrewer, r-cran-ggplot2, r-cran-ggpubr, r-cran-mgcv, r-cran-mcmcpack, r-cran-coda, r-cran-npreg, r-cran-matrixcalc, r-cran-scatterplot3d, r-cran-sf, r-cran-rcpp, r-cran-lubridate, r-cran-matrix, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-kableextra, r-cran-bookdown, r-cran-magick, r-cran-maps, r-cran-loo Filename: pool/dists/noble/main/r-cran-bstfa_0.1.0-1.ca2404.1_amd64.deb Size: 2587984 MD5sum: 5aaafd328f1c21c1da2cc138a7e3d35b SHA1: 0bca45fbbb2ff8ac89cdf22d5cb0192ac5f4e673 SHA256: 990f0db57ea3fe0fed8c2ae96c6cc3893092857cbb5d0cd520e09c2f32b6045b SHA512: fc45cb0ea8bc8422875b0848e98cb0f2810e97c4c81aca5a1ee558c79e42222b0a0961fa5c0856d895be505f2de886436db9ff8e5e7538ac504f3de5ff34026f Homepage: https://cran.r-project.org/package=BSTFA Description: CRAN Package 'BSTFA' (Bayesian Spatio-Temporal Factor Analysis Model) Implements Bayesian spatio-temporal factor analysis models for multivariate data observed across space and time. The package provides tools for model fitting via Markov chain Monte Carlo (MCMC), spatial and temporal interpolation, and visualization of latent factors and loadings to support inference and exploration of underlying spatio-temporal patterns. Designed for use in environmental, ecological, or public health applications, with support for posterior prediction and uncertainty quantification. Includes functions such as BSTFA() for model fitting and plot_factor() to visualize the latent processes. Functions are based on and extended from methods described in Berrett, et al. (2020) . Package: r-cran-bsts Architecture: amd64 Version: 0.9.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8508 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-boomspikeslab, r-cran-zoo, r-cran-xts, r-cran-boom Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bsts_0.9.11-1.ca2404.1_amd64.deb Size: 2522838 MD5sum: 63ccc5959a286b0be6dc54867a174761 SHA1: ddf57f54769cce62c0269df5abbbe354c3a43214 SHA256: e1f8f78d55be0d7d4a293ed8fd5184bafcc82ec07225c023674ecb00e1a43210 SHA512: 5aae7a212cd443aa064e906679b13de38431998be4b81fa5e3bd698e756f8bb8f7802e227effccdca77f0314fa6c4b74ba9ebcc89207781fec4bfd024c2e759e Homepage: https://cran.r-project.org/package=bsts Description: CRAN Package 'bsts' (Bayesian Structural Time Series) Time series regression using dynamic linear models fit using MCMC. See Scott and Varian (2014) , among many other sources. Package: r-cran-bsvars Architecture: amd64 Version: 3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3274 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppprogress, r-cran-rcpptn, r-cran-gigrvg, r-cran-r6, r-cran-stochvol, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-bsvars_3.2-1.ca2404.1_amd64.deb Size: 2191112 MD5sum: cce92f60c48f598137139916496a78a8 SHA1: 5dd10cc7eeb3fdf9476fc2b1aad161edec689806 SHA256: 52f3345958c33d3d6f8c667965015f6447f6d12c244969c77ca1e227f218ac17 SHA512: 780a06600c032e4ca5425a598e20f58376ee887f6dab8733996061e14e87d0375c91c649a39b84e7278388a2c0842a64a505d45c746feed8329c710fad68314e Homepage: https://cran.r-project.org/package=bsvars Description: CRAN Package 'bsvars' (Bayesian Estimation of Structural Vector Autoregressive Models) Provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic, and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity, non-normality, and hypotheses on autoregressive parameters, as well as analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation including the vignette by Woźniak (2024) complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2024) , Lütkepohl & Woźniak (2020) , and Song & Woźniak (2021) . The 'bsvars' package is aligned regarding objects, workflows, and code structure with the R package 'bsvarSIGNs' by Wang & Woźniak (2024) , and they constitute an integrated toolset. Package: r-cran-bsvarsigns Architecture: amd64 Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1617 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpparmadillo, r-cran-bsvars, r-cran-rcpp, r-cran-rcppprogress, r-cran-r6 Suggests: r-cran-knitr, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-bsvarsigns_2.0-1.ca2404.1_amd64.deb Size: 1060822 MD5sum: 1ce90234374245855ed5af10333dccf2 SHA1: d5f52e75d62a2bc604b910b8f5cdab9312144adc SHA256: 17908c59992cedaf01983573a93705309891576ae8047060b6f723f477ad0517 SHA512: 11c4e9fd5f789e4959bff475530e54dcdf5689b58e93350d63e79b86bec7499ccac6880a45b3119376975a97735c086956bd88cf4e4124fd3119302c3f75752a Homepage: https://cran.r-project.org/package=bsvarSIGNs Description: CRAN Package 'bsvarSIGNs' (Bayesian SVARs with Sign, Zero, and Narrative Restrictions) Implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions (SVARs) identified by sign, zero, and narrative restrictions. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors as in Giannone, Lenza, Primiceri (2015) . The sign restrictions are implemented employing the methods proposed by Rubio-Ramírez, Waggoner & Zha (2010) , while identification through sign and zero restrictions follows the approach developed by Arias, Rubio-Ramírez, & Waggoner (2018) . Furthermore, our tool provides algorithms for identification via sign and narrative restrictions, in line with the methods introduced by Antolín-Díaz and Rubio-Ramírez (2018) . Users can also estimate a model with sign, zero, and narrative restrictions imposed at once. The package facilitates predictive and structural analyses using impulse responses, forecast error variance and historical decompositions, forecasting and conditional forecasting, as well as analyses of structural shocks and fitted values. All this is complemented by colourful plots, user-friendly summary functions, and comprehensive documentation including the vignette by Wang & Woźniak (2024) . The 'bsvarSIGNs' package is aligned regarding objects, workflows, and code structure with the R package 'bsvars' by Woźniak (2024) , and they constitute an integrated toolset. It was granted the Di Cook Open-Source Statistical Software Award by the Statistical Society of Australia in 2024. Package: r-cran-bsynth Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10077 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-rcpp, r-cran-cubelyr, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-rstan, r-cran-rstantools, r-cran-scales, r-cran-tibble, r-cran-tidyr, r-cran-vizdraws, r-cran-rlang, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-gsynth, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bsynth_1.0-1.ca2404.1_amd64.deb Size: 2242682 MD5sum: fbc44d093779f855703d427bb9b2731e SHA1: 68a189344b0534c1c9cbe3b1e1cbe2f6a86fb222 SHA256: 41a5c1acae272fd9a92896b710f8d4400a4a102403e9eaf71c732e054dd245ef SHA512: 9147ca5f6f63c31f545dfc51b25ffad261863f7dc8031b1a5ed841963c04d255f035b3cf7b69e269394c475775070ac70f326e2021f680720cc8d6975705f4f6 Homepage: https://cran.r-project.org/package=bsynth Description: CRAN Package 'bsynth' (Bayesian Synthetic Control) Implements the Bayesian Synthetic Control method for causal inference in comparative case studies. This package provides tools for estimating treatment effects in settings with a single treated unit and multiple control units, allowing for uncertainty quantification and flexible modeling of time-varying effects. The methodology is based on the paper by Vives and Martinez (2022) . Package: r-cran-btb Architecture: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1837 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-mapsf, r-cran-rcpp, r-cran-sf, r-cran-rcppparallel, r-cran-magrittr, r-cran-bh, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-btb_0.2.2-1.ca2404.1_amd64.deb Size: 804078 MD5sum: 41a08b09220e88dd5c8c23dff01cd56a SHA1: 1fc3face7a68f61674a94a215a4e60c5ae2509d3 SHA256: db25e4940b870de589d88052112ebb1223cf767a63bafe27295c5c11a39ae2bf SHA512: d489023be107525fa4d99b9f4afd1cdc1c1ae9df4b6d8884660c5f70fe91d7662bf9e53ba1f617c074942a114fc6d257fa327f583141e3257680b7e081aae466 Homepage: https://cran.r-project.org/package=btb Description: CRAN Package 'btb' (Beyond the Border - Kernel Density Estimation for UrbanGeography) The kernelSmoothing() function allows you to square and smooth geolocated data. It calculates a classical kernel smoothing (conservative) or a geographically weighted median. There are four major call modes of the function. The first call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth) for a classical kernel smoothing and automatic grid. The second call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles) for a geographically weighted median and automatic grid. The third call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, centroids) for a classical kernel smoothing and user grid. The fourth call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles, centroids) for a geographically weighted median and user grid. Geographically weighted summary statistics : a framework for localised exploratory data analysis, C.Brunsdon & al., in Computers, Environment and Urban Systems C.Brunsdon & al. (2002) , Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition, Diggle, pp. 83-86, (2003) . Package: r-cran-btllasso Architecture: amd64 Version: 0.1-14-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-stringr, r-cran-psychotools, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-btllasso_0.1-14-1.ca2404.1_amd64.deb Size: 430510 MD5sum: 4a88dacc9aa31aca80150b2ad7c3ffd2 SHA1: 43ec9c24c3085085c6e093d73695f4e0ee5eb723 SHA256: 000b1a64a903d601a64709127c2e2b9da5d584e94d96d76c0108d62f5cb84c4c SHA512: 2c46eaf24ed9d90c3a946f256d491d605db1c146030e11ea58281b3f96f9e8fa448dd0af1fa98ee3cb4601fa09c6f9902f11c9d81cf300abbf491bfd8daea07d Homepage: https://cran.r-project.org/package=BTLLasso Description: CRAN Package 'BTLLasso' (Modelling Heterogeneity in Paired Comparison Data) Performs 'BTLLasso' as described by Schauberger and Tutz (2019) and Schauberger and Tutz (2017) . BTLLasso is a method to include different types of variables in paired comparison models and, therefore, to allow for heterogeneity between subjects. Variables can be subject-specific, object-specific and subject-object-specific and can have an influence on the attractiveness/strength of the objects. Suitable L1 penalty terms are used to cluster certain effects and to reduce the complexity of the models. Package: r-cran-btm Architecture: amd64 Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-udpipe, r-cran-data.table Filename: pool/dists/noble/main/r-cran-btm_0.3.8-1.ca2404.1_amd64.deb Size: 123584 MD5sum: 3bb1f2b3a537e7d1a670757c505df088 SHA1: 1e753af8a3636b667ac9c10dde483ca5059d2674 SHA256: 418b5b57da5445e6f171f1cd6cedae9b1aaa75f2384048f0ffd1861a5e755855 SHA512: 54f7f964c8195d04165def420c951e9eaccf78d43864c967a72584a095fc7ca8900cb93f4b2f31e1de34f1e9bf4cc1a1e8196c148f1363521421a1976b6836ed Homepage: https://cran.r-project.org/package=BTM Description: CRAN Package 'BTM' (Biterm Topic Models for Short Text) Biterm Topic Models find topics in collections of short texts. It is a word co-occurrence based topic model that learns topics by modeling word-word co-occurrences patterns which are called biterms. This in contrast to traditional topic models like Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis which are word-document co-occurrence topic models. A biterm consists of two words co-occurring in the same short text window. This context window can for example be a twitter message, a short answer on a survey, a sentence of a text or a document identifier. The techniques are explained in detail in the paper 'A Biterm Topic Model For Short Text' by Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Xueqi Cheng (2013) . Package: r-cran-btsr Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 764 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-btsr_1.0.2-1.ca2404.1_amd64.deb Size: 542104 MD5sum: 7a0fa79c9f45ad6e6453367953f232c3 SHA1: 394526e6025910de07c27a1477b8763059ba8097 SHA256: b93a7f7fc68aae2cf2352229dbb19a015dd5aca1b436c2aac1c0d5b8bde4dda1 SHA512: 0207cca93ab737c77d0436d5d5919f63b32fc77f55fd1056f622560c215166a1e73963eecbfbd111e6ba66ab8f52575d380ec19f1e557bfa6a79743974c3650a Homepage: https://cran.r-project.org/package=BTSR Description: CRAN Package 'BTSR' (Bounded Time Series Regression) Simulate, estimate and forecast a wide range of regression based dynamic models for bounded time series, covering the most commonly applied models in the literature. The main calculations are done in FORTRAN, which translates into very fast algorithms. Package: r-cran-bttest Architecture: amd64 Version: 0.10.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bttest_0.10.3-1.ca2404.1_amd64.deb Size: 88974 MD5sum: adaf3fec264c28b3e23df0d33b261edd SHA1: 33647be423d1d20e88dc44a043adf8e9fcc36651 SHA256: 565a1e3e9fbb0531066e061d1c027b493acb49ed728c3e7b4812fd276c6cc4ce SHA512: d3b4f24ee40b7ef582527a6771dad1aba1161ba108a3eb7c3146db75b0cdec237e14b71848f8f43f650e96a591a6f746a471821c91a67d0f84cb6baf890a4ce2 Homepage: https://cran.r-project.org/package=BTtest Description: CRAN Package 'BTtest' (Estimate the Number of Factors in Large Nonstationary Datasets) Large panel data sets are often subject to common trends. However, it can be difficult to determine the exact number of these common factors and analyse their properties. The package implements the Barigozzi and Trapani (2022) test, which not only provides an efficient way of estimating the number of common factors in large nonstationary panel data sets, but also gives further insights on factor classes. The routine identifies the existence of (i) a factor subject to a linear trend, (ii) the number of zero-mean I(1) and (iii) zero-mean I(0) factors. Furthermore, the package includes the Integrated Panel Criteria by Bai (2004) that provide a complementary measure for the number of factors. 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It uses historic transaction records to fit a probabilistic model, which then allows to compute quantities of managerial interest on a cohort- as well as on a customer level (Customer Lifetime Value, Customer Equity, P(alive), etc.). This package complements the BTYD package by providing several additional buy-till-you-die models, that have been published in the marketing literature, but whose implementation are complex and non-trivial. These models are: NBD [Ehrenberg (1959) ], MBG/NBD [Batislam et al (2007) ], (M)BG/CNBD-k [Reutterer et al (2020) ], Pareto/NBD (HB) [Abe (2009) ] and Pareto/GGG [Platzer and Reutterer (2016) ]. Package: r-cran-buddle Architecture: amd64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-plyr, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-buddle_2.0.2-1.ca2404.1_amd64.deb Size: 209484 MD5sum: cac09721cd78a30b1a69129c91ad69a6 SHA1: 2a859e4e4e1b5a363ee739dedd07bb0abd7637b5 SHA256: 1883a704c8197370304aec1ba40953390d69a7d983f6009323bc77e3da3aa4df SHA512: 0c6e06fdfb7d9cb0a224f63b5670d358137b948a55aa3c3c2b3f465f74ccad61dac9d37372bbabcafcd9d0087a82642ec2afa43e21b40f1d7113548b943e50bc Homepage: https://cran.r-project.org/package=Buddle Description: CRAN Package 'Buddle' (A Deep Learning for Statistical Classification and RegressionAnalysis with Random Effects) Statistical classification and regression have been popular among various fields and stayed in the limelight of scientists of those fields. Examples of the fields include clinical trials where the statistical classification of patients is indispensable to predict the clinical courses of diseases. Considering the negative impact of diseases on performing daily tasks, correctly classifying patients based on the clinical information is vital in that we need to identify patients of the high-risk group to develop a severe state and arrange medical treatment for them at an opportune moment. Deep learning - a part of artificial intelligence - has gained much attention, and research on it burgeons during past decades: see, e.g, Kazemi and Mirroshandel (2018) . It is a veritable technique which was originally designed for the classification, and hence, the Buddle package can provide sublime solutions to various challenging classification and regression problems encountered in the clinical trials. The Buddle package is based on the back-propagation algorithm - together with various powerful techniques such as batch normalization and dropout - which performs a multi-layer feed-forward neural network: see Krizhevsky et. al (2017) , Schmidhuber (2015) and LeCun et al. (1998) for more details. This package contains two main functions: TrainBuddle() and FetchBuddle(). TrainBuddle() builds a feed-forward neural network model and trains the model. FetchBuddle() recalls the trained model which is the output of TrainBuddle(), classifies or regresses given data, and make a final prediction for the data. Package: r-cran-bunsen Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-boot, r-cran-clustermq, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bunsen_0.1.0-1.ca2404.1_amd64.deb Size: 206574 MD5sum: e857c8bf9cdc9ad5551899f68d48bc15 SHA1: c4cbebb1471e36a737d198200e1896cc63778e1b SHA256: 332bb41aaf9bc7b44239f537f461985db741efebd0d08ed4ceeefe4ee923a219 SHA512: a469436030c9ab36229e6599413396450ea8c56187d298338e5a0a30a40386f47c8b181522dcbaba4770425e99f263a21fb9ccadba47a35813228bf9e609c8a6 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: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-bvarsv_1.1-1.ca2404.1_amd64.deb Size: 175380 MD5sum: 15a0f5abc1791b59f5be0dd22a12a26a SHA1: 040daf69d5b3ba11998c14e0f24c53d643625dfd SHA256: ae5ded90c26421f49c3a94ea35f73ca14155f727ef3acc1ca7772ae0cdbd7d9e SHA512: 16c92671f7160c29ab1bfb5694b4ddcbe428521344086f476db0733ad8bce2b1dade07ded0d2abf34c7f113476164c7ae4072b7de173b091f967ed7d85c5fb4c Homepage: https://cran.r-project.org/package=bvarsv Description: CRAN Package 'bvarsv' (Bayesian Analysis of a Vector Autoregressive Model withStochastic Volatility and Time-Varying Parameters) R/C++ implementation of the model proposed by Primiceri ("Time Varying Structural Vector Autoregressions and Monetary Policy", Review of Economic Studies, 2005), with functionality for computing posterior predictive distributions and impulse responses. 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Functions for posterior simulation, forecasting, impulse response analysis and forecast error variance decomposition are largely based on the introductory texts of Chan, Koop, Poirier and Tobias (2019, ISBN: 9781108437493), Koop and Korobilis (2010) and Luetkepohl (2006, ISBN: 9783540262398). 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It estimates the summed effect of multiple, often moderately to highly correlated, continuous predictors. Its applications can be found in analysis of exposure mixtures. The model was proposed by Hamra, Maclehose, Croen, Kauffman, and Newschaffer (2021) . This implementation includes an extension to model binary outcome. 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The function can be useful for statistically analyze the content of files in a glimpse: text files are shown as a green centered crown, compressed and encrypted files should be shown as equally distributed variations with a very low CV (sigma/mean), and other types of files can be classified between these two categories depending on their text vs binary content, which can be useful to quickly determine how information is stored inside them (databases, multimedia files, etc). Package: r-cran-bzinb Architecture: amd64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/noble/main/r-cran-bzinb_1.0.8-1.ca2404.1_amd64.deb Size: 193488 MD5sum: 509ef39307ed8923d9a1e9b0ddfeb9b9 SHA1: 5e3b2b067a554c604c1af10777acd3febddad509 SHA256: 88a82ec17ac32ff3cc76a5c8b3da73375f90c6d74ae35a300a4cc6c712b1459e SHA512: 60c1ba8d348b3f2c6b1fd59823cbd902974f54436dde91ff92bc31d463e41b07750c8d551c62d16c80e8557fc74251bb1112446b6b33d19469a30cb156d38c50 Homepage: https://cran.r-project.org/package=bzinb Description: CRAN Package 'bzinb' (Bivariate Zero-Inflated Negative Binomial Model Estimator) Provides a maximum likelihood estimation of Bivariate Zero-Inflated Negative Binomial (BZINB) model or the nested model parameters. Also estimates the underlying correlation of the a pair of count data. See Cho, H., Liu, C., Preisser, J., and Wu, D. (In preparation) for details. Package: r-cran-c212 Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1238 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda Filename: pool/dists/noble/main/r-cran-c212_1.0.1-1.ca2404.1_amd64.deb Size: 917726 MD5sum: 4be7c0721367720a1039825025c9397f SHA1: d91f41d44ef734de0696b1812550a13cd6d6429c SHA256: 835bce72063e6dff18f73f867f36289b8e257b0652bf1d4b82ee652180283892 SHA512: 859e7e321ed3582cd55f3470523deb4772f18ea917f4fce198c069968cda6cdc793521c7a72387d1b25b8421535e74fb09e59c801fdd2635e3428ed2db227e84 Homepage: https://cran.r-project.org/package=c212 Description: CRAN Package 'c212' (Methods for Detecting Safety Signals in Clinical Trials UsingBody-Systems (System Organ Classes)) Provides a self-contained set of methods to aid clinical trial safety investigators, statisticians and researchers, in the early detection of adverse events using groupings by body-system or system organ class. This work was supported by the Engineering and Physical Sciences Research Council (UK) (EPSRC) [award reference 1521741] and Frontier Science (Scotland) Ltd. The package title c212 is in reference to the original Engineering and Physical Sciences Research Council (UK) funded project which was named CASE 2/12. Package: r-cran-c3dr Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2026 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-quarto Filename: pool/dists/noble/main/r-cran-c3dr_0.2.0-1.ca2404.1_amd64.deb Size: 1019662 MD5sum: 3798413a23e6b7edab161c3dd239bd8b SHA1: 30370bf4677756b3daca295539459dc9e9ee342b SHA256: 464c95968aac6a5c99679d9a26ebe1dd95a9c58248a2e2e3f2f0bcea463a5d55 SHA512: add528ce2524a252ea21a4691cfd3e46b25d344de68a8a63c31f62aaac103e9671ef6544abc983737aeab37e4eda66f65db241d691cfa691383c7b5fbef09e4f 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: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 562 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), 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/noble/main/r-cran-c50_0.2.0-1.ca2404.1_amd64.deb Size: 316418 MD5sum: 55b7678f2ef6375af792c63fe8d664a4 SHA1: 0d03bf9844573e88b633e5216c29f6c370848d9b SHA256: 99a8c00d35e538ab11eb3a7174b7e422d4ca400ae4abf0692f8a7189c6ce456f SHA512: 71095dfeb9087d815a931cff9817c5d21589c8af2b6c4fefe426e125f4251c7016c3069f6f288f6365b4493baa9b1b32ca598d8cd60f36c5c891e56a2a330164 Homepage: https://cran.r-project.org/package=C50 Description: CRAN Package 'C50' (C5.0 Decision Trees and Rule-Based Models) C5.0 decision trees and rule-based models for pattern recognition that extend the work of Quinlan (1993, ISBN:1-55860-238-0). Package: r-cran-cachem Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-fastmap Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cachem_1.1.0-1.ca2404.1_amd64.deb Size: 67958 MD5sum: b10e14998c15cfdb9e2c386b403940b8 SHA1: 081ac725db10869bcd26280f74da4e64656c5695 SHA256: d6bd9f53752576ba4926a7a4d83b5afc51a88e920cf6e0b16ffc131de79a3eba SHA512: 7b80de43e845d93253b88f91d21e2a78c0dfdddec42cbbf086d5a46fe0748871786161bc1a428aa60927ca7fefa48a0b61069f4dde0b46f9622ad214c9ba4afc Homepage: https://cran.r-project.org/package=cachem Description: CRAN Package 'cachem' (Cache R Objects with Automatic Pruning) Key-value stores with automatic pruning. Caches can limit either their total size or the age of the oldest object (or both), automatically pruning objects to maintain the constraints. Package: r-cran-caesar.suite Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2245 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-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/noble/main/r-cran-caesar.suite_0.3.0-1.ca2404.1_amd64.deb Size: 1872546 MD5sum: 9a1252d41de4b3b05ebb20f62af9ca2f SHA1: c54d104b0830c1cb2ee856db8503183de8a0da89 SHA256: 8cfb0855a49d5d897e946f66378ce28e44407e7df30de8ba99545a888f0510f9 SHA512: 3762389bd63b9b37a9b20537579a5ffab555867dfd6fc74be404428def9c0c0cbfd97addcaeae0e4471ef91a1cc6e1d495e4e95fc6d8abbf12eadf3cba81f1f8 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. 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Besides testing the classical goodness-of-fit null hypothesis of perfect calibration, the confidence bands calculated within that package facilitate inverted goodness-of-fit tests whose rejection allows for a sought-after conclusion of a sufficiently well-calibrated model. The package creates flexible graphical tools to perform these tests. For construction details see also Dimitriadis, Dümbgen, Henzi, Puke, Ziegel (2022) . 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Package: r-cran-caman Architecture: amd64 Version: 0.78-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1498 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-caman_0.78-1.ca2404.1_amd64.deb Size: 1312416 MD5sum: 2cbdb62cc17f116ed79947e6887d211f SHA1: f239da1c3681a8a4c5402dcddef74bda8fcd4f90 SHA256: 89df0b8f8740e3d44a1aa5e1ca000bf961cd26f38e2b2dcda91d3c4480fc42f7 SHA512: 0d0650a7c399be1df8ae1017be4a53bfbbeb45f9889e6ec4a5e89a969e8b28c529d8585fb8cea3e44583f27a27c6a8487b7c355a30a549ae7ba619c51004f748 Homepage: https://cran.r-project.org/package=CAMAN Description: CRAN Package 'CAMAN' (Finite Mixture Models and Meta-Analysis Tools - Based on C.A.MAN) Tools for the analysis of finite semiparametric mixtures. These are useful when data is heterogeneous, e.g. in pharmacokinetics or meta-analysis. 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The method to obtain the estimated fixed-effects coefficients is based on Stammann (2018) , Gaure (2013) , Berge (2018) , and Correia et al. (2020) . Package: r-cran-caramel Architecture: amd64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5101 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), r-base-core (>= 4.6.0), r-api-4.0, r-cran-geometry Suggests: r-cran-markdown, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-caramel_1.5-1.ca2404.1_amd64.deb Size: 739018 MD5sum: 295faba569b638b51ea4144b14800b54 SHA1: 24e2aae9dcd7b394981ce2cfe0681ef89a63510c SHA256: 74c176820273346da9c82072b0aaead20fa64209eca2e647875325a242a77b06 SHA512: 821c014d96bedc885a303e0bd2b224a99c27ac003f9cfd8849318c10dd078c79c8401c5ec3a6df4f0a2ec86a7e00409cac3ff7f7e3f9e3a97b889abbbd39c462 Homepage: https://cran.r-project.org/package=caRamel Description: CRAN Package 'caRamel' (Automatic Calibration by Evolutionary Multi Objective Algorithm) The caRamel optimizer has been developed to meet the requirement for an automatic calibration procedure that delivers a family of parameter sets that are optimal with regard to a multi-objective target (Monteil et al. ). Package: r-cran-carat Architecture: amd64 Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1276 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-gridextra, r-cran-stringr, r-cran-rcpparmadillo Suggests: r-cran-dplyr Filename: pool/dists/noble/main/r-cran-carat_2.2.1-1.ca2404.1_amd64.deb Size: 654414 MD5sum: a6a183849234391c815f13baaa2a0469 SHA1: a5537b3941e08658aaa8b18dbdf97cea471ac940 SHA256: 26beb1752f9936cfc490ede13b78d88b9e621706483564ca7fd7bfa62e20e453 SHA512: 32dd6e9c23ae060cc5f0da4d4a686988a3fa7657957b001da3939617790034c23ea0e0e8fc312b4a6bf7c6995b48e78f8a57aa7483cf8b861c463dd415bdef18 Homepage: https://cran.r-project.org/package=carat Description: CRAN Package 'carat' (Covariate-Adaptive Randomization for Clinical Trials) Provides functions and command-line user interface to generate allocation sequence by covariate-adaptive randomization for clinical trials. 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Package: r-cran-carbayes Architecture: amd64 Version: 6.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1594 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-carbayesdata, r-cran-coda, r-cran-dplyr, r-cran-ggally, r-cran-glmnet, r-cran-igraph, r-cran-mapview, r-cran-mcmcpack, r-cran-rcolorbrewer, r-cran-sf, r-cran-spam, r-cran-spdep, r-cran-truncnorm Filename: pool/dists/noble/main/r-cran-carbayes_6.1.1-1.ca2404.1_amd64.deb Size: 1339008 MD5sum: 00e730d5899e7637d2e2c65e2ae17666 SHA1: c1f90e16e003934164231dc3bd740476e20bbda3 SHA256: ce36e355f04d23c1b9f7b00321c9987c0a79e2ee42e6f5806e78c45fb2d2094d SHA512: 9c46c77c93a0ee5ff5445b91eaa30946b1e4d8b136fb1f278bd680ec173f09dd943319fb518703d31e872998904808cccda747048be608e19b232bb051aaa597 Homepage: https://cran.r-project.org/package=CARBayes Description: CRAN Package 'CARBayes' (Spatial Generalised Linear Mixed Models for Areal Unit Data) Implements a class of univariate and multivariate spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation using a single or multiple Markov chains. The response variable can be binomial, Gaussian, multinomial, Poisson or zero-inflated Poisson (ZIP), and spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. A number of different models are available for univariate spatial data, including models with no random effects as well as random effects modelled by different types of CAR prior, including the BYM model (Besag et al., 1991, ) and Leroux model (Leroux et al., 2000, ). Additionally, a multivariate CAR (MCAR) model for multivariate spatial data is available, as is a two-level hierarchical model for modelling data relating to individuals within areas. Full details are given in the vignette accompanying this package. The initial creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256, and on-going development has been supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1, ESRC grant ES/K006460/1, Innovate UK / Natural Environment Research Council (NERC) grant NE/N007352/1 and the TB Alliance. Package: r-cran-carbayesst Architecture: amd64 Version: 4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2773 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-carbayesdata, r-cran-coda, r-cran-dplyr, r-cran-ggally, r-cran-ggplot2, r-cran-gridextra, r-cran-gtools, r-cran-leaflet, r-cran-matrixstats, r-cran-mcmcpack, r-cran-sf, r-cran-spam, r-cran-spdep, r-cran-truncdist, r-cran-truncnorm Filename: pool/dists/noble/main/r-cran-carbayesst_4.0-1.ca2404.1_amd64.deb Size: 2226398 MD5sum: 9023673a8f2106a0ff23f3b0331cd4fd SHA1: 0928ede0a36f95f280b99b0f5ec1e23bda09bcf2 SHA256: 27d70a0e8031cf795b5a208cadbe49db4d72a94353ac86620eb4ffe7760a5e05 SHA512: 38ce623befed22a18086e2b95f1d90acc28f6555da1d52b43e6c1d3de60266b1820b1c1b8eea79dd4882b9fe5b66b3f4735396686a90fa87f94e43fdb8b283c0 Homepage: https://cran.r-project.org/package=CARBayesST Description: CRAN Package 'CARBayesST' (Spatio-Temporal Generalised Linear Mixed Models for Areal UnitData) Implements a class of univariate and multivariate spatio-temporal generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian, or Poisson, but for some models only the binomial and Poisson data likelihoods are available. The spatio-temporal autocorrelation is modelled by random effects, which are assigned conditional autoregressive (CAR) style prior distributions. A number of different random effects structures are available, including models similar to Rushworth et al. (2014) . Full details are given in the vignette accompanying this package. The creation and development of this package was supported by the Engineering and Physical Sciences Research Council (EPSRC) grants EP/J017442/1 and EP/T004878/1 and the Medical Research Council (MRC) grant MR/L022184/1. Package: r-cran-carbondate Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2114 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-carbondate_1.1.0-1.ca2404.1_amd64.deb Size: 1580496 MD5sum: ccd5ffec5eec40319194e10fb40903d7 SHA1: b426e1b6f68a95e70fbb61ae54cd3604827ad977 SHA256: 1ec369e96d394cade07fac8a4deaf5b19a2714f018932405a72a69d8a08d4d4a SHA512: 233c00e0de903367d6b9d41c4cb9303196aca80ca8381c63fef546f65a0c9911450bd202ff9dfa0a1b04e8fc5cdf70777b35dd1ddcefe7e22aab6ca88ccdf5c4 Homepage: https://cran.r-project.org/package=carbondate Description: CRAN Package 'carbondate' (Calibration and Summarisation of Radiocarbon Dates) Performs Bayesian non-parametric calibration of multiple related radiocarbon determinations, and summarises the calendar age information to plot their joint calendar age density (see Heaton (2022) ). 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Applications of elliptic integrals include probability distributions, geometry, physics, mechanics, electrodynamics, statistical mechanics, astronomy, geodesy, geodesics on conics, and magnetic field calculations. 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These models allow to estimate Generalised Linear Models with CAR (conditional autoregressive) spatial random effects for spatially and temporally misaligned data, provided a suitable Multiple Membership matrix. The main references are Gramatica, Liverani and Congdon (2023) , Petrof, Neyens, Nuyts, Nackaerts, Nemery and Faes (2020) and Gramatica, Congdon and Liverani . Package: r-cran-carms Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-diagram, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-carms_1.0.1-1.ca2404.1_amd64.deb Size: 116846 MD5sum: c3427848ba780f21b45081f39f81de2f SHA1: 9ac1f685e919dd2192969a433482d644b6abdc1b SHA256: 73964fb04a08e4d05c69d5e503954f35c109678b587fb7c0d9a4bad1f8881048 SHA512: 0bd4801fd8394b5bbe60642a8b5265a720ec0931a3e68ef1b798e7f028f48c6eda256132ec28a31999810ac401e7cc56d196e8b232f69adec5e70392b36cb163 Homepage: https://cran.r-project.org/package=CARMS Description: CRAN Package 'CARMS' (Continuous Time Markov Rate Modeling for Reliability Analysis) Emulation of an application originally created by Paul Pukite. Computer Aided Rate Modeling and Simulation. Jan Pukite and Paul Pukite, (1998, ISBN 978-0-7803-3482), William J. Stewart, (1994, ISBN: 0-691-03699-3). Package: r-cran-carrot Architecture: amd64 Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnet, r-cran-doparallel, r-cran-rdpack, r-cran-foreach Filename: pool/dists/noble/main/r-cran-carrot_3.0.2-1.ca2404.1_amd64.deb Size: 106492 MD5sum: 6920c5ac2f050c13bcd78f1655caf143 SHA1: d6ed58d4bae2ecfb4898d1121357f4bca53960db SHA256: a0c553e5ee04d6eeedffc64c83f441321e14b77395f9b63ab2fa8976f4295013 SHA512: 74180b24cf05f8e0d4e7f1c44627ced6087f77b1738fc6079091cab69db6c4117773cabe7d9b540a623ddb2b343b8843ae1242a98b0951783dd4cb0cf94df419 Homepage: https://cran.r-project.org/package=CARRoT Description: CRAN Package 'CARRoT' (Predicting Categorical and Continuous Outcomes Using One in TenRule) Predicts categorical or continuous outcomes while concentrating on a number of key points. These are Cross-validation, Accuracy, Regression and Rule of Ten or "one in ten rule" (CARRoT), and, in addition to it R-squared statistics, prior knowledge on the dataset etc. It performs the cross-validation specified number of times by partitioning the input into training and test set and fitting linear/multinomial/binary regression models to the training set. All regression models satisfying chosen constraints are fitted and the ones with the best predictive power are given as an output. Best predictive power is understood as highest accuracy in case of binary/multinomial outcomes, smallest absolute and relative errors in case of continuous outcomes. For binary case there is also an option of finding a regression model which gives the highest AUROC (Area Under Receiver Operating Curve) value. The option of parallel toolbox is also available. Methods are described in Peduzzi et al. (1996) , Rhemtulla et al. (2012) , Riley et al. (2018) , Riley et al. (2019) . Package: r-cran-carsurv Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-corpcor, r-cran-mboost, r-cran-fdrtool Suggests: r-cran-microbenchmark, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-carsurv_1.0.0-1.ca2404.1_amd64.deb Size: 54698 MD5sum: 9d0c7e05c04b85157118d0c0c2e0e6de SHA1: 576dd08dd8e5b9460e0e6e70bf12b1ede996a5d1 SHA256: bffd6a5f4dd17ab74df54be603a211e93a968a97bf0672e36e28076ef6a16f72 SHA512: bdcebd522f13f3d60c89322911fc23466e2512c2d7b7d1082be8e654f6d07eff3054d2b4011502dfa961c2b0489109149bcffe75961f9ac49044e378ec871f2a Homepage: https://cran.r-project.org/package=carSurv Description: CRAN Package 'carSurv' (Correlation-Adjusted Regression Survival (CARS) Scores) Contains functions to estimate the Correlation-Adjusted Regression Survival (CARS) Scores. The method is described in Welchowski, T. and Zuber, V. and Schmid, M., (2018), Correlation-Adjusted Regression Survival Scores for High-Dimensional Variable Selection, . Package: r-cran-cartogramr Architecture: amd64 Version: 1.5-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1955 Depends: libc6 (>= 2.14), libfftw3-double3 (>= 3.3.10), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-data.table, r-cran-cleancall Suggests: r-cran-lwgeom Filename: pool/dists/noble/main/r-cran-cartogramr_1.5-1-1.ca2404.1_amd64.deb Size: 1898494 MD5sum: c8c99aa73e4529cabf8591a20168b32d SHA1: 1357fa95d4236512d891f4b4b827a86f4b71a465 SHA256: 2c88d07edb61c64dd035cb2ae2b7233a49985e1f803a1df5bbade2425c922c53 SHA512: 91ece601187090d04c480162a83a6402e7d8d3cf86002286965565fbb6f82397fe5ef1f1fbef71b754047f586e59f25bcbe6abcab1606cdd2ac9f76f1687b047 Homepage: https://cran.r-project.org/package=cartogramR Description: CRAN Package 'cartogramR' (Continuous Cartogram) Procedures for making continuous cartogram. Procedures available are: flow based cartogram (Gastner & Newman (2004) ), fast flow based cartogram (Gastner, Seguy & More (2018) ), rubber band based cartogram (Dougenik et al. (1985) ). Package: r-cran-cartographer Architecture: amd64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-rlang, r-cran-sf Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-maps, r-cran-rnaturalearth, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cartographer_0.2.1-1.ca2404.1_amd64.deb Size: 164096 MD5sum: 0e8b34708fd3929d81a363b94606b912 SHA1: 542eba92061e203338df0c29c7038ca19ef33219 SHA256: 4d4d5f3e1eee23791d65b9710e1c1d8deb21bfd5a241e5903e2e8ce61396f361 SHA512: 6b175f3870dde5253adf1daa9501d183513d7672dee583158e81cd43596e5e8fb400899db62f3f0903b309428396bdbff5c3fe9aa335e38b93bb1a2f29b6c34a Homepage: https://cran.r-project.org/package=cartographer Description: CRAN Package 'cartographer' (Turn Place Names into Map Data) A tool for easily matching spatial data when you have a list of place/region names. 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This package helps to design cartographic representations such as proportional symbols, choropleth, typology, flows or discontinuities maps. It also offers several features that improve the graphic presentation of maps, for instance, map palettes, layout elements (scale, north arrow, title...), labels or legends. See Giraud and Lambert (2017) . 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The package implements regression-based covariate adjustment (Rosenblum & van der Laan (2010) ) and a one-step estimator (Van Lancker et al (2024) ) for trials with continuous, binary and count outcomes. The estimation of the minimum sample-size required to reach a specified statistical power for a given estimator uses bisection to find an initial rough estimate, followed by stochastic approximation (Robbins-Monro (1951) ) to improve the estimate, and finally, a grid search to refine the estimate in the neighborhood of the current best solution. Package: r-cran-casebasedreasoning Architecture: amd64 Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 863 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rms, r-cran-r6, r-cran-rcpp, r-cran-rcppparallel, r-cran-ranger, r-cran-survival, r-cran-ggplot2, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-casebasedreasoning_0.4.1-1.ca2404.1_amd64.deb Size: 597472 MD5sum: 9800dc1601346390213ad6a506e98d92 SHA1: bd2bd40f3e4597d380d906af12cb8d62a103054c SHA256: 3e2ad47ef06bc621bb341ac6197f873d913ca235ff57cb294d1ee0ba425e49f8 SHA512: 5868e6e15c6777ef9ab0ad5774a2234fab7e8ddf6225b7d588017f5d54b955f8a17d938d7bde2e037c160ca35cd103568d8f9c597bc349355f1db90d4a603aad Homepage: https://cran.r-project.org/package=CaseBasedReasoning Description: CRAN Package 'CaseBasedReasoning' (Case Based Reasoning) Case-based reasoning is a problem-solving methodology that involves solving a new problem by referring to the solution of a similar problem in a large set of previously solved problems. The key aspect of Case Based Reasoning is to determine the problem that "most closely" matches the new problem at hand. This is achieved by defining a family of distance functions and using these distance functions as parameters for local averaging regression estimates of the final result. The optimal distance function is chosen based on a specific error measure used in regression estimation. This approach allows for efficient problem-solving by leveraging past experiences and adapting solutions from similar cases. The underlying concept is inspired by the work of Dippon J. et al. (2002) . Package: r-cran-casecohortcoxsurvival Architecture: amd64 Version: 0.0.36-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4801 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-nnet Filename: pool/dists/noble/main/r-cran-casecohortcoxsurvival_0.0.36-1.ca2404.1_amd64.deb Size: 4842706 MD5sum: 7e4838cc585d8c378b5ef1606f5c3a9e SHA1: fc061218fcd22d47a8e7658b2ed3f4517f5454cf SHA256: fc6b0d3bc7586cf30e9fc21b1b2b8b832de9a89297afbf22daa4d4870d273dc4 SHA512: ef0f441782deefd84a1561b118ea67c9e3fa3913a1daaf1cde63e5f053acf6305e21b7e9ec8b471f98f32edb850fcce0c24b2720cbb18b2bc781eee90418daf2 Homepage: https://cran.r-project.org/package=CaseCohortCoxSurvival Description: CRAN Package 'CaseCohortCoxSurvival' (Case-Cohort Cox Survival Inference) Cox model inference for relative hazard and covariate-specific pure risk estimated from stratified and unstratified case-cohort data as described in Etievant, L., Gail, M.H. (Lifetime Data Analysis, 2024) . Package: r-cran-casmap Architecture: amd64 Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1263 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-casmap_0.6.1-1.ca2404.1_amd64.deb Size: 489080 MD5sum: 39c3e4489a6bac8651d7ca3af3be08c2 SHA1: 58c25917bc1ec5d35d93ba6fd3ef419975e6d060 SHA256: 1d6e4fd629f0ccd43e8bb57947e683d7a018fd2506f8a1ba7f3e34b11ca83c21 SHA512: 3b1d7473361813943055080563eebfdaaf5c37b1a8afb48109e0123f8d5f6a6c5231548466ff4378c7ecfed9bdf5cd47a74942d0691844f83d08967da6a85482 Homepage: https://cran.r-project.org/package=CASMAP Description: CRAN Package 'CASMAP' (Detection of Statistically Significant Combinations of SNPs inAssociation Mapping) A significant pattern mining-based toolbox for region-based genome-wide association studies and higher-order epistasis analyses, implementing the methods described in Llinares-López et al. (2017) . Package: r-cran-castor Architecture: amd64 Version: 1.8.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3761 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-naturalsort, r-cran-matrix, r-cran-rspectra, r-cran-jsonlite Suggests: r-cran-nloptr, r-cran-ape Filename: pool/dists/noble/main/r-cran-castor_1.8.5-1.ca2404.1_amd64.deb Size: 2667654 MD5sum: 12016833322a45cda663965bc411ad62 SHA1: 459e84804ebf7165a004160dde07718c95501efd SHA256: 1518a744588657ce64395c60493be19bb4975bec90f88c2daac2c76c22430575 SHA512: bf7835924e338f50df7586ed047130608a6dc84c095df98f37d779ef8d84be4ca3b154c40f2be35017b389da41bebefebe6337a96019bfe4188f1618a51696fd Homepage: https://cran.r-project.org/package=castor Description: CRAN Package 'castor' (Efficient Phylogenetics on Large Trees) Efficient phylogenetic analyses on massive phylogenies comprising up to millions of tips. Functions include pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distances. Calculation of phylogenetic signal and mean trait depth (trait conservatism), ancestral state reconstruction and hidden character prediction of discrete characters, simulating and fitting models of trait evolution, fitting and simulating diversification models, dating trees, comparing trees, and reading/writing trees in Newick format. Citation: Louca, Stilianos and Doebeli, Michael (2017) . 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The low-dimensional covariates and the high-dimensional tensors are jointly modeled to predict a categorical outcome in a multi-class discriminant analysis setting. The Covariate-Adjusted Tensor Classification in High-dimensions (CATCH) model is fitted in two steps: (1) adjust for the covariates within each class; and (2) penalized estimation with the adjusted tensor using a cyclic block coordinate descent algorithm. The package can provide a solution path for tuning parameter in the penalized estimation step. Special case of the CATCH model includes linear discriminant analysis model and matrix (or tensor) discriminant analysis without covariates. 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See Wainer (2000) , van der Linden & Pashley (2010) , and Eggen (1999) for more details. 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For mathematical details and software tutorial, see Mahani and Sharabiani (2019) . 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Package: r-cran-cglm Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-nleqslv, r-cran-data.table, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-cglm_1.1-1.ca2404.1_amd64.deb Size: 68004 MD5sum: 6d8a6462a6ec4fb27b3bad8703a43d33 SHA1: 2d52f07afb64fb4b3651d3bee02b1eecef068655 SHA256: 30cbed93350dcbe36d919b35ab113473f471c585ef96a5a47e5da4ec1ef5bd1c SHA512: 92b8f4722af6325f4a2c449b655bd7898c50c70b77a844b6ae03ca5adb762afa6f1c3ddddf470fcbfe9ab082e87f1913f99ccf911b0676e3ceca9a8f0b7d6a74 Homepage: https://cran.r-project.org/package=cglm Description: CRAN Package 'cglm' (Fits Conditional Generalized Linear Models) Estimates the ratio of the regression coefficients and the dispersion parameter in conditional generalized linear models for clustered data. Package: r-cran-cgmanalyzer Architecture: amd64 Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1023 Depends: libc6 (>= 2.7), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cgmanalyzer_1.3.1-1.ca2404.1_amd64.deb Size: 211766 MD5sum: e4f6933bfedffe96c8ddea19ba2986b7 SHA1: 09be17bab3cf457f18815b0354480bd079f13efb SHA256: 8b75dd65ac168167445cf44c55c88401f6cb55e104a67dae2d49560cbf6fc549 SHA512: ba581d4c80a2830fc0327e36fd5fa43424f53782cebf55071929aa5949eb82c099c3e4f50d5d9b62500e06ed4ffd68b1b2b6648105e6ca8d0cccddc8325c55f6 Homepage: https://cran.r-project.org/package=CGManalyzer Description: CRAN Package 'CGManalyzer' (Continuous Glucose Monitoring Data Analyzer) Contains all of the functions necessary for the complete analysis of a continuous glucose monitoring study and can be applied to data measured by various existing 'CGM' devices such as 'FreeStyle Libre', 'Glutalor', 'Dexcom' and 'Medtronic CGM'. 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Package: r-cran-cgmguru Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2081 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-iglu, r-cran-dplyr, r-cran-covr, r-cran-ggplot2, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-cgmguru_1.0.1-1.ca2404.1_amd64.deb Size: 722102 MD5sum: 91763f565690385f2ca3c7b4861c9c81 SHA1: 476328679b2840c4893ea9485ff1215faa569341 SHA256: b4b89ef86d4e533ec837e1c8d16e873bbe9d81a9e5be28b4215e36e8b41d7514 SHA512: a8ca6fa415accd6c4354feaffaa53aac26f519e725318b54bf494c037ae7042e251b35868e59515334408750944fd4517367af8b9a6225ec1b7a91a61a4c86fb Homepage: https://cran.r-project.org/package=cgmguru Description: CRAN Package 'cgmguru' (Advanced Continuous Glucose Monitoring Analysis withHigh-Performance C++ Backend) Tools for advanced analysis of continuous glucose monitoring (CGM) time-series, implementing GRID (Glucose Rate Increase Detector) and GRID-based algorithms for postprandial peak detection, and detection of hypoglycemic and hyperglycemic episodes (Levels 1/2/Extended) aligned with international consensus CGM metrics. Core algorithms are implemented in optimized C++ using 'Rcpp' to provide accurate and fast analysis on large datasets. Package: r-cran-cgvr Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2045 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-cayleyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cgvr_0.1.2-1.ca2404.1_amd64.deb Size: 267096 MD5sum: def1ab7411f87e4e4c82bf4dcf43c935 SHA1: 63c38d8d34dbdf6a13ca51b8ec7b9f29e809069e SHA256: 3188ccec673e087aebc2ae12b5d811c0cf124fec25c5ca812110b51e18724a51 SHA512: d9fe6f665f7fc63441f0736bded091ef35447648b8af5665077fd857e20624ecd1cbf3b44a92a0781bba6b95fbffcff42694087fdf70e5a50170757ed3d74898 Homepage: https://cran.r-project.org/package=cgvR Description: CRAN Package 'cgvR' (Interactive 3D Visualization of Large Cayley Graphs via Vulkan) Provides interactive 3D visualization for large-scale Cayley graphs. Specifically designed for analyzing state spaces of the 'TopSpin' puzzle. Leverages the 'Datoviz' library and Vulkan-based GPU rendering for smooth real-time exploration of large graphs and complex state transitions. Implements efficient coordinate mapping for high-dimensional permutation groups, allowing users to visualize the connectivity and structural properties of the puzzle's state space. The rendering engine provides high-performance visuals and interactive camera controls, making it suitable for mathematical analysis of group-theoretic puzzles within the R environment. Package: r-cran-changepoint.np Architecture: amd64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-changepoint, r-cran-zoo, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-changepoint.np_1.0.5-1.ca2404.1_amd64.deb Size: 217284 MD5sum: 03586a845e9a68e9bde40440e2993fa4 SHA1: bffea58212ccac3fac5dd7d1cfa9183385bf83d5 SHA256: 7b330f0b157833e57a9ca2a0bcbdf1f2848cb55f56c7d63de4a07a6d8ea9526e SHA512: 6872ad98dbf807ce0460277395ef7d1be368ea0a4bd1a61540523d65d7fa9a73b66481e52ea74853d26ce92f030aa50359de5005ece74966b7ea8705ec785399 Homepage: https://cran.r-project.org/package=changepoint.np Description: CRAN Package 'changepoint.np' (Methods for Nonparametric Changepoint Detection) Implements the multiple changepoint algorithm PELT with a nonparametric cost function based on the empirical distribution of the data. This package extends the changepoint package (see Killick, R and Eckley, I (2014) ). Package: r-cran-changepoint Architecture: amd64 Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 905 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-changepoint_2.3-1.ca2404.1_amd64.deb Size: 755946 MD5sum: fc18445672480a68beba826e5cb998bc SHA1: 8498d1e4902c15df7dbb9040103f964538044c38 SHA256: 6a85ed3a8f65f53d93617522db7924b30b2748291b0347ffea7b12a9064989d5 SHA512: 05c41e3bfeb71fcd8cf0003a2dfac866c6313c28c451af2eef454a1aac429e767be78338f3056863cac81ea4c9d26e964098d4be77817ad943e8a80a609df7e6 Homepage: https://cran.r-project.org/package=changepoint Description: CRAN Package 'changepoint' (Methods for Changepoint Detection) Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data. Many popular non-parametric and frequentist methods are included. The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call. Package: r-cran-changepointga Architecture: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1572 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-rcpp, r-cran-clue, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-changepointga_0.1.5-1.ca2404.1_amd64.deb Size: 371678 MD5sum: 127381d25e93f044845a482b41538662 SHA1: b2ffd3a00348a653dc3b87d00923dedf98637791 SHA256: 9293df0096cb5a91b439909f3e31532a5ba3416f8a4c38f0ce19cda2b5f05994 SHA512: 6cbab001d85f5d98e6b2cf1d5b8648bd2a6388e0cdbda0e447cbdf98a3e85c0306b626543e8373a50ebe9fa076442a6d0abdda7428103c27bf7e5a46bde311c5 Homepage: https://cran.r-project.org/package=changepointGA Description: CRAN Package 'changepointGA' (Changepoint Detection via Modified Genetic Algorithms) The Genetic Algorithm (GA) is used to perform changepoint analysis in time series data. The package also includes an extended island version of GA, as described in Lu, Lund, and Lee (2010, ). By mimicking the principles of natural selection and evolution, GA provides a powerful stochastic search technique for solving combinatorial optimization problems. In 'changepointGA', each chromosome represents a changepoint configuration, including the number and locations of changepoints, hyperparameters, and model parameters. The package employs genetic operators—selection, crossover, and mutation—to iteratively improve solutions based on the given fitness (objective) function. Key features of 'changepointGA' include encoding changepoint configurations in an integer format, enabling dynamic and simultaneous estimation of model hyperparameters, changepoint configurations, and associated parameters. The detailed algorithmic implementation can be found in the package vignettes and in the paper of Li (2024, ). Package: r-cran-changepoints Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 868 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-gglasso, r-cran-glmnet, r-cran-ks, r-cran-mass, r-cran-data.tree, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-abind, r-cran-diagrammer, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-changepoints_1.1.0-1.ca2404.1_amd64.deb Size: 531108 MD5sum: 3ceae23dbf25db7cfca34b7b0afbea66 SHA1: 845f34e12087439fae651c8c7289417156c731ef SHA256: efd5386906bc10085064a21d22f2bd2c0c3c60106fecc35f2323a3a3094a90d1 SHA512: a502928f0866fa06ab014d7942e04b132fd4ccf63d945d95aa7ef361c1afc6e85944b235d4b3986eb29fe4817afa744a998fd604ae48beec16357295d6693086 Homepage: https://cran.r-project.org/package=changepoints Description: CRAN Package 'changepoints' (A Collection of Change-Point Detection Methods) Performs a series of offline and/or online change-point detection algorithms for 1) univariate mean: , ; 2) univariate polynomials: ; 3) univariate and multivariate nonparametric settings: , ; 4) high-dimensional covariances: ; 5) high-dimensional networks with and without missing values: , , ; 6) high-dimensional linear regression models: , ; 7) high-dimensional vector autoregressive models: ; 8) high-dimensional self exciting point processes: ; 9) dependent dynamic nonparametric random dot product graphs: ; 10) univariate mean against adversarial attacks: . Package: r-cran-changepointtaylor Architecture: amd64 Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-bench Filename: pool/dists/noble/main/r-cran-changepointtaylor_0.3-1.ca2404.1_amd64.deb Size: 112180 MD5sum: d635ab9c1a9a51f8adfe5b8368979b64 SHA1: 262f1d883d17a1929b27e817badd2594ac2129fc SHA256: 42b9bc857018abc5fd96c2dc0a603f4ba09e59762324c65c4146586d076ad2b2 SHA512: 175ba287e92e6c5cbe3b34289d443173828b8a25d0eb7cb23c02059a50f197da1058ce45c3fd16b54276655895d8abd87c7f092bfee1e48d1c041dfbb3c438c9 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. 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The processes used here have been defined in Bucher, Kojadinovic, Rohmer & Segers and Nasri & Remillard . Package: r-cran-channelattribution Architecture: amd64 Version: 2.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-curl, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-channelattribution_2.2.4-1.ca2404.1_amd64.deb Size: 253016 MD5sum: df09f764fd04042fdf30622060ef49ca SHA1: a8f44520f821db1928651c972ca54c84f2ca3670 SHA256: ed9bf7e07d7712ab7875ba9d886b9b22ad2fba4311e993284669b374f6aeb7a5 SHA512: 73166e9eb72bcfe09ee5943f26399d429bbea58b317e4232316bf59de9f7e7ebcee229a1f1c9e3715fade949f76ad87ca39e3c0c3b0354a7c41b3ad494f48097 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. 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Package: r-cran-chaos01 Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-pbdmpi, r-cran-tuner Filename: pool/dists/noble/main/r-cran-chaos01_1.2.1-1.ca2404.1_amd64.deb Size: 75784 MD5sum: 13486941fd082e3d20426545968c5e89 SHA1: e515c3390a76a7243ad34e19dfbd80e69ad552c9 SHA256: 8800d75d81a2fd8784a294cd4336ea10740ea3b4e049a0b0718f728e0891752b SHA512: 93a8a018558801250b9866919e15cc728054c7cc34270ae9b93d77de5a4b109571609faae3fafaf610b74f5fd4e2e2e0b40b726ee9aa85887089551ffa616ee5 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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Package: r-cran-cheddar Architecture: amd64 Version: 0.1-639-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2906 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cheddar_0.1-639-1.ca2404.1_amd64.deb Size: 1857872 MD5sum: 7e2c6223b004c3d518eb2741ff40476d SHA1: ea00ba9f5f5eb02784de9ec0ae6d5be33e2c03fb SHA256: b30219a7ca2ec9852e6ac204a30b1cad7ee53cf4a76b178d6028fa8c46c671eb SHA512: 4e386d46154424adb8b13310a2d9b5bd43db8b6c04a58bf492f216a8f6ff75cd94af68d082deac1cdab99352530c1163b58b2ab29aa18a54f2b435d74e4fb1e0 Homepage: https://cran.r-project.org/package=cheddar Description: CRAN Package 'cheddar' (Analysis and Visualisation of Ecological Communities) Provides a flexible, extendable representation of an ecological community and a range of functions for analysis and visualisation, focusing on food web, body mass and numerical abundance data. 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Package: r-cran-chillr Architecture: amd64 Version: 0.77-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2111 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-ecmwfr, r-cran-fields, r-cran-gensa, r-cran-ggplot2, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-metr, r-cran-patchwork, r-cran-pls, r-cran-plyr, r-cran-progress, r-cran-purrr, r-cran-r.utils, r-cran-raster, r-cran-rcpp, r-cran-rcurl, r-cran-readxl, r-cran-reshape2, r-cran-rlang, r-cran-rmawgen, r-cran-scales, r-cran-stringr, r-cran-tidyr, r-cran-xml Suggests: r-cran-knitr, r-cran-ncdf4, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-chillr_0.77-1.ca2404.1_amd64.deb Size: 1514352 MD5sum: 85079e16b79570806e44dd7b23c4f28f SHA1: ab1c68be346199eda152ca62c8b1e7e3f9c6591e SHA256: 7dc2159ba3fc39f3a8592e5db7593715df57efb02e65b6c487d7e956b5a4c033 SHA512: 572aa3e559726909f8704eb9a36d7e72c9c26966b27a2306f87de211361cbcb352e0369f018ad30eac19e3e7df5e26d62062c834f6fd81b98614fb85269ea5fc Homepage: https://cran.r-project.org/package=chillR Description: CRAN Package 'chillR' (Statistical Methods for Phenology Analysis in Temperate FruitTrees) The phenology of plants (i.e. the timing of their annual life phases) depends on climatic cues. For temperate trees and many other plants, spring phases, such as leaf emergence and flowering, have been found to result from the effects of both cool (chilling) conditions and heat. Fruit tree scientists (pomologists) have developed some metrics to quantify chilling and heat (e.g. see Luedeling (2012) ). 'chillR' contains functions for processing temperature records into chilling (Chilling Hours, Utah Chill Units and Chill Portions) and heat units (Growing Degree Hours). Regarding chilling metrics, Chill Portions are often considered the most promising, but they are difficult to calculate. This package makes it easy. 'chillR' also contains procedures for conducting a PLS analysis relating phenological dates (e.g. bloom dates) to either mean temperatures or mean chill and heat accumulation rates, based on long-term weather and phenology records (Luedeling and Gassner (2012) ). As of version 0.65, it also includes functions for generating weather scenarios with a weather generator, for conducting climate change analyses for temperature-based climatic metrics and for plotting results from such analyses. Since version 0.70, 'chillR' contains a function for interpolating hourly temperature records. 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Functions are provided for writing balanced reactions to form species from user-selected basis species and for calculating the standard molal properties of species and reactions, including the standard Gibbs energy and equilibrium constant. Calculations of the non-equilibrium chemical affinity and equilibrium chemical activity of species can be portrayed on diagrams as a function of temperature, pressure, or activity of basis species; in two dimensions, this gives a maximum affinity or predominance diagram. The diagrams have formatted chemical formulas and axis labels, and water stability limits can be added to Eh-pH, oxygen fugacity- temperature, and other diagrams with a redox variable. The package has been developed to handle common calculations in aqueous geochemistry, such as solubility due to complexation of metal ions, mineral buffers of redox or pH, and changing the basis species across a diagram ("mosaic diagrams"). CHNOSZ also implements a group additivity algorithm for the standard thermodynamic properties of proteins. Package: r-cran-choicer Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 806 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-nloptr, r-cran-randtoolbox, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-numderiv, r-cran-future.apply, r-cran-goftest Filename: pool/dists/noble/main/r-cran-choicer_0.1.0-1.ca2404.1_amd64.deb Size: 478270 MD5sum: 4186b3ffb8aa83e75dda4646ae32edd4 SHA1: 12bf34981e47af6c2077da2c10ba5e0398d294c0 SHA256: 45948013d4dbba255356961710019ef5a91a34680cc8409ef48993099b56b715 SHA512: 9edefbba3d3e202678ba064ada5131c024e08b16f829031b729be898bb834e092c5cb9ecead2691bff84bbd7e2a3a3f8cf572d6e50c2297287ec0bc5f205e991 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: amd64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libblas3 | libblas.so.3, libc6 (>= 2.23), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-cholwishart_1.1.4-1.ca2404.1_amd64.deb Size: 67544 MD5sum: bf63317890f14e571cf0f4496fc5967a SHA1: c58c92145b5f5c443320db9d429bbbc71d0fc2e4 SHA256: 278de704324108e533a7dc8ab628707d56180d4aedfb2b7833d7cf4612720fd0 SHA512: f38f9542221b970a0c3102e1b1f8cd4aa98b50454e0d206227b03724efbcadf8a32182a4add051e41ad2f1397f91d870ccd619ead044791703579ab3a4acf705 Homepage: https://cran.r-project.org/package=CholWishart Description: CRAN Package 'CholWishart' (Cholesky Decomposition of the Wishart Distribution) Sampling from the Cholesky factorization of a Wishart random variable, sampling from the inverse Wishart distribution, sampling from the Cholesky factorization of an inverse Wishart random variable, sampling from the pseudo Wishart distribution, sampling from the generalized inverse Wishart distribution, computing densities for the Wishart and inverse Wishart distributions, and computing the multivariate gamma and digamma functions. Provides a header file so the C functions can be called directly from other programs. 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The package implements three main inference approaches: (1) Evolutionary Variational Inference for record Linkage (EVIL), (2) Coordinate Ascent Variational Inference (CAVI), and (3) Markov Chain Monte Carlo (MCMC) with split and merge process. The model supports both discrete and continuous fields, and it performs locally-varying hit mechanism for the attributes with multiple truths. It also provides tools for performance evaluation based on either approximated variational factors or posterior samples. The package is designed to support parallel computing with multi-threading support for EVIL to estimate the linkage structure faster. 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This package implements the chopthin resampler, which keeps a bound on the ratio between the largest and the smallest weights after resampling. 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Both nonparametric estimation and direct polytomous regression of cumulative incidence functions (CIFs) are supported. The main functions 'cifcurve()', 'cifplot()', and 'cifpanel()' estimate survival and CIF curves and produce high-quality graphics with risk tables, censoring and competing-risk marks, and multi-panel or inset layouts built on 'ggplot2' and 'ggsurvfit'. The modeling function 'polyreg()' performs direct polytomous regression for coherent joint modeling of all cause-specific CIFs to estimate risk ratios, odds ratios, or subdistribution hazard ratios at user-specified time points. All core functions adopt a formula-and-data syntax and return tidy and extensible outputs that integrate smoothly with 'modelsummary', 'broom', and the broader 'tidyverse' ecosystem. Key numerical routines are implemented in C++ via 'Rcpp'. Package: r-cran-cinterpolate Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cinterpolate_1.0.2-1.ca2404.1_amd64.deb Size: 28370 MD5sum: f9858ca2da9d296c34c6ea429cc756a3 SHA1: 7c17e60f3ead5e418d4b2f369eb94c35c69bfa44 SHA256: c5218972a28f5e7d90f8a3d6c2ec1b112f7ffe86f8f63c2c06f7fb33511b7ce7 SHA512: 2e74561a0181ddb80ba12ea142a02d3e7e48e0fc0283f899da3e119dbb8024b5ea2c2ab3bb8b487523c5f944568e7ad282c1b83228a9fba5a5f0e816cd1e480a Homepage: https://cran.r-project.org/package=cinterpolate Description: CRAN Package 'cinterpolate' (Interpolation From C) Simple interpolation methods designed to be used from C code. Supports constant, linear and spline interpolation. 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We developed the methods described in Jona Lasinio G. et al. (2012) , Wang F. et al. (2014) and Mastrantonio G. et al. (2016) . Package: r-cran-circular Architecture: amd64 Version: 0.5-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 935 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-circular_0.5-2-1.ca2404.1_amd64.deb Size: 827668 MD5sum: ad67fc3522f2c4f5a0bc2bcfdc5057de SHA1: 9034188c7a2ad39f9ab25aecbcee979f737a8f00 SHA256: 53ce22ab95867f727029b1ed2fe1054542c9e32693573d6b8da20c5f003720d6 SHA512: b7624df12ee8a0bd096a9704c3ef780015f431e260e0d7e8ec2d4b000b6297f341cbc19105d80b073d3d154b8494f364a214332bbc359987761c9ad3cf8dbd63 Homepage: https://cran.r-project.org/package=circular Description: CRAN Package 'circular' (Circular Statistics) Circular Statistics, from "Topics in circular Statistics" (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific. 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These algorithms run in linear time on sorted data, in contrast to quadratic time by the definition of silhouette. When used together with the fast and optimal circular clustering method FOCC (Debnath & Song 2021) implemented in R package 'OptCirClust', circular silhouette can be maximized to find the optimal number of circular clusters; it can also be used to estimate the period of noisy periodical data. 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This package provides tools for analyzing and visualizing circular data, including scoring functions for relevant instruments and a generalization of the bootstrapped structural summary method from Zimmermann & Wright (2017) and functions for creating publication-ready tables and figures from the results. 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In addition, the package contains the datasets used within the analysis of the paper. Package: r-cran-cit Architecture: amd64 Version: 2.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-cit_2.3.2-1.ca2404.1_amd64.deb Size: 98218 MD5sum: 1a0b0c206da9bf3a6b8e176f3f990aa3 SHA1: 7b87b43b4298e95be9f322d0fe9b276f3776d745 SHA256: 99289eddeeb6db028750224f77831323d7845323b7782bf455001afad019b8a4 SHA512: ac3a891f418a72646fbc0c5ceea3ae9b8f0827bdee3cdbf6f8a68a989e01179cfcf69b8fc805437bedbe60280013f90874b75935e59173b52c9a662873aba33a Homepage: https://cran.r-project.org/package=cit Description: CRAN Package 'cit' (Causal Inference Test) A likelihood-based hypothesis testing approach is implemented for assessing causal mediation. Described in Millstein, Chen, and Breton (2016), , it could be used to test for mediation of a known causal association between a DNA variant, the 'instrumental variable', and a clinical outcome or phenotype by gene expression or DNA methylation, the potential mediator. Another example would be testing mediation of the effect of a drug on a clinical outcome by the molecular target. The hypothesis test generates a p-value or permutation-based FDR value with confidence intervals to quantify uncertainty in the causal inference. The outcome can be represented by either a continuous or binary variable, the potential mediator is continuous, and the instrumental variable can be continuous or binary and is not limited to a single variable but may be a design matrix representing multiple variables. Package: r-cran-cklrt Architecture: amd64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 554 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mgcv, r-cran-mass, r-cran-nlme, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-cklrt_0.2.3-1.ca2404.1_amd64.deb Size: 308250 MD5sum: 50e6e6db2bc8b77e9b7cee8387987d32 SHA1: 7226b29f989572fa659d11ed2c241eece1dfafd8 SHA256: ebdd376a057dbf1213c071f0690c349526d47283f6fe43023a8c1d3fde84f7fa SHA512: b5974da3f0631657d345287c5d1e2c55d0c9ceec92d2d3f459694c794875d2cefe1d8ad47da98fa412d181962b8941b817df5b065ce98e5afb97148217e65db9 Homepage: https://cran.r-project.org/package=CKLRT Description: CRAN Package 'CKLRT' (Composite Kernel Machine Regression Based on Likelihood RatioTest) Composite Kernel Machine Regression based on Likelihood Ratio Test (CKLRT): in this package, we develop a kernel machine regression framework to model the overall genetic effect of a SNP-set, considering the possible GE interaction. Specifically, we use a composite kernel to specify the overall genetic effect via a nonparametric function and we model additional covariates parametrically within the regression framework. The composite kernel is constructed as a weighted average of two kernels, one corresponding to the genetic main effect and one corresponding to the GE interaction effect. We propose a likelihood ratio test (LRT) and a restricted likelihood ratio test (RLRT) for statistical significance. We derive a Monte Carlo approach for the finite sample distributions of LRT and RLRT statistics. (N. Zhao, H. Zhang, J. Clark, A. Maity, M. Wu. Composite Kernel Machine Regression based on Likelihood Ratio Test with Application for Combined Genetic and Gene-environment Interaction Effect (Submitted).) Package: r-cran-ckmeans.1d.dp Architecture: amd64 Version: 4.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1007 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-ckmeans.1d.dp_4.3.5-1.ca2404.1_amd64.deb Size: 597886 MD5sum: 578918ffec7074270549b99b58d9e40d SHA1: a4bac3440639e3844025809f71c4ce088786b25a SHA256: f7ade1bfdffc98ba58d3e2ea9c79cffdf575e65fdc6b67b3dd5ebb1f9fefa432 SHA512: b9112e4820285ef72a55c93b346a868a525a2269906eb657ffb3da7b59072c2c0f57fe9692199acc355c1a9367911460d7bc372d76f3e415c0e8d3035777d073 Homepage: https://cran.r-project.org/package=Ckmeans.1d.dp Description: CRAN Package 'Ckmeans.1d.dp' (Optimal, Fast, and Reproducible Univariate Clustering) Fast, optimal, and reproducible weighted univariate clustering by dynamic programming. Four problems are solved, including univariate k-means (Wang & Song 2011) (Song & Zhong 2020) , k-median, k-segments, and multi-channel weighted k-means. Dynamic programming is used to minimize the sum of (weighted) within-cluster distances using respective metrics. Its advantage over heuristic clustering in efficiency and accuracy is pronounced when there are many clusters. Multi-channel weighted k-means groups multiple univariate signals into k clusters. An auxiliary function generates histograms adaptive to patterns in data. This package provides a powerful set of tools for univariate data analysis with guaranteed optimality, efficiency, and reproducibility, useful for peak calling on temporal, spatial, and spectral data. Package: r-cran-ckmrpop Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5999 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggforce, r-cran-ggplot2, r-cran-ggraph, r-cran-igraph, r-cran-magrittr, r-cran-purrr, r-cran-rcpp, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-tidygraph, r-cran-tidyr, r-cran-vroom Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-ckmrpop_0.1.3-1.ca2404.1_amd64.deb Size: 3146758 MD5sum: bbe736bb8928779461c820d4c13ad852 SHA1: be5886b291c6688c925a640737f5ce408c52a0eb SHA256: 6f0595dc775171c7cce1547023b154667bfd6f283399d5e660ce9f2fa4798f54 SHA512: 4ae0263109c576b5f1c844b8cfc359bf5e58fafe2feb6523fc0bbefa0ee1cba3367d446a9d8753c4d096e112262db9195d7074010b85be8cdb004ae6197d489f Homepage: https://cran.r-project.org/package=CKMRpop Description: CRAN Package 'CKMRpop' (Forward-in-Time Simulation and Tallying of PairwiseRelationships) Provides an R wrapper around the program 'spip' (), a C program for the simulation of pedigrees within age-structured populations with user-specified life histories. Also includes a variety of functions to parse 'spip' output to compile information about related pairs amongst simulated, sampled individuals, to assess the feasibility and potential accuracy of close-kin mark-recapture (CKMR). Full documentation and vignettes are mirrored at and can be read online there. 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Typically these models must assess the probability of every possible combination of (ancestor state, left descendent state, right descendent state). This means that there are up to (# of states)^3 combinations to investigate, and in biogeographical models, there can easily be hundreds of states, so calculation time becomes an issue. C++ implementation plus clever tricks (many combinations can be eliminated a priori) can greatly speed the computation time over naive R implementations. CITATION INFO: This package is the result of my Ph.D. research, please cite the package if you use it! Type: citation(package="cladoRcpp") to get the citation information. 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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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Parametric and semi-parametric approaches described in Marbac et al. (2020) are implemented. 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Package: r-cran-clusroc Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-rcpp, r-cran-rgl, r-cran-ellipse, r-cran-numderiv, r-cran-ggplot2, r-cran-ggpubr, r-cran-foreach, r-cran-iterators, r-cran-doparallel, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-clusroc_1.0.3-1.ca2404.1_amd64.deb Size: 297916 MD5sum: 8cf58d1d75a2ef472e99279856c4dffe SHA1: 7254f32c203995f889d4e98eee3b032f24b43c95 SHA256: 661a310680f0709bdeff1a45cdc2cdd317a432428bd63ec7eb20c4fac0241586 SHA512: 3f07e6be59efb6e80041b6085161c1fb331b236edca3adf66263ea0945a7d0835425268503dd616b3788e57c35872e640bfb9ad92030b94600d24e7b7ab5f113 Homepage: https://cran.r-project.org/package=ClusROC Description: CRAN Package 'ClusROC' (ROC Analysis in Three-Class Classification Problems forClustered Data) Statistical methods for ROC surface analysis in three-class classification problems for clustered data and in presence of covariates. In particular, the package allows to obtain covariate-specific point and interval estimation for: (i) true class fractions (TCFs) at fixed pairs of thresholds; (ii) the ROC surface; (iii) the volume under ROC surface (VUS); (iv) the optimal pairs of thresholds. Methods considered in points (i), (ii) and (iv) are proposed and discussed in To et al. (2022) . Referring to point (iv), three different selection criteria are implemented: Generalized Youden Index (GYI), Closest to Perfection (CtP) and Maximum Volume (MV). Methods considered in point (iii) are proposed and discussed in Xiong et al. (2018) . Visualization tools are also provided. We refer readers to the articles cited above for all details. Package: r-cran-clustanalytics Architecture: amd64 Version: 0.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 666 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-rcpp, r-cran-mcclust, r-cran-mclust, r-cran-truncnorm, r-cran-boot, r-cran-fossil, r-cran-aricode, r-cran-dplyr, r-cran-rdpack Suggests: r-cran-igraphdata, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clustanalytics_0.5.5-1.ca2404.1_amd64.deb Size: 363700 MD5sum: 7f61af720d88f452e1cda69ace32c692 SHA1: d64bd9c5b493b5f61be927c12451e61bed764664 SHA256: 3589133b1b803f42ad0455e418327ea1dd7c7a824b50eca7d7788e726ac59900 SHA512: 69d9b521a52c82a336239e23d30e8ac645561475496c12bb72e9d18627ed9c034c887c8a41525ea3751033e270af5db18bc25a875139c88f4937c6318fcce8ea Homepage: https://cran.r-project.org/package=clustAnalytics Description: CRAN Package 'clustAnalytics' (Cluster Evaluation on Graphs) Evaluates the stability and significance of clusters on 'igraph' graphs. Supports weighted and unweighted graphs. Implements the cluster evaluation methods defined by Arratia A, Renedo M (2021) . Also includes an implementation of the Reduced Mutual Information introduced by Newman et al. (2020) . Package: r-cran-clustassess Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1265 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-clustassess_1.1.0-1.ca2404.1_amd64.deb Size: 1077132 MD5sum: 8db664a477488705d26e8b8139a34441 SHA1: b11658ac9f6fd0b4e9e9e8858dd7b463493c2a77 SHA256: 163cea2432780d214018fef3aa912714dc25e4b3916723b31fb60c31b63baf7d SHA512: 1041e1e6e1cbc46c3a06179623812a0988aed8380fca200b6d170e40f5b9b5c7440a6a17064e9abf90a2813f187acc667a25f67478559365c0475ee280ae7dfb Homepage: https://cran.r-project.org/package=ClustAssess Description: CRAN Package 'ClustAssess' (Tools for Assessing Clustering) A set of tools for evaluating clustering robustness using proportion of ambiguously clustered pairs (Senbabaoglu et al. (2014) ), as well as similarity across methods and method stability using element-centric clustering comparison (Gates et al. (2019) ). Additionally, this package enables stability-based parameter assessment for graph-based clustering pipelines typical in single-cell data analysis. Package: r-cran-cluster Architecture: amd64 Version: 2.1.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 762 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass, r-cran-matrix Filename: pool/dists/noble/main/r-cran-cluster_2.1.8.2-1.ca2404.1_amd64.deb Size: 568384 MD5sum: 954ad8ed2c055a9624a88c8a3f25a89d SHA1: 9d803e813a98d7b45b59a4d01117f14cb4feba70 SHA256: d2227144db12ec16a2c459ab799bb9e5b24e2c22f749509d468fe631d517b279 SHA512: b2ea85f15411b58a9cce6a23c63b2d0b8eadaf04e0d7da502cab44fe4c6bd06dce0343567bf16352be1ecc480928fe702e8e85a5b12bf00c698d555e23283132 Homepage: https://cran.r-project.org/package=cluster Description: CRAN Package 'cluster' ("Finding Groups in Data": Cluster Analysis Extended Rousseeuw etal.) Methods for Cluster analysis. Much extended the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) "Finding Groups in Data". Package: r-cran-clustercrit Architecture: amd64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 684 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-runit, r-cran-rbenchmark Filename: pool/dists/noble/main/r-cran-clustercrit_1.3.0-1.ca2404.1_amd64.deb Size: 461674 MD5sum: 2c837cfaea8f0681a4e93bcaf4bacf63 SHA1: 0f9672b51824bc56da9ad501599e5f09d513b208 SHA256: aa3eb8a1c4907a1b1f99108a3b673c54f13f28b5d5ca7de6f778ad516fc24ff0 SHA512: b7e4730bb70f2004c0471a3703a9879dd907d821df5be275d3adf3b171e40f8852bbf6af7802ae6d439e406ba8ed31b91e7596693b86b1fb0ca7bdecc94b6ec0 Homepage: https://cran.r-project.org/package=clusterCrit Description: CRAN Package 'clusterCrit' (Clustering Indices) Package providing functions for computing a collection of clustering validation or quality criteria and partition comparison indices. Package: r-cran-clusterggm Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-rlang, r-cran-rcppeigen Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-clusterggm_0.1.1-1.ca2404.1_amd64.deb Size: 229814 MD5sum: ddd927848f5678aa9802fb96fd30c2f6 SHA1: 82a77fc3dc36c239072d08a251436f2f14fe5c5a SHA256: d88342084dc36be029d021b5166b0531262f358e60942563d028e90caa0c9bf1 SHA512: 08f64223a4beaf1709e26c48197d78a9f163d71d3a208eceb9f61224ff3109d81c670cb48131b84d12b2f6fa2f9391d62902293972f43625cc691dcc908525d1 Homepage: https://cran.r-project.org/package=clusterGGM Description: CRAN Package 'clusterGGM' (Sparse Gaussian Graphical Modeling with Variable Clustering) Perform sparse estimation of a Gaussian graphical model (GGM) with node aggregation through variable clustering. Currently, the package implements the clusterpath estimator of the Gaussian graphical model (CGGM) (Touw, Alfons, Groenen & Wilms, 2025; ). Package: r-cran-clusterhd Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mclust, r-cran-ckmeans.1d.dp, r-cran-cluster, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-clusterhd_1.0.2-1.ca2404.1_amd64.deb Size: 98278 MD5sum: 2c8a8bd040644ecf6c7cfabd125d519a SHA1: f25e4824ac205af18b39843672dfcd10ebfce13b SHA256: 52d23d2fed1c4236183284a060d39c22286f8a17942c51b1e5964cbb9fce6e77 SHA512: 1acb3c2825faec686b902fddeb64a4249e2a8a35cd23d7ae804e0313aacdbdf2bad780b8ceb28ec8769e425b134ea808945a9b8cc509c00075d8a781a40e2766 Homepage: https://cran.r-project.org/package=clusterHD Description: CRAN Package 'clusterHD' (Tools for Clustering High-Dimensional Data) Tools for clustering high-dimensional data. In particular, it contains the methods described in , . Package: r-cran-clustering.sc.dp Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-clustering.sc.dp_1.1-1.ca2404.1_amd64.deb Size: 40044 MD5sum: 0ffc70e99adc6b316e2f2cf181bf62a7 SHA1: 03b0300fb1dbf1ef1f6cf9ba8f5a9e5a97e67586 SHA256: cd954c7f6ede70c1192aebee206898481e60150c1c0cfb555140b3fa48de22a1 SHA512: e76bb43d4ac63c73591f4fe1628c30a512cc1ab5ba1a8249f984d375d5378158282cb545161cfb6d31043862b7cbee8fe7dd6cf1ed3815bd5ce301024d3037dd Homepage: https://cran.r-project.org/package=clustering.sc.dp Description: CRAN Package 'clustering.sc.dp' (Optimal Distance-Based Clustering for Multidimensional Data withSequential Constraint) A dynamic programming algorithm for optimal clustering multidimensional data with sequential constraint. The algorithm minimizes the sum of squares of within-cluster distances. The sequential constraint allows only subsequent items of the input data to form a cluster. The sequential constraint is typically required in clustering data streams or items with time stamps such as video frames, GPS signals of a vehicle, movement data of a person, e-pen data, etc. The algorithm represents an extension of 'Ckmeans.1d.dp' to multiple dimensional spaces. Similarly to the one-dimensional case, the algorithm guarantees optimality and repeatability of clustering. Method clustering.sc.dp() can find the optimal clustering if the number of clusters is known. Otherwise, methods findwithinss.sc.dp() and backtracking.sc.dp() can be used. See Szkaliczki, T. (2016) "clustering.sc.dp: Optimal Clustering with Sequential Constraint by Using Dynamic Programming" for more information. Package: r-cran-clustermi Architecture: amd64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1741 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mice, r-cran-micemd, r-cran-mclust, r-cran-mix, r-cran-fpc, r-cran-knockoff, r-cran-withr, r-cran-glmnet, r-cran-clusterr, r-cran-factominer, r-cran-dicer, r-cran-npbayesimputecat, r-cran-e1071, r-cran-rfast, r-cran-cat, r-cran-ggplot2, r-cran-gridextra, r-cran-reshape2, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stargazer, r-cran-vim, r-cran-missmda, r-cran-clustrd, r-cran-clustercrit, r-cran-bookdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-clustermi_1.6-1.ca2404.1_amd64.deb Size: 1434406 MD5sum: 0aa86cfd55653ea679504d78f9ef8ea8 SHA1: bc0d7e522970e599cdc813b4fbf1aff11f7fa20b SHA256: 1046eb41a793e521f7ae1f6c0f5696472c46fa842409aee71654a212b2c0d799 SHA512: fe120dd831a81e84c4b72fa16f6a02b7e7f246fa8e8abab2e69abd39db010c72fdfa9c6356ad6f6004890ea7370d62d2bc91ea12676975e8fc06ee5671fb173c Homepage: https://cran.r-project.org/package=clusterMI Description: CRAN Package 'clusterMI' (Cluster Analysis with Missing Values by Multiple Imputation) Allows clustering of incomplete observations by addressing missing values using multiple imputation. For achieving this goal, the methodology consists in three steps, following Audigier and Niang 2022 . I) Missing data imputation using dedicated models. Four multiple imputation methods are proposed, two are based on joint modelling and two are fully sequential methods, as discussed in Audigier et al. (2021) . II) cluster analysis of imputed data sets. Six clustering methods are available (distances-based or model-based), but custom methods can also be easily used. III) Partition pooling. The set of partitions is aggregated using Non-negative Matrix Factorization based method. An associated instability measure is computed by bootstrap (see Fang, Y. and Wang, J., 2012 ). Among applications, this instability measure can be used to choose a number of clusters with missing values. The package also proposes several diagnostic tools to tune the number of imputed data sets, to tune the number of iterations in fully sequential imputation, to check the fit of imputation models, etc. Package: r-cran-clustermq Architecture: amd64 Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1033 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), libzmq5 (>= 4.0.1+dfsg), r-base-core (>= 4.5.0), r-api-4.0, r-cran-globals, r-cran-progress, r-cran-r6, r-cran-rcpp Suggests: r-bioc-biocparallel, r-cran-callr, r-cran-devtools, r-cran-foreach, r-cran-iterators, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clustermq_0.10.0-1.ca2404.1_amd64.deb Size: 468198 MD5sum: b0bcf15f1bfcd6ea786745711974b971 SHA1: eebda1cef9d7108b1dd5972e82de9c1b9d8e06ab SHA256: 888091731a3c59d72ca71883c6c08c4e18c69f6facdcef3a134040c352de1694 SHA512: 64a1a85ec3ed0034faa17ee9b1f47c4b4fefacc744251a5ba057fee7c74426c02f379db1e83a1dd8ec1c363d31dd785057ad1d02a222b1c8de17bf7096611b3e Homepage: https://cran.r-project.org/package=clustermq Description: CRAN Package 'clustermq' (Evaluate Function Calls on HPC Schedulers (SLURM, LSF, SGE, GCS,OCS, PBS, Torque)) Evaluate arbitrary function calls using workers on HPC schedulers in single line of code. All processing is done on the network without accessing the file system. Remote schedulers are supported via SSH. Package: r-cran-clusterr Architecture: amd64 Version: 1.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1997 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-gmp, r-cran-ggplot2, r-cran-lifecycle, r-cran-rcpparmadillo Suggests: r-cran-openimager, r-cran-fd, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clusterr_1.3.6-1.ca2404.1_amd64.deb Size: 1127812 MD5sum: c375b4b23b20d443d26608093a057ab2 SHA1: 3ee8af2e41d93a3e15d6f0b880a154d530823c99 SHA256: f5ed4120f61f6dc5fc1ecbad8ef8e8f2cc40b6241bb766c50baa6e1f4ce18e72 SHA512: 04ad5232088b01ca8d0cccf7213d93d2efae680c976e82991ff53f641e8f080b7897be2d2317d824f4b45f5b85c6f30da5ad28bd45617eb67ccb3d4e92b02225 Homepage: https://cran.r-project.org/package=ClusterR Description: CRAN Package 'ClusterR' (Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoidsand Affinity Propagation Clustering) Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to speed up the computationally intensive parts of the functions. For more information, see (i) "Clustering in an Object-Oriented Environment" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, ; (ii) "Web-scale k-means clustering" by D. Sculley (2010), ACM Digital Library, ; (iii) "Armadillo: a template-based C++ library for linear algebra" by Sanderson et al (2016), The Journal of Open Source Software, ; (iv) "Clustering by Passing Messages Between Data Points" by Brendan J. Frey and Delbert Dueck, Science 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976, . Package: r-cran-clustersim Architecture: amd64 Version: 0.51-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4013 Depends: libc6 (>= 2.2.5), libstdc++6 (>= 4.3), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-mass, r-cran-ade4, r-cran-e1071 Suggests: r-cran-mlbench, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clustersim_0.51-6-1.ca2404.1_amd64.deb Size: 3593712 MD5sum: 2400158ad641088106f1ffbde3d42b0d SHA1: 510f958cd750d20ee1c2754233e73e6cdc6e774a SHA256: 78c4f1301cbf104a13e410f41dc7bda74284507a0b9d53898a9234e7fcc5e855 SHA512: aabc3a456535ec35d6a7188ab6a5c2df3eabdde5f8a22a0436b76a255554d8662e2f7e3f7c89ece9632b55e449497c53d21843820a8a113dd05a1f169598d2a1 Homepage: https://cran.r-project.org/package=clusterSim Description: CRAN Package 'clusterSim' (Searching for Optimal Clustering Procedure for a Data Set) Distance measures (GDM1, GDM2, Sokal-Michener, Bray-Curtis, for symbolic interval-valued data), cluster quality indices (Calinski-Harabasz, Baker-Hubert, Hubert-Levine, Silhouette, Krzanowski-Lai, Hartigan, Gap, Davies-Bouldin), data normalization formulas (metric data, interval-valued symbolic data), data generation (typical and non-typical data), HINoV method, replication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorical and symbolic interval-valued data). (MILLIGAN, G.W., COOPER, M.C. (1985) , HUBERT, L., ARABIE, P. (1985) , RAND, W.M. (1971) , JAJUGA, K., WALESIAK, M. (2000) , MILLIGAN, G.W., COOPER, M.C. (1988) , JAJUGA, K., WALESIAK, M., BAK, A. (2003) , DAVIES, D.L., BOULDIN, D.W. (1979) , CALINSKI, T., HARABASZ, J. (1974) , HUBERT, L. (1974) , TIBSHIRANI, R., WALTHER, G., HASTIE, T. (2001) , BRECKENRIDGE, J.N. (2000) , WALESIAK, M., DUDEK, A. (2008) ). Package: r-cran-clusterstability Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-cluster, r-cran-copula, r-cran-weightedcluster Filename: pool/dists/noble/main/r-cran-clusterstability_1.0.4-1.ca2404.1_amd64.deb Size: 93418 MD5sum: 0a4cdf694379681fe6a2356721a30678 SHA1: 7953cb0284d3fbce3db3b5827ec4899367da3ffe SHA256: 527511505f62cac736305f03ddadf833f738d5ffcd2d32f3c0999ea9ecd25961 SHA512: ba7b07d7bdd1d8b17cec9f14c9d644643a0474be34afee9419f23b36ed8514742e1654c39d6b8c83755f883e5e7289ce0124d1673b28ed190c0ef37d55e90614 Homepage: https://cran.r-project.org/package=ClusterStability Description: CRAN Package 'ClusterStability' (Assessment of Stability of Individual Objects or Clusters inPartitioning Solutions) Allows one to assess the stability of individual objects, clusters and whole clustering solutions based on repeated runs of the K-means and K-medoids partitioning algorithms. Package: r-cran-clustord Architecture: amd64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1645 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-nnet, r-cran-flexclust, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-formatr, r-cran-rmarkdown, r-cran-testthat, r-cran-multgee Filename: pool/dists/noble/main/r-cran-clustord_2.0.1-1.ca2404.1_amd64.deb Size: 1010874 MD5sum: 7d483d80b5a0ffb448face99c540b80b SHA1: f58393963a22c2b6625675ea90a465864f5ba80d SHA256: e4bede29f4ce7ea8eef9c8bdbe9712a830a04386f0fae74624362c09a5d494c5 SHA512: 46ea7344fa24dc4ce3dfbf8d268cc04dbc88d6ae2c39121d16899ecbcc331a6e52ebf4c235238651111a5c2b5733d76fc47092c4c074b5df16c4af3cf4b4ddb3 Homepage: https://cran.r-project.org/package=clustord Description: CRAN Package 'clustord' (Cluster Ordinal Data via Proportional Odds or Ordered Stereotype) Biclustering, row clustering and column clustering using the proportional odds model (POM), ordered stereotype model (OSM) or binary model for ordinal categorical data. Fernández, D., Arnold, R., Pledger, S., Liu, I., & Costilla, R. (2019) . Package: r-cran-clusttmb Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3741 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-clustmixtype, r-cran-fmesher, r-cran-lme4, r-cran-matrix, r-cran-mclust, r-cran-reformulas, r-cran-moeclust, r-cran-sf, r-cran-tmb, r-cran-rcppeigen Suggests: r-cran-bookdown, r-cran-covr, r-cran-cowplot, r-cran-dplyr, r-cran-fmsmsnreg, r-cran-ggally, r-cran-ggplot2, r-cran-ggspatial, r-cran-giscor, r-cran-inlabru, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-mixsim, r-cran-mvnfast, r-cran-mvtnorm, r-cran-palmerpenguins, r-cran-rmarkdown, r-cran-sdmtmb, r-cran-sp, r-cran-spdata, r-cran-splancs, r-cran-testthat, r-cran-tidyr, r-cran-tweedie, r-cran-wesanderson Filename: pool/dists/noble/main/r-cran-clusttmb_0.1.0-1.ca2404.1_amd64.deb Size: 1036410 MD5sum: a138adba005f9ff661652021fa747651 SHA1: 668fcb86c15a222ce8fb75ca077ab183ec61a998 SHA256: 59f74c9cba78bc6f09d7bc98348a13d8003cdf8777e19946d3a359ff06d0f134 SHA512: 31f6ce54387786d624dbdd4c345267fae5028362dc03cb51e1f30b44351171ce2214e62b73480a7b9ddaf643a92bde765b3fcbef978aa6eb1d2c364e912ac3b3 Homepage: https://cran.r-project.org/package=clustTMB Description: CRAN Package 'clustTMB' (Spatio-Temporal Finite Mixture Model using 'TMB') Fits a spatio-temporal finite mixture model using 'TMB'. Covariate, spatial and temporal random effects can be incorporated into the gating formula using multinomial logistic regression, the expert formula using a generalized linear mixed model framework, or both. Package: r-cran-clustur Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1719 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-testthat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-clustur_0.1.4-1.ca2404.1_amd64.deb Size: 567842 MD5sum: f8a070163cce85881071ce90fc895d94 SHA1: 7fe5ce65e779f5c00455832644cfe8549546df0f SHA256: 743f0efa9198b234649812a62fa10fa2c51b0b4d5eb3a60e4ed48ec478707c72 SHA512: 3a1263af52c63a765fb85321c5436cc5aced30035cdf567f1f0d8350312a472e05328ead7b9529bb8c33948ee5bcc0ba2e8b3371dfc923070e94da19dcaa0a4e Homepage: https://cran.r-project.org/package=clustur Description: CRAN Package 'clustur' (Clustering) A tool that implements the clustering algorithms from 'mothur' (Schloss PD et al. (2009) ). 'clustur' make use of the cluster() and make.shared() command from 'mothur'. Our cluster() function has five different algorithms implemented: 'OptiClust', 'furthest', 'nearest', 'average', and 'weighted'. 'OptiClust' is an optimized clustering method for Operational Taxonomic Units, and you can learn more here, (Westcott SL, Schloss PD (2017) ). The make.shared() command is always applied at the end of the clustering command. This functionality allows us to generate and create clustering and abundance data efficiently. Package: r-cran-clustvarlv Architecture: amd64 Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 745 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-plyr, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clustvarlv_2.1.1-1.ca2404.1_amd64.deb Size: 550370 MD5sum: 222db8490b6b4d87f51e8642ec94bd44 SHA1: 3e706d111cf939add3d8343c8be2de98ea509876 SHA256: 533ef9c7d61d3278abf67b3ce36c88479d6b6fbe5cea64b1ae31b0cab0e9a939 SHA512: 7d4b30cce5a3e1f8e1789367cc7cb8d7d3be5ecab90c7a31be8a57effc669d17cc250bbad89c73893884b13bfc2a281433aa1768a74307c7caae1e296eb5ec7f Homepage: https://cran.r-project.org/package=ClustVarLV Description: CRAN Package 'ClustVarLV' (Clustering of Variables Around Latent Variables) Functions for the clustering of variables around Latent Variables, for 2-way or 3-way data. Each cluster of variables, which may be defined as a local or directional cluster, is associated with a latent variable. External variables measured on the same observations or/and additional information on the variables can be taken into account. A "noise" cluster or sparse latent variables can also be defined. Package: r-cran-clusvis Architecture: amd64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-mgcv, r-cran-mvtnorm, r-cran-rmixmod, r-cran-varsellcm, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-clusvis_1.2.0-1.ca2404.1_amd64.deb Size: 116492 MD5sum: 6b55956d92358de8100f5ee090c1e9bc SHA1: 373f058c23453cbd0717e808e88150a34bea53ce SHA256: e1ff91ca2115eed881ee1b6c4f6aa3a15b43643c614eb977a2ef2592cf2f6b1e SHA512: 5157406848a22ec6b0d50afc8343f1082f7b3ebe22537646747981884683435824f0dedfb49352fdb569d114d2e88edd64e90c3949282dfdba24a97743d44eb6 Homepage: https://cran.r-project.org/package=ClusVis Description: CRAN Package 'ClusVis' (Gaussian-Based Visualization of Gaussian and Non-GaussianModel-Based Clustering) Gaussian-Based Visualization of Gaussian and Non-Gaussian Model-Based Clustering done on any type of data. Visualization is based on the probabilities of classification. Package: r-cran-clv Architecture: amd64 Version: 0.3-2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-class Filename: pool/dists/noble/main/r-cran-clv_0.3-2.5-1.ca2404.1_amd64.deb Size: 212014 MD5sum: cc9e79e66240fa84d16cda37b7a2c17b SHA1: f90cff1a4b754de9e357d0ab1a48f8692cfdbe38 SHA256: 0a38609b61d9dda7300da2334cbc012f01aadc589ca77e3b4272ac214d0c5082 SHA512: 6c7a95e32cc5117f6e8713b2e1e83377ad4834d07e29aa94038824ad70eb5ef625d9baff1da811f14ae53fd03cd4899f9d2d4630a832137bd5fba2e4457da16e Homepage: https://cran.r-project.org/package=clv Description: CRAN Package 'clv' (Cluster Validation Techniques) Contains most of the popular internal and external cluster validation methods ready to use for the most of the outputs produced by functions coming from package "cluster". Package contains also functions and examples of usage for cluster stability approach that might be applied to algorithms implemented in "cluster" package as well as user defined clustering algorithms. Package: r-cran-clvtools Architecture: amd64 Version: 0.12.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3355 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-digest, r-cran-formula, r-cran-ggplot2, r-cran-lubridate, r-cran-numderiv, r-cran-matrix, r-cran-mass, r-cran-optimx, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppgsl, r-cran-testthat Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-xml2, r-cran-lmtest, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-clvtools_0.12.1-1.ca2404.1_amd64.deb Size: 2128156 MD5sum: f1ccafb8c5f2c2c809b3d20e6d519084 SHA1: c57a429083302fb2dbb1cbc5d5b9fcd2e5306747 SHA256: 3d0a7d534045cdce966fa88258b00cce6d6aabfeed050f171ca0afd8485e382d SHA512: ce660b917215d6a8a573a0626b3cac0ad410f40549429d225d92937604c26a1f38c8367faf110c9ef90e79ef9b8f55b23149ec34859d0f3a4579fd7d46fa9710 Homepage: https://cran.r-project.org/package=CLVTools Description: CRAN Package 'CLVTools' (Tools for Customer Lifetime Value Estimation) A set of state-of-the-art probabilistic modeling approaches to derive estimates of individual customer lifetime values (CLV). Commonly, probabilistic approaches focus on modelling 3 processes, i.e. individuals' attrition, transaction, and spending process. Latent customer attrition models, which are also known as "buy-'til-you-die models", model the attrition as well as the transaction process. They are used to make inferences and predictions about transactional patterns of individual customers such as their future purchase behavior. Moreover, these models have also been used to predict individuals’ long-term engagement in activities such as playing an online game or posting to a social media platform. The spending process is usually modelled by a separate probabilistic model. Combining these results yields in lifetime values estimates for individual customers. This package includes fast and accurate implementations of various probabilistic models for non-contractual settings (e.g., grocery purchases or hotel visits). All implementations support time-invariant covariates, which can be used to control for e.g., socio-demographics. If such an extension has been proposed in literature, we further provide the possibility to control for time-varying covariates to control for e.g., seasonal patterns. Currently, the package includes the following latent attrition models to model individuals' attrition and transaction process: [1] Pareto/NBD model (Pareto/Negative-Binomial-Distribution), [2] the Extended Pareto/NBD model (Pareto/Negative-Binomial-Distribution with time-varying covariates), [3] the BG/NBD model (Beta-Gamma/Negative-Binomial-Distribution) and the [4] GGom/NBD (Gamma-Gompertz/Negative-Binomial-Distribution). Further, we provide an implementation of the Gamma/Gamma model to model the spending process of individuals. Package: r-cran-cmapss Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5314 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-cmapss_0.1.1-1.ca2404.1_amd64.deb Size: 5403886 MD5sum: cf77b095b38d6164a3db4b4272fe0bce SHA1: 308d26e11428dc438903755fd1e26e65ee4af81c SHA256: 094fc3389f971ff9ae8f20ad25d72ee10a008b13b80987681a15a9556112735b SHA512: 6e351d02514470e0057b00bbd86bfaafe777290a0895ffb8969f89d60e704bfdc323059868fbfdd2c2d3ca51e0b2529fda6079a3539d066bff5eaaa127f859ca Homepage: https://cran.r-project.org/package=CMAPSS Description: CRAN Package 'CMAPSS' (Commercial Modular Aero-Propulsion System Simulation Data Set) Contains the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data set. Package: r-cran-cmbclust Architecture: amd64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-hdclassif, r-cran-mixsim, r-cran-mclust Filename: pool/dists/noble/main/r-cran-cmbclust_0.0.2-1.ca2404.1_amd64.deb Size: 143688 MD5sum: bf76fa412ad85e6e45bef365037e145f SHA1: 475ad17c9f52f9c3ba0aac63d395bad4fa3d578b SHA256: ef4596215128c204000d7a044fb403b92efba5c1f4d19de1acf279dea53b3d4a SHA512: fc3399f49d34daea624a49c4e6a9f86e435def7d5dd8b0164f176b4755b5945f069862c1d85ce27543cab00f7c7a2f36ceee0946d99aac8d24e24bd9ad28acb5 Homepage: https://cran.r-project.org/package=cmbClust Description: CRAN Package 'cmbClust' (Conditional Mixture Modeling and Model-Based Clustering) Conditional mixture model fitted via EM (Expectation Maximization) algorithm for model-based clustering, including parsimonious procedure, optimal conditional order exploration, and visualization. Package: r-cran-cmenet Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-glmnet, r-cran-hiernet, r-cran-sparsenet, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-cmenet_0.1.2-1.ca2404.1_amd64.deb Size: 91656 MD5sum: 3908a694fa0efb1f836334c51dbaa2d2 SHA1: 58d691f6dfae6fae68329bd9fc63146336e18801 SHA256: e63429be220bff0c320376d785980687d8bfd40a7107a92ce2e97b2d989dfbb5 SHA512: ecbff86c7d2bb3d13445d6a954143a859f6714d33c114a433b677f7d3bc39b02dd33f7bdee20d0309d9db085c388b0e11b515ef5a7fae9f7acd899024c51ccc7 Homepage: https://cran.r-project.org/package=cmenet Description: CRAN Package 'cmenet' (Bi-Level Selection of Conditional Main Effects) Provides functions for implementing cmenet - a bi-level variable selection method for conditional main effects (see Mak and Wu (2018) ). CMEs are reparametrized interaction effects which capture the conditional impact of a factor at a fixed level of another factor. Compared to traditional two-factor interactions, CMEs can quantify more interpretable interaction effects in many problems. The current implementation performs variable selection on only binary CMEs; we are working on an extension for the continuous setting. Package: r-cran-cmf Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: libc6 (>= 2.11), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cpp11 Filename: pool/dists/noble/main/r-cran-cmf_1.0.3-1.ca2404.1_amd64.deb Size: 85664 MD5sum: 7fcd2774005ff05276fc88254fada9e0 SHA1: 4cd0780555be529ac41d05838d62660df7fca424 SHA256: 61af6260285c75f05fb7baa33c46f2c202193e103d7b0984e29c6fa238d31060 SHA512: 32da50c5fa72a34c76dbd051b40a699ec62dd5a0605e685fd554ed9a0143eb41727f3f679677e37573d95ef5510915d8b716e7148d8f026ed5636ab5d5c6a693 Homepage: https://cran.r-project.org/package=CMF Description: CRAN Package 'CMF' (Collective Matrix Factorization) Collective matrix factorization (CMF) finds joint low-rank representations for a collection of matrices with shared row or column entities. This code learns a variational Bayesian approximation for CMF, supporting multiple likelihood potentials and missing data, while identifying both factors shared by multiple matrices and factors private for each matrix. For further details on the method see Klami et al. (2014) . The package can also be used to learn Bayesian canonical correlation analysis (CCA) and group factor analysis (GFA) models, both of which are special cases of CMF. This is likely to be useful for people looking for CCA and GFA solutions supporting missing data and non-Gaussian likelihoods. See Klami et al. (2013) and Virtanen et al. (2012) for details on Bayesian CCA and GFA, respectively. Package: r-cran-cmfrec Architecture: amd64 Version: 3.5.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 971 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 6), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-matrix, r-cran-matrixextra, r-cran-rhpcblasctl, r-cran-recosystem, r-cran-recommenderlab, r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-cmfrec_3.5.1-3-1.ca2404.1_amd64.deb Size: 549280 MD5sum: 66adf94445243e08d9ef2b4ee5c7fd5e SHA1: 28e2ba2213c4b859f2a03017cd9714f2bd470352 SHA256: 2fa4c41115719b775841e2dbcaf071cd9502f66b71d4d5766e8f83c292af7528 SHA512: c0190753d2f29743ba0e5ef825a672a2e1db5be47d6ccacf5693726419c83c71a4b9a3b26b5b67eb410b03770156b17327b236dc158c10774a3412349768cd00 Homepage: https://cran.r-project.org/package=cmfrec Description: CRAN Package 'cmfrec' (Collective Matrix Factorization for Recommender Systems) Collective matrix factorization (a.k.a. multi-view or multi-way factorization, Singh, Gordon, (2008) ) tries to approximate a (potentially very sparse or having many missing values) matrix 'X' as the product of two low-dimensional matrices, optionally aided with secondary information matrices about rows and/or columns of 'X', which are also factorized using the same latent components. The intended usage is for recommender systems, dimensionality reduction, and missing value imputation. Implements extensions of the original model (Cortes, (2018) ) and can produce different factorizations such as the weighted 'implicit-feedback' model (Hu, Koren, Volinsky, (2008) ), the 'weighted-lambda-regularization' model, (Zhou, Wilkinson, Schreiber, Pan, (2008) ), or the enhanced model with 'implicit features' (Rendle, Zhang, Koren, (2019) ), with or without side information. Can use gradient-based procedures or alternating-least squares procedures (Koren, Bell, Volinsky, (2009) ), with either a Cholesky solver, a faster conjugate gradient solver (Takacs, Pilaszy, Tikk, (2011) ), or a non-negative coordinate descent solver (Franc, Hlavac, Navara, (2005) ), providing efficient methods for sparse and dense data, and mixtures thereof. Supports L1 and L2 regularization in the main models, offers alternative most-popular and content-based models, and implements functionality for cold-start recommendations and imputation of 2D data. Package: r-cran-cmgfm Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 414 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-irlba, r-cran-mass, r-cran-gfm, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cmgfm_1.1-1.ca2404.1_amd64.deb Size: 158044 MD5sum: a85d76cef39f0194c66646f538922514 SHA1: 49e1e5f8518248177d1f043be1f55f0965e90874 SHA256: 496565cdefb29225b96defbd5c29f6d2807276537a0b30561080e27f14aa98cc SHA512: 19c77f0737f32a2a08e6ef9fd8df25e92f962305a202b0d7e1af345c60cbbda779beaacb507c3ca72fffb5699537324bd3963356587521e2c6a955d392dd8379 Homepage: https://cran.r-project.org/package=CMGFM Description: CRAN Package 'CMGFM' (Covariate-Augumented Generalized Factor Model) Covariate-augumented generalized factor model is designed to account for cross-modal heterogeneity, capture nonlinear dependencies among the data, incorporate additional information, and provide excellent interpretability while maintaining high computational efficiency. Package: r-cran-cmpp Architecture: amd64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 392 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-numderiv, r-cran-cmprsk, r-cran-tidyselect, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-cmpp_0.0.2-1.ca2404.1_amd64.deb Size: 205390 MD5sum: 75abb59a3aca57d1f7fad8d2567e1231 SHA1: c96704ea378d06b1272820014c1519172358c2dc SHA256: c3582737abd83d0c91a10680d8b7f24ea8705805d36614261d5ff8ffcb5af2f5 SHA512: d10cd0263eb4e89c6b8f6aee2f099ca5e9a7d8e7529af1fd8a4a2098c06c33e9ddabb504a67d93dad00fe2f89e384f1da3a4c85a6265cd725ef7eaf0cdc6dbbf Homepage: https://cran.r-project.org/package=cmpp Description: CRAN Package 'cmpp' (Direct Parametric Inference for the Cumulative IncidenceFunction in Competing Risks) Implements parametric (Direct) regression methods for modeling cumulative incidence functions (CIFs) in the presence of competing risks. Methods include the direct Gompertz-based approach and generalized regression models as described in Jeong and Fine (2006) and Jeong and Fine (2007) . The package facilitates maximum likelihood estimation, variance computation, with applications to clinical trials and survival analysis. Package: r-cran-cmprsk Architecture: amd64 Version: 2.2-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-cmprsk_2.2-12-1.ca2404.1_amd64.deb Size: 84784 MD5sum: 65a6da345592130be1509c32ab22e391 SHA1: 193121e9514fe72d363fed93a3134a40f1139d83 SHA256: a576c980085dd9abe9dee6e92ed1e5ed34fa25443dceb616a610dd6dd4ccaacb SHA512: 0981e283163102ff0263aefc919b63d6324c22963a744be770554d2a3f92eed344700ddc4492ba0815f7275115c0a82daebd121f2600314a0c09cb35bd327e5f 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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In general, it can be used for objective comparison of striated tool marks, and in our examples, we specifically use it for bullet signatures comparisons. The CMPS score is expected to be large if two signatures are similar. So it can also be considered as a feature that measures the similarity of two bullet signatures. 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'cmstatr' contains statistical methods that are published in the Composite Materials Handbook, Volume 1 (2012, ISBN: 978-0-7680-7811-4), while 'cmstatrExt' contains statistical methods that are not included in that handbook. 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Package: r-cran-cna Architecture: amd64 Version: 4.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1936 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-matrixstats, r-cran-car Suggests: r-cran-dplyr, r-cran-frscore, r-cran-causalhypergraph Filename: pool/dists/noble/main/r-cran-cna_4.0.3-1.ca2404.1_amd64.deb Size: 1373074 MD5sum: 0158bc6cabd57f5ecc0d35ba4e7d6f75 SHA1: e94308286f045bcc6d1e7037d03c48979f9ce1a3 SHA256: 53adea1226cf3c7f838a8657f428ffd7e7c62e5086b389812fb1706bbb96092b SHA512: 4de507623a68540e427a1067c2fbe5906fd8c72cd0de5722e52304c1ae793a3be407643e0588b9137dedf88de55f724dbbb96ed4333ad7c37ed1157cfd0f3876 Homepage: https://cran.r-project.org/package=cna Description: CRAN Package 'cna' (Causal Modeling with Coincidence Analysis) Provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) , and generalized in Baumgartner & Ambuehl (2020) . CNA is designed to recover INUS-causation from data, which is particularly relevant for analyzing processes featuring conjunctural causation (component causation) and equifinality (alternative causation). CNA is currently the only method for INUS-discovery that allows for multiple effects (outcomes/endogenous factors), meaning it can analyze common-cause and causal chain structures. Moreover, as of version 4.0, it is the only method of its kind that provides measures for model evaluation and selection that are custom-made for the problem of INUS-discovery. Package: r-cran-cnaopt Architecture: amd64 Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cna, r-cran-rcpp, r-cran-matrixstats, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-cnaopt_0.5.3-1.ca2404.1_amd64.deb Size: 161894 MD5sum: 73669b536f4b27d3bce11564f774e77c SHA1: 39ac5510c24481ed0934eb74b5e3acc595e87465 SHA256: 854b54a087f1db0bae3044aad71cf9a686b8ba1fb7f17c44dbf3dcaea35ff852 SHA512: 4fc65b278ff406cecadb163228d9c766389c452b4ca4ea14f1d1d774ae5c82b404b5d69c82ab62783f9b9dbd29494deaface807471465571ec02ac827b7f4af1 Homepage: https://cran.r-project.org/package=cnaOpt Description: CRAN Package 'cnaOpt' (Optimizing Consistency and Coverage in Configurational CausalModeling) This is an add-on to the 'cna' package comprising various functions for optimizing consistency and coverage scores of models of configurational comparative methods as Coincidence Analysis (CNA) and Qualitative Comparative Analysis (QCA). The function conCovOpt() calculates con-cov optima, selectMax() selects con-cov maxima among the con-cov optima, DNFbuild() can be used to build models actually reaching those optima, and findOutcomes() identifies those factor values in analyzed data that can be modeled as outcomes. For a theoretical introduction to these functions see Baumgartner and Ambuehl (2021) . 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This package supports the casual scale naming system and the respective SI prefix systems used in mainland China and Taiwan: "The State Council's Order on the Unified Implementation of Legal Measurement Units in Our Country" The State Council of the People's Republic of China (1984) "Names, Definitions and Symbols of the Legal Units of Measurement and the Decimal Multiples and Submultiples" Ministry of Economic Affairs (2019) . 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More details can be referred to Liu et al. (2024) . 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Package: r-cran-cohortmethod Architecture: amd64 Version: 6.0.2-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3142 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-databaseconnector, r-cran-cyclops, r-cran-featureextraction, r-cran-andromeda, r-cran-ggplot2, r-cran-gridextra, r-cran-readr, r-cran-plyr, r-cran-dplyr, r-cran-rlang, r-cran-rcpp, r-cran-sqlrender, r-cran-survival, r-cran-parallellogger, r-cran-checkmate, r-cran-empiricalcalibration, r-cran-jsonlite, r-cran-r6, r-cran-digest Suggests: r-cran-testthat, r-cran-proc, r-cran-knitr, r-cran-rmarkdown, r-cran-eunomia, r-cran-zip, r-cran-withr, r-cran-r.utils, r-cran-rsqlite, r-cran-resultmodelmanager, r-cran-markdown, r-cran-psweight Filename: pool/dists/noble/main/r-cran-cohortmethod_6.0.2-1.ca2404.2_amd64.deb Size: 2193788 MD5sum: 364b708bd3aa5c3e892a39da00df5ac0 SHA1: 24abd08c532a34d14afd08e3db512918d6742730 SHA256: 57874e43c9d895c52c247154031b1f63d19d8e5830121c669b39bbc334971cba SHA512: a6c15e4c1624543905f3c74e5892cffe4832ecd6495ddccc08ed3df73b37aa2f0c4339feac05099258cbab44c51539fc12546034a653cd83d507f75b75245eeb Homepage: https://cran.r-project.org/package=CohortMethod Description: CRAN Package 'CohortMethod' (Comparative Cohort Method with Large Scale Propensity andOutcome Models) Functions for performing comparative cohort studies in an observational database in the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Can extract all necessary data from a database. This implements large-scale propensity scores (LSPS) as described in Tian et al. (2018) , using a large set of covariates, including for example all drugs, diagnoses, procedures, as well as age, comorbidity indexes, etc. Large scale regularized regression is used to fit the propensity and outcome models as described in Suchard et al. (2013) . Functions are included for trimming, stratifying, (variable and fixed ratio) matching and weighting by propensity scores, as well as diagnostic functions, such as propensity score distribution plots and plots showing covariate balance before and after matching and/or trimming. Supported outcome models are (conditional) logistic regression, (conditional) Poisson regression, and (stratified) Cox regression. Also included are Kaplan-Meier plots that can adjust for the stratification or matching. 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Package: r-cran-collapse Architecture: amd64 Version: 2.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9235 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-fastverse, r-cran-data.table, r-cran-magrittr, r-cran-kit, r-cran-xts, r-cran-zoo, r-cran-plm, r-cran-fixest, r-cran-vars, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-tibble, r-cran-dplyr, r-cran-ggplot2, r-cran-scales, r-cran-microbenchmark, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-bit64 Filename: pool/dists/noble/main/r-cran-collapse_2.1.7-1.ca2404.1_amd64.deb Size: 5039370 MD5sum: 5531dc3f5aa64d2b8680aee046245f91 SHA1: e3be7512fcf773359945f5f71c8fbb1e14a30d33 SHA256: ae1c1fbad7c10bc67e17bb28556025f279c3fdf4245a7563473a5360bf40b902 SHA512: 4e4f0f499ee4258d5a512efdc5a1d52019a1de499dda8c1af64f53ff2789100118cd97e0c85a6931b87c565b73705e076beee805d4f61980bdc67af4ccfba9d5 Homepage: https://cran.r-project.org/package=collapse Description: CRAN Package 'collapse' (Advanced and Fast Data Transformation) A large C/C++-based package for advanced data transformation and statistical computing in R that is extremely fast, class-agnostic, robust, and programmer friendly. Core functionality includes a rich set of S3 generic grouped and weighted statistical functions for vectors, matrices and data frames, which provide efficient low-level vectorizations, OpenMP multithreading, and skip missing values by default. These are integrated with fast grouping and ordering algorithms (also callable from C), and efficient data manipulation functions. The package also provides a flexible and rigorous approach to time series and panel data in R, fast functions for data transformation and common statistical procedures, detailed (grouped, weighted) summary statistics, powerful tools to work with nested data, fast data object conversions, functions for memory efficient R programming, and helpers to effectively deal with variable labels, attributes, and missing data. It seamlessly supports base R objects/classes as well as 'units', 'integer64', 'xts'/ 'zoo', 'tibble', 'grouped_df', 'data.table', 'sf', and 'pseries'/'pdata.frame'. For a concise overview of the package see Krantz (2026) . Package: r-cran-collections Architecture: amd64 Version: 0.3.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-collections_0.3.12-1.ca2404.1_amd64.deb Size: 66726 MD5sum: a96769820ae5ebe2766538240de2678d SHA1: 930b8231c2eb028cee0073bb1ce0a83911aa67af SHA256: 42205b6d2f3bafb67c03eafe001b433f9cb40fb223c0981f99115985a410f9e0 SHA512: 60497b1935c1dea739120a8657557dc035f38980eafff6144017450cd15357efe66767e919720fe8362b6eded5394b39f2c3dc707861f728c69f8c415072f7f3 Homepage: https://cran.r-project.org/package=collections Description: CRAN Package 'collections' (High Performance Container Data Types) Provides high performance container data types such as queues, stacks, deques, dicts and ordered dicts. Benchmarks have shown that these containers are asymptotically more efficient than those offered by other packages. Package: r-cran-collpcm Architecture: amd64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 671 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-network, r-cran-latentnet, r-cran-gtools Filename: pool/dists/noble/main/r-cran-collpcm_1.4-1.ca2404.1_amd64.deb Size: 571826 MD5sum: 2ee3b5dde12ba2115c6a683e2e7d0b50 SHA1: 907f47279f82391c7ccc3ee1f8cf0816345004fd SHA256: 1a637982891df81a5c46d212c01225e5bba6b0f1b445808101865f67af615979 SHA512: 4555d3e8a5b2bec3e77b9a0b78aeef3177573493572739a6d05cf77486314243c6c5b533834baab01476fe4fe542ecccd8b6200809ffdce1e1e1d13300b95686 Homepage: https://cran.r-project.org/package=collpcm Description: CRAN Package 'collpcm' (Collapsed Latent Position Cluster Model for Social Networks) Markov chain Monte Carlo based inference routines for collapsed latent position cluster models or social networks, which includes searches over the model space (number of clusters in the latent position cluster model). The label switching algorithm used is that of Nobile and Fearnside (2007) which relies on the algorithm of Carpaneto and Toth (1980) . Package: r-cran-collutils Architecture: amd64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2476 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-collutils_1.0.5-1.ca2404.1_amd64.deb Size: 2121150 MD5sum: 7eaf9d14debfd23ee02e9c5098cf4cea SHA1: e7c9203bc4e089bdc6fb51b995c942724cb4911c SHA256: bfe4cb1c1dd343bca3d59ec1738f8661c4bafcf4e6b6f229d6d93e0f91083651 SHA512: 9263177579149c86ecb19c547b9dc7f930b069b42e9d97159298f53c15c565209eb36eed467c39346783701c3972cde15fd57cecb6892efac5d3ef4582b9ce16 Homepage: https://cran.r-project.org/package=collUtils Description: CRAN Package 'collUtils' (Auxiliary Package for Package 'CollapsABEL') Provides some low level functions for processing PLINK input and output files. Package: r-cran-colorednoise Architecture: amd64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 484 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-rcpp, r-cran-data.table, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-colorednoise_1.1.2-1.ca2404.1_amd64.deb Size: 254302 MD5sum: 58d45912a88b740003b3e463795a9099 SHA1: d0c9932973d30f56690fd5b72f7f2f28d14fe65b SHA256: 1a2cf2d2ac4a5e32e1eb2e8ea2e7024385d38ba6c87ae87cdc9c4522e1afaf21 SHA512: e0b569db15ea704252fbdb9b30e1cae1f1e321ec2be391a3047066e756e65aa9c5de05f64198c42b3d3d292e478966fc7fd728252fc8249a442b02a9ea9c9d5b Homepage: https://cran.r-project.org/package=colorednoise Description: CRAN Package 'colorednoise' (Simulate Temporally Autocorrelated Populations) Temporally autocorrelated populations are correlated in their vital rates (growth, death, etc.) from year to year. It is very common for populations, whether they be bacteria, plants, or humans, to be temporally autocorrelated. This poses a challenge for stochastic population modeling, because a temporally correlated population will behave differently from an uncorrelated one. This package provides tools for simulating populations with white noise (no temporal autocorrelation), red noise (positive temporal autocorrelation), and blue noise (negative temporal autocorrelation). The algebraic formulation for autocorrelated noise comes from Ruokolainen et al. (2009) . Models for unstructured populations and for structured populations (matrix models) are available. Package: r-cran-colorfast Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-colorfast_1.0.1-1.ca2404.1_amd64.deb Size: 40958 MD5sum: 0e546b0064ca6f8cbf725141e4776e4f SHA1: 7630eb8cd48f3908377a7bf0cf6fd85a8aca847e SHA256: 3a86ee680d8bfaa475c8294c295a8bd11c5892d0fca52f52538f8dc415460088 SHA512: 708c3ba742c6e67e0d6bbbaa65162b8b8bcff076e60a31fa17764bd31b5ca0804e8495a1bc01c7fb959d3f0edcf6d46478bf9fa74c3d74d4a8b205137c784ce1 Homepage: https://cran.r-project.org/package=colorfast Description: CRAN Package 'colorfast' (Fast Conversion of R Colors to Color Component Values and NativePacked Integer Format) Color values in R are often represented as strings of hexadecimal colors or named colors. This package offers fast conversion of these color representations to either an array of red/green/blue/alpha values or to the packed integer format used in native raster objects. Functions for conversion are also exported at the 'C' level for use in other packages. This fast conversion of colors is implemented using an order-preserving minimal perfect hash derived from Majewski et al (1996) "A Family of Perfect Hashing Methods" . Package: r-cran-colorspace Architecture: amd64 Version: 2.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4047 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-kernsmooth, r-cran-mass, r-cran-kernlab, r-cran-mvtnorm, r-cran-vcd, r-cran-shiny, r-cran-shinyjs, r-cran-ggplot2, r-cran-dplyr, r-cran-scales, r-cran-png, r-cran-jpeg, r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer, r-cran-rcartocolor, r-cran-scico, r-cran-viridis, r-cran-wesanderson Filename: pool/dists/noble/main/r-cran-colorspace_2.1-2-1.ca2404.1_amd64.deb Size: 2524652 MD5sum: 0bdd4c6aec4eff9c016ded5f36273e8f SHA1: af131c4877e53bc14fab751ce4bb520eb7400603 SHA256: fd4918101643ea982a511deaec57d2451a7941aed657cc09c91337947b1c2b56 SHA512: 352d2d48bccc177f47c9f4cdcdc0dee8fe8867d5d93a33bcd78404af3db0bd5e052ae9dff466c93aa8d3d0e40322b5576a64d10d9e7e66f792a1092bffb4bdcd Homepage: https://cran.r-project.org/package=colorspace Description: CRAN Package 'colorspace' (A Toolbox for Manipulating and Assessing Colors and Palettes) Carries out mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB, and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided along with corresponding ggplot2 color scales. Color palette choice is aided by an interactive app (with either a Tcl/Tk or a shiny graphical user interface) and shiny apps with an HCL color picker and a color vision deficiency emulator. Plotting functions for displaying and assessing palettes include color swatches, visualizations of the HCL space, and trajectories in HCL and/or RGB spectrum. Color manipulation functions include: desaturation, lightening/darkening, mixing, and simulation of color vision deficiencies (deutanomaly, protanomaly, tritanomaly). Details can be found on the project web page at and in the accompanying scientific paper: Zeileis et al. (2020, Journal of Statistical Software, ). Package: r-cran-colossus Architecture: amd64 Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4343 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-rlang, r-cran-callr, r-cran-stringr, r-cran-processx, r-cran-dplyr, r-cran-tibble, r-cran-lubridate, r-cran-rcppeigen, r-cran-testthat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-xml2, r-cran-pandoc, r-cran-spelling, r-cran-survival, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-colossus_1.5.1-1.ca2404.1_amd64.deb Size: 1795710 MD5sum: 59969263f8cc5a130e30cafbe78d4240 SHA1: 9fc9f3ca806c044891910abe800dd86c553d5034 SHA256: 31149b86e7cdcf5f02b8ff83e0dffcc923e6275063406be241bac0c19a518701 SHA512: 08429230bdc0f2f65917526351b74ddb6545f610789f2a4a8ec14f9cf880e13b0829c53f056ab3dc9f2956a3ff69ed102c48797a59a51b64e102f2fbf7201e57 Homepage: https://cran.r-project.org/package=Colossus Description: CRAN Package 'Colossus' ("Risk Model Regression and Analysis with Complex Non-LinearModels") Performs survival analysis using general non-linear models. Risk models can be the sum or product of terms. Each term is the product of exponential/linear functions of covariates. Additionally sub-terms can be defined as a sum of exponential, linear threshold, and step functions. Cox Proportional hazards , Poisson , and Fine-Gray competing risks regression are supported. This work was sponsored by NASA Grants 80NSSC19M0161 and 80NSSC23M0129 through a subcontract from the National Council on Radiation Protection and Measurements (NCRP). The computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CNS-1006860, EPS-1006860, EPS-0919443, ACI-1440548, CHE-1726332, and NIH P20GM113109. Package: r-cran-colourvalues Architecture: amd64 Version: 0.3.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1928 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-covr, r-cran-microbenchmark, r-cran-scales, r-cran-testthat, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-colourvalues_0.3.11-1.ca2404.1_amd64.deb Size: 554528 MD5sum: 74c395c5f147317c39733b43844fbe22 SHA1: 1fb473edeb006822ff774301e0cb4ce6cddeff72 SHA256: b0dad25213fdf260569d84bd9e1d7f709f75b9c5e664317913e7f984df1ee278 SHA512: 789640f88028334e1496dc945edceb2cea0c7f52905ad670a33479dc8ac99c20775220b6433ef9cb7825ed328057e75b31063a48e1029d3df28d769fd2352d93 Homepage: https://cran.r-project.org/package=colourvalues Description: CRAN Package 'colourvalues' (Assigns Colours to Values) Maps one of the viridis colour palettes, or a user-specified palette to values. Viridis colour maps are created by Stéfan van der Walt and Nathaniel Smith, and were set as the default palette for the 'Python' 'Matplotlib' library . Other palettes available in this library have been derived from 'RColorBrewer' and 'colorspace' packages. Package: r-cran-comat Architecture: amd64 Version: 0.9.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 558 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-tinytest, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-comat_0.9.7-1.ca2404.1_amd64.deb Size: 191958 MD5sum: 69c8fc48bb0a5a980a7ddc99b6626add SHA1: 25d421b172e2110d892f0777faa71a018f136df0 SHA256: 7a874728061d3311efcc0487ae745db6a45e23205e9819e204d1bbbc0ac3ee05 SHA512: d343252db4d578e653ada1525fea148d883ceb6eb38c860227d10fc590b6a7210ace22b22abb9eaaa7f97b71379f30681dfb768985d775910f1bb784d084108b Homepage: https://cran.r-project.org/package=comat Description: CRAN Package 'comat' (Creates Co-Occurrence Matrices of Spatial Data) Builds co-occurrence matrices based on spatial raster data. It includes creation of weighted co-occurrence matrices (wecoma) and integrated co-occurrence matrices (incoma; Vadivel et al. (2007) ). Package: r-cran-combinit Architecture: amd64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 458 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-rcpparmadillo Suggests: r-cran-mass, r-cran-matrix, r-cran-testthat Filename: pool/dists/noble/main/r-cran-combinit_2.0.1-1.ca2404.1_amd64.deb Size: 235676 MD5sum: a169ced3d9b39973848d33ae7d70cd09 SHA1: 84a69488aff9daf299d82cff76b8add9e298056e SHA256: 9e5ed22d9e49a27308bcb4330d800cabc0e961618e1d050547b979dd8fdff051 SHA512: 91d672c9562b9760fc8c513d5eeb5af799f233252fa08f0fde1464d47b178865b4261b82545730ac62f942126ecd94dbfff9a02e26c87b77bc17722fe8a9ba05 Homepage: https://cran.r-project.org/package=combinIT Description: CRAN Package 'combinIT' (A Combined Interaction Test for Unreplicated Two-Way Tables) There are several non-functional-form-based interaction tests for testing interaction in unreplicated two-way layouts. However, no single test can detect all patterns of possible interaction and the tests are sensitive to a particular pattern of interaction. This package combines six non-functional-form-based interaction tests for testing additivity. These six tests were proposed by Boik (1993) , Piepho (1994), Kharrati-Kopaei and Sadooghi-Alvandi (2007) , Franck et al. (2013) , Malik et al. (2016) and Kharrati-Kopaei and Miller (2016) . The p-values of these six tests are combined by Bonferroni, Sidak, Jacobi polynomial expansion, and the Gaussian copula methods to provide researchers with a testing approach which leverages many existing methods to detect disparate forms of non-additivity. This package is based on the following published paper: Shenavari and Kharrati-Kopaei (2018) "A Method for Testing Additivity in Unreplicated Two-Way Layouts Based on Combining Multiple Interaction Tests". In addition, several sentences in help files or descriptions were copied from that paper. Package: r-cran-combiter Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-iterators, r-cran-itertools, r-cran-rcpp Suggests: r-cran-combinat, r-cran-foreach, r-cran-testthat Filename: pool/dists/noble/main/r-cran-combiter_1.0.3-1.ca2404.1_amd64.deb Size: 70316 MD5sum: d16c46d8b94ae712da4c3421d2084e08 SHA1: 58036db7ec3a3e313c3218d930d0311655482632 SHA256: 56e22a816b3b5432d72ce9c01b02db39b828c42fa10a4e0f3d8628d9391589c2 SHA512: 7cffa6bfac37f24070250c750c8e633db1cc425a17734dfc701b7180c46264f91a004708b9579d61c1b4422b35b54a0b5a6e81b2a44b9d3a2b6c299820756915 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-cometexacttest Architecture: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 921 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-cometexacttest_0.1.5-1.ca2404.1_amd64.deb Size: 841806 MD5sum: 25c0c2c1614427343c75402a57bbcda6 SHA1: e84d5cca9deb62df3c10052753645ef2d57d1f03 SHA256: 8309e03ab3d2f3cf0dd5478b68e3c9d0a197d1d43cd4d188d6458ccc0094b5ed SHA512: f4a0b864440c98449d5ff4279fefbfcc498b6023149c94b27e5d5cd6c553bd6edf8e161340cfd53309b548a83c04b705a507c74b954d090e440608d2601f65d6 Homepage: https://cran.r-project.org/package=cometExactTest Description: CRAN Package 'cometExactTest' (Exact Test from the Combinations of Mutually ExclusiveAlterations (CoMEt) Algorithm) An algorithm for identifying combinations of mutually exclusive alterations in cancer genomes. CoMEt represents the mutations in a set M of k genes with a 2^k dimensional contingency table, and then computes the tail probability of observing T(M) exclusive alterations using an exact statistical test. Package: r-cran-comets Architecture: amd64 Version: 0.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-comets_0.2-2-1.ca2404.1_amd64.deb Size: 147098 MD5sum: f897d78c72a66cf34efb034d86c3afb7 SHA1: c683da55a2c74afdca039a56ad4af978f4f5fd2b SHA256: 54a48b2a9d2a92a70f51a5cc448864893abb315d51e128c66d0070c03922f8a8 SHA512: 04be99e1cecbe67ddd0c05060a3e05b41f7e1264e83726997142c75fba0bfdaa093512e335683eec0975d5f73ad10704a278c7429909a50548adef5d6fb55ad6 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, ). 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For more information, see Gorsky, Chan and Ma (2024) . Package: r-cran-commonmark Architecture: amd64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-curl, r-cran-testthat, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-commonmark_2.0.0-1.ca2404.1_amd64.deb Size: 130266 MD5sum: dca1e50c35320743f6bece46b972bd33 SHA1: d35f9b29b5b787335eaaab9b11d7dfd1c272cc3c SHA256: 0ef128bad46572fcdf3e66fed51c4e709ac990547d7b204244175a3eb808baff SHA512: 6250da4cfcf3e6272d524bfc8ac12fd1757db5deb982de1010447f1a55b88ccff79ea968c251d38b341b63bd68ed79d467dd3eb3204b2d682ba96dfbd81e0cc2 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. 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This is based on the monograph by Svetunkov & Svetunkov (2024) . Package: r-cran-complexlm Architecture: amd64 Version: 1.1.3-1.ca2404.1 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/noble/main/r-cran-complexlm_1.1.3-1.ca2404.1_amd64.deb Size: 233210 MD5sum: 381cd65b383bb5f3ed7552d08010f7ba SHA1: 16cdeee627b11675832adf48197bd83adbc95a25 SHA256: 337fea0fc7fa02d78add13ebe94a6c8c2863f1db3ad032aa78fd5398a855e029 SHA512: 0e217cf1af6a0e2f68b6d1b6365c75311329d942b56c11adaf4b741f890059945723b9e7fd245746b33046d7a29443ec36d0d902b4d95d906987f784421dbaed 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. 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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. Package: r-cran-compoissonreg Architecture: amd64 Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 825 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-numderiv Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-compoissonreg_0.8.1-1.ca2404.1_amd64.deb Size: 604962 MD5sum: 4cdb4d90e4360943b630ee66e5cda262 SHA1: 1de39fd2120e723af48c6aebd03104728446b939 SHA256: 11aec5f080a6385d443c9b35ea333de33373ad8a824843e1869d009d20e72ac5 SHA512: ffa5faef675a03a15e6be8d1db029fe3d7c6f3cb08d588ad58027bc5a55d0bd6b4a074bffb1eb5e5262c07740d7a5908b3ed8f34a23f2905e6475580b439d8d2 Homepage: https://cran.r-project.org/package=COMPoissonReg Description: CRAN Package 'COMPoissonReg' (Conway-Maxwell Poisson (COM-Poisson) Regression) Fit Conway-Maxwell Poisson (COM-Poisson or CMP) regression models to count data (Sellers & Shmueli, 2010) . 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Package: r-cran-compositionalrf Architecture: amd64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-compositional, r-cran-rcppparallel, r-cran-rcpp, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-compositionalrf_1.6-1.ca2404.1_amd64.deb Size: 110876 MD5sum: 58d8cdaeb6fc98af181d0ee057741210 SHA1: d22b8eb35ff0cb291bd05c230451ce500f738be1 SHA256: 06573c33d97cb9419e767078384ad0f389e902986b7ed199bf1a14fd2e5737ba SHA512: d74e6da5115de98d2fecc72d6a011a6cc4e2842a40ef93a64f9a6b59073a71affc4f1f477c2d51bf09c05178b00bb84b79861e307a64cf29d6721a8df9d29ed6 Homepage: https://cran.r-project.org/package=CompositionalRF Description: CRAN Package 'CompositionalRF' (Multivariate Random Forest with Compositional Responses) Multivariate random forests with compositional responses and Euclidean predictors is performed. The compositional data are first transformed using the additive log-ratio transformation, or the alpha-transformation of Tsagris, Preston and Wood (2011), , and then the multivariate random forest of Rahman R., Otridge J. and Pal R. (2017), , is applied. 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Aitchison and V. Pawlowsky-Glahn. Package: r-cran-compquadform Architecture: amd64 Version: 1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.3), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-compquadform_1.4.4-1.ca2404.1_amd64.deb Size: 43352 MD5sum: 5abc91237ddd0fd349273ba583e0b040 SHA1: fb5cee4eaeb84444545397a0bbe5c8079c1ead39 SHA256: d4143df192df71960b9c248dc01126fc961a7452d283f40863e7292ead3097bb SHA512: 7fc541907fb630b35c8dc43b358f35060334ed402d62871f8a388de516d95d218a559b34e547b32301d2a4ed2b820008fbf3c9997243714388072fefefb317f1 Homepage: https://cran.r-project.org/package=CompQuadForm Description: CRAN Package 'CompQuadForm' (Distribution Function of Quadratic Forms in Normal Variables) Computes the distribution function of quadratic forms in normal variables using Imhof's method, Davies's algorithm, Farebrother's algorithm or Liu et al.'s algorithm. Package: r-cran-concom Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-english, r-cran-rcpp, r-cran-rgl, r-cran-rvcg, r-cran-bh Suggests: r-cran-rmarchingcubes Filename: pool/dists/noble/main/r-cran-concom_1.0.0-1.ca2404.1_amd64.deb Size: 50442 MD5sum: a4d728b1ee1a41a01022a696616a4a58 SHA1: e2dc7c1e49c9940bb97c08d15aeaef9c115faa3d SHA256: 3dcb45be4e98e494bbd36ede7b01d28e8bb74db2c719465a7afabd83f6fc56ee SHA512: 3715285308e6405831152f0d16a59c39d50eebb1865acab80b58c386ce00397241f7f0230cb19ccac152b3dabc8071af332715be86376044af8f55ea4b981773 Homepage: https://cran.r-project.org/package=concom Description: CRAN Package 'concom' (Connected Components of an Undirected Graph) Provides a function for fast computation of the connected components of an undirected graph (though not faster than the components() function of the 'igraph' package) from the edges or the adjacency matrix of the graph. 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This includes functions that are ccpl (resp. ccpq) on a convex set (i.e. an interval or a point) and infinite out of the domain. These functions can be very useful for a large class of optimisation problems. Efficient manipulation (such as log(N) insertion) of such data structure is obtained with map standard template library of C++ (that hides balanced trees). This package is a wrapper on such a class based on Rcpp modules. Package: r-cran-condsurv Architecture: amd64 Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernsmooth, r-cran-np, r-cran-survival, r-cran-doparallel, r-cran-dorng, r-cran-foreach Filename: pool/dists/noble/main/r-cran-condsurv_2.0.4-1.ca2404.1_amd64.deb Size: 161050 MD5sum: b32a82200cd43b860e174a5ad7699e82 SHA1: 4ad1105466fb29fc08b4240f94e056937ffb4fbb SHA256: 1feb1414b4ec05d7cad2b0796baf39995cdee4c85ee9490f47f5698433f16da0 SHA512: c92efd758d24a621b3a938a6f1eff6444fc8a290534d4dd3c81dd2085cc6358c064f93a54d9827f3410a49dd3e34ba02630fc27c09e91c5eee688dccdc9f6274 Homepage: https://cran.r-project.org/package=condSURV Description: CRAN Package 'condSURV' (Estimation of the Conditional Survival Function for OrderedMultivariate Failure Time Data) Method to implement some newly developed methods for the estimation of the conditional survival function. See Meira-Machado, Sestelo and Goncalves (2016) . 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See Mary C. Meyer (2013) for more details. 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Package: r-cran-conleyreg Architecture: amd64 Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1213 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-rcpp, r-cran-data.table, r-cran-lmtest, r-cran-foreach, r-cran-doparallel, r-cran-rdpack, r-cran-fixest, r-cran-matrix, r-cran-lwgeom, r-cran-s2, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-conleyreg_0.1.9-1.ca2404.1_amd64.deb Size: 493300 MD5sum: 4b8784ad7f671545068b99b91cf658e7 SHA1: 05251e5943c15910b5c5b47054ce043051dfc32a SHA256: ad77af47c3193b5e8ca62b5c67757447f2aaed922bc3367c7f6cb0e67e12ee81 SHA512: 78c71fad294f1db4d140ec1674346cd5207ae0bde666089bfe003d9cc80bfeb2827a0326406d426e1c6f5c9595007917186db1a975c0f14edd454e6bdb012f84 Homepage: https://cran.r-project.org/package=conleyreg Description: CRAN Package 'conleyreg' (Estimations using Conley Standard Errors) Functions calculating Conley (1999) standard errors. The package started by merging and extending multiple packages and other published scripts on this econometric technique. It strongly emphasizes computational optimization. Details are available in the function documentation and in the vignette. Package: r-cran-conmition Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-conmition_0.3.0-1.ca2404.1_amd64.deb Size: 103774 MD5sum: 9a169080e5cb8c3b090a1757df8048d2 SHA1: 426fe967e8731a28ea66292ede2e19499db6d0ef SHA256: 1eff3592b5dbf817eca4324c8481991aac14e4d32162e4eefa27374d44267748 SHA512: a419c8efc4bec63561aa1a2dc5c22cd92c36484d781eb93bd096ca99ee6ccfe4c5d641567cb42f5249f8e93f9e3938f16b67c56509738855b9a381244a5c0a44 Homepage: https://cran.r-project.org/package=conMItion Description: CRAN Package 'conMItion' (Conditional Mutual Information Estimation for Multi-Omics Data) The biases introduced in association measures, particularly mutual information, are influenced by factors such as tumor purity, mutation burden, and hypermethylation. This package provides the estimation of conditional mutual information (CMI) and its statistical significance with a focus on its application to multi-omics data. Utilizing B-spline functions (inspired by Daub et al. (2004) ), the package offers tools to estimate the association between heterogeneous multi- omics data, while removing the effects of confounding factors. This helps to unravel complex biological interactions. In addition, it includes methods to evaluate the statistical significance of these associations, providing a robust framework for multi-omics data integration and analysis. This package is ideal for researchers in computational biology, bioinformatics, and systems biology seeking a comprehensive tool for understanding interdependencies in omics data. Package: r-cran-conos Architecture: amd64 Version: 1.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2273 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-igraph, r-cran-abind, r-cran-cowplot, r-bioc-complexheatmap, r-cran-dendextend, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-gridextra, r-cran-irlba, r-cran-leidenalg, r-cran-magrittr, r-cran-n2r, r-cran-pagoda2, r-cran-r6, r-cran-reshape2, r-cran-rlang, r-cran-rtsne, r-cran-sccore, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-rcppprogress Suggests: r-bioc-annotationdbi, r-bioc-biocparallel, r-cran-drat, r-bioc-deseq2, r-cran-entropy, r-cran-ggrastr, r-bioc-go.db, r-cran-jsonlite, r-cran-knitr, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db, r-cran-pma, r-cran-plyr, r-bioc-rhdf5, r-cran-rmarkdown, r-cran-rmumps, r-cran-seurat, r-cran-shinycssloaders, r-bioc-summarizedexperiment, r-cran-testthat, r-cran-tibble, r-cran-uwot, r-cran-zoo Filename: pool/dists/noble/main/r-cran-conos_1.5.4-1.ca2404.1_amd64.deb Size: 1680404 MD5sum: 7ee2d9ea97b4283d10d71be718219f83 SHA1: 8de79728a6f6c28aa606dc4f980ef9198397f06f SHA256: bdb28634ce3e6b96a1721b52d19fc9d8a75ca1a3958c714c693895345f62ab54 SHA512: 9d06cb841e930b8e1895c39f01e171a825ede1a69976f835af5649e5048cbe03182432030f8f0b18a9411ce1cd0020a5e8545978694b7828c559e9793b6ceebb Homepage: https://cran.r-project.org/package=conos Description: CRAN Package 'conos' (Clustering on Network of Samples) Wires together large collections of single-cell RNA-seq datasets, which allows for both the identification of recurrent cell clusters and the propagation of information between datasets in multi-sample or atlas-scale collections. 'Conos' focuses on the uniform mapping of homologous cell types across heterogeneous sample collections. For instance, users could investigate a collection of dozens of peripheral blood samples from cancer patients combined with dozens of controls, which perhaps includes samples of a related tissue such as lymph nodes. This package interacts with data available through the 'conosPanel' package, which is available in a 'drat' repository. To access this data package, see the instructions at . The size of the 'conosPanel' package is approximately 12 MB. Package: r-cran-conquer Architecture: amd64 Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1803 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-matrixstats, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-conquer_1.3.3-1.ca2404.1_amd64.deb Size: 519434 MD5sum: 6c6b22c91c24f1ed193959d6daaeea4f SHA1: 3dce8ef856bc9fa7294062986def8c7182440a33 SHA256: 69e6684e0a24738295e1ac6dc03e611381e1318c7b83885231ea869dae316ffe SHA512: cbcc8849172da96a27222830f0b446a833fd102a43d1ebce26961572a108e111883b2b6ac0e928e8fc98c2234c22e7ba1ab011eeb256af18d42ef2c24868766a Homepage: https://cran.r-project.org/package=conquer Description: CRAN Package 'conquer' (Convolution-Type Smoothed Quantile Regression) Estimation and inference for conditional linear quantile regression models using a convolution smoothed approach. In the low-dimensional setting, efficient gradient-based methods are employed for fitting both a single model and a regression process over a quantile range. Normal-based and (multiplier) bootstrap confidence intervals for all slope coefficients are constructed. In high dimensions, the conquer method is complemented with flexible types of penalties (Lasso, elastic-net, group lasso, sparse group lasso, scad and mcp) to deal with complex low-dimensional structures. Package: r-cran-conquestr Architecture: amd64 Version: 1.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3057 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-kableextra, r-cran-magrittr, r-cran-rcpp, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-zlib Suggests: r-cran-knitr, r-cran-gridextra, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-conquestr_1.5.5-1.ca2404.1_amd64.deb Size: 1574774 MD5sum: a94d3baf6e52b730355c22c5fe884628 SHA1: e4371750cecdc9997675cf60d700e32e5c762f8b SHA256: 02d504d73e1c2e7ed840fd0e106178d31dcc9bf74ac2fcd6a4f3652a35dc7204 SHA512: fc675c37cb87c5b95d6b78899b7cc9a8ec5ac30342f703981e801b9841d5fc2ec56a62b4b078eeb0bd9ff9479686ca7f18104b7a68682d788bf54d7f484f70b4 Homepage: https://cran.r-project.org/package=conquestr Description: CRAN Package 'conquestr' (An R Package to Extend 'ACER ConQuest') Extends 'ACER ConQuest' through a family of functions designed to improve graphical outputs and help with advanced analysis (e.g., differential item functioning). Allows R users to call 'ACER ConQuest' from within R and read 'ACER ConQuest' System Files (generated by the command `put` ). Requires 'ACER ConQuest' version 5.40 or later. A demonstration version can be downloaded from . Package: r-cran-consrank Architecture: amd64 Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 510 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlist, r-cran-proxy, r-cran-gtools, r-cran-tidyr, r-cran-rcpp Suggests: r-cran-rgl, r-cran-plotly Filename: pool/dists/noble/main/r-cran-consrank_3.0-1.ca2404.1_amd64.deb Size: 355668 MD5sum: b222fad5541ea8a18a1228c4e940febf SHA1: a9ac25bef7122be99305d826146d051d6da2acba SHA256: 8ee7484c4a274af3c42732bee45ea530d45652230f9edd80b08d855268a60a81 SHA512: 1b00dc2f81ff5b19a506acb9a16883242bca301761eae7f6ddb96ea632529792882525e96405be69e37156446120e06a95524830084edd4b4ff5e30c94fd1c69 Homepage: https://cran.r-project.org/package=ConsRank Description: CRAN Package 'ConsRank' (Compute the Median Ranking(s) According to the Kemeny'sAxiomatic Approach) Compute the median ranking according to the Kemeny's axiomatic approach. Rankings can or cannot contain ties, rankings can be both complete or incomplete. The package contains both branch-and-bound algorithms and heuristic solutions recently proposed. The searching space of the solution can either be restricted to the universe of the permutations or unrestricted to all possible ties. The package also provide some useful utilities for deal with preference rankings, including both element-weight Kemeny distance and correlation coefficient. This release includes also the median constrained bucket order algorithm. This release removes the functions previously declared as deprecated. These functions are now defunct and no longer available in the package. Essential references: Emond, E.J., and Mason, D.W. (2002) ; D'Ambrosio, A., Amodio, S., and Iorio, C. (2015) ; Amodio, S., D'Ambrosio, A., and Siciliano R. (2016) ; D'Ambrosio, A., Mazzeo, G., Iorio, C., and Siciliano, R. (2017) ; Albano, A., and Plaia, A. (2021) ; D'Ambrosio, A., Iorio, C., Staiano, M. and Siciliano, R (2019) . Package: r-cran-consreg Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-forecast, r-cran-rlang, r-cran-nloptr, r-cran-fme, r-cran-mcmcpack, r-cran-rsolnp, r-cran-deoptim, r-cran-dfoptim, r-cran-ga, r-cran-gensa, r-cran-metrics, r-cran-ggplot2, r-cran-adaptmcmc, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-consreg_0.1.0-1.ca2404.1_amd64.deb Size: 247052 MD5sum: eeb0af4908098496a465aad1a713fb4d SHA1: eb74a773484bc604b3dde69fd260f188ec56f6bc SHA256: 2997a94bbeb30fa400b7eb1f3735287afa8a7ccc9da64e39d8fc1c5faeb92769 SHA512: f957003050b283561f1188662df25f2f9229f3175a4382084a9ce1b94f421836caa1bb86519b5713b11cbe0a61dddbb2037a55f1d59b3e2c1fbd374f0ad84326 Homepage: https://cran.r-project.org/package=ConsReg Description: CRAN Package 'ConsReg' (Fits Regression & ARMA Models Subject to Constraints to theCoefficient) Fits or generalized linear models either a regression with Autoregressive moving-average (ARMA) errors for time series data. The package makes it easy to incorporate constraints into the model's coefficients. The model is specified by an objective function (Gaussian, Binomial or Poisson) or an ARMA order (p,q), a vector of bound constraints for the coefficients (i.e beta1 > 0) and the possibility to incorporate restrictions among coefficients (i.e beta1 > beta2). The references of this packages are the same as 'stats' package for glm() and arima() functions. See Brockwell, P. J. and Davis, R. A. (1996, ISBN-10: 9783319298528). For the different optimizers implemented, it is recommended to consult the documentation of the corresponding packages. 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(2011) "constrainedKriging: An R-package for customary, constrained and covariance-matching constrained point or block kriging" ). This package supplies functions for two-dimensional spatial interpolation by constrained (Cressie, N. (1993) "Aggregation in geostatistical problems" ), covariance-matching constrained (Aldworth, J. and Cressie, N. (2003) "Prediction of nonlinear spatial functionals" ) and universal (external drift) Kriging for points or blocks of any shape from data with a non-stationary mean function and an isotropic weakly stationary covariance function. The linear spatial interpolation methods, constrained and covariance-matching constrained Kriging, provide approximately unbiased prediction for non-linear target values under change of support. This package extends the range of tools for spatial predictions available in R and provides an alternative to conditional simulation for non-linear spatial prediction problems with local change of support. Package: r-cran-construct Architecture: amd64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4780 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-caroline, r-cran-gtools, r-cran-foreach, r-cran-doparallel, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-maps Filename: pool/dists/noble/main/r-cran-construct_1.0.6-1.ca2404.1_amd64.deb Size: 1525642 MD5sum: fac2da87be4de4b1860808cce870ec29 SHA1: 07f66b0aeaabb51b373ffc61498d56eb877b345a SHA256: 65f2a6b4d602306a4c3eb5d94ddea818d864d076abc75355ea8df4942d81d1d3 SHA512: b838b29bd93686b5a423cea583bb0ed2e57e70ce292a72a026240d6b548ec81060c7a735a5456381bbcbbab38908c4e3af1a538b992d63fc724da4def1222b80 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. 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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: amd64 Version: 1.1-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7309 Depends: libc6 (>= 2.4), 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/noble/main/r-cran-copula_1.1-7-1.ca2404.1_amd64.deb Size: 5241024 MD5sum: 65aa05eee94269ac0b5fcb2bdbab8a2f SHA1: 37f45c126424fd102321278df8726c2fbd00b4c2 SHA256: fa822d6354c9612a3343a1a4b6c2a5cf14dae1fc034c7eef19e7c4308135e165 SHA512: 0c61a4893be78c08f90326064e95835728e7b9b954026291bd6bdce70af1b8aca6ad89927fecd45f84f9a010c62e56d133bb8606bbd159b0a56140ced2abf18c 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. 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For more details and background on these methodologies, see the works of Kowal and Canale (2020) , Kowal and Wu (2022) , King and Kowal (2023) , and Kowal and Wu (2023) . 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Provides implementations of the Hungarian method (Kuhn 1955) , Jonker-Volgenant shortest path algorithm (Jonker and Volgenant 1987) , Auction algorithm (Bertsekas 1988) , cost-scaling (Goldberg and Kennedy 1995) , scaling algorithms (Gabow and Tarjan 1989) , push-relabel (Goldberg and Tarjan 1988) , and Sinkhorn entropy-regularized transport (Cuturi 2013) . Designed for matching plots, sites, samples, or any pairwise optimization problem. Supports rectangular matrices, forbidden assignments, data frame inputs, batch solving, k-best solutions, and pixel-level image morphing for visualization. Includes automatic preprocessing with variable health checks, multiple scaling methods (standardized, range, robust), greedy matching algorithms, and comprehensive balance diagnostics for assessing match quality using standardized differences and distribution comparisons. 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Package: r-cran-covcombr Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1117 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-nlme, r-cran-cholwishart Suggests: r-cran-knitr, r-cran-plyr, r-cran-spcov, r-cran-qgraph, r-cran-igraph Filename: pool/dists/noble/main/r-cran-covcombr_1.0-1.ca2404.1_amd64.deb Size: 977022 MD5sum: b7ffeba678e3d35c413ba472091816f0 SHA1: 7a42001915fabef96fb1f4d8d5f0ce836a1cfabc SHA256: 5378f24fd4678cfb5fd224b2b0ec950a8e3a08de52cf2ce4a731cc184574f3b1 SHA512: ad632ae53c7a923ff100afa22c2c3443a15cb0a2c51ef4f72c3ffd3179ad6340a05f5de4bdd9f2caa0743191a991cfbbb4da1c35b845f66aba743a509dd4fed0 Homepage: https://cran.r-project.org/package=CovCombR Description: CRAN Package 'CovCombR' (Combine Partial Covariance / Relationship Matrices) Combine partial covariance matrices using a Wishart-EM algorithm. Methods are described in the November 2019 article by Akdemir et al. . It can be used to combine partially overlapping covariance matrices from independent trials, partially overlapping multi-view relationship data from genomic experiments, partially overlapping Gaussian graphs described by their covariance structures. High dimensional covariance estimation, multi-view data integration. high dimensional covariance graph estimation. 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Package: r-cran-covregrf Architecture: amd64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1520 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-data.tree, r-cran-diagrammer Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-covregrf_2.0.1-1.ca2404.1_amd64.deb Size: 919194 MD5sum: 73d05b8a1d6eda611dd5003732b5107d SHA1: ec7abdaa9702d8d3b42d0858028fd928284a33e2 SHA256: d81fe1ba03419b0d316c2d651554464d5553616e9092453d6475520616db418a SHA512: f1d831b06ac4d6dd421bd606f9e11a7a44c164ba0d7cd17d0202e4bbeef1b14951678a457fdbcf4dd1fabaa6c188df889128dca69f2a58dcdd09576580fc0237 Homepage: https://cran.r-project.org/package=CovRegRF Description: CRAN Package 'CovRegRF' (Covariance Regression with Random Forests) Covariance Regression with Random Forests (CovRegRF) is a random forest method for estimating the covariance matrix of a multivariate response given a set of covariates. 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Package: r-cran-covtestr Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 434 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-purrr, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-covtestr_0.1.4-1.ca2404.1_amd64.deb Size: 188406 MD5sum: 6758b9884529a49d11e5bcfd86a5adff SHA1: f04ce8732a8130f441228f7ec7a653f65ef04919 SHA256: 7166568c7e96b2d05cdd397fe97a2f4714578252abd761bf4994a1c0065b7952 SHA512: 3ae8bf7b5a8a4bfa0788f229b171520980132f252dd2c44bdf7bb0753361173b3226c1dbba21265ebd0b7ebc90d97265f73bd849c9bbef62f8e1154a255a623c Homepage: https://cran.r-project.org/package=covTestR Description: CRAN Package 'covTestR' (Covariance Matrix Tests) Testing functions for Covariance Matrices. 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We provide a rich collection of geometric and inferential tools for convenient analysis of covariance structures, topics including distance measures, mean covariance estimator, covariance hypothesis test for one-sample and two-sample cases, and covariance estimation. For an introduction to covariance in multivariate statistical analysis, see Schervish (1987) . Package: r-cran-coxboost Architecture: amd64 Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 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/noble/main/r-cran-coxboost_1.5.1-1.ca2404.1_amd64.deb Size: 250664 MD5sum: 0815df53dfba643ccdd0e3bf37b1fe9b SHA1: b475b87b53edcd6e03bd95b79544ba908bd27ae1 SHA256: ba3d0ef000f0d5c4f5a87636a8383a105fa6db116eb56038409f3bcb814d1184 SHA512: 6bde4ec7fd08fefeeeb6cc163b2b1897d8e8f607e280d7f583a5f0ad0b91256c27085088fef6fbacdbaba02ee7ac27c3edb066bf57e0309f6faf409ac510e110 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) . 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The adopted measurement error model has minimal assumptions on the dependence structure, and an instrumental variable is supposed to be available. 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The random effects can have a general form, of which familial interactions (a "kinship" matrix) is a particular special case. Note that the simplest case of a mixed effects Cox model, i.e. a single random per-group intercept, is also called a "frailty" model. The approach is based on Ripatti and Palmgren, Biometrics 2002. Package: r-cran-coxmos Architecture: amd64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5581 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-cowplot, r-cran-furrr, r-cran-future, r-cran-ggrepel, r-cran-ggplot2, r-cran-ggpubr, r-cran-glmnet, r-cran-mass, r-bioc-mixomics, r-cran-patchwork, r-cran-progress, r-cran-purrr, r-cran-rdpack, r-cran-scattermore, r-bioc-survcomp, r-cran-survival, r-cran-survminer, r-cran-svglite, r-cran-tidyr Suggests: r-cran-ggforce, r-cran-knitr, r-cran-nsroc, r-cran-rcolorconesa, r-cran-risksetroc, r-cran-rmarkdown, r-cran-smoothroctime, r-cran-survivalroc Filename: pool/dists/noble/main/r-cran-coxmos_1.1.5-1.ca2404.1_amd64.deb Size: 4150588 MD5sum: cae9ae2c869e875fc17e6b74741db88b SHA1: d238ae0a723ce7f06a1a0891ba9d4e6dec5f6f76 SHA256: d467df874cf2d8b344be3908e984f56cce6be4984842fd8e03e6f2f40fdf8d84 SHA512: 1fca9f62beb01d22fa115c10025fc1cf5eb2b6e0b1b1fa32ace78931847809621cb456b2b40acd82b8808097bc7e76a53f504ac0927193864d90837ccbe8533d 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) . 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The program fits profile penalized likelihood confidence intervals which were proved to outperform Wald confidence intervals. 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The engine is highly configurable in order to tune the detection algorithms and obtain the best possible results. Package: r-cran-cppcontainers Architecture: amd64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8029 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cppcontainers_1.0.5-1.ca2404.1_amd64.deb Size: 2021112 MD5sum: 8e295036674d951455e07373dfeebd7f SHA1: ede5bb958f3fb1a57e1c8e5c9ab07e395741d32b SHA256: 59c4aba746d5e701af0d248656aa372657c998245a2ada94b5e0b116a2e7ad4b SHA512: 44ab03704411ef621eef4c77b4c918d2182bc4c22168bedfe2302e19189d2971a32332671d8e02351abf51c4ef87e54fe96018aa2ce048cc2d0f24d342ad5971 Homepage: https://cran.r-project.org/package=cppcontainers Description: CRAN Package 'cppcontainers' ('C++' Standard Template Library Containers) Use 'C++' Standard Template Library containers interactively in R. Includes sets, unordered sets, multisets, unordered multisets, maps, unordered maps, multimaps, unordered multimaps, stacks, queues, priority queues, vectors, deques, forward lists, and lists. Package: r-cran-cppdoubles Architecture: amd64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-bench, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cppdoubles_0.4.0-1.ca2404.1_amd64.deb Size: 41958 MD5sum: bf8fc94c055d0ffca3e54e89c48c02b8 SHA1: eadec53c25269086252194ddb150ada29056701f SHA256: 651d59d93d20de1047320876edfe56110747bed6f6d2028d26c9a355596c4021 SHA512: d0a8e5935e7fc7b870db22bb5de3a47801e75b0d5fa7da933bbfd3fa650b496ffac511e80f1e5255575b4a45bf107043b2346b547b3bf9af834ab41e64981d7d Homepage: https://cran.r-project.org/package=cppdoubles Description: CRAN Package 'cppdoubles' (Fast Relative Comparisons of Floating Point Numbers in 'C++') Compare double-precision floating point vectors using relative differences. All equality operations are calculated using 'cpp11'. Package: r-cran-cpprouting Architecture: amd64 Version: 3.2-1.ca2404.1 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-rcpp, r-cran-rcppparallel, r-cran-rcppprogress, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-igraph Filename: pool/dists/noble/main/r-cran-cpprouting_3.2-1.ca2404.1_amd64.deb Size: 311284 MD5sum: 87669b9d275d84fdc35af99d56ce54c0 SHA1: e33430fdbf07b848e6656cf786ad38070468fa7e SHA256: 8e875df1044d1d267bbc95099df5a271ab993308e6adac54be04fd6e15b05e50 SHA512: 74e6eee397692ac59f289b61bb5f8cbcfc95f35756bad44587aa18d3c1b496a922e432bef2ac3ee6469cf2fe312b4da2754f46ad672589140256030b09cae476 Homepage: https://cran.r-project.org/package=cppRouting Description: CRAN Package 'cppRouting' (Algorithms for Routing and Solving the Traffic AssignmentProblem) Calculation of distances, shortest paths and isochrones on weighted graphs using several variants of Dijkstra algorithm. Proposed algorithms are unidirectional Dijkstra (Dijkstra, E. W. (1959) ), bidirectional Dijkstra (Goldberg, Andrew & Fonseca F. Werneck, Renato (2005) ), A* search (P. E. Hart, N. J. Nilsson et B. Raphael (1968) ), new bidirectional A* (Pijls & Post (2009) ), Contraction hierarchies (R. Geisberger, P. Sanders, D. Schultes and D. Delling (2008) ), PHAST (D. Delling, A.Goldberg, A. Nowatzyk, R. Werneck (2011) ). Algorithms for solving the traffic assignment problem are All-or-Nothing assignment, Method of Successive Averages, Frank-Wolfe algorithm (M. Fukushima (1984) ), Conjugate and Bi-Conjugate Frank-Wolfe algorithms (M. Mitradjieva, P. O. Lindberg (2012) ), Algorithm-B (R. B. Dial (2006) ). Package: r-cran-cppsim Architecture: amd64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3493 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-data.table, r-cran-foreach, r-cran-knitr, r-cran-rlist, r-cran-rmarkdown, r-cran-cli, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cppsim_0.2-1.ca2404.1_amd64.deb Size: 3239904 MD5sum: 64be87d9dcd70ebb409c8cff54a77c0b SHA1: c087e7f7ec2c7b2dc71e9aa5e2ac24660bce7281 SHA256: b8e6d6ae1e242152c441f05fabffafd98b9b5c75a4f163d4b365eb9ea4e8ea7d SHA512: 46dac1f2d34d96d87ec3a4ca336a9a64ffcb7d8cea8cceaab76dee7d5e65fe6f0a5a196247cc9220342b60e8c6301467f9c8188563f0a6e29abebdd074f0eb40 Homepage: https://cran.r-project.org/package=cppSim Description: CRAN Package 'cppSim' (Fast and Memory Efficient Spatial Interaction Models) Building on top of the 'RcppArmadillo' linear algebra functionalities to do fast spatial interaction models in the context of urban analytics, geography, transport modelling. It uses the Newton root search algorithm to determine the optimal cost exponent and can run country level models with thousands of origins and destinations. It aims at implementing an easy approach based on matrices, that can originate from various routing and processing steps earlier in an workflow. Currently, the simplest form of production, destination and doubly constrained models are implemented. Schlosser et al. (2023) . Package: r-cran-cpr Architecture: amd64 Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2568 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-lme4, r-cran-plot3d, r-cran-rcpp, r-cran-rgl, r-cran-scales, r-cran-rcpparmadillo Suggests: r-cran-matrix, r-cran-geepack, r-cran-ggpubr, r-cran-knitr, r-cran-qwraps2 Filename: pool/dists/noble/main/r-cran-cpr_0.4.1-1.ca2404.1_amd64.deb Size: 1807134 MD5sum: 280d8a50d6636cf2a02fd1496fe5fd86 SHA1: 09b8d55bd7e742f6a309408e3fd5f34b878c7436 SHA256: d0c22ff712264c0983bd1fe389fa4d9dc5f3939e2c8b8dc709e8db597036b1b1 SHA512: ee762579f6e094e804b393bee93a8079a9b2be724efc2509f915bdfbb0d49fee58426dec26077b62d57656e6496130041eb6ab26c3c25a4376f0f5988af89491 Homepage: https://cran.r-project.org/package=cpr Description: CRAN Package 'cpr' (Control Polygon Reduction) Implementation of the Control Polygon Reduction and Control Net Reduction methods for finding parsimonious B-spline regression models. Package: r-cran-cpss Architecture: amd64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 596 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-magrittr, r-cran-mvtnorm, r-cran-rfast, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-ggplot2, r-cran-gridextra, r-cran-rcpparmadillo Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-cpss_0.0.3-1.ca2404.1_amd64.deb Size: 304754 MD5sum: 39846a378e51f819389b513e5416bf87 SHA1: a4b4abe87d43b2e912e2f13aef0fe201ea3c7c20 SHA256: cdef76663a3d4ebc83eb852c81eb6a829f2270c1bfd781532605421cac964dea SHA512: 151211443d17ed503bfcd069a8d54d095dabf4dd69505371eadebda3687446d2095045585624eb6e0b03f41e5f9f7d77d17cf7255e22a8bc19a6c6f859cdf9ed Homepage: https://cran.r-project.org/package=cpss Description: CRAN Package 'cpss' (Change-Point Detection by Sample-Splitting Methods) Implements multiple change searching algorithms for a variety of frequently considered parametric change-point models. 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Package: r-cran-cptnonpar Architecture: amd64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-doparallel, r-cran-parallelly, r-cran-foreach, r-cran-rfast, r-cran-iterators Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cptnonpar_0.3.2-1.ca2404.1_amd64.deb Size: 124002 MD5sum: 0914443e4559fdfbdd9e0eeacf81b421 SHA1: 0ed05dfd448446d77abea5d03334f994cf2750b4 SHA256: 95d5b52a1a89a3462b0a06d67cef82e0472fb9ac9f12834886ec086320b40456 SHA512: f6a0dc4acfd18bc2a121669e6f4fca355fb12293f9ad6e1e673fadd0d065d3dd8ba32500fbed268970a012876fcfbaed6ab88bf8dc759067f534c4bb106c072c Homepage: https://cran.r-project.org/package=CptNonPar Description: CRAN Package 'CptNonPar' (Nonparametric Change Point Detection for Multivariate TimeSeries) Implements the nonparametric moving sum procedure for detecting changes in the joint characteristic function (NP-MOJO) for multiple change point detection in multivariate time series. See McGonigle, E. T., Cho, H. (2025) for description of the NP-MOJO methodology. Package: r-cran-cqrreg Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 564 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-quantreg, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-cqrreg_1.2.1-1.ca2404.1_amd64.deb Size: 238956 MD5sum: e1bd963213dd5d72edd62888c9316b21 SHA1: 99938b4dd055458ba073520647d03f99d13a1667 SHA256: b01adf9e6570740a13d9dd4a392a1fd0c309ec5ca832b9ffc33f2edf43d88c6e SHA512: 378f7ac223e1daa04cdb150b5bebefcb7220fd24c966258770aa29a0250571ca6bbb0d76ee9ad4d3a36e3ba760fecc8a5d9a4ce7df7c98a3c65a89d1e1248829 Homepage: https://cran.r-project.org/package=cqrReg Description: CRAN Package 'cqrReg' (Quantile, Composite Quantile Regression and Regularized Versions) Estimate quantile regression(QR) and composite quantile regression (cqr) and with adaptive lasso penalty using interior point (IP), majorize and minimize(MM), coordinate descent (CD), and alternating direction method of multipliers algorithms(ADMM). Package: r-cran-cramer Architecture: amd64 Version: 0.9-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-cramer_0.9-4-1.ca2404.1_amd64.deb Size: 54376 MD5sum: eb160aa9e52674cb86d7796c1284af66 SHA1: 383624b9499804a36bf60b10d0f2d8446abc10f5 SHA256: 105d8b7fef336f2e90447dd034fbe7d0ac06421ee9be8e8da2143db8761b8679 SHA512: 6570da907362c829bc2da01fe8d1e1e6b6f5f7ff763d3c79489be17d4efa243cb98ad5cd60ca3d4893abe21e35d562e91391d5bd08c9e669960b563881ec6652 Homepage: https://cran.r-project.org/package=cramer Description: CRAN Package 'cramer' (Multivariate Nonparametric Cramer-Test for theTwo-Sample-Problem) Provides R routine for the so called two-sample Cramer-Test. This nonparametric two-sample-test on equality of the underlying distributions can be applied to multivariate data as well as univariate data. It offers two possibilities to approximate the critical value both of which are included in this package. 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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: amd64 Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1195 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-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/noble/main/r-cran-crawl_2.3.1-1.ca2404.1_amd64.deb Size: 861212 MD5sum: f5ef29cf829923ab5667d4e8f7d07b7c SHA1: 19941f97d07e5293fd649892c8b0b11ca9a6be25 SHA256: 62d6eaa114aeaf8ea17337d0686ef9e0391ef7334a81cc7559e52d12d5661c00 SHA512: a8dd887c059e669bfdac26592603390b4041766ea9dc5d693b00e42d3f778a6cd728bedf93eb219a2356ddc0f1c8887f9fd293624264548ac713b158616ef4d5 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: amd64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidycpp Filename: pool/dists/noble/main/r-cran-crc32c_0.0.3-1.ca2404.1_amd64.deb Size: 26378 MD5sum: f1a2196b781cb4d7f2953f86abd5c61a SHA1: 6d7f8a0f7de7e7d503cbcaa48cb597f9fb95699e SHA256: 672b909a443572ce9fd320a5c301518607a59f8700834b8b426bdefa35e0683f SHA512: b793ab9f94c5f16571968fa07450c9168736ba064250fb0ee8c28815a04adef57aeb7c90f149b9270d8bca10e1987fe91c3f380c3c4d9ba06454ba310808f877 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' . 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First, continuous distributions can be used for the (right and/or left censored or truncated) response with separate linear predictors for the mean and variance. Second, cumulative link models for ordinal data (obtained by interval-censoring continuous data) can be employed for heteroscedastic extended logistic regression (HXLR). In the latter type of models, the intercepts depend on the thresholds that define the intervals. Infrastructure for working with censored or truncated normal, logistic, and Student-t distributions, i.e., d/p/q/r functions and distributions3 objects. 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A full description of the methods can be found in Watson et al. (2023) . 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See for more information about the model and for license details for the 'C' code. Package: r-cran-credule Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-credule_0.1.4-1.ca2404.1_amd64.deb Size: 58972 MD5sum: 91d0513ae4905274d402271f50642940 SHA1: e5b0e485c31e3acec148465b6e5b9bd70387a400 SHA256: 6416207fea97adc9bd08f3fc5b0bdfae304905591b2e359a28d612d88664c31d SHA512: e307a0f1d89aadd54bdbb900bbb85d947619b2d69570de7fe54e1178bfc6eaba8e60a1b76cb1398ce1717b5db519095850607cac3af2f916fff6470ce57e4e79 Homepage: https://cran.r-project.org/package=credule Description: CRAN Package 'credule' (Credit Default Swap Functions) It provides functions to bootstrap Credit Curves from market quotes (Credit Default Swap - CDS - spreads) and price Credit Default Swaps - CDS. 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Package: r-cran-crimcv Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 374 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-crimcv_1.0.0-1.ca2404.1_amd64.deb Size: 229502 MD5sum: 5cf091040a4bc52abfea1cbbf66d8863 SHA1: 33b54bdab64bdb1fb4ffeec4d3af7002d37869e6 SHA256: f45d8f11d1c184ca74cb6879bb8e445613ace148513733ec478c0c4203906e95 SHA512: 90ff2e0c45920d5a1123630d14100d76b91b18d1b154ca701faa9349d85f28e90bbef3c825034e9a9eba3d245844a0cb05778d69898fdf7112562367b8260cf8 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. 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Package: r-cran-crmreg Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn, r-cran-ggplot2, r-cran-gplots, r-cran-pcapp, r-cran-plyr, r-cran-robustbase, r-cran-rrcov Filename: pool/dists/noble/main/r-cran-crmreg_1.0.2-1.ca2404.1_amd64.deb Size: 92710 MD5sum: 66463bc02baf21134d23e8bdcf36cec7 SHA1: 40e480801d1f182b947f391123dcf5dc749c0b74 SHA256: a57b4f63a8887edb02db930674324c7f5fd31b52f7f0a80565dbc790994f4c89 SHA512: f73cb8ba8b4f619e5ac3f548a26da9f08a6052e278fad2f9fdbcd7ef135096ed7c8ab34a12b2b346699bdbbbb7e76130ff632ef8e0fe2f0687bf7a4e1f7f41fe Homepage: https://cran.r-project.org/package=crmReg Description: CRAN Package 'crmReg' (Cellwise Robust M-Regression and SPADIMO) Method for fitting a cellwise robust linear M-regression model (CRM, Filzmoser et al. 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The methods follow those described in Cannon et al. (2025, Fire Ecology 21:71, ). 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(2016) , as well as supporting functions. 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Different methods for computing recurrence, cross vs. multidimensional or profile iti.e., only looking at the diagonal recurrent points, as well as functions for optimization and plotting are proposed. in-depth measures of the whole cross-recurrence plot, Please refer to Coco and others (2021) , Coco and Dale (2014) and Wallot (2018) for further details about the method. Package: r-cran-crrsc Architecture: amd64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-crrsc_1.1.2-1.ca2404.1_amd64.deb Size: 85750 MD5sum: 8c10b07ab5144c189dc467c572e004ce SHA1: f0fd23e4bcacbb6514b243878f47321eec4d8eb9 SHA256: d550d3953c75b927e5fa8c3f8584210c8cc9ee7cbcfb0ed3356e9eebb145406d SHA512: c31be7b4117c5bb50a80ce6db7025fe5880691d2ae625df4d74c1dda779b63c75ff3063c79f6e40107374160ff97c1869e3e450e0b65877bbc2bffba7c39e76f 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) . 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I would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, ), the Social Sciences and Humanities Research Council of Canada (SSHRC, ), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, ). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints. 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Package: r-cran-cts Architecture: amd64 Version: 1.0-26-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cts_1.0-26-1.ca2404.1_amd64.deb Size: 325242 MD5sum: 95530c565911053ff8011323b8b7dcad SHA1: 71b8309dcf62578c491aa6315d0f8ae9c9ebfc0a SHA256: 6a74fbca05fe4dd9f98ade59afc82ba2a0e8e2aad3bf952df5d801e78caf0102 SHA512: 3d54d40ab5366d989fe32f8f6b9a09fdf7c5dc5ba18ee8290076dc1be3c103315cc5f8aae844174a12035a32e289f1b4142bd904859a6a7bd5e075c9301b6aa5 Homepage: https://cran.r-project.org/package=cts Description: CRAN Package 'cts' (Continuous Time Autoregressive Models) Provides tools for fitting continuous-time autoregressive (CAR) and complex CAR (CZAR) models for irregularly sampled time series using an exact Gaussian state-space formulation and Kalman filtering/smoothing. Implements maximum-likelihood estimation with stable parameterizations of characteristic roots, model selection via AIC, residual and spectral diagnostics, forecasting and simulation, and extraction of fitted state estimates. Methods are described in Wang (2013) . Package: r-cran-ctsem Architecture: amd64 Version: 3.10.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11544 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-code, r-cran-data.table, r-cran-deriv, r-cran-expm, r-cran-ggplot2, r-cran-mass, r-cran-matrix, r-cran-mize, r-cran-mvtnorm, r-cran-plyr, r-cran-rstan, r-cran-rstantools, r-cran-tibble, r-cran-parallelly, r-cran-corpcor, r-cran-png, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-testthat, r-cran-devtools, r-cran-tinytex, r-cran-lme4, r-cran-shiny, r-cran-gridextra, r-cran-arules, r-cran-collapse, r-cran-qgam, r-cran-papaja, r-cran-future, r-cran-future.apply, r-cran-diagis, r-cran-pdftools, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-ctsem_3.10.6-1.ca2404.1_amd64.deb Size: 5395822 MD5sum: 1f8ff7964ad0e978140e76c56d41acf1 SHA1: 1748f5ed634f5ca69fde509530b6f018d0c5b4f8 SHA256: db8af1f67895436c38af96142b2f9b2a42b408ebad8158cb9369f6f589217f12 SHA512: 5cedcf6737e37681da5b7dc992fceb869556220fd2d828d823843d20191dec6f431ada507bc0c5d97ed222075d05140a1f7a1e2d138b26a5fb93c55b4f3f588a Homepage: https://cran.r-project.org/package=ctsem Description: CRAN Package 'ctsem' (Continuous Time Structural Equation Modelling) Hierarchical continuous (and discrete) time state space modelling, for linear and nonlinear systems measured by continuous variables, with limited support for binary data. The subject specific dynamic system is modelled as a stochastic differential equation (SDE) or difference equation, measurement models are typically multivariate normal factor models. Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default. Nonlinearities, (state dependent parameters) and random effects on all parameters are possible, using either max likelihood / max a posteriori optimization (with optional importance sampling) or Stan's Hamiltonian Monte Carlo sampling. See for details. See for a detailed tutorial. Priors may be used. For the conceptual overview of the hierarchical Bayesian linear SDE approach, see . Exogenous inputs may also be included, for an overview of such possibilities see . contains some tutorial blog posts. Package: r-cran-ctsmtmb Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2392 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-deriv, r-cran-geomtextpath, r-cran-ggfortify, r-cran-ggplot2, r-cran-matrix, r-cran-patchwork, r-cran-r6, r-cran-rcppxptrutils, r-cran-rtmb, r-cran-stringr, r-cran-tmb, r-cran-rcppeigen, r-cran-zigg Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ctsmtmb_1.0.1-1.ca2404.1_amd64.deb Size: 1557394 MD5sum: 6986dfdd3327cc92953e721af285f294 SHA1: 935df30b7850e90a64de071c6a062aee95d8d4b1 SHA256: e52770a559eaad4994aec78980bad4409287663567a238ba779838f02fb5f5c9 SHA512: e56e2febbdfba1b3e83be1d8af5c3186b5493b11a70aef1d1805d8aba843041d830388a1d20b6a86cb7c20693e48239d6b734036557abff1b4c04e849b373dfc Homepage: https://cran.r-project.org/package=ctsmTMB Description: CRAN Package 'ctsmTMB' (Continuous Time Stochastic Modelling using Template ModelBuilder) Perform state and parameter inference, and forecasting, in stochastic state-space systems using the 'ctsmTMB' class. This class, built with the 'R6' package, provides a user-friendly interface for defining and handling state-space models. Inference is based on maximum likelihood estimation, with derivatives efficiently computed through automatic differentiation enabled by the 'TMB'/'RTMB' packages (Kristensen et al., 2016) . The available inference methods include Kalman filters, in addition to a Laplace approximation-based smoothing method. For further details of these methods refer to the documentation of the 'CTSMR' package and Thygesen (2025) . Forecasting capabilities include moment predictions and stochastic path simulations, both implemented in 'C++' using 'Rcpp' (Eddelbuettel et al., 2018) for computational efficiency. Package: r-cran-ctypesio Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-jpeg Filename: pool/dists/noble/main/r-cran-ctypesio_0.1.3-1.ca2404.1_amd64.deb Size: 170954 MD5sum: f6d105a94b3ed7e6554c8a83d9f47290 SHA1: 25b1e0c691e3b6124b72e96a0ba3ca60e766a8da SHA256: d4d99f42aacb92a94b8c8499ed7c60e93d13dd4c1b7bb25abef4bcbbbfe3a04b SHA512: 98590a387938d8e522290074e2816242968522254fb3e0acf83c9d62cf99f8ec0f4de58235dc79bb6a2b9811bb6c4a3fbac893baea8966ff9d05609a5f8fc549 Homepage: https://cran.r-project.org/package=ctypesio Description: CRAN Package 'ctypesio' (Read and Write Standard 'C' Types from Files, Connections andRaw Vectors) Interacting with binary files can be difficult because R's types are a subset of what is generally supported by 'C'. This package provides a suite of functions for reading and writing binary data (with files, connections, and raw vectors) using 'C' type descriptions. These functions convert data between 'C' types and R types while checking for values outside the type limits, 'NA' values, etc. Package: r-cran-cubature Architecture: amd64 Version: 2.1.4-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3322 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-mvtnorm, r-cran-bench, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cubature_2.1.4-1-1.ca2404.1_amd64.deb Size: 1731890 MD5sum: 9bf7f382cb32f47c7a874dfe567e5db7 SHA1: 5f75efdd776e5b68b73cb293120a773c104dacbd SHA256: 62b54a1b8bed0714ab642fa00c27226f137a64ec1d23724c766bf387f28c8cc9 SHA512: 43ed271f8b54bd9d4d31b0e1bdd17294bcc238f25613adcdcade878fa1da582ef409d28198491124bd7c4cc468d81c2269f4df885b7295e18dc78c531b86eb40 Homepage: https://cran.r-project.org/package=cubature Description: CRAN Package 'cubature' (Adaptive Multivariate Integration over Hypercubes) R wrappers around the cubature C library of Steven G. Johnson for adaptive multivariate integration over hypercubes and the Cuba C library of Thomas Hahn for deterministic and Monte Carlo integration. Scalar and vector interfaces for cubature and Cuba routines are provided; the vector interfaces are highly recommended as demonstrated in the package vignette. Package: r-cran-cubfits Architecture: amd64 Version: 0.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2378 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-foreach Suggests: r-cran-seqinr, r-cran-vgam, r-cran-emcluster Filename: pool/dists/noble/main/r-cran-cubfits_0.1-4-1.ca2404.1_amd64.deb Size: 1666706 MD5sum: c333b9c04fc4452db9d048e200cd3cb3 SHA1: ef1526bdaf5fed907b4b7be7a2634880d00d788a SHA256: b429058bfabe1dcf31b03480c37c2acd845cf0a2831f7d399c304de65ed2d9cb SHA512: 13a4b610eb6171bd216acd3d36139bd832f32ee47c6106f99f51cd35a20e1623558c8d43c1d5833aa80622ee6635b6a3b4f2017898813fd6cc692f2452ccf082 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) . 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Package: r-cran-cubing Architecture: amd64 Version: 1.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2985 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl Filename: pool/dists/noble/main/r-cran-cubing_1.0-5-1.ca2404.1_amd64.deb Size: 2930926 MD5sum: e173d27ea8bd0e67032d46ce220be85d SHA1: 126047f3888dbfcd2531b50910147e396e65a158 SHA256: ee232b58ea66d0b6a0b418ea8ecc67e3e44ef65deb60feac282e9f45fd9b7caa SHA512: be77ddfb812d0f0fac79b978d31aa1c9ecab5c423b3dba279a5596b61e10f6ee264a235b109a13e2b0a765c7df1cfa1218aee4b3c0cdfb0a7b91fa00460e8430 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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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: amd64 Version: 4.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3650 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgfortran5 (>= 10), libstdc++6 (>= 13.1), 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/noble/main/r-cran-daisie_4.6.0-1.ca2404.1_amd64.deb Size: 2201406 MD5sum: 76060c2e4de4114de983bf6623d1a838 SHA1: bafebae562bbd9e8b689bb60f947f96b73a0fc01 SHA256: d7ce27bc570e00bf93a7e5b6afaf7fa9d9b6b8f96efd1056821bd3f1725bb329 SHA512: e62a0feb09d3c7415df45d23c0cd1b08f107a4ddc1019d5651aa8e4a48bc474bed148cca711077b4b4282205d7e320e53b7b64e362b5dcde232e6e8a046a64b3 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. See Valente et al. (2015) . Package: r-cran-daly Architecture: amd64 Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1395 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-daly_1.5.0-1.ca2404.1_amd64.deb Size: 1166098 MD5sum: 56527abd3266449c1d041373bfe31724 SHA1: 8ff59b21997df60e827e1bc40fee9f935dcf3bd1 SHA256: d74118158db357ef03ef11e89143dcfb70385d75095d23b742fa174d2ec69ea6 SHA512: 3667ea251c2000aed71a7689628feffc6256751f1d02ebce29781702589da30d88f624ec7479a40098cbebf031d4e1bb973dc8dfc3602f107fc81a2758c1001c Homepage: https://cran.r-project.org/package=DALY Description: CRAN Package 'DALY' (The DALY Calculator - Graphical User Interface for ProbabilisticDALY Calculation in R) The DALY Calculator is a free, open-source Graphical User Interface (GUI) for stochastic disability-adjusted life year (DALY) calculation. Package: r-cran-dang Architecture: amd64 Version: 0.0.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/noble/main/r-cran-dang_0.0.17-1.ca2404.1_amd64.deb Size: 105472 MD5sum: 87d5d21c17ed77c93a5c527c785717b5 SHA1: 880b044b9f6e9bc950854d2350cfeeefd858a54b SHA256: 19017d7098f990b855ab7308b8dda8ef7bdab496bc3146a3fbc3e6a7ed7b6273 SHA512: e447f59bf79d6c41a79711a7b6fae69e67bd8f071c9fb0d39e5c3ec66a7186bbb46fa62e5f3e78b40fc53c60b2e4d9e3e566443669ec7653587a046b74039c2f 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: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-ggplot2, r-cran-stringr, r-cran-rlang, r-cran-fpc, r-cran-rcpp, r-cran-hardhat, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-mlbench, r-cran-dplyr, r-cran-magrittr, r-cran-recipes Filename: pool/dists/noble/main/r-cran-dann_1.1.0-1.ca2404.1_amd64.deb Size: 237742 MD5sum: 49a6f232ffd19be6883dda6b82f14108 SHA1: da9ae99534dd1f16aedc3a59c7c1b57115c13cad SHA256: 5f29eedbd02bfe3a33dcd1fce5a80d7a5fbd07e15496b211ba1cd959d3f45974 SHA512: 77961aa42e7d07aaaabdfe7ab0d91e22bdddf591c6c78c56f67eb6a354b98e5ee0eb1b8a6acd12d40d1fe104f869b0fa518adf0ea88f3bc59ce32ebc805c4a82 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: amd64 Version: 1.18.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5353 Depends: libc6 (>= 2.38), libgomp1 (>= 6), zlib1g (>= 1:1.2.2), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-bit64, r-cran-bit, r-cran-r.utils, r-cran-xts, r-cran-zoo, r-cran-yaml, r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-data.table_1.18.4-1.ca2404.1_amd64.deb Size: 2518252 MD5sum: 8cd3c80f39e91a4ee4f06881eddf86de SHA1: d7a5c1eee9b7ccd48e566a9c63d491f4e4f6bf8d SHA256: 66cbb10f8f5441ca6911da89462682a563deafb19fbcd64e8dff05b149b03f72 SHA512: 1ef00e0c83aaaa69d110024ad10f58f32432da5bcf3a3e1d7ae75d9a9a6bade390063aac2003eda6e4751d6893ce00ec4edc3754400a41aed86ddcd296c28735 Homepage: https://cran.r-project.org/package=data.table Description: CRAN Package 'data.table' (Extension of `data.frame`) Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. 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Package: r-cran-databionicswarm Architecture: amd64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3408 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-deldir, r-cran-generalizedumatrix, r-cran-abcanalysis, r-cran-ggplot2, r-cran-rcpparmadillo Suggests: r-cran-datavisualizations, r-cran-knitr, r-cran-rmarkdown, r-cran-plotrix, r-cran-geometry, r-cran-sp, r-cran-spdep, r-cran-rgl, r-cran-png, r-cran-projectionbasedclustering, r-cran-paralleldist, r-cran-pracma, r-cran-dendextend Filename: pool/dists/noble/main/r-cran-databionicswarm_2.0.0-1.ca2404.1_amd64.deb Size: 839448 MD5sum: f8aa44aee4f2648afe5d5c9cbf6d9e07 SHA1: a71b520e9b361015eaba42b6bf4969dcbb786a48 SHA256: d1ead1b24d68069eaa8393c0ce7d0b66a97f1ef63fef78b12338d1d89fafaeb4 SHA512: 5e185ba01129563a836094a7aed7ff727bbe89c4845d5a42edf4f4b45c83c5e5b60e091c57108e4bd495ee8ecfa793ff4fcca36681f1de6933ef087c5bd36fb4 Homepage: https://cran.r-project.org/package=DatabionicSwarm Description: CRAN Package 'DatabionicSwarm' (Swarm Intelligence for Self-Organized Clustering) Algorithms implementing populations of agents that interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here, a swarm system called Databionic swarm (DBS) is introduced which was published in Thrun, M.C., Ultsch A.: "Swarm Intelligence for Self-Organized Clustering" (2020), Artificial Intelligence, . DBS is able to adapt itself to structures of high-dimensional data such as natural clusters characterized by distance and/or density based structures in the data space. The first module is the parameter-free projection method called Pswarm (Pswarm()), which exploits the concepts of self-organization and emergence, game theory, swarm intelligence and symmetry considerations. The second module is the parameter-free high-dimensional data visualization technique, which generates projected points on the topographic map with hypsometric tints defined by the generalized U-matrix (GeneratePswarmVisualization()). The third module is the clustering method itself with non-critical parameters (DBSclustering()). Clustering can be verified by the visualization and vice versa. The term DBS refers to the method as a whole. It enables even a non-professional in the field of data mining to apply its algorithms for visualization and/or clustering to data sets with completely different structures drawn from diverse research fields. The comparison to common projection methods can be found in the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) . 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We use an efficient model representation and a genetic algorithm-based estimation process to generate simple deterministic approximations that explain most of the structure of complex stochastic processes. We have applied the software to empirical data, and demonstrated it's ability to recover known data-generating processes by simulating data with agent-based models and correctly deriving the underlying decision models for multiple agent models and degrees of stochasticity. 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Can save a data frame, collection of data frames and sequences of data frames and individual vectors. For more information see . 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Package: r-cran-datassim Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-datassim_1.0-1.ca2404.1_amd64.deb Size: 50970 MD5sum: ca9e709b5601589d42b10a92ccd57381 SHA1: d5a33515382d7201250aebb9d1cb245357076399 SHA256: e30a19cfe4dc036a3b8ec0b29dae13dcb966cc17d95d243ea0d35c8146296533 SHA512: 3bb02a0644e93fbe77afd2e9d7216bf6815ac49a170f8dfcfdb5d0d3594cba090c254b815ff6c316975b95cb39f928bb1599eacada4d75bf285eb3bfd6164fc5 Homepage: https://cran.r-project.org/package=DatAssim Description: CRAN Package 'DatAssim' (Data Assimilation) For estimation of a variable of interest using Kalman filter by incorporating results from previous assessments, i.e. through development weighted estimates where weights are assigned inversely proportional to the variance of existing and new estimates. For reference see Ehlers et al. (2017) . 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Bundles the 'blast' decompressor from 'zlib' contrib/blast to decode 'PKWare DCL' compressed 'DBC' files and parses 'DBF' records directly for efficient import into tibbles. See the 'DATASUS' file transfer site and Adler (2003) for details on the underlying data and compression format. Package: r-cran-datavisualizations Architecture: amd64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5348 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-ggplot2, r-cran-sp, r-cran-pracma, r-cran-reshape2 Suggests: r-cran-plyr, r-cran-mba, r-cran-ggmap, r-cran-plotrix, r-cran-rworldmap, r-cran-rgl, r-cran-abcanalysis, r-cran-paralleldist, r-cran-knitr, r-cran-rmarkdown, r-cran-vioplot, r-cran-ggextra, r-cran-plotly, r-cran-htmlwidgets, r-cran-diptest, r-cran-moments, r-cran-signal, r-cran-ggrepel, r-cran-mass, r-cran-rocit, r-cran-scatterdensity, r-cran-colorspace, r-cran-viridis, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-datavisualizations_1.4.0-1.ca2404.1_amd64.deb Size: 3771602 MD5sum: 0a56173fd6697c34e922b90fdf837ed9 SHA1: 62636fe5ffbeb17c4f74f35274df2af806669a33 SHA256: d98d9e7302a82f3c602b05e46ec8135df86c78134d8416e1f7a7c86dab8c8b9f SHA512: 3d126b1bd42171f53bd6d442b6949f88dffe9ad43ff841b67f1ed013dd53da0dfb6dd24bdc3773b8c2f7fd196ef40b3ebdd620f27da44379d5ea7e2d65c73e28 Homepage: https://cran.r-project.org/package=DataVisualizations Description: CRAN Package 'DataVisualizations' (Visualizations of High-Dimensional Data) Gives access to data visualisation methods that are relevant from the data scientist's point of view. The flagship idea of 'DataVisualizations' is the mirrored density plot (MD-plot) for either classified or non-classified multivariate data published in Thrun, M.C. et al.: "Analyzing the Fine Structure of Distributions" (2020), PLoS ONE, . The MD-plot outperforms the box-and-whisker diagram (box plot), violin plot and bean plot and geom_violin plot of ggplot2. Furthermore, a collection of various visualization methods for univariate data is provided. In the case of exploratory data analysis, 'DataVisualizations' makes it possible to inspect the distribution of each feature of a dataset visually through a combination of four methods. One of these methods is the Pareto density estimation (PDE) of the probability density function (pdf). Additionally, visualizations of the distribution of distances using PDE, the scatter-density plot using PDE for two variables as well as the Shepard density plot and the Bland-Altman plot are presented here. Pertaining to classified high-dimensional data, a number of visualizations are described, such as f.ex. the heat map and silhouette plot. A political map of the world or Germany can be visualized with the additional information defined by a classification of countries or regions. By extending the political map further, an uncomplicated function for a Choropleth map can be used which is useful for measurements across a geographic area. For categorical features, the Pie charts, slope charts and fan plots, improved by the ABC analysis, become usable. More detailed explanations are found in the book by Thrun, M.C.: "Projection-Based Clustering through Self-Organization and Swarm Intelligence" (2018) . 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The throw chart method is a line chart used to illustrate paired data sets (such as before-after, male-female). 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Also serves as a drop-in replacement for package 'BayesTree'. Package: r-cran-dblcens Architecture: amd64 Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dblcens_1.1.9-1.ca2404.1_amd64.deb Size: 56550 MD5sum: 40663457d5307ae67562d6895dbef68c SHA1: 2b64cff10e31675d554acef7fd85d342eb00ddde SHA256: b60f977d168be64cf3c17570b5361b99de74d8fdaeafa731b9768b93e52d9ff4 SHA512: 2e2e587b8f2c82b66f8ffc394ea900fb7fac43ee29b54fd45b3c945b90b97c531fef5a55d05195471d4e025862e61c8bae2c9c6429c916546157b34fa63a7097 Homepage: https://cran.r-project.org/package=dblcens Description: CRAN Package 'dblcens' (Compute the NPMLE of Distribution Function from Doubly CensoredData, Plus the Empirical Likelihood Ratio for F(T)) Doubly censored data, as described in Chang and Yang (1987) ), are commonly seen in many fields. We use EM algorithm to compute the non-parametric MLE (NPMLE) of the cummulative probability function/survival function and the two censoring distributions. One can also specify a constraint F(T)=C, it will return the constrained NPMLE and the -2 log empirical likelihood ratio for this constraint. This can be used to test the hypothesis about the constraint and, by inverting the test, find confidence intervals for probability or quantile via empirical likelihood ratio theorem. Influence functions of hat F may also be calculated, but currently, the it may be slow. 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Extends some of the functionality offered by the 'bnlearn' package to learn the networks from data and perform exact inference. It offers three structure learning algorithms for dynamic Bayesian networks: Trabelsi G. (2013) , Santos F.P. and Maciel C.D. (2014) , Quesada D., Bielza C. and Larrañaga P. (2021) . It also offers the possibility to perform forecasts of arbitrary length. A tool for visualizing the structure of the net is also provided via the 'visNetwork' package. Further detailed information and examples can be found in our Journal of Statistical Software paper Quesada D., Larrañaga P. and Bielza C. (2025) . Package: r-cran-dbscan Architecture: amd64 Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4331 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-generics, r-cran-rcpp Suggests: r-cran-dendextend, r-cran-fpc, r-cran-igraph, r-cran-knitr, r-cran-microbenchmark, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-dbscan_1.2.4-1.ca2404.1_amd64.deb Size: 2867362 MD5sum: 3357f6e488d0c7bfbe3b84f213aa308d SHA1: 0ee0b8eaab3f5e56b42f430c6cfb1594a5433706 SHA256: 33d545d6f1ad222bad6aef3073b4b6e804e9e101fe63f0f3366fda27b963b629 SHA512: b620ec44098aeccfb89dabb6d752ab25dae0d348a66472e2f566bc08df2795875beda1138c199ca380df6b7c6a530851e7e51eb593a9113bbf34a2f3fb29da69 Homepage: https://cran.r-project.org/package=dbscan Description: CRAN Package 'dbscan' (Density-Based Spatial Clustering of Applications with Noise(DBSCAN) and Related Algorithms) A fast reimplementation of several density-based algorithms of the DBSCAN family. Includes the clustering algorithms DBSCAN (density-based spatial clustering of applications with noise) and HDBSCAN (hierarchical DBSCAN), the ordering algorithm OPTICS (ordering points to identify the clustering structure), shared nearest neighbor clustering, and the outlier detection algorithms LOF (local outlier factor) and GLOSH (global-local outlier score from hierarchies). The implementations use the kd-tree data structure (from library ANN) for faster k-nearest neighbor search. An R interface to fast kNN and fixed-radius NN search is also provided. Hahsler, Piekenbrock and Doran (2019) . Package: r-cran-dcca Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-dcca_0.1.1-1.ca2404.1_amd64.deb Size: 97296 MD5sum: a63acdbb17594ff715c7a9ab6fd714af SHA1: a5fb0a96b45638bb68c55bb0c44e499c890551a4 SHA256: 0adcfc6cf75f9fd61a01f1222d2c491565c722fa46b1d866b0bd17193c0ea90c SHA512: c16d44400ef5340137cd07050cfc5ea93e484b556b7a7953f7a25003bf8a94ac99067fd2425768275eb74197507cdfc00575b6d684a662a95a32b0f39daf461e Homepage: https://cran.r-project.org/package=DCCA Description: CRAN Package 'DCCA' (Detrended Fluctuation and Detrended Cross-Correlation Analysis) A collection of functions to perform Detrended Fluctuation Analysis (DFA) and Detrended Cross-Correlation Analysis (DCCA). This package implements the results presented in Prass, T.S. and Pumi, G. (2019). "On the behavior of the DFA and DCCA in trend-stationary processes" . Package: r-cran-dcce Architecture: amd64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1136 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-dcce_0.4.2-1.ca2404.1_amd64.deb Size: 761628 MD5sum: b8fde367f21cc4b0ec8f808c932bbc04 SHA1: b58928afb850a7276b7650f34fbf7d243279e524 SHA256: 744618520272cabfaf55ca3a496782ad1a57c6dcd20358e96b6a1d3aea2f1483 SHA512: 14e9e48dbc67058ee56f183ccd27ff7f3bca7e79f1729053bd0644c7c355635fb4971cc478c5b37453d17e1b4cca892111eab1cb1f97132d3746b00d7ae85456 Homepage: https://cran.r-project.org/package=dcce Description: CRAN Package 'dcce' (Dynamic Common Correlated Effects Estimation for Panel Data) Estimates heterogeneous coefficient models for large panels with cross-sectional dependence. Implements the Mean Group (MG) estimator of Pesaran and Smith (1995) , the Common Correlated Effects (CCE) and Dynamic CCE (DCCE) estimators of Pesaran (2006) and Chudik and Pesaran (2015) , the regularized CCE of Juodis (2022), the Augmented Mean Group (AMG) of Eberhardt and Teal (2010), the Interactive Fixed Effects (IFE) estimator of Bai (2009) , and long-run estimators including Cross-Sectionally augmented Distributed Lag (CS-DL), Cross-Sectionally augmented Autoregressive Distributed Lag (CS-ARDL), and Pooled Mean Group (PMG) (Chudik et al. 2016; Shin et al. 1999). Also provides rolling-window estimation, high-dimensional fixed effect absorption, spatial CCE via user-supplied weight matrices, and structural break tests (Chow and sup-Wald) following Andrews (1993), Bai and Perron (1998), and Ditzen, Karavias and Westerlund (2024). Supplies a comprehensive cross-sectional dependence (CD) test suite including the Pesaran (2015) CD test , the Juodis and Reese (2022) randomized weighted CD (CDw) test, the Baltagi et al. (2012) bias-adjusted weighted CD (CDw+) test, the Fan et al. (2015) Power Enhancement Approach (PEA) test, and the Pesaran and Xie (2021) bias-corrected CD (CD*) test. Further diagnostics include the Pesaran (2007) Cross-sectionally Augmented IPS (CIPS) panel unit root test , the Westerlund (2007) panel cointegration tests, the Dumitrescu and Hurlin (2012) panel Granger causality test, the Im-Pesaran-Shin (IPS) and Levin-Lin-Chu (LLC) panel unit root tests, the Pedroni (2004) and Kao (1999) residual cointegration tests, the Swamy (1970) and Pesaran and Yamagata (2008) slope homogeneity tests, a Hausman-type test for MG versus pooled, the exponent of cross-sectional dependence from Bailey et al. (2016) , information criteria for Cross-Sectional Average (CSA) selection, the rank condition classifier, impulse response functions, cross-section and wild bootstrap inference, and 'broom'-compatible methods. Package: r-cran-dccmidas Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 588 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-maxlik, r-cran-rumidas, r-cran-rugarch, r-cran-roll, r-cran-xts, r-cran-rdpack, r-cran-zoo, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dccmidas_0.1.2-1.ca2404.1_amd64.deb Size: 503082 MD5sum: 175ed0602a9d9a1fb81278e0897b677c SHA1: 58123defee8c3e9c040a4586b761137f69534cc8 SHA256: 39344c56ddfa6f232e1e499d1de70ecb55eda04720d07e820cf845e5b22e5d59 SHA512: 2788ae1df68d2dfc84ac831f693eb6b6acbaaedb601612c70677f36495e6e6b63f12b08d3ab386a952c77592a0e9e29872064378c8b44149bd920d273f981625 Homepage: https://cran.r-project.org/package=dccmidas Description: CRAN Package 'dccmidas' (DCC Models with GARCH and GARCH-MIDAS Specifications in theUnivariate Step, RiskMetrics, Moving Covariance and Scalar andDiagonal BEKK Models) Estimates a variety of Dynamic Conditional Correlation (DCC) models. More in detail, the 'dccmidas' package allows the estimation of the corrected DCC (cDCC) of Aielli (2013) , the DCC-MIDAS of Colacito et al. (2011) , the Asymmetric DCC of Cappiello et al. , and the Dynamic Equicorrelation (DECO) of Engle and Kelly (2012) . 'dccmidas' offers the possibility of including standard GARCH , GARCH-MIDAS and Double Asymmetric GARCH-MIDAS models in the univariate estimation. Moreover, also the scalar and diagonal BEKK models can be estimated. Finally, the package calculates also the var-cov matrix under two non-parametric models: the Moving Covariance and the RiskMetrics specifications. Package: r-cran-dccpp Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dccpp_0.1.0-1.ca2404.1_amd64.deb Size: 57848 MD5sum: acfd81c1422d01dc75e0f2a301418408 SHA1: 1800dfacfbba3797278331550b43bf2cc9b3b3f2 SHA256: 418d9e25dd88fa4e5c256b93d2109bc0dcf58d36c39d3d5a689fb8ae32f64e28 SHA512: 4dfd5e3acf0110358cc6c1c8813b8dda072d4a20bb911ed2ea4c080492aed5adb8cd8aad0672faf6ab7dfb09f3e9093a5fcdaefe59f95c17bf81baa7a7911d8d Homepage: https://cran.r-project.org/package=dccpp Description: CRAN Package 'dccpp' (Fast Computation of Distance Correlations) Fast computation of the distance covariance 'dcov' and distance correlation 'dcor'. The computation cost is only O(n log(n)) for the distance correlation (see Chaudhuri, Hu (2019) ). The functions are written entirely in C++ to speed up the computation. Package: r-cran-dcem Architecture: amd64 Version: 2.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-dcem_2.0.6-1.ca2404.1_amd64.deb Size: 173088 MD5sum: f475ed75e4e062e37c35662cc67f1143 SHA1: 4fd8bf805ef613a83d8482f6947747fefe69549b SHA256: b3ac65e685739089c6762408a8dc5dce6b416f557f1c0bbc5b0db065267a5bcf SHA512: 7d0fc948ccd56c9c89ad044b1d3db08749348b7f6b2cf0d9c4cdf86c77135fa890ebd18a0f0325a3b67d4c50f1766cbd65a2e9e8f542b3ed8c2fe7d188527fcc Homepage: https://cran.r-project.org/package=DCEM Description: CRAN Package 'DCEM' (Clustering Big Data using Expectation Maximization Star (EM*)Algorithm) Implements the Improved Expectation Maximisation EM* and the traditional EM algorithm for clustering big data (gaussian mixture models for both multivariate and univariate datasets). This version implements the faster alternative-EM* that expedites convergence via structure based data segregation. The implementation supports both random and K-means++ based initialization. Reference: Parichit Sharma, Hasan Kurban, Mehmet Dalkilic (2022) . Hasan Kurban, Mark Jenne, Mehmet Dalkilic (2016) . Package: r-cran-dcifer Architecture: amd64 Version: 1.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1057 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dcifer_1.5.2-1.ca2404.1_amd64.deb Size: 589734 MD5sum: cfab8ac88e5bbd44287b55644f5ceeb1 SHA1: ca28b1e648c53f9cbe9c45e1660c7ae12e43cb31 SHA256: d1b84f9aee0ddc6795d283b7ec133b0cd29a35115b04a62498514e65766917c4 SHA512: 2b9805849b1980b85146565629ed722634298952f79e57b2e04560a8b1124d20bd525d8702e2860fc55a5f7925fae817975eaa9b3cbf639fbe69176e6cb81bc5 Homepage: https://cran.r-project.org/package=dcifer Description: CRAN Package 'dcifer' (Genetic Relatedness Between Polyclonal Infections) An implementation of Dcifer (Distance for complex infections: fast estimation of relatedness), an identity by descent (IBD) based method to calculate genetic relatedness between polyclonal infections from biallelic and multiallelic data. The package includes functions that format and preprocess the data, implement the method, and visualize the results. Gerlovina et al. (2022) . 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Our methods won both sub-challenges 2 and 3 of the Allen Institute Cell Lineage Reconstruction DREAM Challenge in 2020. References: Gong et al. (2021) , Gong et al. (2022) . Package: r-cran-dcluster Architecture: amd64 Version: 0.2-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-spdep, r-cran-mass Suggests: r-cran-sp, r-cran-sf Filename: pool/dists/noble/main/r-cran-dcluster_0.2-10-1.ca2404.1_amd64.deb Size: 202418 MD5sum: 62e798f442f142b4a2efd85e643de60e SHA1: 42e3efd5730436cecccbd239da9c68216cbcf90d SHA256: 7c1f86322aa928d505a70cd6fd019b522bd87998fd9a78271e4cb9307646702f SHA512: 8e90ad1eb469001bc0df6e7d5e249a0adad686d6d38057594324c40ad40a037a707b6d6e214ecf0500392e6b31be018e94f1f70245ae913750a29580c39b9fd4 Homepage: https://cran.r-project.org/package=DCluster Description: CRAN Package 'DCluster' (Functions for the Detection of Spatial Clusters of Diseases) A set of functions for the detection of spatial clusters of disease using count data. Bootstrap is used to estimate sampling distributions of statistics. 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(2016) . These functions provide multiple sources of information for model fit according to the M2 statistic, including the M2 statistic, the *p* value for that M2 statistic, and the Root Mean Square Error of Approximation based on the M2 statistic. 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(2007) ), generalized version thereof (Sejdinovic, et al. (2013) ) and corresponding tests (Berschneider, Bottcher (2018) . Distance standard deviation methods (Edelmann, et al. (2020) ) and distance correlation methods for survival endpoints (Edelmann, et al. (2021) ) are also included. 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'DCPO' uses a population-level graded response model with country-specific item bias terms. Sampling is conducted with 'Stan'. References: Solt (2020) . 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The Koopman, Wang and Wei (2014) decomposition splits country-level exports into 9 value added components, and the Wang, Wei and Zhu (2013) decomposition splits bilateral exports into 16 value added components. Various GVC indicators based on these decompositions are computed in the complimentary 'gvc' package. --- References: --- Hummels, D., Ishii, J., & Yi, K. M. (2001). The nature and growth of vertical specialization in world trade. Journal of international Economics, 54(1), 75-96. Koopman, R., Wang, Z., & Wei, S. J. (2014). Tracing value-added and double counting in gross exports. American Economic Review, 104(2), 459-94. Wang, Z., Wei, S. J., & Zhu, K. (2013). Quantifying international production sharing at the bilateral and sector levels (No. w19677). National Bureau of Economic Research. 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Here we use a probabilistic model of the observed data to apply a whitening transformation. This Gaussian Inverse Wishart Empirical Bayes model substantially reduces computational complexity, and regularizes the eigen-values of the sample covariance matrix to improve out-of-sample performance. 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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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The methods used are described in Xu, Z., Zhen, B., Park, Y., & Zhu, B. (2017) . Package: r-cran-deldir Architecture: amd64 Version: 2.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-polyclip Filename: pool/dists/noble/main/r-cran-deldir_2.0-4-1.ca2404.1_amd64.deb Size: 275960 MD5sum: 7a48c1f39862a496fc328c66fcbde5ae SHA1: c600b588f679bda3e1d957d845c71851c9375a05 SHA256: a575337f36e43458b86f3e5c51ad18408da66da2dbdf44b7445ef50640452ceb SHA512: 63ce42c80506526c20e7e655ee010a4e899bc02076c4a06634f3e32e341861413c81ec1ba83ab3930e2af01c8cbb1a98a7a41a6d48e64578977fd1cd5fa8a0aa Homepage: https://cran.r-project.org/package=deldir Description: CRAN Package 'deldir' (Delaunay Triangulation and Dirichlet (Voronoi) Tessellation) Calculates the Delaunay triangulation and the Dirichlet or Voronoi tessellation (with respect to the entire plane) of a planar point set. Plots triangulations and tessellations in various ways. Clips tessellations to sub-windows. Calculates perimeters of tessellations. Summarises information about the tiles of the tessellation. Calculates the centroidal Voronoi (Dirichlet) tessellation using Lloyd's algorithm. 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See for more information and examples. Package: r-cran-dendser Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 792 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gclus, r-cran-seriation Suggests: r-cran-hsaur2, r-bioc-iyer517, r-cran-rcolorbrewer, r-cran-mvtnorm, r-cran-scagnostics, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dendser_1.0.3-1.ca2404.1_amd64.deb Size: 515634 MD5sum: 2fb8299e576eeb1b69ed96e50a645ca8 SHA1: f75e1561f16f2223df9ddeaf68cea8b197d99797 SHA256: 74f24a2734a75724447151963574837d9ccb566e34348a0438aa9cd917f06265 SHA512: e48b71e959424656d911dcf0d0c85e2cd5c42f3298170d870d1f729ea6182d5b8f7c737bc5c564a3b12650b9343a2dd82c3faa7d33b67703a64e248ceb095594 Homepage: https://cran.r-project.org/package=DendSer Description: CRAN Package 'DendSer' (Dendrogram Seriation: Ordering for Visualisation) Re-arranges a dendrogram to optimize visualisation-based cost functions. 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Package: r-cran-densestbayes Architecture: amd64 Version: 1.0-2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3562 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-nlme, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/noble/main/r-cran-densestbayes_1.0-2.2-1.ca2404.1_amd64.deb Size: 2507908 MD5sum: ea1cf8ed843888829bf352dfcf2cec33 SHA1: 61326ad7581031017f8f2ee9608c7123a90a0fef SHA256: f72f8b0b97faf860aef0a3e3970457a340251165bad433d1594e1e1fef849cc2 SHA512: f6381987c892865ca52faa97af47ed5ff4f9deba2baf7a70e516d088a06283889253d95a783a330f655dfdfad26652d9cdd596aad38a9eef97a45ef799500b91 Homepage: https://cran.r-project.org/package=densEstBayes Description: CRAN Package 'densEstBayes' (Density Estimation via Bayesian Inference Engines) Bayesian density estimates for univariate continuous random samples are provided using the Bayesian inference engine paradigm. The engine options are: Hamiltonian Monte Carlo, the no U-turn sampler, semiparametric mean field variational Bayes and slice sampling. The methodology is described in Wand and Yu (2020) . 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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. Package: r-cran-deoptim Architecture: amd64 Version: 2.2-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-foreach, r-cran-iterators, r-cran-colorspace, r-cran-lattice, r-cran-parallelly Filename: pool/dists/noble/main/r-cran-deoptim_2.2-8-1.ca2404.1_amd64.deb Size: 122778 MD5sum: a4973768ec2fc7b274e60ef35131f67d SHA1: 40d3a22b01f2ab18ea79141d2a90575eab467394 SHA256: 81b60902519773ee92184587478fd723452312965ccb33ef2b696154f66cc317 SHA512: 5509f4ab61904072b8f513122b2873c6e8aa39e1b272f540bb15cfa281bad371a87a9ad7cb657e2fcd532cbaf1e047d3a24ea8cee0b300806404c4959e7e2f16 Homepage: https://cran.r-project.org/package=DEoptim Description: CRAN Package 'DEoptim' (Global Optimization by Differential Evolution) Implements the Differential Evolution algorithm for global optimization of a real-valued function of a real-valued parameter vector as described in Mullen et al. (2011) . Package: r-cran-depcache Architecture: amd64 Version: 0.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-codetools Filename: pool/dists/noble/main/r-cran-depcache_0.1-2-1.ca2404.1_amd64.deb Size: 40934 MD5sum: e9eeed029e9dc9253831825e1fa20717 SHA1: 485a0a4b41b2c0d2621a41fa70368925683800fc SHA256: cf3dcd688e7b2f6dc8d93110245f75f007ecee45a9205889bd1f48afe13ee325 SHA512: 1be56c80dbf5cbf015f235c0339a63348a64db9120957cd119b31a8c82ccd9f08f950175bd26cd4470188ac306a393aa5dd02134c27b6423b97f8d2f56ec6c97 Homepage: https://cran.r-project.org/package=depcache Description: CRAN Package 'depcache' (Cache R Expressions, Taking Their Dependencies into Account) Hash an expression with its dependencies and store its value, reloading it from a file as long as both the expression and its dependencies stay the same. Package: r-cran-depcoeff Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 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-copula Suggests: r-cran-mass, r-cran-testthat Filename: pool/dists/noble/main/r-cran-depcoeff_0.1.1-1.ca2404.1_amd64.deb Size: 93236 MD5sum: ecd72ff6559346b61cf931d5bf25c34c SHA1: 329204172dc8432f4a7d236890208807d856634c SHA256: d9cefcb08147795892186c5d9951e10dab76cd77e6cf3e4fc729da8b31ffe8d9 SHA512: 90c179d50923a5e8ff66cd8c13017a67b07aad045579df19dce417505902c1f6e8ccdab6a87091602c96325b6d649affaf8546d4165bf59fac7f50f5906ef4b4 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. 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See Visser et al. (2009, ) for examples and applications. 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"Polarization: concepts, measurement, estimation". Econometrica, 72(6): 1737--1772. . The index may be computed for a single or for a range of values of the alpha-parameter and bootstrapping is also available. 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Package: r-cran-dfms Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2990 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-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/noble/main/r-cran-dfms_1.0.0-1.ca2404.1_amd64.deb Size: 2192174 MD5sum: f7cddffb3d28301e6f8cd1f91854c171 SHA1: 7f54af5bc3654726e4d61d5de3603677efd0e42d SHA256: c18ba9cde5970e4719e57ca9f6332b9565417aa34d0ecddba6db02b252f27180 SHA512: 8f4cec3ae0180634ab97bbd0b0168e33cb46414f0e78270ae393dda8644c1e6a15cc7a19b0797403fc97062dc8e80eb906a1b6c8fb1313bb9ac041f79c02ac63 Homepage: https://cran.r-project.org/package=dfms Description: CRAN Package 'dfms' (Dynamic Factor Models) Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm or Two-Step (2S) estimation, supporting datasets with missing data and mixed-frequency nowcasting applications. Factors follow a stationary VAR process of order p. Estimation options include: running the Kalman Filter and Smoother once with PCA initial values (2S) as in Doz, Giannone and Reichlin (2011) ; iterated Kalman Filtering and Smoothing until EM convergence as in Doz, Giannone and Reichlin (2012) ; or the adapted EM algorithm of Banbura and Modugno (2014) , allowing arbitrary missing-data patterns and monthly-quarterly mixed-frequency datasets. The implementation uses the 'Armadillo' 'C++' library and the 'collapse' package for fast estimation. A comprehensive set of methods supports interpretation and visualization, forecasting, and decomposition of the 'news' content of macroeconomic data releases following Banbura and Modugno (2014). Information criteria to choose the number of factors are also provided, following Bai and Ng (2002) . Package: r-cran-dfmta Architecture: amd64 Version: 1.7-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpparmadillo, r-cran-bh, r-cran-rcppprogress, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-dfmta_1.7-8-1.ca2404.1_amd64.deb Size: 120008 MD5sum: 9ffdd8eaf3c099c767dbe9de05288d20 SHA1: dd2c438addd4f47de343d12c60d5e654f1e9a91b SHA256: c99147a71b60fd025034eaac89205489d8419519ea67ffefe3906b75ac85cfa1 SHA512: 0f5d29db21fcb05751b43f1a1db3ad33cffec93150d3223b37e7c53b485eca83d0ba3283c3ef2946331d43b753d511d429843f2ffc0868dc9142e8d4ea63b2e9 Homepage: https://cran.r-project.org/package=dfmta Description: CRAN Package 'dfmta' (Phase I/II Adaptive Dose-Finding Design for MTA) Phase I/II adaptive dose-finding design for single-agent Molecularly Targeted Agent (MTA), according to the paper "Phase I/II Dose-Finding Design for Molecularly Targeted Agent: Plateau Determination using Adaptive Randomization", Riviere Marie-Karelle et al. (2016) . Package: r-cran-dfped Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-dfped_1.1-1.ca2404.1_amd64.deb Size: 176424 MD5sum: c24dd8ac8a281fdda895fd1b3afa136d SHA1: dd0bf9a6db2afb327e6811ed2970c3ed9ecb15b3 SHA256: 4fae05cad60d086bc65e606c9e99aaa48653e4dd1918275305f85cbc768fbd7e SHA512: c58e87def1a60832eb996cfbacc5e361784a20ebeffd3f132596841b4ac58d1762e670af9d47a49d5d20f7f7b05cfbdc5909056a5989c6f45da006b6ce1d0d6b Homepage: https://cran.r-project.org/package=dfped Description: CRAN Package 'dfped' (Extrapolation and Bridging of Adult Information in Early PhaseDose-Finding Paediatrics Studies) A unified method for designing and analysing dose-finding trials in paediatrics, while bridging information from adults, is proposed in the 'dfped' package. The dose range can be calculated under three extrapolation methods: linear, allometry and maturation adjustment, using pharmacokinetic (PK) data. To do this, it is assumed that target exposures are the same in both populations. The working model and prior distribution parameters of the dose-toxicity and dose-efficacy relationships can be obtained using early phase adult toxicity and efficacy data at several dose levels through 'dfped' package. Priors are used into the dose finding process through a Bayesian model selection or adaptive priors, to facilitate adjusting the amount of prior information to differences between adults and children. This calibrates the model to adjust for misspecification if the adult and paediatric data are very different. User can use his/her own Bayesian model written in Stan code through the 'dfped' package. A template of this model is proposed in the examples of the corresponding R functions in the package. Finally, in this package you can find a simulation function for one trial or for more than one trial. These methods are proposed by Petit et al, (2016) . 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(2017) . Package: r-cran-dgof Architecture: amd64 Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dgof_1.5.1-1.ca2404.1_amd64.deb Size: 54812 MD5sum: 7374cb5faaffc0c4d4541681f7b0810b SHA1: 60be410e90e58464e9725c827f6562541a393888 SHA256: 146d5a73acb3e1c9da18a6d04ac2f7022768929dff811c1f7163f675c37131af SHA512: 8f611c281b6f6631a070bcc03415514f4a72691294855bd0a4bb9201f7e61f4f89098feb939a2706ca62141573e83727cf57a9cdf328109dcf74b7e6cc4fbed7 Homepage: https://cran.r-project.org/package=dgof Description: CRAN Package 'dgof' (Discrete Goodness-of-Fit Tests) A revision to the stats::ks.test() function and the associated ks.test.Rd help page. With one minor exception, it does not change the existing behavior of ks.test(), and it adds features necessary for doing one-sample tests with hypothesized discrete distributions. 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Given a network one can define different types of Laplacian (or transition) matrices corresponding to different continuous-time random walks dynamics on the network. This package enables the evaluation of Laplacians, stochastic matrices, and the corresponding diffusion distance matrices. The metric structure induced by the network-driven process is richer and more robust than the one given by shortest-paths and allows to study the geometry induced by different types of diffusion-like communication mechanisms taking place on complex networks. For more details see: De Domenico, M. (2017) and Bertagnolli, G. and De Domenico, M. (2021) . Package: r-cran-difm Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1016 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-laplacesdemon, r-cran-spdep, r-cran-gridextra, r-cran-sp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-difm_1.0.1-1.ca2404.1_amd64.deb Size: 524500 MD5sum: 361bcf447787e24a932445190f75e644 SHA1: d9a243084ab45cd535eb1185aa04d59bfbf9266a SHA256: 3af952b79e72d41ed8aeee9c4059758e65d2cd0d7f6ed19c602cfd7e2d1bb392 SHA512: 6be5e0a4312bd91e3baf87384d25c9e0767300dfde75c0750325b2f0f8da742da88b67eed306ff8e656d653bf9df14cda656670dbe62f39ad3ede30874607a86 Homepage: https://cran.r-project.org/package=DIFM Description: CRAN Package 'DIFM' (Dynamic ICAR Spatiotemporal Factor Models) Bayesian factor models are effective tools for dimension reduction. This is especially applicable to multivariate large-scale datasets. It allows researchers to understand the latent factors of the data which are the linear or non-linear combination of the variables. Dynamic Intrinsic Conditional Autocorrelative Priors (ICAR) Spatiotemporal Factor Models 'DIFM' package provides function to run Markov Chain Monte Carlo (MCMC), evaluation methods and visual plots from Shin and Ferreira (2023). Our method is a class of Bayesian factor model which can account for spatial and temporal correlations. By incorporating these correlations, the model can capture specific behaviors and provide predictions. Package: r-cran-digest Architecture: amd64 Version: 0.6.39-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 484 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-simplermarkdown, r-cran-rbenchmark Filename: pool/dists/noble/main/r-cran-digest_0.6.39-1.ca2404.1_amd64.deb Size: 207626 MD5sum: eb8b469e0753a13acdcaaf51d8bacfba SHA1: 7ec5149419b0a6dc102b94ef9ce0c4e71c18cc42 SHA256: 0525bcb82d4595bbdb6ed3a71cbf88f5fcb7205681fa8c60bcd9bedf289f7cbd SHA512: 7cfe6a7e569fb0a351e3c89bac235a069a956cefd245f3658c319d8e56b6125b783f40883251f9bba0d45c69107b5a79661479d4c50c8f6cc07c93f05ec784f8 Homepage: https://cran.r-project.org/package=digest Description: CRAN Package 'digest' (Create Compact Hash Digests of R Objects) Implementation of a function 'digest()' for the creation of hash digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 'crc32', 'xxhash', 'murmurhash', 'spookyhash', 'blake3', 'crc32c', 'xxh3_64', and 'xxh3_128' algorithms) permitting easy comparison of R language objects, as well as functions such as 'hmac()' to create hash-based message authentication code. 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Package: r-cran-dime Architecture: amd64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dime_1.3.0-1.ca2404.1_amd64.deb Size: 169228 MD5sum: 49bc6652575d29c4ba8e8a065156bfff SHA1: afd9693042cf347270cbdca972920f5655bce68e SHA256: 5b0f6221bb3588b53d0bedc0af2fc7c7cf86de9fc1726dbd5c8e7b3970719776 SHA512: 443ced490d4fe5948e15cefaff5d6d88064660202567cfad85b59a41964b21b2fb6a6f9d1a309ee483996e015829c2bcd78f30bea74ef7e1cd95aab8b990afb6 Homepage: https://cran.r-project.org/package=DIME Description: CRAN Package 'DIME' (Differential Identification using Mixture Ensemble) A robust identification of differential binding sites method for analyzing ChIP-seq (Chromatin Immunoprecipitation Sequencing) comparing two samples that considers an ensemble of finite mixture models combined with a local false discovery rate (fdr) allowing for flexible modeling of data. Methods for Differential Identification using Mixture Ensemble (DIME) is described in: Taslim et al., (2011) . Package: r-cran-dimodal Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-statmod Filename: pool/dists/noble/main/r-cran-dimodal_1.0.4-1.ca2404.1_amd64.deb Size: 405582 MD5sum: a1f1b46323f64eb36e7668236ff2f73b SHA1: 7d02f4c8786522e20c517ee659f83d6359f48a75 SHA256: f655a2f276097b7087f9f56edd4767dba9edcdaf6ef681524df4e9d87de2b871 SHA512: d239f681c80dc0c065c1f0bdf9ba0be89df948e258a2b559601e0e8df211a66ca93b22b2510e57e4ca6d32413594c9678befe9e747ed54778f7f9662d7164392 Homepage: https://cran.r-project.org/package=Dimodal Description: CRAN Package 'Dimodal' (Spacing Tests for Multi-Modality) Tests for modality of data using its spacing. 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Package: r-cran-dipm Architecture: amd64 Version: 1.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: libc6 (>= 2.14), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-partykit, r-cran-ggplot2, r-cran-rlang Filename: pool/dists/noble/main/r-cran-dipm_1.12-1.ca2404.1_amd64.deb Size: 141438 MD5sum: 5c373cbbb180ff8a6b035ce05c1f6982 SHA1: 05764b6573f890c75f2076091ab3b6fb8ece1a9a SHA256: 85fa9286d0fcce1e357f1341cda6d4a930b32951eb1aab2b3f11391e397a3f2f SHA512: 815c09d92dfab9e50f3c96bcb7d52a5801ac2b6b9246e23527772603ce119b47579c5c8a809069be19fedd2cabdfc4f45b0fe02262884ef5d3fadae94f30d1a5 Homepage: https://cran.r-project.org/package=dipm Description: CRAN Package 'dipm' (Depth Importance in Precision Medicine (DIPM) Method) An implementation by Chen, Li, and Zhang (2022) of the Depth Importance in Precision Medicine (DIPM) method in Chen and Zhang (2022) and Chen and Zhang (2020) . The DIPM method is a classification tree that searches for subgroups with especially poor or strong performance in a given treatment group. Package: r-cran-dipsaus Architecture: amd64 Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2335 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-shiny, r-cran-cli, r-cran-stringr, r-cran-jsonlite, r-cran-future, r-cran-future.apply, r-cran-parallelly, r-cran-progressr, r-cran-fastmap, r-cran-base64enc, r-cran-digest, r-cran-rlang, r-cran-rstudioapi Suggests: r-cran-knitr, r-cran-promises, r-cran-later, r-cran-rmarkdown, r-cran-testthat, r-cran-microbenchmark, r-cran-yaml, r-cran-future.callr Filename: pool/dists/noble/main/r-cran-dipsaus_0.3.5-1.ca2404.1_amd64.deb Size: 1090572 MD5sum: 7f7c34e31bf42db302514de82881ce66 SHA1: ee049f53bcca06a142763417878be7c54c812572 SHA256: f82a4e8a8b79910a365144f293a53f0f3ff0a1869d25078ccebfab9b077c576f SHA512: 4c3122e0fac2ace0e5e4352f879e61ff3caf0cb7d5c9bd364dde40f4056b2b40a76b4ae991c904c78865cfde6deb4a717c0e6a0f866e89ae47e8112689c945a7 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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It estimates the number of clusters directly from the data using a Dirichlet-process prior. See Fu, A. Q., Russell, S., Bray, S. and Tavare, S. (2013) Bayesian clustering of replicated time-course gene expression data with weak signals. The Annals of Applied Statistics. 7(3) 1334-1361. . Package: r-cran-dirichletreg Architecture: amd64 Version: 0.7-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: libc6 (>= 2.4), 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/noble/main/r-cran-dirichletreg_0.7-2-1.ca2404.1_amd64.deb Size: 349354 MD5sum: 31a4bed019428e4194f8c3fab3466b75 SHA1: 4701d0e16c6b86b3ef5158adaeeabf421d2776b0 SHA256: 68f2264369f7909ea3c3bdd516812779b7dcf10ac3957c7c381b764ac27da189 SHA512: e68c28b3102f3d85e35005c9cc6efcbc1ccaeaaf23b6da41362bdae24cca056a629058c62b8bb8f9bb2e1765423a17303e749de6dc5db8c043c3575e355a3c30 Homepage: https://cran.r-project.org/package=DirichletReg Description: CRAN Package 'DirichletReg' (Dirichlet Regression) Implements Dirichlet regression models. 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Supports maximum likelihood estimation ('MLE') and method-of-moments ('MOM') parameter estimation for the Dirichlet distribution. Provides two prediction strategies; averaging-based predictions (average of responses within terminal nodes) and parameter-based predictions (expected value derived from the estimated Dirichlet parameters within terminal nodes). For more details see Masoumifard, van der Westhuizen, and Gardner-Lubbe (2026, ISBN:9781032903910). Package: r-cran-dirstats Architecture: amd64 Version: 0.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-movmf, r-cran-rotasym Suggests: r-cran-viridislite Filename: pool/dists/noble/main/r-cran-dirstats_0.1.10-1.ca2404.1_amd64.deb Size: 128696 MD5sum: baa1e55ff77f95b575ff2f35d8c79fc8 SHA1: 2c6cde034caf68cbeb3071cf956cb90541cefb14 SHA256: 36fec5ca8573c2c9ab84ccf7a787bf3381ee3cb14cf3bce2e635c27ebb539fc4 SHA512: 18c5c17e9d2f0f31cd19ae81a8f4ef408c0eccbc5a79af3b06fdb9c26a4bc15588035ad8d465e2958c097d598d0171490dc21526b0543a1be24a67a6bdc44f25 Homepage: https://cran.r-project.org/package=DirStats Description: CRAN Package 'DirStats' (Nonparametric Methods for Directional Data) Nonparametric kernel density estimation, bandwidth selection, and other utilities for analyzing directional data. Implements the estimator in Bai, Rao and Zhao (1987) , the cross-validation bandwidth selectors in Hall, Watson and Cabrera (1987) and the plug-in bandwidth selectors in García-Portugués (2013) . Package: r-cran-disaggregation Architecture: amd64 Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1754 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-splancs, r-cran-matrix, r-cran-tmb, r-cran-dplyr, r-cran-ggplot2, r-cran-cowplot, r-cran-rspde, r-cran-sparsemvn, r-cran-fmesher, r-cran-tidyterra, r-cran-terra, r-cran-sf, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spatialepi Filename: pool/dists/noble/main/r-cran-disaggregation_0.4.1-1.ca2404.1_amd64.deb Size: 838778 MD5sum: 7678ff0325aef6734138bd22c6d7fe68 SHA1: c842d4f017db8546a468fe03af6ab7d0ab88b6f6 SHA256: 614fb06a97b285ccbea5fe9f6366c12a8328555a78d0d736f5d7ce9f6648939c SHA512: aa126fa6ceeb9869ad002525e26e792cd68b4600a63c190fa1699e0256c6d42879b6bf8ee2092d233aa5bb7814fce098af0d32a090a9fd9cdf0ea388f4f83f7d Homepage: https://cran.r-project.org/package=disaggregation Description: CRAN Package 'disaggregation' (Disaggregation Modelling) Fits disaggregation regression models using 'TMB' ('Template Model Builder'). When the response data are aggregated to polygon level but the predictor variables are at a higher resolution, these models can be useful. Regression models with spatial random fields. The package is described in detail in Nandi et al. (2023) . Package: r-cran-disbayes Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4443 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-tibble, r-cran-generics, r-cran-rcpp, r-cran-rstan, r-cran-mgcv, r-cran-shelf, r-cran-ggplot2, r-cran-loo, r-cran-matrixstats, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rstantools, r-cran-tempdisagg, r-cran-testthat, r-cran-codetools Filename: pool/dists/noble/main/r-cran-disbayes_1.1.1-1.ca2404.1_amd64.deb Size: 1509760 MD5sum: f59c05b1454b7217c55b6e921087aeb5 SHA1: b3340bad624625e8164b08f7997def24f0cd1f3c SHA256: 87d2aa29bf412bcde318e3716165beb07bda84c9e9008752a19436868c6ff2f7 SHA512: 8be12c42158814cbaff2410291ff3096019c5e44af3e63e64b49923fe29732f89cf9e4f4afa51505c4d4c4f4a6436cf953fc49d78f7e88e00a1235b83bb66805 Homepage: https://cran.r-project.org/package=disbayes Description: CRAN Package 'disbayes' (Bayesian Multi-State Modelling of Chronic Disease Burden Data) Estimation of incidence and case fatality for a chronic disease, given partial information, using a multi-state model. 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Package: r-cran-disclapmix2 Architecture: amd64 Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-cluster Suggests: r-cran-testthat, r-cran-disclapmix, r-cran-readxl Filename: pool/dists/noble/main/r-cran-disclapmix2_0.6.1-1.ca2404.1_amd64.deb Size: 449518 MD5sum: 45d9518f858453398797a3454a08e340 SHA1: 1a09382f06d58a6f2d10625597b717c717489637 SHA256: f17c45ab968632e61cc6d2631e50c4c4213f47e8773e864a0764b40a6987f2c4 SHA512: 60db8739582cb7c7352ced5ff969a219a01cc7bb17a77af607e418c9b67667f050ec2991d236aeaedb46e9a298a1d17e2960f082243e8cbe60acff74983c5173 Homepage: https://cran.r-project.org/package=disclapmix2 Description: CRAN Package 'disclapmix2' (Mixtures of Discrete Laplace Distributions using NumericalOptimisation) Fit a mixture of Discrete Laplace distributions using plain numerical optimisation. This package has similar applications as the 'disclapmix' package that uses an EM algorithm. 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This can e.g. be used for modelling the distribution of Y chromosomal haplotypes as described in [1, 2] (refer to the URL section). Package: r-cran-discretedists Architecture: amd64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 519 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-discretedists_1.1.2-1.ca2404.1_amd64.deb Size: 377532 MD5sum: 08fd100a53eccebcdf131e553afd3b32 SHA1: ab723973e890ad3f24ff0a4e0c7eb03c64bca527 SHA256: e73a33c90cc89382bf9cfa42443ae191471a09123faf013eca10f4e70e8b9644 SHA512: 8dcc32e1d66b7fc857b1d19fb20bacbaceb76a26fb03ffc5efc48be7371e82ee3cb0eaabd07c37f787260a94ad1ee196e46f7a7520364ff8564d401ca41ec870 Homepage: https://cran.r-project.org/package=DiscreteDists Description: CRAN Package 'DiscreteDists' (Discrete Statistical Distributions) Implementation of new discrete statistical distributions. Each distribution includes the traditional functions as well as an additional function called the family function, which can be used to estimate parameters within the 'gamlss' framework. Package: r-cran-discretedlm Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-statmod, r-cran-bayeslogit, r-cran-dlnm, r-cran-dplyr, r-cran-ggplot2, r-cran-ggridges, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-discretedlm_1.0.0-1.ca2404.1_amd64.deb Size: 277616 MD5sum: f19ac42f5881d2857f702752817aebdf SHA1: 8d288e670aab91c85b98f53286184a85f7edbd2a SHA256: 25e5ad6967f3cc72addb26de6f06e249b783e73d82c4f1d9f4a985a7a45a2f46 SHA512: 991488213d61f970b3a909e0d175d8f7fcfb8ef98b5e866aa83dff6f5d163bbc63a5a5319616772d6b4103ab1c53ee04ed621e535f0ac826580180ba2a7c47cd Homepage: https://cran.r-project.org/package=DiscreteDLM Description: CRAN Package 'DiscreteDLM' (Bayesian Distributed Lag Model Fitting for Binary and CountResponse Data) Tools for fitting Bayesian Distributed Lag Models (DLMs) to longitudinal response data that is a count or binary. Count data is fit using negative binomial regression and binary is fit using quantile regression. The contribution of the lags are fit via b-splines. In addition, infers the predictor inclusion uncertainty. Multimomial models are not supported. Based on Dempsey and Wyse (2025) . Package: r-cran-discretefdr Architecture: amd64 Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2135 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-discretetests, r-cran-lifecycle, r-cran-checkmate, r-cran-discretedatasets, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-r.rsp, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-discretefdr_2.1.1-1.ca2404.1_amd64.deb Size: 1132568 MD5sum: 43e705a1d506934f370243c33fb04be8 SHA1: 9499774d0576e4c4f0f9f16930ac75b487f724a9 SHA256: 7fa3cfd95dc56d317d142c19bcfe31cd6b743f1db7c7296d33008870215d5c87 SHA512: 9492bd11db25b198b29a60e71c1ba16fcb5d235ed1ba5d9890495dcfeb050063ce5c1752ac91e26003106d27367ebeaa0091ee5b5f777c90d1e25d777c614285 Homepage: https://cran.r-project.org/package=DiscreteFDR Description: CRAN Package 'DiscreteFDR' (FDR Based Multiple Testing Procedures with Adaptation forDiscrete Tests) Implementations of the multiple testing procedures for discrete tests described in the paper Döhler, Durand and Roquain (2018) "New FDR bounds for discrete and heterogeneous tests" . The main procedures of the paper (HSU and HSD), their adaptive counterparts (AHSU and AHSD), and the HBR variant are available and are coded to take as input the results of a test procedure from package 'DiscreteTests', or a set of observed p-values and their discrete support under their nulls. A shortcut function to obtain such p-values and supports is also provided, along with a wrapper allowing to apply discrete procedures directly to data. Package: r-cran-discretefit Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-dgof, r-cran-cvmdisc, r-cran-bench, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-discretefit_0.1.3-1.ca2404.1_amd64.deb Size: 92466 MD5sum: 1624454b8df8e73e92feb5f3c2d99c51 SHA1: eae670453aa36d3346ca9bc85b7635e8f2e9c90e SHA256: 78caae9e3da13ee0aa1de35c26d687f78973b108f71def0dac4664671cb8800f SHA512: 56437908e6d8041843ebc0eab6b25d16c5941e32d09db7069928bb7a259195c447b05f5269e308e8b69e52799a89fe3707684568ce8f638592ddf53ebf8626e2 Homepage: https://cran.r-project.org/package=discretefit Description: CRAN Package 'discretefit' (Simulated Goodness-of-Fit Tests for Discrete Distributions) Implements fast Monte Carlo simulations for goodness-of-fit (GOF) tests for discrete distributions. This includes tests based on the Chi-squared statistic, the log-likelihood-ratio (G^2) statistic, the Freeman-Tukey (Hellinger-distance) statistic, the Kolmogorov-Smirnov statistic, the Cramer-von Mises statistic as described in Choulakian, Lockhart and Stephens (1994) , and the root-mean-square statistic, see Perkins, Tygert, and Ward (2011) . Package: r-cran-discretefwer Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-checkmate, r-cran-discretefdr, r-cran-rcpparmadillo Suggests: r-cran-discretedatasets, r-cran-discretetests Filename: pool/dists/noble/main/r-cran-discretefwer_1.0.0-1.ca2404.1_amd64.deb Size: 178032 MD5sum: d46a3f1b5a2d21f74d4870dac9b12f82 SHA1: 161bdbb97b3e25217d0319f3ba787a8a8f1d60b1 SHA256: 8c93dd7ecd9e91c72841a0f00cf91b29332f8c98c1786faf65af13412df9b260 SHA512: 62bc88bc94643f56e82a42365287683281ab7e5668bd1dc17a9e614dc3c13d195b8b0b8be88f0e589e16aa170c709541d23973d68badd0c8a2a4f240e2ca4330 Homepage: https://cran.r-project.org/package=DiscreteFWER Description: CRAN Package 'DiscreteFWER' (FWER-Based Multiple Testing Procedures with Adaptation forDiscrete Tests) Implementations of several multiple testing procedures that control the family-wise error rate (FWER) designed specifically for discrete tests. Included are discrete adaptations of the Bonferroni, Holm, Hochberg and Šidák procedures as described in the papers Döhler (2010) "Validation of credit default probabilities using multiple-testing procedures" and Zhu & Guo (2019) "Family-Wise Error Rate Controlling Procedures for Discrete Data" . The main procedures of this package take as input the results of a test procedure from package 'DiscreteTests' or a set of observed p-values and their discrete support under their nulls. A shortcut function to apply discrete procedures directly to data is also provided. Package: r-cran-discretetests Architecture: amd64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 504 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-checkmate, r-cran-lifecycle, r-cran-cli, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-discretetests_0.4.0-1.ca2404.1_amd64.deb Size: 346272 MD5sum: 6b8ba144f1a8b72b1018a5eb1dc7a992 SHA1: 72d7b773db94c60421951bf0d1867a891c0617d2 SHA256: 029e1ad6306baecfa6ae32a6e0fa9a1521db7b0e26b6e8a381528434090f2155 SHA512: d2e1384332e8c311c0e2dfe237c2d8543507fa02c9d16dd3b0c94a8843b106b6c07d140bfc817d12785a8ea8c333fb61a95c5660957480694d646e4d1a501439 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-disk.frame Architecture: amd64 Version: 0.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1205 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rcpp, r-cran-glue, r-cran-future.apply, r-cran-fs, r-cran-jsonlite, r-cran-pryr, r-cran-stringr, r-cran-fst, r-cran-future, r-cran-data.table, r-cran-crayon, r-cran-bigreadr, r-cran-bit64, r-cran-benchmarkme, r-cran-purrr, r-cran-globals, r-cran-rlang, r-cran-arrow Suggests: r-cran-nycflights13, r-cran-magrittr, r-cran-shiny, r-cran-laf, r-cran-readr, r-cran-rstudioapi, r-cran-broom, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-disk.frame_0.8.3-1.ca2404.1_amd64.deb Size: 1037382 MD5sum: e0b5fb6346df69a6d7f4282e2ab4c451 SHA1: fc82a380fef1b4022ba7c34cd5258f5f48d52caa SHA256: 518b026dfaece6eb4670907f2a7ab21105998d21417209820fc95345b33d797a SHA512: 5fba1d02f14425abb54b800dfdcaae135f03e5bb55124ba6ea590e2bf3ea30b713f6bb7b1958de27cb2f1f8f786d5c4d0d5ba3a0889c68c5727775f89db61b32 Homepage: https://cran.r-project.org/package=disk.frame Description: CRAN Package 'disk.frame' (Larger-than-RAM Disk-Based Data Manipulation Framework) A disk-based data manipulation tool for working with large-than-RAM datasets. Aims to lower the barrier-to-entry for manipulating large datasets by adhering closely to popular and familiar data manipulation paradigms like 'dplyr' verbs and 'data.table' syntax. Package: r-cran-dismo Architecture: amd64 Version: 1.3-16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2946 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-dismo_1.3-16-1.ca2404.1_amd64.deb Size: 1893250 MD5sum: da4e19ebbbe615f2d49ff506709747d2 SHA1: 0dbaf50e4273f83f7b0bb8f5750d0da24b19b18a SHA256: 44450c56b62ed59ecf9545b3f3f38388c1a2c1146ab7dfeb0816a95e7e66551c SHA512: d3a50a2e549a1f9b63e56d071c4fa9edf62857df0bc837d2779bdce9785459b523ae58000ee02a8ccf60bbb5e18066d129e66c0cae1a196d9194c48e30817051 Homepage: https://cran.r-project.org/package=dismo Description: CRAN Package 'dismo' (Species Distribution Modeling) Methods for species distribution modeling, that is, predicting the environmental similarity of any site to that of the locations of known occurrences of a species. Package: r-cran-disperse Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-sf Filename: pool/dists/noble/main/r-cran-disperse_1.1-1.ca2404.1_amd64.deb Size: 292142 MD5sum: 52f6cf13a932e44c455c7033cdfe29c3 SHA1: 3b2d71e50b4ae816b2b3968607e74d995ec0f296 SHA256: 6ee1d6e927a1327385068943630a662f6090f444fd8eaaf4be483f4e47d21370 SHA512: 155e19a8d5fb9afb60bc3abc268358df7be0e936b0d61835af83a578083e1e4237f8b0bf20cbbd0ce817270d53014f429fdd3a033632286b7ac8f920fdb150a8 Homepage: https://cran.r-project.org/package=dispeRse Description: CRAN Package 'dispeRse' (Simulation of Demic Diffusion with Environmental Constraints) Simulates demic diffusion building on models previously developed for the expansion of Neolithic and other food-producing economies during the Holocene (Fort et al. (2012) , Souza et al. (2021) ). Growth and emigration are modelled as density-dependent processes using logistic growth and an asymptotic threshold model. Environmental and terrain layers, which can change over time, affect carrying capacity, growth and mobility. Multiple centres of origin with their respective starting times can be specified. Package: r-cran-disprity Architecture: amd64 Version: 1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2923 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-ade4, r-cran-castor, r-cran-claddis, r-cran-ellipse, r-cran-geometry, r-cran-get, r-cran-mass, r-cran-mnormt, r-cran-phangorn, r-cran-phyclust, r-cran-phylolm, r-cran-vegan, r-cran-scales, r-cran-zoo Suggests: r-cran-mcmcglmm, r-cran-geoscale, r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-disprity_1.9-1.ca2404.1_amd64.deb Size: 2778660 MD5sum: 8b2b04b298a2cb77a687fa720905c368 SHA1: 5f6e11a4454559ed6ae9dd6f789c8eab1c671b74 SHA256: 5df8abedc9fa5e6b21ebed7d31ea2114e39a85aeec6aa8574cc53940b45e33ed SHA512: 70e29ca53363a6c79669d4b456ecba02e813d3f98d888e467e2e2add937f0b54ec6b755bf46366b1b0e461353a37bc55165262e76f7c25858430c5c57dadfad3 Homepage: https://cran.r-project.org/package=dispRity Description: CRAN Package 'dispRity' (Measuring Disparity) A modular package for measuring disparity (multidimensional space occupancy). Disparity can be calculated from any matrix defining a multidimensional space. The package provides a set of implemented metrics to measure properties of the space and allows users to provide and test their own metrics. The package also provides functions for looking at disparity in a serial way (e.g. disparity through time) or per groups as well as visualising the results. Finally, this package provides several statistical tests for disparity analysis. Package: r-cran-dissimilarities Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-microbenchmark, r-cran-proxy Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dissimilarities_0.3.0-1.ca2404.1_amd64.deb Size: 86936 MD5sum: 7464e9be864d092f1f1a1e5087b028da SHA1: 4d0679ab9cd528b95c300e5d42d95ba58c325ab9 SHA256: 28f83a8484756afbefe314ba92e37402653e4053c0348c6438feae127a355216 SHA512: 01580634048c67812c3355b7a3b8cc33b63e2ec4ded17fd3f7a1c28d29fde1e7b52554a4a419a9f19cc62df287baa504bffa2258449854c4b1fb853125d2f1ae Homepage: https://cran.r-project.org/package=dissimilarities Description: CRAN Package 'dissimilarities' (Creating, Manipulating, and Subsetting "dist" Objects) Efficiently creates, manipulates, and subsets "dist" objects, commonly used in cluster analysis. Designed to minimise unnecessary conversions and computational overhead while enabling seamless interaction with distance matrices. Package: r-cran-distances Architecture: amd64 Version: 0.1.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-distances_0.1.13-1.ca2404.1_amd64.deb Size: 70160 MD5sum: 044290ee5c369f5e153f69c26bf37113 SHA1: 3e4f79569d5959857400954c24c07d3c688a001f SHA256: 59fd8ca0109ae6aa1426e37e2246a0b537fab20a94bf561a0fe03781947036b1 SHA512: 3cb5fe7f99abbf1f931e80e2c0f93f1d89b42cb82c716b06e99de90a770c0395220f62018f2cf063c5ace90b9fe08726afdc0db58e7983233d7b7ce0515e294d Homepage: https://cran.r-project.org/package=distances Description: CRAN Package 'distances' (Tools for Distance Metrics) Provides tools for constructing, manipulating and using distance metrics. Package: r-cran-distantia Architecture: amd64 Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2035 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-distantia_2.0.3-1.ca2404.1_amd64.deb Size: 1702130 MD5sum: e0327061a90ffc48bfd1492aa408a122 SHA1: 6a52b4a5040ada5e51bacb4b7af1dab71b92a7a1 SHA256: 7b3fe7d97892e732e1d76cc6806305f8be07f037cd8a0f1aec0042ae60b6d87e SHA512: 3797b5b8fb62ea71642ec3ceb81e273342f961083f7c47078b9ce3f6aafa3447f2a964b2533754d187ab441dd898451f39828190392db856baae021910da7724 Homepage: https://cran.r-project.org/package=distantia Description: CRAN Package 'distantia' (Advanced Toolset for Efficient Time Series DissimilarityAnalysis) Fast C++ implementation of Dynamic Time Warping for time series dissimilarity analysis, with applications in environmental monitoring and sensor data analysis, climate science, signal processing and pattern recognition, and financial data analysis. Built upon the ideas presented in Benito and Birks (2020) , provides tools for analyzing time series of varying lengths and structures, including irregular multivariate time series. Key features include individual variable contribution analysis, restricted permutation tests for statistical significance, and imputation of missing data via GAMs. Additionally, the package provides an ample set of tools to prepare and manage time series data. Package: r-cran-distcomp Architecture: amd64 Version: 1.3-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3352 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-shiny, r-cran-httr, r-cran-digest, r-cran-jsonlite, r-cran-stringr, r-cran-r6, r-cran-dplyr, r-cran-rlang, r-cran-magrittr, r-cran-homomorpher, r-cran-gmp Suggests: r-cran-opencpu, r-cran-knitr, r-cran-covr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-distcomp_1.3-4-1.ca2404.1_amd64.deb Size: 1156782 MD5sum: f2d3191d94ec16b0385dd0ca925fb904 SHA1: b26e645cd523122220f5334718054a22641ecc76 SHA256: 53f181907e73e7ad118ab5f97b646eaf42bb670bcc5319d3ae947dc164586fca SHA512: 0dac7c234c22c32539f4511a8cd7d57bc75cdda314be78bc66fc96a18035e31c6760c15740bc27ae6d1f67052dac61b1af57f9659bf4b1c952d2a575254d7d6c Homepage: https://cran.r-project.org/package=distcomp Description: CRAN Package 'distcomp' (Computations over Distributed Data without Aggregation) Implementing algorithms and fitting models when sites (possibly remote) share computation summaries rather than actual data over HTTP with a master R process (using 'opencpu', for example). A stratified Cox model and a singular value decomposition are provided. The former makes direct use of code from the R 'survival' package. (That is, the underlying Cox model code is derived from that in the R 'survival' package.) Sites may provide data via several means: CSV files, Redcap API, etc. An extensible design allows for new methods to be added in the future and includes facilities for local prototyping and testing. Web applications are provided (via 'shiny') for the implemented methods to help in designing and deploying the computations. Package: r-cran-distops Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-desc, r-cran-fs, r-cran-glue, r-cran-rcpp, r-cran-rcppparallel, r-cran-usethis Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-distops_0.1.0-1.ca2404.1_amd64.deb Size: 94582 MD5sum: e8f15bd833736c0eb11372b76a848566 SHA1: b86635f0ab0d343f50084fd1dc0af9176a67fa74 SHA256: 90ddc069d3c27037b3647a07154948ab29f50a20ba22c9d1b2292041e4270194 SHA512: b5064799e9c0e0f30c68620457090172c9fedab11232979c35fc3b92f0f587bbade9c04a505d84be47fd4ad677a463890329d48f1f04888a5588e738c81e1b2c Homepage: https://cran.r-project.org/package=distops Description: CRAN Package 'distops' (Usual Operations for Distance Matrices in R) It provides the subset operator for dist objects and a function to compute medoid(s) that are fully parallelized leveraging the 'RcppParallel' package. It also provides functions for package developers to easily implement their own parallelized dist() function using a custom 'C++'-based distance function. Package: r-cran-distory Architecture: amd64 Version: 1.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape Filename: pool/dists/noble/main/r-cran-distory_1.4.5-1.ca2404.1_amd64.deb Size: 90784 MD5sum: a4bd477d11e75772a0d45dfec9500a24 SHA1: 8b6181097012e5e2c7ac319503b0ae3655a50481 SHA256: b2b4cb81a3cb54233471659fb53411081076fde459e45b184422f2d3351eaeec SHA512: a4173114d1b9132dbd510949f854faf01d337f2859561c51db4d98d1351d4fac795d4091ef41cd3dd7e4d0a718ed17b5fa4242687466f7f619188694ad0545a0 Homepage: https://cran.r-project.org/package=distory Description: CRAN Package 'distory' (Distance Between Phylogenetic Histories) Geodesic distance between phylogenetic trees and associated functions. The theoretical background of 'distory' is published in Billera et al. (2001) "Geometry of the space of phylogenetic trees." . Package: r-cran-distr Architecture: amd64 Version: 2.9.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2865 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-startupmsg, r-cran-sfsmisc, r-cran-mass Suggests: r-cran-distrex, r-cran-svunit, r-cran-knitr, r-cran-distrmod, r-cran-roptest Filename: pool/dists/noble/main/r-cran-distr_2.9.7-1.ca2404.1_amd64.deb Size: 2139982 MD5sum: 5f3e120b3cc55ae2d736857378d8b7e9 SHA1: 9ea614201145ebd9595d75626236a48d4209f05f SHA256: 0ce9ea87c017084d5e56595f6fd18872cee1e2d5b0f127f276edb69a25ed1d89 SHA512: 949535caa3d27ac7a5d87b8f77e8d2b721be739a12934f270d6af96eadda7a19573c1cf49f41d699ed4de1e5fa306699441ba040e687c5d52022cf44d0bbb70c Homepage: https://cran.r-project.org/package=distr Description: CRAN Package 'distr' (Object Oriented Implementation of Distributions) S4-classes and methods for distributions. Package: r-cran-distrex Architecture: amd64 Version: 2.9.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3269 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-distr, r-cran-startupmsg Filename: pool/dists/noble/main/r-cran-distrex_2.9.6-1.ca2404.1_amd64.deb Size: 2880874 MD5sum: 71bc215d9c71b6fff8d03dc0c2cd744d SHA1: 9e15ef7a10d0a5210ffd37ecc03f1d7fc1d1c696 SHA256: 418e5ef882325b98fb3128bb16e7f9be13d1aaaa874a1c6f076ae5678b73a005 SHA512: 5d2cb800615045d46d01f5ab3e31bdae795630b092e67cb77618fda6a45e709dc5f8c7814e921145323e9ef11fa86a1e80f87935afa6ac4a849a91b50f1cbb28 Homepage: https://cran.r-project.org/package=distrEx Description: CRAN Package 'distrEx' (Extensions of Package 'distr') Extends package 'distr' by functionals, distances, and conditional distributions. Package: r-cran-distributionutils Architecture: amd64 Version: 0.6-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-generalizedhyperbolic, r-cran-variancegamma, r-cran-skewhyperbolic, r-cran-runit Filename: pool/dists/noble/main/r-cran-distributionutils_0.6-2-1.ca2404.1_amd64.deb Size: 173852 MD5sum: d07effabb702dae6943b094659fb5c95 SHA1: 7f9beeb85227178099e389c14f5d2cc19afff253 SHA256: a2cefffa004b38968760d1038d818bdb81dc25a72c9ee8ec97b1a089835b2862 SHA512: d70bf9d3b3985d34c855aaeeafc10f5616ff082b0ad1106794b58331932937abfef9b295ca22f756d6eab999c0360810520179f61343ddde6f171b82a9d38912 Homepage: https://cran.r-project.org/package=DistributionUtils Description: CRAN Package 'DistributionUtils' (Distribution Utilities) Utilities are provided which are of use in the packages I have developed for dealing with distributions. Currently these packages are GeneralizedHyperbolic, VarianceGamma, and SkewHyperbolic and NormalLaplace. Each of these packages requires DistributionUtils. Functionality includes sample skewness and kurtosis, log-histogram, tail plots, moments by integration, changing the point about which a moment is calculated, functions for testing distributions using inversion tests and the Massart inequality. Also includes an implementation of the incomplete Bessel K function. Package: r-cran-divdyn Architecture: amd64 Version: 0.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2032 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-vegan, r-cran-icosa Filename: pool/dists/noble/main/r-cran-divdyn_0.8.3-1.ca2404.1_amd64.deb Size: 1787544 MD5sum: 3f0f8bc577b6c6ca4feb3e95a6d2cfe5 SHA1: d7408c6289becec45fa9298cd961ec201dc39260 SHA256: 9e04aa57db0ed07a83bc10667163a98990036dfa16752aeafa478ac56193aa3f SHA512: 8483e64ea4034944a0f8e699dd363d8666c894501f84fa74338523208ae187c59cd6c4495758bb018f5c9f182d99df7a703bf264bb4bf6869ed9a71d3a993c65 Homepage: https://cran.r-project.org/package=divDyn Description: CRAN Package 'divDyn' (Diversity Dynamics using Fossil Sampling Data) Functions to describe sampling and diversity dynamics of fossil occurrence datasets (e.g. from the Paleobiology Database). The package includes methods to calculate range- and occurrence-based metrics of taxonomic richness, extinction and origination rates, along with traditional sampling measures. A powerful subsampling tool is also included that implements frequently used sampling standardization methods in a multiple bin-framework. The plotting of time series and the occurrence data can be simplified by the functions incorporated in the package, as well as other calculations, such as environmental affinities and extinction selectivity testing. Details can be found in: Kocsis, A.T.; Reddin, C.J.; Alroy, J. and Kiessling, W. (2019) . 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Miscellaneous functions for handling location data are also provided. Package: r-cran-divent Architecture: amd64 Version: 0.5-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1297 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-alphahull, r-cran-ape, r-cran-cli, r-cran-dbmss, r-cran-dplyr, r-cran-ggplot2, r-cran-igraph, r-cran-entropyestimation, r-cran-rcolorbrewer, r-cran-rcppparallel, r-cran-rdpack, r-cran-rlang, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-tibble, r-cran-tidyr, r-cran-vegan Suggests: r-cran-ade4, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-species, r-cran-testthat Filename: pool/dists/noble/main/r-cran-divent_0.5-4-1.ca2404.1_amd64.deb Size: 1088334 MD5sum: 11734ed6a6f1c37aa181dcfcf8abcf37 SHA1: 68114e4d1a0fbab5ea6687f1530c089be818e246 SHA256: f8a295705c0f1aa96fa917e941cc21a5207ad1a4c9dbac810355a805e5ac2ec0 SHA512: 6d5dae3210bb6ec07e0ee9595c681192fbaea7b5073d076816a7b771284add6bb5afd13e41ef0b7b5965f8938d6c94d34f2cfcf616d20e00c9a9249806aecde8 Homepage: https://cran.r-project.org/package=divent Description: CRAN Package 'divent' (Entropy Partitioning to Measure Diversity) Measurement and partitioning of diversity, based on Tsallis entropy, following Marcon and Herault (2015) . 'divent' provides functions to estimate alpha, beta and gamma diversity of communities, including phylogenetic and functional diversity. Package: r-cran-diversitree Architecture: amd64 Version: 0.10-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1668 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 4.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-desolve, r-cran-subplex, r-cran-rcpp Suggests: r-cran-numderiv, r-cran-minqa, r-cran-lubridate, r-cran-expm, r-cran-caper, r-cran-geiger Filename: pool/dists/noble/main/r-cran-diversitree_0.10-1-1.ca2404.1_amd64.deb Size: 1121258 MD5sum: a4c820126e1181517fe3ce0ca60de286 SHA1: 41a0f12d0e81abcc9a97ac59416f1350a56dcf45 SHA256: 1a1c9ebe7014b1a3740493a52e5cbf7b650e854631d04931884f26d2c6c63d94 SHA512: 3cc40fd1169ca785c525186bee6e5787a125335c7df57e4c040f48881d2a473a4ed838b0300ddf67366da6055b0503b3b418bdcba910e99707ab5e042771533f Homepage: https://cran.r-project.org/package=diversitree Description: CRAN Package 'diversitree' (Comparative 'Phylogenetic' Analyses of Diversification) Contains a number of comparative 'phylogenetic' methods, mostly focusing on analysing diversification and character evolution. Contains implementations of 'BiSSE' (Binary State 'Speciation' and Extinction) and its unresolved tree extensions, 'MuSSE' (Multiple State 'Speciation' and Extinction), 'QuaSSE', 'GeoSSE', and 'BiSSE-ness' Other included methods include Markov models of discrete and continuous trait evolution and constant rate 'speciation' and extinction. Package: r-cran-diversityforest Architecture: amd64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1404 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-ggplot2, r-cran-ggpubr, r-cran-scales, r-cran-nnet, r-cran-sgeostat, r-cran-rms, r-cran-mapgam, r-cran-gam, r-cran-rlang, r-cran-rcolorbrewer, r-cran-rcppeigen, r-cran-survival, r-cran-patchwork Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-diversityforest_0.6.0-1.ca2404.1_amd64.deb Size: 827312 MD5sum: 75d5d21050dcb7f410946757413d3e56 SHA1: 79f309fe1698f5d10aa82591b6d9b1be22fe6984 SHA256: aeb1a6858c51b5e33f5aa40ccbc6b5732d414cc7f9eb77663fdb7182672b642b SHA512: 16db25c7c3a88b1121ee24995a99c4b3a661336a05cdea46fef661a754473db90047d6f6ed66e65b4e4c501ecf9f3108c421cff919995d03c7373ba0fb0dfa81 Homepage: https://cran.r-project.org/package=diversityForest Description: CRAN Package 'diversityForest' (Innovative Complex Split Procedures in Random Forests ThroughCandidate Split Sampling) Implementation of three methods based on the diversity forest (DF) algorithm (Hornung, 2022, ), a split-finding approach that enables complex split procedures in random forests. The package includes: 1. Interaction forests (IFs) (Hornung & Boulesteix, 2022, ): Model quantitative and qualitative interaction effects using bivariable splitting. Come with the Effect Importance Measure (EIM), which can be used to identify variable pairs that have well-interpretable quantitative and qualitative interaction effects with high predictive relevance. 2. Two random forest-based variable importance measures (VIMs) for multi-class outcomes: the class-focused VIM, which ranks covariates by their ability to distinguish individual outcome classes from the others, and the discriminatory VIM, which measures overall covariate influence irrespective of class-specific relevance. 3. The basic form of diversity forests that uses conventional univariable, binary splitting (Hornung, 2022). Except for the multi-class VIMs, all methods support categorical, metric, and survival outcomes. The package includes visualization tools for interpreting the identified covariate effects. Built as a fork of the 'ranger' R package (main author: Marvin N. Wright), which implements random forests using an efficient C++ implementation. 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(2015). Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions. Cognitive Psychology, 78, 148-174. Ulrich et al. (2015) . Decision processes within choice reaction-time (CRT) tasks are often modelled using evidence accumulation models (EAMs), a variation of which is the Diffusion Decision Model (DDM, for a review, see Ratcliff & McKoon, 2008). Ulrich et al. (2015) introduced a Diffusion Model for Conflict tasks (DMC). The DMC model combines common features from within standard diffusion models with the addition of superimposed controlled and automatic activation. The DMC model is used to explain distributional reaction time (and error rate) patterns in common behavioural conflict-like tasks (e.g., Flanker task, Simon task). This R-package implements the DMC model and provides functionality to fit the model to observed data. Further details are provided in the following paper: Mackenzie, I.G., & Dudschig, C. (2021). DMCfun: An R package for fitting Diffusion Model of Conflict (DMC) to reaction time and error rate data. Methods in Psychology, 100074. . 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Package: r-cran-dnatools Architecture: amd64 Version: 0.2-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1112 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsolnp, r-cran-multicool, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcppprogress Suggests: r-cran-testthat, r-cran-testthis, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dnatools_0.2-5-1.ca2404.1_amd64.deb Size: 620158 MD5sum: ba9f5f939e94bf030eb95d2f5127016a SHA1: 3a229cabc45d421aeb86c667bcb456494ff1db0d SHA256: 261b61e3108aaadcfc0b72f9d6a7f45d9049920eaeb3986a222b17026e993149 SHA512: 26db3748dfd97dabdfbd12a4af80471104475a49879b84ea5837bdb12da2faadd2a204d6058e537a638dea47314bb053fb30e44716108de21db80d3f89130c81 Homepage: https://cran.r-project.org/package=DNAtools Description: CRAN Package 'DNAtools' (Tools for Analysing Forensic Genetic DNA Data) Computationally efficient tools for comparing all pairs of profiles in a DNA database. The expectation and covariance of the summary statistic is implemented for fast computing. Routines for estimating proportions of close related individuals are available. The use of wildcards (also called F- designation) is implemented. Dedicated functions ease plotting the results. See Tvedebrink et al. (2012) . Compute the distribution of the numbers of alleles in DNA mixtures. See Tvedebrink (2013) . Package: r-cran-dng Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 458 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dng_1.0.0-1.ca2404.1_amd64.deb Size: 199768 MD5sum: 07e82f97694ecc423b1513d030983011 SHA1: ae9c43649f40756b54f23fa38611fe54618e681e SHA256: f88c73c020966a88c258b18c1d1ced3cf6ee6f0f85c09b031c9670b495da840e SHA512: cd3ea02d48f22e01ddadf4b846fc8a896ad8542900f61754f894e303dbd95592160e9d13f93d2e6e01d0a719aeeee728d06efc118222901f0b972c8b8eea155e Homepage: https://cran.r-project.org/package=dng Description: CRAN Package 'dng' (Distributions and Gradients) Provides density, distribution function, quantile function and random generation for the split normal and split-t distributions, and computes their mean, variance, skewness and kurtosis for the two distributions (Li, F, Villani, M. and Kohn, R. (2010) ). Package: r-cran-dnn Architecture: amd64 Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-lpl, r-cran-rcpp, r-cran-survival, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-dnn_0.0.7-1.ca2404.1_amd64.deb Size: 248502 MD5sum: 58af7c74600f19f1dfe08045cc1ee242 SHA1: 982eacc6ea9519e5e992e01362070bb6c7e6858f SHA256: 8a692a588f9002c2f12b2434a6b8a37fb9dea5884c6c2d777873d7605af2f306 SHA512: fdbfe4f7ddc1306af42b3370cb54e454db00634d40b7d3cbbaa03005e1dfa471b1985f93310fb8b61ba4adafa628e995ec4e19622da3f4825b68dd73107ce483 Homepage: https://cran.r-project.org/package=dnn Description: CRAN Package 'dnn' (Deep Neural Network Tools for Probability and Statistic Models) Contains a robust set of tools designed for constructing deep neural networks, which are highly adaptable with user-defined loss function and probability models. 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Package: r-cran-doc2vec Architecture: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5232 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tokenizers.bpe, r-cran-word2vec, r-cran-uwot, r-cran-dbscan, r-cran-udpipe Filename: pool/dists/noble/main/r-cran-doc2vec_0.2.2-1.ca2404.1_amd64.deb Size: 5172590 MD5sum: 6d11ef961c46064a977b0711e187d776 SHA1: 1c4bc29729ea0e55d343d01a7002c3b39918542e SHA256: 0ae978cb532015de7bc38eea407569d98f217618bb86364051d8fccb4f1a62e5 SHA512: cc40f43f3c7310a136deca7e4772ce33c8d4cee54c10678bb16c317ad74e1e0c2f12c1b92a2904ea241c78b6147ae8a8bcd75ad1b66bb5c7fe0bc98cac9ca1ef Homepage: https://cran.r-project.org/package=doc2vec Description: CRAN Package 'doc2vec' (Distributed Representations of Sentences, Documents and Topics) Learn vector representations of sentences, paragraphs or documents by using the 'Paragraph Vector' algorithms, namely the distributed bag of words ('PV-DBOW') and the distributed memory ('PV-DM') model. The techniques in the package are detailed in the paper "Distributed Representations of Sentences and Documents" by Mikolov et al. (2014), available at . The package also provides an implementation to cluster documents based on these embedding using a technique called top2vec. Top2vec finds clusters in text documents by combining techniques to embed documents and words and density-based clustering. It does this by embedding documents in the semantic space as defined by the 'doc2vec' algorithm. Next it maps these document embeddings to a lower-dimensional space using the 'Uniform Manifold Approximation and Projection' (UMAP) clustering algorithm and finds dense areas in that space using a 'Hierarchical Density-Based Clustering' technique (HDBSCAN). These dense areas are the topic clusters which can be represented by the corresponding topic vector which is an aggregate of the document embeddings of the documents which are part of that topic cluster. In the same semantic space similar words can be found which are representative of the topic. More details can be found in the paper 'Top2Vec: Distributed Representations of Topics' by D. Angelov available at . 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Builds from and improves on previous package from IPEA validaRA . It can check individual registers or help creating a table summarizing validity of a set. 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Weighted directed graphs have weights from A to B which may differ from those from B to A. Dual-weighted directed graphs have two sets of such weights. A canonical example is a street network to be used for routing in which routes are calculated by weighting distances according to the type of way and mode of transport, yet lengths of routes must be calculated from direct distances. 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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. 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It provides functions for: multiple contrast tests, fitting non-linear dose-response models (using Bayesian and non-Bayesian estimation), calculating optimal designs and an implementation of the MCPMod methodology (Pinheiro et al. (2014) ). Package: r-cran-dotcall64 Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-microbenchmark, r-cran-rhpcblasctl, r-cran-rcolorbrewer, r-cran-roxygen2, r-cran-spam, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dotcall64_1.2-1.ca2404.1_amd64.deb Size: 31282 MD5sum: da0d979a6333882b5922ebff0db196f6 SHA1: 3ca1c657f036cba73e41f0b19d3debca9723f07a SHA256: 76a989d62ce1244dd41a1bf58a9bc34dffd1e6626f4684026e80ecc4a175f9e5 SHA512: 410f38c04d675df2af561bf744884a218d413e619e685f1cdcceca6badb16491e7fb171502d13b321cc0c27432b0b55c1f21e66ab6081f6abc126ba25d5b9d66 Homepage: https://cran.r-project.org/package=dotCall64 Description: CRAN Package 'dotCall64' (Enhanced Foreign Function Interface Supporting Long Vectors) Provides .C64(), which is an enhanced version of .C() and .Fortran() from the foreign function interface. .C64() supports long vectors, arguments of type 64-bit integer, and provides a mechanism to avoid unnecessary copies of read-only and write-only arguments. 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Package: r-cran-doubcens Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-doubcens_1.1-1.ca2404.1_amd64.deb Size: 20476 MD5sum: 91539335ec75ec4cd3fad6bcf8471f3d SHA1: 0ce5db7592d2e2e1bf4235ae442c339041a0caa2 SHA256: 342768a06d3546c04577c397c6f59291f4b08b9c5845a7b9588f9cd5cd044597 SHA512: 6927307bdfc6ff494da631a38c9cd665c72815453d61ca1736ace4c32a699c016eb31168cafcb8a338872e7fa1fd649aea7a85b72a462c890d6179effbd943c7 Homepage: https://cran.r-project.org/package=doubcens Description: CRAN Package 'doubcens' (Survivor Function Estimation for Doubly Interval-CensoredFailure Time Data) Contains the discrete nonparametric survivor function estimation algorithm of De Gruttola and Lagakos for doubly interval-censored failure time data and the discrete nonparametric survivor function estimation algorithm of Sun for doubly interval-censored left-truncated failure time data [Victor De Gruttola & Stephen W. Lagakos (1989) ] [Jianguo Sun (1995) ]. Package: r-cran-dove Architecture: amd64 Version: 1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 910 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-dove_1.11-1.ca2404.1_amd64.deb Size: 635976 MD5sum: d3acce69d0db76f8bd11d7cf2a1ef9cd SHA1: ae9ebc49a18df9d5c1b3d7034e519fc7283a651d SHA256: 2b420e1b7f3a94c223d9b5c4d918aae690b73fad68e60191b14da4a4a7030141 SHA512: b4c6dd2231698103d5a4cd6f05564ed29f689dd597bc1afc0e2ab57e6eb7cab7330e8efad66f92185f6e1e7efe715501d14fd44edde51125b94f69e092fc5612 Homepage: https://cran.r-project.org/package=DOVE Description: CRAN Package 'DOVE' (Durability of Vaccine Efficacy) Implements maximum likelihood methods for evaluating the durability of vaccine efficacy in a randomized, placebo-controlled clinical trial with staggered enrollment of participants and potential crossover of placebo recipients before the end of the trial. Lin, D. Y., Zeng, D., and Gilbert, P. B. (2021) and Lin, D. Y., Gu, Y., Zeng, D., Janes, H. E., and Gilbert, P. B. (2021) . Package: r-cran-dparser Architecture: amd64 Version: 1.3.1-13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 830 Depends: libc6 (>= 2.33), r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest Suggests: r-cran-rex, r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-devtools Filename: pool/dists/noble/main/r-cran-dparser_1.3.1-13-1.ca2404.1_amd64.deb Size: 324748 MD5sum: ec86943f48bd657d22e14eb84774d845 SHA1: 43db370a62b01950af6b4bbff89ca05bde326dc4 SHA256: 4561b66d9d085fbd5a5b714c12ddfa98ae206113556544e6ea340306cebf155d SHA512: cede3079fa6b10e4873313831f6ee6b13ff822bffa36b331a76de402214eda05ee9d99ca1b3973951811ab0ffa1a83a34223b440e5f764d7fd0249cb528c61cc Homepage: https://cran.r-project.org/package=dparser Description: CRAN Package 'dparser' (Port of 'Dparser' Package) A Scannerless GLR parser/parser generator. Note that GLR standing for "generalized LR", where L stands for "left-to-right" and R stands for "rightmost (derivation)". For more information see . This parser is based on the Tomita (1987) algorithm. (Paper can be found at ). The original 'dparser' package documentation can be found at . This allows you to add mini-languages to R (like rxode2's ODE mini-language Wang, Hallow, and James 2015 ) or to parse other languages like 'NONMEM' to automatically translate them to R code. To use this in your code, add a LinkingTo dparser in your DESCRIPTION file and instead of using #include use #include . This also provides a R-based port of the make_dparser command called mkdparser(). Additionally you can parse an arbitrary grammar within R using the dparse() function, which works on most OSes and is mainly for grammar testing. The fastest parsing, of course, occurs at the C level, and is suggested. 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Package: r-cran-dpp Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 999 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-coda Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-dpp_0.1.2-1.ca2404.1_amd64.deb Size: 595108 MD5sum: 42f5eb7e8ff64ad06097969cdf8b666f SHA1: 55b89d06d8909d946e4faa6fe076a1c64f70755e SHA256: 26c9b918846cf124248fcee4f151d5658e49ca7cb0bae1fbab90fb1b32245b49 SHA512: 53416ae7e9bfe06f47361d8f9871b9042261fc6b9535baf660e06c99a78d77306e8fae3283c91e08955a21d268697c463bef516fbdd85d69afa305769dce55aa Homepage: https://cran.r-project.org/package=DPP Description: CRAN Package 'DPP' (Inference of Parameters of Normal Distributions from a Mixtureof Normals) This MCMC method takes a data numeric vector (Y) and assigns the elements of Y to a (potentially infinite) number of normal distributions. The individual normal distributions from a mixture of normals can be inferred. Following the method described in Escobar (1994) we use a Dirichlet Process Prior (DPP) to describe stochastically our prior assumptions about the dimensionality of the data. Package: r-cran-dpq Architecture: amd64 Version: 0.6-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2783 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sfsmisc Suggests: r-cran-rmpfr, r-cran-dpqmpfr, r-cran-gmp, r-cran-mass, r-cran-mgcv, r-cran-scatterplot3d, r-cran-interp, r-cran-cobs Filename: pool/dists/noble/main/r-cran-dpq_0.6-1-1.ca2404.1_amd64.deb Size: 2534514 MD5sum: 558690322a731bdaec8903835a7f79f0 SHA1: 2a2e974a0d98cff9a21dbdbcb2f1c73ee6990807 SHA256: b8b902755fbb2b480276e99a28c0e938da611f304df6de94f3e7f0efde9ef6bb SHA512: 8432001aa3524b72244cef908311b1767d38cd8e21248f4e3b356e254b625501def129f376a7929d050bdc64576376717440754f4fa5fe09949ee6202f149636 Homepage: https://cran.r-project.org/package=DPQ Description: CRAN Package 'DPQ' (Density, Probability, Quantile ('DPQ') Computations) Computations for approximations and alternatives for the 'DPQ' (Density (pdf), Probability (cdf) and Quantile) functions for probability distributions in R. Primary focus is on (central and non-central) beta, gamma and related distributions such as the chi-squared, F, and t. -- For several distribution functions, provide functions implementing formulas from Johnson, Kotz, and Kemp (1992) and Johnson, Kotz, and Balakrishnan (1995) for discrete or continuous distributions respectively. This is for the use of researchers in these numerical approximation implementations, notably for my own use in order to improve standard R pbeta(), qgamma(), ..., etc: {'"dpq"'-functions}. Package: r-cran-dpseg Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2004 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-markdown, r-cran-knitr, r-cran-htmltools, r-cran-rcppdynprog, r-cran-microbenchmark, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-dpseg_0.1.1-1.ca2404.1_amd64.deb Size: 1364604 MD5sum: dd5ea7f143e26249767020332fc7cf78 SHA1: dc02921198d6029c7752d47af00b8547ead96dc2 SHA256: 4cff6ed51d986d2b1bae5aad9d8ccc96a0a66f0dc064e24555e4964b8b18deff SHA512: fbc2b3ca72f830c14388033583e7f9467cb208371c4824005917248039afb10a48785af35e56eb5cab9b69ed802eaf35e0d6c34d1f3c5b2272a44e0fd7c8aa30 Homepage: https://cran.r-project.org/package=dpseg Description: CRAN Package 'dpseg' (Piecewise Linear Segmentation by Dynamic Programming) Piecewise linear segmentation of ordered data by a dynamic programming algorithm. The algorithm was developed for time series data, e.g. growth curves, and for genome-wide read-count data from next generation sequencing, but is broadly applicable. Generic implementations of dynamic programming routines allow to scan for optimal segmentation parameters and test custom segmentation criteria ("scoring functions"). Package: r-cran-dptm Architecture: amd64 Version: 3.0.2-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-bayesiantools, r-cran-purrr, r-cran-mass, r-cran-coda, r-cran-parabar, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-dptm_3.0.2-1.ca2404.2_amd64.deb Size: 253114 MD5sum: 185ce89334f1ac9374714226673c6b26 SHA1: 0689c606c6fa98c1d6991f3b16176a37e96b50f0 SHA256: f2d3719faa0d18dbbb61aae640ae4e1f96ed6493373e4591ad6dbde4d27a2d7b SHA512: e669650883afd9ce7a77aec36445eb477d8216116c81c3ed3e184019a8f6e95240ab60e868280c08b8b74c877ff0d109307d944bbbb8f58dc7b8b9694e23f51f Homepage: https://cran.r-project.org/package=DPTM Description: CRAN Package 'DPTM' (Dynamic Panel Multiple Threshold Model with Fixed Effects) Compute the fixed effects dynamic panel threshold model suggested by Ramírez-Rondán (2020) , and dynamic panel linear model suggested by Hsiao et al. (2002) , where maximum likelihood type estimators are used. Multiple thresholds estimation based on Markov Chain Monte Carlo (MCMC) is allowed, and model selection of linear model, threshold model and multiple threshold model is also allowed. Package: r-cran-dqrng Architecture: amd64 Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 703 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bh, r-cran-sitmo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mvtnorm, r-cran-bench Filename: pool/dists/noble/main/r-cran-dqrng_0.4.1-1.ca2404.1_amd64.deb Size: 176706 MD5sum: 45dad1d5e77ec0cc0517e20aee18e583 SHA1: 5508ff390da99fac8123a9c8f6d9eb806ec03e7a SHA256: 364daf15daf9c7013f251203ecef4824a2a5558b67657ee2dc06c05ffa9bb261 SHA512: 9de39ed3f9a50f599812dbde97d8899e26f857b898e240f8c7a7ccbaa712e52d23eac139a72cd862751a5c4ecfd1322b7c99aebae73ad981203eaf0bd1cbbf96 Homepage: https://cran.r-project.org/package=dqrng Description: CRAN Package 'dqrng' (Fast Pseudo Random Number Generators) Several fast random number generators are provided as C++ header only libraries: The PCG family by O'Neill (2014 ) as well as the Xoroshiro / Xoshiro family by Blackman and Vigna (2021 ). In addition fast functions for generating random numbers according to a uniform, normal and exponential distribution are included. The latter two use the Ziggurat algorithm originally proposed by Marsaglia and Tsang (2000, ). The fast sampling methods support unweighted sampling both with and without replacement. These functions are exported to R and as a C++ interface and are enabled for use with the default 64 bit generator from the PCG family, Xoroshiro128+/++/** and Xoshiro256+/++/** as well as the 64 bit version of the 20 rounds Threefry engine (Salmon et al., 2011, ) as provided by the package 'sitmo'. Package: r-cran-dr.sc Architecture: amd64 Version: 3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3924 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.geom, r-cran-compquadform, r-cran-irlba, r-cran-cowplot, r-cran-ggplot2, r-cran-mass, r-cran-matrix, r-cran-mclust, r-cran-purrr, r-bioc-s4vectors, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-seurat, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dr.sc_3.7-1.ca2404.1_amd64.deb Size: 3360526 MD5sum: 7e42cd749288901b90a3c2477a6bec7e SHA1: dfa60aa3861fcaaf041b8e1f5ac10ec12497984d SHA256: 898862df3e4768e51dfad31ff001aa64be3422815d28ae8f2dd4024e5b347390 SHA512: 1b7784d4abc962d532102676f4096a399353343630969e247de2ae4880a57d10be062f3c62fcb99ab68134a4824d80a9ffb2ab3f41a5c701fe16751a3aba5adb Homepage: https://cran.r-project.org/package=DR.SC Description: CRAN Package 'DR.SC' (Joint Dimension Reduction and Spatial Clustering) Joint dimension reduction and spatial clustering is conducted for Single-cell RNA sequencing and spatial transcriptomics data, and more details can be referred to Wei Liu, Xu Liao, Yi Yang, Huazhen Lin, Joe Yeong, Xiang Zhou, Xingjie Shi and Jin Liu. (2022) . It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well. Package: r-cran-dracor Architecture: amd64 Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 963 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-spelling, r-cran-covr Filename: pool/dists/noble/main/r-cran-dracor_0.2.6-1.ca2404.1_amd64.deb Size: 268370 MD5sum: 007d22965fa8a373d3a41dd5e04a920e SHA1: 171a83115af8ec9639fa87b259580951539705eb SHA256: 78991427e4bf7452dd0e100585515dac5aad4a220f8533f59867108ceb02c06b SHA512: 56bd24961c08b6d12e6ae44a180c8fed613a60e92e40f101893b93b39996e35475113c1b819aaf7bdd867f1fdb58b3a4ca9a432a2a7032acb3aea6abd430882d Homepage: https://cran.r-project.org/package=dracor Description: CRAN Package 'dracor' (Decode Draco Format 3D Mesh Data) Decodes meshes and point cloud data encoded by the Draco mesh compression library from Google. Note that this is only designed for basic decoding and not intended as a full scale wrapping of the Draco library. Package: r-cran-drclust Architecture: amd64 Version: 0.1.1-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-fpc, r-cran-cluster, r-cran-factoextra, r-cran-pheatmap Filename: pool/dists/noble/main/r-cran-drclust_0.1.1-1.ca2404.1_amd64.deb Size: 324248 MD5sum: 90e67f4cd153dcb19a7adfcdc771b33b SHA1: 1623fb3a9871c0c2e33127b910e30a9e8096ae2e SHA256: fac06baf45e0f38f014762661155a935024deae8115640f7fda3681b61b980dc SHA512: 1a1f33b1100cf64c811c6b33fa01068d463582940fcb52306752814a8ffcaa502d4b5d653c9db6e54be971b24f5dbcd14d09520ac73c7f36c8466205427c448b Homepage: https://cran.r-project.org/package=drclust Description: CRAN Package 'drclust' (Simultaneous Clustering and (or) Dimensionality Reduction) Methods for simultaneous clustering and dimensionality reduction such as: Double k-means, Reduced k-means, Factorial k-means, Clustering with Disjoint PCA but also methods for exclusively dimensionality reduction: Disjoint PCA, Disjoint FA. The statistical methods implemented refer to the following articles: de Soete G., Carroll J. (1994) "K-means clustering in a low-dimensional Euclidean space" ; Vichi M. (2001) "Double k-means Clustering for Simultaneous Classification of Objects and Variables" ; Vichi M., Kiers H.A.L. (2001) "Factorial k-means analysis for two-way data" ; Vichi M., Saporta G. (2009) "Clustering and disjoint principal component analysis" ; Vichi M. (2017) "Disjoint factor analysis with cross-loadings" . Package: r-cran-drdid Architecture: amd64 Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 928 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-trust, r-cran-bmisc, r-cran-rcpp, r-cran-fastglm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-drdid_1.2.3-1.ca2404.1_amd64.deb Size: 810594 MD5sum: b9d624d02d71ea88f5f88f96d6de1200 SHA1: 1be834d7c52e39ebb76c6187adf526f46e28e2d1 SHA256: e8e430f220cbf6e3b64c3e9f6d98b25693d8a6c278e3ba67b166f7f333208957 SHA512: e211cb1d9f83d62f2ec40383c9297c5978f0820c03af0d31e8eba3351d6e9b3383e4f482759fb8b1f653012329e8d619367b26e9b7bba1aec627da8d4fd7df88 Homepage: https://cran.r-project.org/package=DRDID Description: CRAN Package 'DRDID' (Doubly Robust Difference-in-Differences Estimators) Implements the locally efficient doubly robust difference-in-differences (DiD) estimators for the average treatment effect proposed by Sant'Anna and Zhao (2020) . The estimator combines inverse probability weighting and outcome regression estimators (also implemented in the package) to form estimators with more attractive statistical properties. Two different estimation methods can be used to estimate the nuisance functions. Package: r-cran-dream Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1535 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-collapse, r-cran-data.table, r-cran-foreach, r-cran-doparallel, r-cran-rcpp, r-cran-lifecycle Filename: pool/dists/noble/main/r-cran-dream_1.1.1-1.ca2404.1_amd64.deb Size: 1179228 MD5sum: 29fb52f1a18bae52b1c7ff8b8a8e3476 SHA1: 268558ff97761406913ef08c3c866ceced669d7e SHA256: 973fbb03fe198d0b8ef92738bcb3febe032c7a83e071bdd32106df7a1ebfdfe6 SHA512: 27d97301bab77de722014d6bb0430222ccd0b4b3356c99a902b737a1e8cc3f1e11010501ed489b97082cbb769d4122c4f06a0a40095e1b744e7c98bd3ea3f95a Homepage: https://cran.r-project.org/package=dream Description: CRAN Package 'dream' (Dynamic Relational Event Analysis and Modeling) A set of tools for relational and event analysis, including two- and one-mode network brokerage and structural measures, and helper functions optimized for relational event analysis with large datasets, including creating relational risk sets, computing network statistics, estimating relational event models, and simulating relational event sequences. For more information on relational event models, see Butts (2008) , Lerner and Lomi (2020) , Bianchi et al. (2024) , and Butts et al. (2023) . In terms of the structural measures in this package, see Leal (2025) , Burchard and Cornwell (2018) , and Fujimoto et al. (2018) . This package was developed with support from the National Science Foundation’s (NSF) Human Networks and Data Science Program (HNDS) under award number 2241536 (PI: Diego F. Leal). Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. Package: r-cran-dregar Architecture: amd64 Version: 0.1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-msgps Filename: pool/dists/noble/main/r-cran-dregar_0.1.3.0-1.ca2404.1_amd64.deb Size: 50216 MD5sum: aa11d40e9fc7ddc8d5af1433f4984d68 SHA1: f1c31bf0fd4cfa3f3f249e98ea2903e2a6ba5a52 SHA256: a05649239ca7f0b94ad0da74e8f5d3c8f90280e8022f1e6a5c509f5051af1071 SHA512: a2ec3562de4560d5adc1e37e0d82db4b115886f8701a950cd677ca8103669f6fe7f3be48acf0c81fed5cdec576d753cefff76b93342d3f3215f154e9972c9f87 Homepage: https://cran.r-project.org/package=DREGAR Description: CRAN Package 'DREGAR' (Regularized Estimation of Dynamic Linear Regression in thePresence of Autocorrelated Residuals (DREGAR)) A penalized/non-penalized implementation for dynamic regression in the presence of autocorrelated residuals (DREGAR) using iterative penalized/ordinary least squares. It applies Mallows CP, AIC, BIC and GCV to select the tuning parameters. Package: r-cran-dress.graph Architecture: amd64 Version: 0.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: libc6 (>= 2.14), libgomp1 (>= 4.9), r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dress.graph_0.8.3-1.ca2404.1_amd64.deb Size: 84442 MD5sum: 3f840a6b74437b94852f6f99dc12aedf SHA1: 709ec092c18504f4c4b0f5436178aff58715f4e9 SHA256: ae0234ccc4194ed8a0e7265800e4d2aa5ff1f74ec13c06b82f1fd01f7c2d95c7 SHA512: 5d56fc65743bacd29cb2bba68864cd909e157d75b101426e4e05904910ad03b4daa018690aa595ac0bbd1a71a8758a9c46bcdc841d084b541673bd4ef47f9388 Homepage: https://cran.r-project.org/package=dress.graph Description: CRAN Package 'dress.graph' (DRESS - A Continuous Framework for Structural Graph Refinement) DRESS is a deterministic, parameter-free framework for continuous structural graph refinement. It iterates a nonlinear dynamical system on real-valued edge similarities and produces a graph fingerprint as a sorted edge-value vector once the iteration reaches a prescribed stopping criterion. The resulting fingerprint is self-contained, isomorphism-invariant by construction, reproducible across vertex labelings under the reference implementation, numerically robust in practice, and efficient to compute with straightforward parallelization and distribution. Package: r-cran-drf Architecture: amd64 Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 604 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastdummies, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-diagrammer Filename: pool/dists/noble/main/r-cran-drf_1.3.1-1.ca2404.1_amd64.deb Size: 249502 MD5sum: c5bcb6e998bbfea2ba84d96412871b50 SHA1: 72428d1b94a02470fc7e40fcb4862d60449de7f0 SHA256: 2b4201fa7b9c7d925e8abeeaf164707351bc9fca4cb749f74e4d42b28459785d SHA512: 5118c4ce9484dcd5aaa5fd35ddcea459472eda6d15bc6a17c401662ff7b069bed265d9a78b60ff5dc4b3cfcb36f14cf74afd65ef02b9e1b7186efb5a81a4dc67 Homepage: https://cran.r-project.org/package=drf Description: CRAN Package 'drf' (Distributional Random Forests) An implementation of distributional random forests as introduced in Cevid & Michel & Naf & Meinshausen & Buhlmann (2022) . Package: r-cran-drgee Architecture: amd64 Version: 1.1.10-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nleqslv, r-cran-survival, r-cran-rcpp, r-cran-data.table, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-drgee_1.1.10-4-1.ca2404.1_amd64.deb Size: 192984 MD5sum: 7fbb915d4dab30bf69249bb443e2f566 SHA1: d7debaf8b80cc8514b35a9b7c7be6059094f53fc SHA256: b0558494dc1ed4d1e12ee253415e25a8afc86e36001b7bda572763a3fcba998f SHA512: 0b40bb1fbcca990012e702410b237c9f22ea2f1a2885cd5c19d4d230d83ddcc19a1f912f7b07c60937daea0e2cd45619e7279e64edd0ef3329a3a66a11e939df Homepage: https://cran.r-project.org/package=drgee Description: CRAN Package 'drgee' (Doubly Robust Generalized Estimating Equations) Estimates the conditional association between an exposure and an outcome given covariates. Three methods are implemented: O-estimation, where a nuisance model for the association between the covariates and the outcome is used; E-estimation where a nuisance model for the association between the covariates and the exposure is used, and doubly robust (DR) estimation where both nuisance models are used. In DR-estimation, the estimates will be consistent when at least one of the nuisance models is correctly specified, not necessarily both. For more information, see Zetterqvist and Sjölander (2015) . Package: r-cran-driftbursthypothesis Architecture: amd64 Version: 0.4.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-xts, r-cran-zoo, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-driftbursthypothesis_0.4.0.1-1.ca2404.1_amd64.deb Size: 133238 MD5sum: 23f6aa56a56c48c075a997014086e661 SHA1: 6c4e7557f931ccd494c63f7f2e6a787a0df5e492 SHA256: fca200ebf8f7288f676e9dd6c3064f5fbd0d7e82a3bd7e79ec8217b53427589e SHA512: aa21eaa1e84532af205be2529e7519f097a2dbb1bff1fd942a68d2d812fb37a5941e0b20abde83dece278790b7429b2dcef79bf2991e40a81297113bc058eef6 Homepage: https://cran.r-project.org/package=DriftBurstHypothesis Description: CRAN Package 'DriftBurstHypothesis' (Calculates the Test-Statistic for the Drift Burst Hypothesis) Calculates the T-Statistic for the drift burst hypothesis from the working paper Christensen, Oomen and Reno (2018) . 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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: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1551 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-roxygen2, r-cran-irlba Filename: pool/dists/noble/main/r-cran-drimpute_1.0-1.ca2404.1_amd64.deb Size: 1359346 MD5sum: 30158806754fa570118113e686bc942b SHA1: e44d9beeec35c61d4c29aa32aedb174274d4a750 SHA256: 8f03cdca0f300b02c152777fef197762e9e9d3e1a94c3b54d841794eb14203c5 SHA512: 784d183e1861a2be6aa230df605de06f98f9a54cb4990c442cc86c39bfa370f51345841936ffe28efbf0859943130928d942d95afe11eed97888e3d41780d05b 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. 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The implemented methods are based on the following research: Qiu, P. (1998) , Qiu, P. and Yandell, B. (1997) , Qiu, P. (2009) , Kang, Y. and Qiu, P. (2014) , Qiu, P. and Kang, Y. (2015) , Kang, Y., Mukherjee, P.S. and Qiu, P. (2018) , Kang, Y. (2020) . Package: r-cran-drogonr Architecture: amd64 Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3018 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-drogonr_0.1.6-1.ca2404.1_amd64.deb Size: 944164 MD5sum: 51470103cb6e390c52e57cea9e3ab1e0 SHA1: 9b93c7ac105316ac777d596fb13d7997d7beda6f SHA256: a8cbe6a9514bc9c3ebb0b78727a12fffe362f5eae7aa0242b6f60a1060847d56 SHA512: e0a5cb1c55c7d7e9b73586f25771f72ae188211097aca5d71202dfa360baa93e0b61af7f8581468f722a4f1e3f099bf2a4ba148b3e90c78141dfbe1ae8f29980 Homepage: https://cran.r-project.org/package=drogonR Description: CRAN Package 'drogonR' (High-Performance HTTP Server for R via 'Drogon') Provides an 'R' interface to the 'Drogon' high-performance 'C++' 'HTTP' server framework (). 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Package: r-cran-dropout Architecture: amd64 Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dropout_2.2.0-1.ca2404.1_amd64.deb Size: 378992 MD5sum: e332406eab15b32465693f11623a8f4d SHA1: f73911d20aec57da0ab502882ca9c662a52d8d13 SHA256: d4d90f338e89cac173f2680ae947b52170db3a47b1f07c980f0626ab9383e046 SHA512: 6b066b425ddb1208e09bfd1b38e771264aa60f691e088a53ee3335e70cfda8923321bdbbb96c50af7e42151cda06e8b9e47ee5446af082e7949efa0b5d63a986 Homepage: https://cran.r-project.org/package=dropout Description: CRAN Package 'dropout' (Handling Incomplete Responses in Survey Data Analysis) Offers robust tools to identify and manage incomplete responses in survey datasets, thereby enhancing the quality and reliability of research findings. 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Package: r-cran-drrglm Architecture: amd64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6745 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-glmnet, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-drrglm_0.3.2-1.ca2404.1_amd64.deb Size: 6786314 MD5sum: 4330c09a6e84c36bcd4927d20de41e69 SHA1: 18ea3eaebe5afd054caaea79767aa5d8cc0ccc73 SHA256: fb7447834698095e823e2a94a9a522bc9b7a61f5ef2f7120688fa1c760cebec2 SHA512: 53b54f1c335c7deb71579d600a8ff82506341719b12f8fb7167e9fe90bc6c200ca19af838094367d487269b0ef293571c9ae860526a7fe788df5ffd6492b6d73 Homepage: https://cran.r-project.org/package=drrglm Description: CRAN Package 'drrglm' (Doubly Regularized Matrix-Variate Regression) The doubly regularized matrix-variate regression solves a low-rank-plus-sparse structure for matrix-variate generalized linear models through a weighted combination of nuclear-norm and L1-norm. The methodology implemented by this package is described in the paper "Doubly Regularized Matrix-Variate Regression", which has been tentatively accepted for publication but does not yet have a DOI or URL. A formal citation will be added in a future update once the final publication details are available. Package: r-cran-drsurvcrt Architecture: amd64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 386 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-frailtyem, r-cran-survival, r-cran-ggplot2, r-cran-pracma, r-cran-abind, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-drsurvcrt_0.0.1-1.ca2404.1_amd64.deb Size: 228010 MD5sum: d99544625b16340c9d60e2a031273f31 SHA1: aa21e517320ff2baa1d7d9bb5ec098c716e71ce3 SHA256: cc6821090572529af2d919ea1316762dc9e9d7f798311bd5995d848bf5a6d760 SHA512: 14fdd738632a9184c73c255779454aebac9d15df692f2b6a88d1e3e1c1105eff7bb08b92b7c387523ef2730fb363a4a50e017c2851a8088d27e5f21f82256baf Homepage: https://cran.r-project.org/package=DRsurvCRT Description: CRAN Package 'DRsurvCRT' (Doubly-Robust Estimation for Survival Outcomes inCluster-Randomized Trials) Cluster-randomized trials (CRTs) assign treatment to groups rather than individuals, so valid analyses must distinguish cluster-level and individual-level effects and define estimands within a potential-outcomes framework. This package supports right-censored survival outcomes for both single-state (binary) and multi-state settings. For single-state outcomes, it provides estimands based on stage-specific survival contrasts (SPCE) and restricted mean survival time (RMST). For multi-state outcomes, it provides SPCE as well as a generalized win-based restricted mean time-in-favor estimand (RMT-IF). The package implements doubly robust estimators that accommodate covariate-dependent censoring and remain consistent if either the outcome model or the censoring model is correctly specified. Users can choose marginal Cox or gamma-frailty Cox working models for nuisance estimation, and inference is supported via leave-one-cluster-out jackknife variance and confidence interval estimation. Methods are described in Fang et al. (2025) "Estimands and doubly robust estimation for cluster-randomized trials with survival outcomes" . Package: r-cran-drugdemand Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 432 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-rlang, r-cran-purrr, r-cran-stringr, r-cran-plotly, r-cran-survival, r-cran-mvtnorm, r-cran-erify, r-cran-mass, r-cran-nlme, r-cran-l1pack, r-cran-eventpred, r-cran-foreach, r-cran-doparallel, r-cran-dorng Filename: pool/dists/noble/main/r-cran-drugdemand_0.1.3-1.ca2404.1_amd64.deb Size: 290114 MD5sum: 303a19ea6ea4832f99cf15e9aa76e474 SHA1: 0073312838d08f5c7e87726ff87183231a753149 SHA256: 24d6333458cb16d3c411fbd5fee55744b82f5f74e361ffaa53180d66565d6e40 SHA512: 052242737ea2f1ad8866b00f909d4abe168d57c11b16fca3d1dcbc6915dbffcbec621887a8c385f211e50b410508db1c69d27f8c6a8a9f223b312ea2f91ef284 Homepage: https://cran.r-project.org/package=drugDemand Description: CRAN Package 'drugDemand' (Drug Demand Forecasting) Performs drug demand forecasting by modeling drug dispensing data while taking into account predicted enrollment and treatment discontinuation dates. The gap time between randomization and the first drug dispensing visit is modeled using interval-censored exponential, Weibull, log-logistic, or log-normal distributions (Anderson-Bergman (2017) ). The number of skipped visits is modeled using Poisson, zero-inflated Poisson, or negative binomial distributions (Zeileis, Kleiber & Jackman (2008) ). The gap time between two consecutive drug dispensing visits given the number of skipped visits is modeled using linear regression based on least squares or least absolute deviations (Birkes & Dodge (1993, ISBN:0-471-56881-3)). The number of dispensed doses is modeled using linear or linear mixed-effects models (McCulloch & Searle (2001, ISBN:0-471-19364-X)). Package: r-cran-dscore Architecture: amd64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2620 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rcpp, r-cran-stringi, r-cran-tidyr, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-kableextra, r-cran-knitr, r-cran-lme4, r-cran-matrix, r-cran-patchwork, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dscore_2.0.0-1.ca2404.1_amd64.deb Size: 1861282 MD5sum: 6859afdde1697a84625914a0af37ebf3 SHA1: 17cb1d499345897cc5b7e8a559a67390cdf3346b SHA256: 8fdd14a2ce894e7f44c9f2617a14b3a732a06f982320006401352ebdd8df3eca SHA512: e83f6fffafca63ffb2f8149b452711f40fb50f71cfb6df86a9dc0840901db676bb8b12c5e3049bc9c6b7c52494b0a74b05813e8a22a5df5ccb9833d16496dc34 Homepage: https://cran.r-project.org/package=dscore Description: CRAN Package 'dscore' (D-Score for Child Development) The D-score summarizes a child's performance on developmental milestones into a single number. Its key feature is its generic nature. The method does not depend on a specific measurement instrument. The statistical method underlying the D-score is described in van Buuren et al. (2025) . This package implements model keys to convert milestone scores to D-scores; maps instrument-specific item names to a generic 9-position naming convention; computes D-scores and their precision from a child's milestone scores; and converts D-scores to Development-for-Age Z-scores (DAZ) using age-conditional reference standards. Package: r-cran-dsdp Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-dsdp_0.1.1-1.ca2404.1_amd64.deb Size: 460832 MD5sum: a27268ed648eaa328750923925a74e94 SHA1: f0984303c43bf3c7ff688e342f01104bf8e7c7f4 SHA256: 39522d50a67001c31bcec162860f06558a41cfd12aab6f6a93d24213ed902918 SHA512: 35ad8ba37d5e000f942b363af8edf07eb04ceb9edc9a7dc266f03ff5bd40f2d6a454530d7a7b712a4de199da5556795dc895189019e0937183d5548a376ec3e0 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. Using a maximum likelihood method, 'dsdp' computes parameters of Gaussian or exponential distributions together with degrees of polynomials by a grid search, and coefficient of polynomials by a variant of semidefinite programming. It adopts Akaike Information Criterion for model selection. See a vignette for a tutorial and more on our 'Github' repository . Package: r-cran-dsem Architecture: amd64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5648 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-tmb, r-cran-matrix, r-cran-igraph, r-cran-rtmb, r-cran-ggraph, r-cran-ggplot2, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-aer, r-cran-phylopath, r-cran-rmarkdown, r-cran-reshape, r-cran-gridextra, r-cran-dynlm, r-cran-marss, r-cran-ggpubr, r-cran-vars, r-cran-testthat, r-cran-dharma Filename: pool/dists/noble/main/r-cran-dsem_2.0.1-1.ca2404.1_amd64.deb Size: 2571966 MD5sum: c122c19cc094c6bc0e2f117dc0f38c72 SHA1: cac10f26db3e57ef9a30bd0bb5e0227e83d48b23 SHA256: d5b3c61c76d642d20006ed24e3d803ba31d97a247e4bb40e148bf98192199f68 SHA512: 97ec13870752577db329c8ce56261ffb0c5646f3dba8571b5fc318b20849285f353aa55a374bf29217f2f0bf9c8cc50b9257ca3eb54dcfddeed0adaf26ffb940 Homepage: https://cran.r-project.org/package=dsem Description: CRAN Package 'dsem' (Dynamic Structural Equation Models) Applies dynamic structural equation models to time-series data with generic and simplified specification for simultaneous and lagged effects. Methods are described in Thorson et al. (2024) "Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms." Package: r-cran-dsl Architecture: amd64 Version: 0.1-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-hive Filename: pool/dists/noble/main/r-cran-dsl_0.1-7-1.ca2404.1_amd64.deb Size: 292400 MD5sum: ed584fee058b54448b5ebed8a82204bb SHA1: d2f9704ccfd5e977db336ade6b349a9617a8611a SHA256: a30d26f7df520ee64ed2a3336f5231db8a85e7d7f43c022a187222367e2ba79e SHA512: b098739ebe9416194a4bc355c7a92b438e462af91a74a610bd83b109149d1c31faaf892ec3dd091a1b35c0ebb4c3c2df1ccbd19051545ffc630cdf4e115d96e8 Homepage: https://cran.r-project.org/package=DSL Description: CRAN Package 'DSL' (Distributed Storage and List) An abstract DList class helps storing large list-type objects in a distributed manner. Corresponding high-level functions and methods for handling distributed storage (DStorage) and lists allows for processing such DLists on distributed systems efficiently. In doing so it uses a well defined storage backend implemented based on the DStorage class. Package: r-cran-dslice Architecture: amd64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1653 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-scales Filename: pool/dists/noble/main/r-cran-dslice_1.2.2-1.ca2404.1_amd64.deb Size: 1515666 MD5sum: f7957e7b0e8a766922659fee4afe1e17 SHA1: 382deb28cf316f7b728bbc40bb36337638c92a63 SHA256: b2fcb8f0845ab5fdfcd97d963ac5c998224f5957968af0a24108292a568f4f02 SHA512: f6df5a9a8c0442d8088d2559035d48e7fe62c0b55a2405941af75c7db1783b08540a00eac6b2ebf0138d9230c7c0a264441b7ff54ada615eff1fe7f46fda9cd9 Homepage: https://cran.r-project.org/package=dslice Description: CRAN Package 'dslice' (Dynamic Slicing) Dynamic slicing is a method designed for dependency detection between a categorical variable and a continuous variable. It could be applied for non-parametric hypothesis testing and gene set enrichment analysis. Package: r-cran-dsmisc Architecture: amd64 Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-stringr Suggests: r-cran-covr, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-dsmisc_0.3.3-1.ca2404.1_amd64.deb Size: 53086 MD5sum: de7ea89798734fe8074bbd5f4fcaa862 SHA1: 2d78c7a1c23e0acb9208ea281ce7c659813d6cec SHA256: 3e8e7d890abe095f80d3f369a93709eab5fed39d5d142c7ca163bfb3d2c1b06f SHA512: e989455813b1279587b0b25a503d15c0ecd6a6f7065ca5333c8a33fdf57144fcf510955400f66bdb31bda146ddf1dec476e97c56a3ba7ca179c231d466f5b06d Homepage: https://cran.r-project.org/package=dsmisc Description: CRAN Package 'dsmisc' (Data Science Box of Pandora Miscellaneous) Tool collection for common and not so common data science use cases. This includes custom made algorithms for data management as well as value calculations that are hard to find elsewhere because of their specificity but would be a waste to get lost nonetheless. Currently available functionality: find sub-graphs in an edge list data.frame, find mode or modes in a vector of values, extract (a) specific regular expression group(s), generate ISO time stamps that play well with file names, or generate URL parameter lists by expanding value combinations. Package: r-cran-dsmmr Architecture: amd64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-discreteweibull Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dsmmr_1.0.5-1.ca2404.1_amd64.deb Size: 233108 MD5sum: cc2c46cd183eae2f6d03e21c15ca2311 SHA1: f12ecba6ab3a8407757ccc3374c916307ec3b0f0 SHA256: 7cffa1b7be8e66912fd149e711a1a45411c9750ced5378f5d094cddc658a36aa SHA512: c3967c6fa9178bd6c86edc1c4bbf9659295a7d46ae85432dd474e5796eaf8b84b3d359102d05122c9f85c96b80e01d2c50c575520f1e666ffb94204538ea2475 Homepage: https://cran.r-project.org/package=dsmmR Description: CRAN Package 'dsmmR' (Estimation and Simulation of Drifting Semi-Markov Models) Performs parametric and non-parametric estimation and simulation of drifting semi-Markov processes. The definition of parametric and non-parametric model specifications is also possible. Furthermore, three different types of drifting semi-Markov models are considered. These models differ in the number of transition matrices and sojourn time distributions used for the computation of a number of semi-Markov kernels, which in turn characterize the drifting semi-Markov kernel. For the parametric model estimation and specification, several discrete distributions are considered for the sojourn times: Uniform, Poisson, Geometric, Discrete Weibull and Negative Binomial. The non-parametric model specification makes no assumptions about the shape of the sojourn time distributions. Semi-Markov models are described in: Barbu, V.S., Limnios, N. (2008) . Drifting Markov models are described in: Vergne, N. (2008) . Reliability indicators of Drifting Markov models are described in: Barbu, V. S., Vergne, N. (2019) . We acknowledge the DATALAB Project (financed by the European Union with the European Regional Development fund (ERDF) and by the Normandy Region) and the HSMM-INCA Project (financed by the French Agence Nationale de la Recherche (ANR) under grant ANR-21-CE40-0005). Package: r-cran-dsp Architecture: amd64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 576 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-coda, r-cran-fda, r-cran-matrix, r-cran-mcmcpack, r-cran-msm, r-cran-pgdraw, r-cran-rcpp, r-cran-rcppziggurat, r-cran-spam, r-cran-progress, r-cran-stochvol, r-cran-bayeslogit, r-cran-truncdist, r-cran-mgcv, r-cran-purrr, r-cran-rlang, r-cran-lifecycle, r-cran-glue, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dsp_1.4.0-1.ca2404.1_amd64.deb Size: 461626 MD5sum: 08e3492eb040ea99ba1b69ff86f57702 SHA1: de2ead7a60209d1721213685c2dfe50186c606d7 SHA256: 1140a5389a4aab68c76d22a5866bc48e429abc386b25d226ce454ba5abf75820 SHA512: 9a1c2d214a5476a4607ac8dba7b0bc54ab263bc1c4622796c50ab16dad92c5fedc1c34fadea20d6a3a41b70cbd908606f7f0e156629b870ed53641b28a93fec5 Homepage: https://cran.r-project.org/package=dsp Description: CRAN Package 'dsp' (Dynamic Shrinkage Process and Change Point Detection) Provides efficient Markov chain Monte Carlo (MCMC) algorithms for dynamic shrinkage processes, which extend global-local shrinkage priors to the time series setting by allowing shrinkage to depend on its own past. These priors yield locally adaptive estimates, useful for time series and regression functions with irregular features. The package includes full MCMC implementations for trend filtering using dynamic shrinkage on signal differences, producing locally constant or linear fits with adaptive credible bands. Also included are models with static shrinkage and normal-inverse-Gamma priors for comparison. Additional tools cover dynamic regression with time-varying coefficients and B-spline models with shrinkage on basis differences, allowing for flexible curve-fitting with unequally spaced data. Some support for heteroscedastic errors, outlier detection, and change point estimation. Methods in this package are described in Kowal et al. (2019) , Wu et al. (2024) , Schafer and Matteson (2024) , and Cho and Matteson (2024) . Package: r-cran-dspline Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2699 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rlang, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dspline_1.0.4-1.ca2404.1_amd64.deb Size: 1885032 MD5sum: f36474965ae725fd739f9aaeb0aa3aef SHA1: 0b387dbf6cd61486c50ede0acb6092b5c446c143 SHA256: bbbd84827c81520786c1678cbdab8ba3b55f77d2e98dab659cf97aa447199501 SHA512: 6a6070e4a7f4d6605c5c63c9594b7ba2809feeb9321db9004f42b3fddb6fd04f16db010c1d7d1c4e62f4b939dffb8e4cce129778e78f50c812fa640840b764b5 Homepage: https://cran.r-project.org/package=dspline Description: CRAN Package 'dspline' (Tools for Computations with Discrete Splines) Discrete splines are a class of univariate piecewise polynomial functions which are analogous to splines, but whose smoothness is defined via divided differences rather than derivatives. Tools for efficient computations relating to discrete splines are provided here. These tools include discrete differentiation and integration, various matrix computations with discrete derivative or discrete spline bases matrices, and interpolation within discrete spline spaces. These techniques are described in Tibshirani (2020) . Package: r-cran-dssp Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 567 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcse, r-cran-posterior, r-cran-rust, r-cran-sp, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-cowplot, r-cran-ggplot2, r-cran-gstat, r-cran-interp, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dssp_0.1.1-1.ca2404.1_amd64.deb Size: 334962 MD5sum: aebd3feb1db9f82a6d398560c0086a5f SHA1: ef831f8b76b1ea14089eac2dab540740aa41d2b4 SHA256: eda214a97f9bf5d329026ed2df8f59dc9d709c853ad0741d42cefb10951e1d44 SHA512: 1ab18662302329173cd7370c165742f6492186e4b33ea84241f32307a79afb79dd4c615a851cf78e6dfede194132652208c560d15a3de8981cf3814a297a9a73 Homepage: https://cran.r-project.org/package=DSSP Description: CRAN Package 'DSSP' (Implementation of the Direct Sampling Spatial Prior) Draw samples from the direct sampling spatial prior model as described in G. White, D. Sun, P. Speckman (2019) . The basic model assumes a Gaussian likelihood and derives a spatial prior based on thin-plate splines. Package: r-cran-dstarm Architecture: amd64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 445 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-deoptim, r-cran-rwiener, r-cran-rtdists, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dstarm_0.5.0-1.ca2404.1_amd64.deb Size: 281820 MD5sum: 1fa40d24a51c9783bb68e228e1cd0a7c SHA1: 14b4bcb4a1a28a9958a852d64f4fd215197c777c SHA256: b48e5539969a5cd89c841ff60bfbd781277ff3353e26efb1631e4bbb6d3d41c8 SHA512: 55b7eff139095ae92aac33ce3377f64e47e573e5a3ee53df227a7b4f5163e3719ecc1672d3616c4a8f92ab3d5ae2987fea785fa85db8a86564195b2fbdc0f6a0 Homepage: https://cran.r-project.org/package=DstarM Description: CRAN Package 'DstarM' (Analyze Two Choice Reaction Time Data with the D*M Method) A collection of functions to estimate parameters of a diffusion model via a D*M analysis. Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function. 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Current functionalities include creating a multi-dimensional power curve model, performing power curve function comparison, covariate matching, and energy decomposition. Relevant works for the developed functions are: funGP() - Prakash et al. (2022) , AMK() - Lee et al. (2015) , tempGP() - Prakash et al. (2022) , ComparePCurve() - Ding et al. (2021) , deltaEnergy() - Latiffianti et al. (2022) , syncSize() - Latiffianti et al. (2022) , imptPower() - Latiffianti et al. (2022) , All other functions - Ding (2019, ISBN:9780429956508). 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The estimator generalizes the Efron-Petrosian NPMLE (Non-Parametric Maximun Likelihood Estimator) to the competing risks setting. Efron, B. and Petrosian, V. (1999) . 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Package: r-cran-dtrkernsmooth Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-dtrkernsmooth_1.1.0-1.ca2404.1_amd64.deb Size: 105226 MD5sum: 8edc5c809e5ddc9a84feac66be04a411 SHA1: 5a8f5f1e6565a0794b1edaf1bbf77234149fde6f SHA256: c05540aaa8f287ea58af7172d424d5717e4ccb99221610e2fddc9c264624ff9e SHA512: 310aa54d6b342100f2279378f50e87f4f4e2102d5328465b57c8487b3324a256d5111da1a53d20044dbf292eaed4c775e89b74d5e652f020943b5f7c19b1bf20 Homepage: https://cran.r-project.org/package=DTRKernSmooth Description: CRAN Package 'DTRKernSmooth' (Estimate and Make Inference About Optimal Treatment Regimes viaSmoothed Methods) Methods to estimate the optimal treatment regime among all linear regimes via smoothed estimation methods, and construct element-wise confidence intervals for the optimal linear treatment regime vector, as well as the confidence interval for the optimal value via wild bootstrap procedures, if the population follows treatments recommended by the optimal linear regime. See more details in: Wu, Y. and Wang, L. (2021), "Resampling-based Confidence Intervals for Model-free Robust Inference on Optimal Treatment Regimes", Biometrics, 77: 465– 476, . 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Package: r-cran-dtw Architecture: amd64 Version: 1.23-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 709 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-proxy Filename: pool/dists/noble/main/r-cran-dtw_1.23-2-1.ca2404.1_amd64.deb Size: 601428 MD5sum: dc9cc22c57d87d84de3fef4a3989f6df SHA1: d678f00896a5e02e3aa65c72261388a11dcff6c9 SHA256: 557a8cf5255b3b8cf868d5b8c59a148f181e58017607544159899be39e4429c1 SHA512: 7b506ff0a5931c1bc6b2722654f0ba8f38859723537048e0e1673947d6d4f31e987b73e3846d14850af0900c60579756a12063ec20661f78e13371d6ed97ef78 Homepage: https://cran.r-project.org/package=dtw Description: CRAN Package 'dtw' (Dynamic Time Warping Algorithms) A comprehensive implementation of dynamic time warping (DTW) algorithms in R. DTW computes the optimal (least cumulative distance) alignment between points of two time series. 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Implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole clustering are available. Functionality can be easily extended with custom distance measures and centroid definitions. Implementations of DTW barycenter averaging, a distance based on global alignment kernels, and the soft-DTW distance and centroid routines are also provided. All included distance functions have custom loops optimized for the calculation of cross-distance matrices, including parallelization support. Several cluster validity indices are included. 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Illustrative examples from the original dynamic trees paper (Gramacy, Taddy & Polson (2011); ) are facilitated by demos in the package; see demo(package="dynaTree"). 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All models are explained in detail by Hellmann et al. (2023; Preprint available at , published version: ). Implemented models are the dynaViTE model, dynWEV model, the 2DSD model (Pleskac & Busemeyer, 2010, ), and various race models. C++ code for dynWEV and 2DSD is based on the 'rtdists' package by Henrik Singmann. Package: r-cran-dynmix Architecture: amd64 Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-zoo, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-dynmix_2.2-1.ca2404.1_amd64.deb Size: 200266 MD5sum: d98c68951ee2af3f118be324834c8033 SHA1: bc211d0260a7cc0b0f5451523eedc3963ec53d07 SHA256: eca99ee62551e8a4681f396bd46443dd00dff240542c127f9c481aa8251edc21 SHA512: 52f08f0f347f6a62ee26f5b21493d15006281706ffc242959e4a002a83efd2a804a0349418d767645704b98096f398106ff5043fab19a9463c7e8107aa2f6a88 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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A fast algorithm is used to make the computation really fast. The data in package 'DysPIAData' is needed. 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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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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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As a special case of the 'EBglmnet' package (also available on CRAN), this package encourages a grouping effects to select relevant variables and estimate the corresponding non-zero effects. Package: r-cran-ebglmnet Architecture: amd64 Version: 6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-ebglmnet_6.0-1.ca2404.1_amd64.deb Size: 356672 MD5sum: 71b786a9919e5c9fb114eccec90f53f0 SHA1: 9d028d56951d2c26485710e39cebb8ccad4b1293 SHA256: 4580824a32e0de5756c9b836b2d92ec4a67993e18321187985e89ae8393dfb78 SHA512: 3514923e34a6585a2a3d1c4a2872d8e3d2f8bf6b6f04704f88e913f542087d9845a2beea2e4fec328388a4d3b0f19c87271bb8650ea32f49b700b521c677f846 Homepage: https://cran.r-project.org/package=EBglmnet Description: CRAN Package 'EBglmnet' (Empirical Bayesian Lasso and Elastic Net Methods for GeneralizedLinear Models) Provides empirical Bayesian lasso and elastic net algorithms for variable selection and effect estimation. Key features include sparse variable selection and effect estimation via generalized linear regression models, high dimensionality with p>>n, and significance test for nonzero effects. This package outperforms other popular methods such as lasso and elastic net methods in terms of power of detection, false discovery rate, and power of detecting grouping effects. Please reference its use as A Huang and D Liu (2016) . Package: r-cran-ebmaforecast Architecture: amd64 Version: 1.0.33-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 565 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-plyr, r-cran-separationplot, r-cran-hmisc, r-cran-abind, r-cran-gtools, r-cran-glue Filename: pool/dists/noble/main/r-cran-ebmaforecast_1.0.33-1.ca2404.1_amd64.deb Size: 322650 MD5sum: 9f0f829428ae924f65a8cc85d8010cc3 SHA1: 3de47e85a62de662cb9c2999a85d80aecd2f6b40 SHA256: ec876959436f6ce0757e0ff36d5a64f346d9c417413cc7b41f14b85a6f11eb04 SHA512: 4e4658603da049eaee56574d7b016f32af0728dffe6ff0089632dcff75cb9b2529a6fc4ff97c86b0466758f6be3b2eae4f74a3645d517e6cecaf9a328a127e8f Homepage: https://cran.r-project.org/package=EBMAforecast Description: CRAN Package 'EBMAforecast' (Estimate Ensemble Bayesian Model Averaging Forecasts using GibbsSampling or EM-Algorithms) Create forecasts from multiple predictions using ensemble Bayesian model averaging (EBMA). EBMA models can be estimated using an expectation maximization (EM) algorithm or as fully Bayesian models via Gibbs sampling. The methods in this package are Montgomery, Hollenbach, and Ward (2015) and Montgomery, Hollenbach, and Ward (2012) . Package: r-cran-ebmstate Architecture: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 511 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-mstate, r-cran-rcpp, r-cran-hdinterval Filename: pool/dists/noble/main/r-cran-ebmstate_0.1.5-1.ca2404.1_amd64.deb Size: 400122 MD5sum: eb0f02147201386089b7651423b11701 SHA1: 8f3a895c9d48ce7c5a9c313762059c3ed9873d07 SHA256: 186a3230b3dbab36e3b1398a0e3329b8370c54cc4b4e733ec621ed4b17768380 SHA512: bdc20344b923bac4cc15d1875d8d4a76b8416b2b310c866caf0b7d2291103d3bf8608a18656a4c93f6eb88040e89a96d2188b44e91a2e01a89fa138a9284448a Homepage: https://cran.r-project.org/package=ebmstate Description: CRAN Package 'ebmstate' (Empirical Bayes Multi-State Cox Model) Implements an empirical Bayes, multi-state Cox model for survival analysis. Run "?'ebmstate-package'" for details. See also Schall (1991) . 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The method handles a non-parametric estimation for the correlation of the errors. See "Krivobokova", "Serra", "Rosales" and "Klockmann" (2021) for details. Package: r-cran-ebtobit Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-rebayes Filename: pool/dists/noble/main/r-cran-ebtobit_1.0.2-1.ca2404.1_amd64.deb Size: 153940 MD5sum: 997fd0aa7955811940b43b524b1038d4 SHA1: 1c624e36bf965832c8f1449d155f057b75ceddaf SHA256: ee48a87df87c2d9b7a4e99834a455dbd7d8172c4e4ea5880a540f9e34ca52fc3 SHA512: ee5436bcc35bb6edba72919a5574fa37b24b457bd5978deee024afaf637aa4216adbf95b94bfd1d2b92dd4af82c1c88b6b617328ebf07e2113d1292a0312edb9 Homepage: https://cran.r-project.org/package=ebTobit Description: CRAN Package 'ebTobit' (Empirical Bayesian Tobit Matrix Estimation) Estimation tools for multidimensional Gaussian means using empirical Bayesian g-modeling. Methods are able to handle fully observed data as well as left-, right-, and interval-censored observations (Tobit likelihood); descriptions of these methods can be found in Barbehenn and Zhao (2023) . Additional, lower-level functionality based on Kiefer and Wolfowitz (1956) and Jiang and Zhang (2009) is provided that can be used to accelerate many empirical Bayes and nonparametric maximum likelihood problems. 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The 'INLA' package can be obtained from . 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(2008) and Olano et al. (2009) . Package: r-cran-ecgoftestdx Architecture: amd64 Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-orthopolynom, r-cran-bootstrap Filename: pool/dists/noble/main/r-cran-ecgoftestdx_0.5-1.ca2404.1_amd64.deb Size: 135922 MD5sum: 40b7deed24790c9e08195e2cc3aaa0e1 SHA1: 5f3a43dc162217e7fabcc81ffc5c449d39a05513 SHA256: da7c812dbba40f2c1336484253e2e6e19a1ecbba1729a8dd19194336f3d4bf74 SHA512: f9f3c7be48b2676d030e4f68bcca96e8a514a96bca2a6924c946be424fb32ed9159fc586d0eb398d2da80a80ae7e01732f9f225ee4ac190c328efd619e95acfc Homepage: https://cran.r-project.org/package=ECGofTestDx Description: CRAN Package 'ECGofTestDx' (A Goodness-of-Fit Test for Elliptical Distributions withDiagnostic Capabilities) A goodness-of-fit test for elliptical distributions with diagnostic capabilities. Gilles R. Ducharme, Pierre Lafaye de Micheaux (2020) . 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Package: r-cran-ecolmod Architecture: amd64 Version: 1.2.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1162 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rootsolve, r-cran-shape, r-cran-diagram, r-cran-desolve Suggests: r-cran-maps, r-cran-seacarb, r-cran-scatterplot3d, r-cran-deldir Filename: pool/dists/noble/main/r-cran-ecolmod_1.2.6.4-1.ca2404.1_amd64.deb Size: 703874 MD5sum: 795c0351282cf8cbd1583c9416fd2959 SHA1: 58e3b39958da4f4bf6f8c08dffee3dca55f0efe4 SHA256: e89d2b3f5dc09ced8a5e7fbd605289bf1b00acfb484348ff1f03da2fdc18952c SHA512: dec2bf6e6458905b4b137c0a5ff36115b2109ef9018f3aacc0dd495fd6386167ca451d062549b34eb55863b961b859b6939bb28d14f74982c050cdc505dae62b Homepage: https://cran.r-project.org/package=ecolMod Description: CRAN Package 'ecolMod' ("A Practical Guide to Ecological Modelling - Using R as aSimulation Platform") Figures, data sets and examples from the book "A practical guide to ecological modelling - using R as a simulation platform" by Karline Soetaert and Peter MJ Herman (2009). Springer. All figures from chapter x can be generated by "demo(chapx)", where x = 1 to 11. The R-scripts of the model examples discussed in the book are in subdirectory "examples", ordered per chapter. Solutions to model projects are in the same subdirectories. Package: r-cran-ecolrxc Architecture: amd64 Version: 0.1.1-16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-scales Filename: pool/dists/noble/main/r-cran-ecolrxc_0.1.1-16-1.ca2404.1_amd64.deb Size: 253640 MD5sum: 5a18e0a5576a436b7d2d03819345ec50 SHA1: 0010c8a8c240431ff3f4d72e9ba5778d07680014 SHA256: 7d105b88eb73772146d5c813d54b89b163e3a5f3e091713702398dcd0964a331 SHA512: 9f77b2cf7f239e831fc54e1b20f9946b0df294e601db523a2f9637311e0598d2367fe1f129e5e76041fb6f9e0267d9096816d26224ac47cc557b79f3bc889f83 Homepage: https://cran.r-project.org/package=ecolRxC Description: CRAN Package 'ecolRxC' (Ecological Inference of RxC Tables by Latent StructureApproaches) Estimates RxC (R by C) vote transfer matrices (ecological contingency tables) from aggregate data building on Thomsen (1987) and Park (2008) approaches. References: Park, W.-H. (2008). ''Ecological Inference and Aggregate Analysis of Election''. PhD Dissertation. University of Michigan. Pavía, J.M. and Thomsen, S.R. (2025) ''ecolRxC: Ecological inference estimation of RxC tables using latent structure approaches''. Political Science Research and Methods, 13(4), 943-961. Thomsen, S.R. (1987, ISBN:87-7335-037-2). ''Danish Elections 1920 79: a Logit Approach to Ecological Analysis and Inference''. Politica, Aarhus, Denmark. Acknowledgements: The authors wish to thank Generalitat Valenciana (Conselleria de Educacion, Cultura y Universidades), grant CIACIO/2023/031, and Ministerio de Economia e Innovacion, grant PID2021-128228NB-I00, for supporting this research. Package: r-cran-econetgen Architecture: amd64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-ggplot2 Suggests: r-cran-spelling, r-cran-testthat, r-cran-covr, r-cran-ggraph Filename: pool/dists/noble/main/r-cran-econetgen_0.2.4-1.ca2404.1_amd64.deb Size: 365790 MD5sum: 97a770a1d765b9f0d4e6737e564e530c SHA1: 78af58baa7fd1fa9abb720e994494603d8c985bf SHA256: 34cad58f3f1fd48c709f4a58ff5bb6ddec3e32eac2694645c5af96365dedf4c9 SHA512: 591d6bdb54df7a3e927a3e46133a9b682d6488eb32333d4ad073f01d49fc8ac28af84f3bafb336176e2dfdc265c239fed6f99a83d54672a007f9bd698e038cd8 Homepage: https://cran.r-project.org/package=EcoNetGen Description: CRAN Package 'EcoNetGen' (Simulate and Sample from Ecological Interaction Networks) Randomly generate a wide range of interaction networks with specified size, average degree, modularity, and topological structure. 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(2018) . Package: r-cran-edith Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2342 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ocnet, r-cran-rivnet, r-cran-bayesiantools, r-cran-laplacesdemon, r-cran-dharma, r-cran-terra, r-cran-fields Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-edith_1.1.0-1.ca2404.1_amd64.deb Size: 2018348 MD5sum: d293ca69bfb651d282e9f976b7a98aae SHA1: b889b35be096b309db4fc3307d1792b9f177e7b6 SHA256: 0802d98199201d6b0d12e070c0efaf33eba697271dc32f589c48840919a32e87 SHA512: b16710017056878a17b3cdfd49e5061a155eb07c67f3d14391ed351fdc10755daf04848d41496ffc491656bcfacc368e9acd944b963e61e762a57f7f7d93fdfd Homepage: https://cran.r-project.org/package=eDITH Description: CRAN Package 'eDITH' (Model Transport of Environmental DNA in River Networks) Runs the eDITH (environmental DNA Integrating Transport and Hydrology) model, which implements a mass balance of environmental DNA (eDNA) transport at a river network scale coupled with a species distribution model to obtain maps of species distribution. eDITH can work with both eDNA concentration (e.g., obtained via quantitative polymerase chain reaction) or metabarcoding (read count) data. Parameter estimation can be performed via Bayesian techniques (via the 'BayesianTools' package) or optimization algorithms. An interface to the 'DHARMa' package for posterior predictive checks is provided. See Carraro and Altermatt (2024) for a package introduction; Carraro et al. (2018) and Carraro et al. (2020) for methodological details. Package: r-cran-edlibr Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-stringr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-edlibr_1.0.3-1.ca2404.1_amd64.deb Size: 81310 MD5sum: f5ffa7009e24d3825e5bc40615dbcb9a SHA1: 02c6c5b08bb44db656ae000a4020371c23086851 SHA256: e64c9ff4e2b52bdffa4012dc0dabff1ad7a0f1e9ce701080e28d16c2d33a2a36 SHA512: 9697c71e0dc0463a641f4f4c9a24fb328803b52b5809f983f344964a3b14ca7ac8df397c772eb611c6a8d43736c1e325cdc98497367f0d44006ffcc0e620e913 Homepage: https://cran.r-project.org/package=edlibR Description: CRAN Package 'edlibR' (R Integration for Edlib, the C/C++ Library for Exact PairwiseSequence Alignment using Edit (Levenshtein) Distance) Bindings to edlib, a lightweight performant C/C++ library for exact pairwise sequence alignment using edit distance (Levenshtein distance). The algorithm computes the optimal alignment path, but also can be used to find only the start and/or end of the alignment path for convenience. Edlib was designed to be ultrafast and require little memory, with the capability to handle very large sequences. Three alignment methods are supported: global (Needleman-Wunsch), infix (Hybrid Wunsch), and prefix (Semi-Hybrid Wunsch). The original C/C++ library is described in "Edlib: a C/C++ library for fast, exact sequence alignment using edit distance", M. Šošić, M. Šikić, . Package: r-cran-edma Architecture: amd64 Version: 1.5-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 699 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-xts, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-edma_1.5-4-1.ca2404.1_amd64.deb Size: 378580 MD5sum: 23837315f59511bf59be8bde06984374 SHA1: 0fd83ebd44d16b0ffc64900bead21d7c4123d1e2 SHA256: da48faec8fa96c8fa9d94702990ba4dd24c2df8538c74e3e68261bfd3f9cbcbd SHA512: 5bdb897dc641765b237fab386512d4749d373d2b1c658dce9b30363abab69b27f16f98e602a7b6a63eb0fbdf61695bc3fd3a2652c6bc5d39aba4b50e6c1b8a14 Homepage: https://cran.r-project.org/package=eDMA Description: CRAN Package 'eDMA' (Dynamic Model Averaging with Grid Search) Perform dynamic model averaging with grid search as in Dangl and Halling (2012) using parallel computing. Package: r-cran-edmcr Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 629 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-igraph, r-cran-lbfgs, r-cran-truncnorm, r-cran-mass, r-cran-nloptr, r-cran-vegan, r-cran-sdpt3r Filename: pool/dists/noble/main/r-cran-edmcr_0.2.0-1.ca2404.1_amd64.deb Size: 581126 MD5sum: b007f4b24380afda3e4ee83f848fc326 SHA1: 3cdac676f9a4c9036b020ec851dbbd79a73b29af SHA256: 9868caaf354efb96f43916910087aee18787ecf208a51dd547d9ba4581067d76 SHA512: ed32b88f2d8d6ad3463bc023738995de2caf03e8109361daad3c0fc2bcf0cdd8e7500c566518389a70a808c6fda2624bee9d693a8d782d93002815733d7d4e13 Homepage: https://cran.r-project.org/package=edmcr Description: CRAN Package 'edmcr' (Euclidean Distance Matrix Completion Tools) Implements various general algorithms to estimate missing elements of a Euclidean (squared) distance matrix. Includes optimization methods based on semi-definite programming found in Alfakih, Khadani, and Wolkowicz (1999), a non-convex position formulation by Fang and O'Leary (2012), and a dissimilarity parameterization formulation by Trosset (2000). When the only non-missing distances are those on the minimal spanning tree, the guided random search algorithm will complete the matrix while preserving the minimal spanning tree following Rahman and Oldford (2018). Point configurations in specified dimensions can be determined from the completions. Special problems such as the sensor localization problem, as for example in Krislock and Wolkowicz (2010), as well as reconstructing the geometry of a molecular structure, as for example in Hendrickson (1995), can also be solved. These and other methods are described in the thesis of Adam Rahman(2018). Package: r-cran-edmeasure Architecture: amd64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-energy, r-cran-dhsic, r-cran-rbayesianoptimization Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-edmeasure_1.2.0-1.ca2404.1_amd64.deb Size: 96376 MD5sum: 6368b5f5a6258e5c828c4d7735bb0fb6 SHA1: 95c363206da53fb9f7aded6e131b7a6938d34db9 SHA256: 5a541a0f659325033c598a068992fe9dc0350beea385df1e1784988b6015a660 SHA512: f7f322d6074fe14838e3ec6e8f417309d353b8a3c1e373f70bfb15fc0c371c977d41ef12323db3b5e63a853b4d4105de055b414f972b36c71f03b48cf27f8fcf Homepage: https://cran.r-project.org/package=EDMeasure Description: CRAN Package 'EDMeasure' (Energy-Based Dependence Measures) Implementations of (1) mutual dependence measures and mutual independence tests in Jin, Z., and Matteson, D. S. 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Models can be used with count data via traditional survey methods (i.e., trapping, electrofishing, visual) and replicated eDNA detection/nondetection data via polymerase chain reaction (i.e., PCR or qPCR) from multiple survey locations. Estimated parameters include probability of a false positive eDNA detection, a site-level covariates that scale the sensitivity of eDNA surveys relative to traditional surveys, and gear scaling coefficients for traditional gear types. Models are implemented with a Bayesian framework (Markov chain Monte Carlo) using the 'Stan' probabilistic programming language. Package: r-cran-edotrans Architecture: amd64 Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cabcanalysis, r-cran-opgmmassessment, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-edotrans_0.3.5-1.ca2404.1_amd64.deb Size: 120944 MD5sum: b127d70fb33fc7fe9f0744462e64f7f7 SHA1: 379d0a26144aa5ed4d84b1d02811333195c2501e SHA256: c9f86a447c31dbd02a37c9ab54be2cf38b38fcdab4bc7e1a26c3fcda90278258 SHA512: cd8c75ea61259f61998beb7fc59bfd1bfe5bab1697c36425f45d06d56f63b34cc557d93043492b306098d3b1fdc8a6e8885954d89deb7bbe5425166d079d31eb 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. 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Traditional methods such as the scree plot by Cattell (1966) , Kaiser-Guttman Criterion (KGC) by Guttman (1954) and Kaiser (1960) , and flexible Parallel Analysis (PA) by Horn (1965) based on eigenvalues form PCA or EFA are readily available. This package also implements several newer methods, such as the Empirical Kaiser Criterion (EKC) by Braeken and van Assen (2017) , Comparison Data (CD) by Ruscio and Roche (2012) , and Hull method by Lorenzo-Seva et al. (2011) , as well as some AI-based methods like Comparison Data Forest (CDF) by Goretzko and Ruscio (2024) and Factor Forest (FF) by Goretzko and Buhner (2020) . Additionally, it includes a deep neural network (DNN) trained on large-scale datasets that can efficiently and reliably determine the number of factors. Package: r-cran-efatools Architecture: amd64 Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1984 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-psych, r-cran-crayon, r-cran-stringr, r-cran-ggplot2, r-cran-tibble, r-cran-magrittr, r-cran-dplyr, r-cran-cli, r-cran-rcpp, r-cran-viridislite, r-cran-future.apply, r-cran-future, r-cran-gparotation, r-cran-checkmate, r-cran-tidyr, r-cran-progressr, r-cran-progress, r-cran-rlang, r-cran-clue, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-efatools_0.7.1-1.ca2404.1_amd64.deb Size: 1326078 MD5sum: 571e6a6f4270bb314a7049628eea1d91 SHA1: d4eba25a5dc23b286dcadd12776f545e1480aa10 SHA256: 30adf26df3b5c003986196239535816a3e68ef05708bc251379a0730763d721f SHA512: 86245c13c4cbe1552e40e3a0abe80f2b9199ce25faa078c5ff9ddd1bc4c1034f18f6ff0bfcd6357bb1006e303495f18ec7ff9164631707de79a00e17c66965c3 Homepage: https://cran.r-project.org/package=EFAtools Description: CRAN Package 'EFAtools' (Fast and Flexible Implementations of Exploratory Factor AnalysisTools) Provides functions to perform exploratory factor analysis (EFA) procedures and compare their solutions. The goal is to provide state-of-the-art factor retention methods and a high degree of flexibility in the EFA procedures. This way, for example, implementations from R 'psych' and 'SPSS' can be compared. Moreover, functions for Schmid-Leiman transformation and the computation of omegas are provided. To speed up the analyses, some of the iterative procedures, like principal axis factoring (PAF), are implemented in C++. Package: r-cran-efcm Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2141 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-nsrfa, r-cran-ismev, r-cran-fields, r-cran-mnormt, r-cran-numderiv, r-cran-pbmcapply, r-cran-boot, r-cran-progress, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-efcm_1.0-1.ca2404.1_amd64.deb Size: 1903474 MD5sum: 5216acc96e720bc9aeb08f01190750ac SHA1: 92dafcb5d64b98ced6054b12190016869c7665e8 SHA256: 5b450cebc05400060a489b2a1bac30ef6f3e168888db0694076c818ac8a0aa66 SHA512: 716ef4b9fadf069b2a8177b048699e705422a5ea89ecd4956c3d258c45db76d90db5ab74c28aa414993ea558c3bce81c957ea9de38bea563b5167df0d2d12822 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: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 634 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-collapse, r-cran-ggplot2, r-cran-labeling, r-cran-patchwork, r-cran-plotly, r-cran-rcpp, r-cran-scales Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-effectplots_0.2.2-1.ca2404.1_amd64.deb Size: 249352 MD5sum: 1dba0fb731d347239a40bb24e50c2019 SHA1: afdf02bad84deb257beb89f010c3214e022d3eeb SHA256: 17755dbd07d5592feda6d443871a534bad4fb37a3c4c9f57d50ec422aee1667d SHA512: 6b6f9b08224cbb9ee74639f0b43fbd80a0945ce55e37276b44b1a73a99f66e3fff16f7925602dcccb40f362c9fe042e118adfd2ccadda92cea17411b4008fe2c Homepage: https://cran.r-project.org/package=effectplots Description: CRAN Package 'effectplots' (Effect Plots) High-performance implementation of various effect plots useful for regression and probabilistic classification tasks. The package includes partial dependence plots (Friedman, 2021, ), accumulated local effect plots and M-plots (both from Apley and Zhu, 2016, ), as well as plots that describe the statistical associations between model response and features. It supports visualizations with either 'ggplot2' or 'plotly', and is compatible with most models, including 'Tidymodels', models wrapped in 'DALEX' explainers, or models with case weights. Package: r-cran-eganet Architecture: amd64 Version: 2.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4076 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-clue, r-cran-dendextend, r-cran-future, r-cran-future.apply, r-cran-ggally, r-cran-ggplot2, r-cran-ggpubr, r-cran-glasso, r-cran-glassofast, r-cran-gparotation, r-cran-igraph, r-cran-lavaan, r-cran-matrix, r-cran-network, r-cran-progressr, r-cran-qgraph, r-cran-semplot, r-cran-sna Suggests: r-cran-fitdistrplus, r-cran-gridextra, r-cran-pbapply, r-cran-progress, r-cran-psych, r-cran-pwr, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-eganet_2.4.1-1.ca2404.1_amd64.deb Size: 3829572 MD5sum: 76ae6319965b433f4ec55bde01f771fc SHA1: d3a9a99ae35d3743b75d6867996c47caa8c6813b SHA256: 1548979e657849a1919b63da65a9f767e9dad2426b80524f22264002fa918dfa SHA512: e3498f9aed2179b9452bcd86f53a6ed6e85249d12a8cb0ab834dc80a2b38132a2c7046726121e1e08121afa1fc600ff49459c8cb51791739cbdc7f3f56a727d3 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. 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Package: r-cran-eha Architecture: amd64 Version: 2.11.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4074 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-eha_2.11.5-1.ca2404.1_amd64.deb Size: 2094316 MD5sum: 360aa5e0af923541c031e84722ed638b SHA1: b0b027f9f16c3efaacdcc5015948697bed052231 SHA256: 603bf2b481710584763d0ec1ba11456cf35076925f09cbbe4b87760c6ad3140a SHA512: 9684bf8c07d60a7c6fac265bb64bf9f7edcc3683d4f2f4e3fe20e93ac45b8eea20e46378d072d8482a747169bff4f164db4fb2067db5c84f3d439ab5bfae9095 Homepage: https://cran.r-project.org/package=eha Description: CRAN Package 'eha' (Event History Analysis) Parametric proportional hazards fitting with left truncation and right censoring for common families of distributions, piecewise constant hazards, and discrete models. 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Everest et al. (2022) , . Package: r-cran-elfdistr Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-elfdistr_1.0.1-1.ca2404.1_amd64.deb Size: 55012 MD5sum: 42782cf497eb253231c32a3f771bd35a SHA1: 85ea061f697db503813b0b0e8a82b97f59bde9a4 SHA256: e604887d31d307aaf6055b28a9b810c62505d99faea0725a2b87c2232be8e801 SHA512: 38486cacf6a8485f99eafd0e1699043e8589697ff5f9d7fa9093e0e8bf9016a037e3ac24fb84f6f672437327305f31e00873bbaf8ef656fe75636f5279d3b80c Homepage: https://cran.r-project.org/package=elfDistr Description: CRAN Package 'elfDistr' (Kumaraswamy Complementary Weibull Geometric (Kw-CWG) ProbabilityDistribution) Density, distribution function, quantile function and random generation for the Kumaraswamy Complementary Weibull Geometric (Kw-CWG) lifetime probability distribution proposed in Afify, A.Z. et al (2017) . Package: r-cran-elgbd Architecture: amd64 Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppprogress Suggests: r-cran-melt, r-cran-spelling Filename: pool/dists/noble/main/r-cran-elgbd_0.9.0-1.ca2404.1_amd64.deb Size: 206546 MD5sum: da03b2cc2c59a622ddd2dc1f6bd19320 SHA1: 521a968dd121f1dc7bd310bdcbf1c95e35e5b27c SHA256: f2abd45dd399456a4fc4bd7817528d396ae20dce83ab3585cf5df0856db5030d SHA512: 65ed57e287414fed544666b069d0507ca24cfbc3dbd22e1d1d1f5f02b394d5b6cb7fd0828ba70b2916ca13c08d363a12bec79885d75cb3bce733d3803cd9959f Homepage: https://cran.r-project.org/package=elgbd Description: CRAN Package 'elgbd' (Empirical Likelihood for General Block Designs) Performs hypothesis testing for general block designs with empirical likelihood. The core computational routines are implemented using the 'Eigen' 'C++' library and 'RcppEigen' interface, with 'OpenMP' for parallel computation. Details of the methods are given in Kim, MacEachern, and Peruggia (2023) . This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552. Package: r-cran-ellipsis Architecture: amd64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ellipsis_0.3.2-1.ca2404.1_amd64.deb Size: 35014 MD5sum: 954fa07fb1c1a65503896bae5bcda365 SHA1: 27683404ba3998242d46f3cb57670d95552566ba SHA256: f03230979d7df822edcdd21b9da581dcb76da7bb43c6c876e3e75e5201ee339e SHA512: 7828aa989ebca8cd0383c11b1fb86d53992f431a6085688efed0ea60e1c9e48fe36e3992798309888a93d57cbc9f11ad29106f6d5a2ad6301da4b8384474634e Homepage: https://cran.r-project.org/package=ellipsis Description: CRAN Package 'ellipsis' (Tools for Working with ...) The ellipsis is a powerful tool for extending functions. Unfortunately this power comes at a cost: misspelled arguments will be silently ignored. 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Package: r-cran-elmnnrcpp Architecture: amd64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernelknn, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-elmnnrcpp_1.0.5-1.ca2404.1_amd64.deb Size: 504160 MD5sum: e9cedc390797b893b741e8af8d9a9de8 SHA1: 146a27c8598d11db360867191f3e24e29fbef625 SHA256: 9bfc6ea70a5f127288b788813513c73a713672ba75e12f8ce4fb0136b8d16197 SHA512: 1678feecbe9e72ccc76d8bcfca86dc4f331df6ff6d7b0e5c3e5f7a4fb8a102dc05efa59a5e2078418e9f04a551c9be6fe2d87c592e055d59585d10cee7853d4f Homepage: https://cran.r-project.org/package=elmNNRcpp Description: CRAN Package 'elmNNRcpp' (The Extreme Learning Machine Algorithm) Training and predict functions for Single Hidden-layer Feedforward Neural Networks (SLFN) using the Extreme Learning Machine (ELM) algorithm. The ELM algorithm differs from the traditional gradient-based algorithms for very short training times (it doesn't need any iterative tuning, this makes learning time very fast) and there is no need to set any other parameters like learning rate, momentum, epochs, etc. This is a reimplementation of the 'elmNN' package using 'RcppArmadillo' after the 'elmNN' package was archived. For more information, see "Extreme learning machine: Theory and applications" by Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew (2006), Elsevier B.V, . Package: r-cran-elo Architecture: amd64 Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 544 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-proc Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-elo_3.0.2-1.ca2404.1_amd64.deb Size: 266140 MD5sum: 7435a9f85184299d9de9bfe02d7b097b SHA1: f55d9668c65d6bc2a996c63468266805e1d2ad9b SHA256: caa359ed9979382dc1bd72cf6e2d855285d5dbde18f7e4cafd28abdca651bc91 SHA512: b286864c11b7ccc3a651e4264332f457d4596fa81a2a8d9c35bbb7a6da5bde6d292443bdeb6d3703fe14f412b3dcb673dd6ca2fc34a3c0c72ac58ed7c521f8b0 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: amd64 Version: 0.29.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-psychotools, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-xtable, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-elochoice_0.29.4-1.ca2404.1_amd64.deb Size: 224338 MD5sum: 5f98e141e97f0b5b49c20075d999e029 SHA1: 7d5ce746de9623a226c3e0a9de7eaed11f19ac37 SHA256: 4b6bf272225dbf4efcd01edb788c0c5b7eb7ef220d7640f995b923506cd0c9df SHA512: c33bc674fe44eeafeccbf974c51a69bbf45b8f1c24f558b4cd853a2eeaff0574a9574c770d1ac90fe5d990d0fbf37f06edab43eae31426b597fa5e11da61ba01 Homepage: https://cran.r-project.org/package=EloChoice Description: CRAN Package 'EloChoice' (Preference Rating for Visual Stimuli Based on Elo Ratings) Allows calculating global scores for characteristics of visual stimuli as assessed by human raters. 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In addition, this package offers other methods to measure local indicators of spatial associations (LISA). Furthermore, global spatial structure can be measured using a variogram-like diagram, called entrogram. For more information, please check that paper: Naimi, B., Hamm, N. A., Groen, T. A., Skidmore, A. K., Toxopeus, A. G., & Alibakhshi, S. (2019) . Package: r-cran-elyp Architecture: amd64 Version: 0.7-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-elyp_0.7-6-1.ca2404.1_amd64.deb Size: 149998 MD5sum: 41ff90e29a0d90ae540daa6a7f2c0c7f SHA1: 7c1ee9abcca7f9bdc8a012fec81b3796ee4a7f32 SHA256: f9cf76dc2ca674f19d812221fc4c4d170f95780f4a0feb4484e32498424ece4c SHA512: a5c652b4d6c212027e4e8cdd9e01ba555d0fc391a1c21537ff3938d0f2171a043982a25e9610c0c4bc693289f1b6a4fea43c7f7341d0ea87d4a855c5ca869e55 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. 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This package provides implementations of the spike-and-slab quantile (group) LASSO which have been developed along the line of Bayesian hierarchical models but deeply rooted in frequentist regularization methods by utilizing Expectation–Maximization (EM) algorithm. The spike-and-slab quantile LASSO can handle data irregularity in terms of skewness and outliers in response variables, compared to its non-robust alternative, the spike-and-slab LASSO, which has also been implemented in the package. In addition, procedures for fitting the spike-and-slab quantile group LASSO and its non-robust counterpart have been implemented in the form of quantile/least-square varying coefficient mixed effect models for high-dimensional longitudinal data. The core module of this package is developed in 'C++'. Package: r-cran-embc Architecture: amd64 Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1176 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-sp, r-cran-rcolorbrewer, r-cran-mnormt, r-cran-suntools, r-cran-rcpparmadillo Suggests: r-cran-move, r-cran-sf, r-cran-rgl, r-cran-knitr Filename: pool/dists/noble/main/r-cran-embc_2.0.4-1.ca2404.1_amd64.deb Size: 921940 MD5sum: c70016841a15085cd972005506549d9f SHA1: fd05364901ead91631d05ac701931c83d25ea195 SHA256: c96c2cb33f6eb818087e607384ce672dc59d693c1da69ab5e299c646d0fb375a SHA512: 22eff7435cba8e57d3b457d8b027a2795ebff5ba11fa8846e07ed65f018a180870f8a448d0a3d96c6b488d595e336c8715fb1ae20be91992d693d75b159d9641 Homepage: https://cran.r-project.org/package=EMbC Description: CRAN Package 'EMbC' (Expectation-Maximization Binary Clustering) Unsupervised, multivariate, binary clustering for meaningful annotation of data, taking into account the uncertainty in the data. A specific constructor for trajectory analysis in movement ecology yields behavioural annotation of trajectories based on estimated local measures of velocity and turning angle, eventually with solar position covariate as a daytime indicator, ("Expectation-Maximization Binary Clustering for Behavioural Annotation"). Package: r-cran-embedsom Architecture: amd64 Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 682 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-matrix, r-cran-rtsne, r-cran-umap, r-cran-uwot Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-embedsom_2.2.1-1.ca2404.1_amd64.deb Size: 360144 MD5sum: a3ff7b45e9120543f3bfd8b30ba3ea35 SHA1: c7f8a03ccf4f490c84c72173fcf515890b5b0fff SHA256: 5d3aa855f4bb14b779124171ebc1582ba8fc84e825442e1d2cd6577d7a03508f SHA512: e5ded23a7eff0c8bcf7ac725a6546330482efe4e19d3d4932d72e8b608d5a4fcc8539dac7144c3c791a32046348cb35f46c810ab62e78cc75eee276c33bfd067 Homepage: https://cran.r-project.org/package=EmbedSOM Description: CRAN Package 'EmbedSOM' (Fast Embedding Guided by Self-Organizing Map) Provides a smooth mapping of multidimensional points into low-dimensional space defined by a self-organizing map. Designed to work with 'FlowSOM' and flow-cytometry use-cases. See Kratochvil et al. (2019) . Package: r-cran-emc2 Architecture: amd64 Version: 3.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6322 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-coda, r-cran-magic, r-cran-mass, r-cran-matrixcalc, r-cran-msm, r-cran-mvtnorm, r-cran-matrix, r-cran-rcpp, r-cran-brobdingnag, r-cran-corrplot, r-cran-colorspace, r-cran-psych, r-cran-lpsolve, r-cran-wienr, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-diagrammer Filename: pool/dists/noble/main/r-cran-emc2_3.4.1-1.ca2404.1_amd64.deb Size: 3904058 MD5sum: 905ea5e14387524f60c292a7dddb482f SHA1: 382c4b4b175e7a624d8632804d3303a4b4da7f4d SHA256: c2342de84c8049ef92ca9c99a36aebf684d6379822b0991717bb1da96563429d SHA512: db029fee67020fa573e74ed667f8a4356ec4b211eadac1d00c4e258fb01855fdeb875355745c4f633415678578c23007d23a0b1b07b812a9eca7c77d0ffb8fc0 Homepage: https://cran.r-project.org/package=EMC2 Description: CRAN Package 'EMC2' (Bayesian Hierarchical Analysis of Cognitive Models of Choice) Fit Bayesian (hierarchical) cognitive models using a linear modeling language interface using particle Metropolis Markov chain Monte Carlo sampling with Gibbs steps. The diffusion decision model (DDM), linear ballistic accumulator model (LBA), racing diffusion model (RDM), and the lognormal race model (LNR) are supported. Additionally, users can specify their own likelihood function and/or choose for non-hierarchical estimation, as well as for a diagonal, blocked or full multivariate normal group-level distribution to test individual differences. Prior specification is facilitated through methods that visualize the (implied) prior. A wide range of plotting functions assist in assessing model convergence and posterior inference. Models can be easily evaluated using functions that plot posterior predictions or using relative model comparison metrics such as information criteria or Bayes factors. References: Stevenson et al. (2024) . Package: r-cran-emcadr Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1136 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-dplyr, r-cran-umap, r-cran-dbscan, r-cran-logistf, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-emcadr_1.3-1.ca2404.1_amd64.deb Size: 789880 MD5sum: 9a491f28ab565af0401ddfdb58b992b8 SHA1: 4bbbc5f743cf05416f9e69b0fd9b658abe90b344 SHA256: 867c474533acee768ea06a88fa22adb2f8c469e2afc586a3cc270d7cc7b50c26 SHA512: b6bfd840fd5cc061ad700395b00dc8e215119e64564ea58ca519b572263ca19bf2c85cf53e65e5a9a860c0bb2c3c32816b79de46d9b220447f08bcb6ebc0f5fb Homepage: https://cran.r-project.org/package=emcAdr Description: CRAN Package 'emcAdr' (Evolutionary Version of the Metropolis-Hastings Algorithm) Provides computational methods for detecting adverse high-order drug interactions from individual case safety reports using statistical techniques, allowing the exploration of higher-order interactions among drug cocktails. Package: r-cran-emcluster Architecture: amd64 Version: 0.2-17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1030 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix Filename: pool/dists/noble/main/r-cran-emcluster_0.2-17-1.ca2404.1_amd64.deb Size: 835902 MD5sum: 6c9b75879f0edc73be805fa3fed49e48 SHA1: f8750a66c72d7bf346cf3af9278ec02b85fb9e94 SHA256: bbe6e885b2f85d659e48f56ff744224cdf709323804c0c53b596e898fad180ad SHA512: 1de97bfd044697c53d2fdcee18cd78f9ea6ca734b93812b8773a1882796d635eadf4448e6570275d8e3274c51b1ad0de7eefda3354a316b2f969e2c5ed4a5d13 Homepage: https://cran.r-project.org/package=EMCluster Description: CRAN Package 'EMCluster' (EM Algorithm for Model-Based Clustering of Finite MixtureGaussian Distribution) EM algorithms and several efficient initialization methods for model-based clustering of finite mixture Gaussian distribution with unstructured dispersion in both of unsupervised and semi-supervised learning. Package: r-cran-emd Architecture: amd64 Version: 1.5.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields, r-cran-locfit Filename: pool/dists/noble/main/r-cran-emd_1.5.9-1.ca2404.1_amd64.deb Size: 388814 MD5sum: 9fc5587d8bc66157b0230f70234b4177 SHA1: d05c9556ad20e435738682184b0876f16a81a9ff SHA256: 67d4ae3a1ed1d9ab321cd77ed2f76d2a3c60638446ad1fab944e1fa113f8b522 SHA512: 2652c8b41fc1b2ba50896a1fc0449cf1bba1730149fcb02421c6cb320ae9c5d10ea5a7315ce0548c0b0d0f48cf7d47fa8537bb795d63bd2f8a1c78d9fae975d8 Homepage: https://cran.r-project.org/package=EMD Description: CRAN Package 'EMD' (Empirical Mode Decomposition and Hilbert Spectral Analysis) For multiscale analysis, this package carries out empirical mode decomposition and Hilbert spectral analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46). Package: r-cran-emdist Architecture: amd64 Version: 0.3-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-emdist_0.3-3-1.ca2404.1_amd64.deb Size: 25428 MD5sum: 03183eac1fe4e8be25924d30fdffe637 SHA1: ccddc23a45bad80fd6ade9be5fac9fbbbf5ad23e SHA256: 3622fa2b0357a45700471a328fbd4de91f35044164d8775bc6b3049a9399cf95 SHA512: 55f228c6bc7ba85592fdf6811dc8c14954d83bbe6270584679b191f96765c638c68796df6fb121424eb4a1ba80ddf2515bdf53f380e5c3c02532c8b2d18a16bf Homepage: https://cran.r-project.org/package=emdist Description: CRAN Package 'emdist' (Earth Mover's Distance) Package providing calculation of Earth Mover's Distance (EMD). Package: r-cran-emgaussian Architecture: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrixcalc, r-cran-matrix, r-cran-lavaan, r-cran-glasso, r-cran-glassofast, r-cran-caret, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-psych, r-cran-bootnet, r-cran-qgraph, r-cran-cglasso Filename: pool/dists/noble/main/r-cran-emgaussian_0.2.2-1.ca2404.1_amd64.deb Size: 122816 MD5sum: 5c4695b637d7f665a7e1561e0c84137c SHA1: 0e9941c752ac12f8ecb18773d2b22aec4ef4bc50 SHA256: 9de7e6a69eea06aec167b5a9ea3ef088f56232613cea64f71d0e69dd43f38e19 SHA512: 7224711e1aa720c742c467326372e2c3173d290fd219b249e1599949f852e5c07fbf167a2b159223082394f1618a1e20e574c225af572b147a10a6ad6e10fa99 Homepage: https://cran.r-project.org/package=EMgaussian Description: CRAN Package 'EMgaussian' (Expectation-Maximization Algorithm for Multivariate Normal(Gaussian) with Missing Data) Initially designed to distribute code for estimating the Gaussian graphical model with Lasso regularization, also known as the graphical lasso (glasso), using an Expectation-Maximization (EM) algorithm based on work by Städler and Bühlmann (2012) . As a byproduct, code for estimating means and covariances (or the precision matrix) under a multivariate normal (Gaussian) distribution is also available. Package: r-cran-emir Architecture: amd64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1668 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-tictoc, r-cran-ggplot2, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-gganimate, r-cran-mathjaxr, r-cran-data.table, r-cran-rcppprogress, r-cran-testthat Suggests: r-cran-xml2 Filename: pool/dists/noble/main/r-cran-emir_1.0.6-1.ca2404.1_amd64.deb Size: 698398 MD5sum: b78d7645e927204ae72f51657ea476b0 SHA1: 57dc91c008b52e15a251b2d10361f11bff184569 SHA256: 6bb369b49da8087fe379e842c2c7fb9c526131c1be86ef11e379b303c18368ac SHA512: 1cdee30e15d07aef1e397d1010adb19023cf786b77d8bf3ef40987ed614190100792a68e1337d3012d60285f35dd39035d15a94ae3e0e45c0c0488956df25183 Homepage: https://cran.r-project.org/package=EmiR Description: CRAN Package 'EmiR' (Evolutionary Minimizer for R) A C++ implementation of the following evolutionary algorithms: Bat Algorithm (Yang, 2010 ), Cuckoo Search (Yang, 2009 ), Genetic Algorithms (Holland, 1992, ISBN:978-0262581110), Gravitational Search Algorithm (Rashedi et al., 2009 ), Grey Wolf Optimization (Mirjalili et al., 2014 ), Harmony Search (Geem et al., 2001 ), Improved Harmony Search (Mahdavi et al., 2007 ), Moth-flame Optimization (Mirjalili, 2015 ), Particle Swarm Optimization (Kennedy et al., 2001 ISBN:1558605959), Simulated Annealing (Kirkpatrick et al., 1983 ), Whale Optimization Algorithm (Mirjalili and Lewis, 2016 ). 'EmiR' can be used not only for unconstrained optimization problems, but also in presence of inequality constrains, and variables restricted to be integers. Package: r-cran-emirt Architecture: amd64 Version: 0.0.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2972 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pscl, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-emirt_0.0.15-1.ca2404.1_amd64.deb Size: 2475650 MD5sum: a03f16b814b3db8a3280f080385944e9 SHA1: 7e7b66178ed91b3123dceb80b369ea02edb7bb76 SHA256: 4e9f4989f4325076d89cd94a7771464c48aeb21542d776fe9214390fca30ec50 SHA512: d46ffd8957558907190d87f51bf9054fe4da19a0f809ff83e86be88e24672cb0bde6bdef4b78f4e5881743942611a9f97a0e1d634aec4182b8c10b20c2d6a11d Homepage: https://cran.r-project.org/package=emIRT Description: CRAN Package 'emIRT' (EM Algorithms for Estimating Item Response Theory Models) Various Expectation-Maximization (EM) algorithms are implemented for item response theory (IRT) models. The package includes IRT models for binary and ordinal responses, along with dynamic and hierarchical IRT models with binary responses. The latter two models are fitted using variational EM. The package also includes variational network and text scaling models. The algorithms are described in Imai, Lo, and Olmsted (2016) . Package: r-cran-emmixgene Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2458 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mclust, r-cran-reshape, r-cran-ggplot2, r-cran-scales, r-cran-rcpparmadillo, r-cran-bh Filename: pool/dists/noble/main/r-cran-emmixgene_0.1.4-1.ca2404.1_amd64.deb Size: 2286894 MD5sum: a66f6bc60413d7c04fb4e47d4e821b69 SHA1: 9263c6850b7d323edc84ca8b1da4ac6fc13bd1e7 SHA256: ea50d840398d4ecc1cae24bd20792e213078015f30aaf269db7b81c98fe4df49 SHA512: e001747b960fdad97d723390d9a4fa7c3dbdfe5046706bd12f20e8bf27eeac5229fe118a1ae5ef77def15344a0c211efd9759c9fa073a5c3ca158b4128a52d74 Homepage: https://cran.r-project.org/package=EMMIXgene Description: CRAN Package 'EMMIXgene' (A Mixture Model-Based Approach to the Clustering of MicroarrayExpression Data) Provides unsupervised selection and clustering of microarray data using mixture models. Following the methods described in McLachlan, Bean and Peel (2002) a subset of genes are selected based one the likelihood ratio statistic for the test of one versus two components when fitting mixtures of t-distributions to the expression data for each gene. The dimensionality of this gene subset is further reduced through the use of mixtures of factor analyzers, allowing the tissue samples to be clustered by fitting mixtures of normal distributions. Package: r-cran-emmixmfa Architecture: amd64 Version: 2.0.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mvtnorm, r-cran-ggally, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-emmixmfa_2.0.14-1.ca2404.1_amd64.deb Size: 217904 MD5sum: cd3bdd00ef0f334f926b81bb2aaae6aa SHA1: 97ca848fce28e68e4e31f4a17f785ec9529af26e SHA256: a191df16ddbea7ce71f84288e3197d4ed098fba0440e3afaa93a52a54d65862a SHA512: 50e5d2732efa4683fd03ce8b4015fb6a6198d81fa1f024be7c182c39e775015fbcb38d813747a60fd29d9fea254bf8e3d8b756e2808aabb546bc4237f70131c1 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: amd64 Version: 0.5-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-emoa_0.5-3-1.ca2404.1_amd64.deb Size: 139948 MD5sum: b0e230a6ae3a8cf83b91b9c1965234f3 SHA1: 6c399cc5b630f5ae58ffdbc43bb519af12e11249 SHA256: 1a6ecbfaca04e243b148760a7fcfe5382f04a0b8acf3d9ede17c029a4844c3a0 SHA512: c894cf7dbb895c0f39c66d00ed6e79af0ad40adc95d854101d1beca8f14b8861ea5a90c1ec06864168256cefa9643121172d76b598710115bfcdd82cdf6fd504 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: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-spelling, r-cran-covr Filename: pool/dists/noble/main/r-cran-empichar_1.0.1-1.ca2404.1_amd64.deb Size: 56224 MD5sum: eabeddd57789825f7ac8e1360b7ad299 SHA1: a05eab912975fad2733c122f935face19057c519 SHA256: 7ef06152072a324088564d1f9a5ba48b3538dd61cb90518753bbbe3ea7476d60 SHA512: 2d3e09b6506f33b05739e924c8f7b4e8972eda9367d1070b7ae046d69caad989733a6e17ea9c6adc1d7837963dd5ca0c734e64474472174db4c94fc02d70d414 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. Package: r-cran-empiricalcalibration Architecture: amd64 Version: 3.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1834 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-rlang, r-cran-rcpp Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat, r-cran-cyclops, r-cran-survival, r-cran-sequential Filename: pool/dists/noble/main/r-cran-empiricalcalibration_3.1.4-1.ca2404.1_amd64.deb Size: 1564730 MD5sum: 783f666dd2559bba0e06c6c2032ca444 SHA1: 70f4f3d80d4e1a46c31efaefd093024b1d716e8d SHA256: a870d11b11ebcf420d8cfec65e04a3d7c99724ba4e2139bc854582bd2952cdb3 SHA512: bbea178473a96059084d651ba4244e97145a76807062f036b3073b83b8c1773cf4e0670b6bc2c5f578a0b44e6820f51f51762b086e630e7efeb88aac99128c21 Homepage: https://cran.r-project.org/package=EmpiricalCalibration Description: CRAN Package 'EmpiricalCalibration' (Routines for Performing Empirical Calibration of ObservationalStudy Estimates) Routines for performing empirical calibration of observational study estimates. 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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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: amd64 Version: 1.7-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 508 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-boot, r-cran-gsl Suggests: r-cran-mass, r-cran-compquadform, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-energy_1.7-12-1.ca2404.1_amd64.deb Size: 309274 MD5sum: 819b5218193a43c4ba403c9e2c16a3bd SHA1: 032b0582c5dfd62c750297adf8732fb70d5f7a1a SHA256: ef1a0ce9917a269ed06755ce4baa778e03ee3421476e502a9e7cf40f393af22f SHA512: a757de7aac9314917e76dca3fdc8efb99cd661196055c3b1a988d45d032cfb35b5ef876cda6e5fbf2388a8c34218d5b8925dad5d48c8e5669cbba4d7c0a5903c 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: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-energymethod_1.1-1.ca2404.1_amd64.deb Size: 77424 MD5sum: 63931ba907e17133c0168dc9a160c037 SHA1: b44a571414e7bed111f1a2c1c379c53f9a9c8c58 SHA256: 47110d0ddd7ee77c9e34b4d9c9229a8e443d8a6751acf422dc39da2463b97b47 SHA512: 5982f7022fc9dda13f1c6ede2d2f87596bed62c1b7a316bfd95d74a7b9be4b8112b36fd79737e6d78122ddf483af42699cc2a244198c45fed216e0aba3fa0d72 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: amd64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1469 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-enerscape_1.2.0-1.ca2404.1_amd64.deb Size: 1210052 MD5sum: 4fed567b72f57c33b9010fc6021fc210 SHA1: cf9401eb1e897f2cfee0b642e3f1aefa606d4613 SHA256: cba20c28e500f17a5811fead73cee9876576b38a973678a2aa3acc20bdee64c3 SHA512: 0d6526b65799faf1b51f2dca2fc72003468f8be148d4daef62f00ff6a8c89bf0926abd6c177a435323e382b0139d49ba76922a544c1a854927024e7cc127072e 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: amd64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3173 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dosnow, r-cran-ellipse, r-cran-foreach, r-cran-mgcv, r-cran-rcpp, r-cran-snow, r-cran-terra, r-cran-vegan Filename: pool/dists/noble/main/r-cran-enmpa_0.2.3-1.ca2404.1_amd64.deb Size: 3001784 MD5sum: 4811c396e7fb90905e1bc42004d61528 SHA1: f59ea5c05f89ba44c01dd48f90fefe178027c366 SHA256: 09837ead8565df9c196fe54c302bb7d39f530ff4cb431a12058eaafa2204e941 SHA512: 8c6f36d29551427f6560a8b740c2481a8b09882174bb5cb7655daf025348fa5c8dad30052929e8bc6f8e5ff74cb958e33735360dfc6e8f819bd7ea6e2d16857e 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) . 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Currently provides Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA). The multilevel GSEA algorithm is derived from the 'fgsea' package. Methods are described in Subramanian et al. (2005) and Korotkevich et al. (2021) . Package: r-cran-entropyestimation Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-entropyestimation_1.2.1-1.ca2404.1_amd64.deb Size: 63140 MD5sum: 5db685aa481a1420b1671c97f7da26cc SHA1: 6e324552fe1ec88048ba83572dfc826c12301b03 SHA256: 271440c27c5ade8a61d2a6a6605b9ae34c0c75e4fdbbd0647aca8f8811e6a92d SHA512: b50aa577de49096d5a7898e6edc0c2bb0d23c984daad034121e88e001899db7ce2812a960e2117a9661bba3ea31608834cd713f2b0104b66adda4bfe481f6838 Homepage: https://cran.r-project.org/package=EntropyEstimation Description: CRAN Package 'EntropyEstimation' (Estimation of Entropy and Related Quantities) Contains methods for the estimation of Shannon's entropy, variants of Renyi's entropy, mutual information, Kullback-Leibler divergence, and generalized Simpson's indices. The estimators used have a bias that decays exponentially fast. Package: r-cran-entropymcmc Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rann, r-cran-mixtools Suggests: r-cran-rmpi, r-cran-snow Filename: pool/dists/noble/main/r-cran-entropymcmc_1.0.4-1.ca2404.1_amd64.deb Size: 189712 MD5sum: 58f4c9cd72f1814701d878fa00451d90 SHA1: cd162d312c9de216dfe7b87234211ac7c82564e9 SHA256: 38dd341c244c3e77b3957b4b1cecfa485d88240da56ede6c5f2201ca49800786 SHA512: ea5f561ff4e23262f9a674a9f11685c21df3400d2d9647a34eebb3041de9384a5ab14f382f7d99aba267e380fe4a9f89024b67826567bb3ff3cbc5e4282a27f4 Homepage: https://cran.r-project.org/package=EntropyMCMC Description: CRAN Package 'EntropyMCMC' (MCMC Simulation and Convergence Evaluation using Entropy andKullback-Leibler Divergence Estimation) Tools for Markov Chain Monte Carlo (MCMC) simulation and performance analysis. Simulate MCMC algorithms including adaptive MCMC, evaluate their convergence rate, and compare candidate MCMC algorithms for a same target density, based on entropy and Kullback-Leibler divergence criteria. MCMC algorithms can be simulated using provided functions, or imported from external codes. This package is based upon work starting with Chauveau, D. and Vandekerkhove, P. (2013) and next articles. Package: r-cran-envcpt Architecture: amd64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-changepoint, r-cran-mass, r-cran-zoo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-envcpt_1.1.5-1.ca2404.1_amd64.deb Size: 83876 MD5sum: ccdf719212792871a4b8d5d510456c46 SHA1: 99944607052be456167ecfbac4a003191210a7a1 SHA256: b09914495b04560b24cfa20c864ad47384afea82c298c299c09870e53795a1c1 SHA512: 158643274c0f87fb3a0393a8c80e63e011f20dca98d5e617ee4425ed142bf502570b5e655b8ec97b8a355e1eba411256320e54590163c729d92fef0686e4c134 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. 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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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Package: r-cran-epinow2 Architecture: amd64 Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13117 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-futile.logger, r-cran-ggplot2, r-cran-lifecycle, r-cran-lubridate, r-cran-patchwork, r-cran-posterior, r-cran-primarycensored, r-cran-purrr, r-cran-r.utils, r-cran-rcpp, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-runner, r-cran-scales, r-cran-truncnorm, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-future, r-cran-future.apply, r-cran-knitr, r-cran-parallelly, r-cran-progressr, r-cran-rmarkdown, r-cran-scoringutils, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-epinow2_1.8.0-1.ca2404.1_amd64.deb Size: 6713150 MD5sum: bbe2616f85d90bb9416851aa3ceed66b SHA1: 8ba87146b965347a2c720d50bce7641d648cca32 SHA256: 1af60566ea8594a9ea3761cc94c89fb1e61d1b32dafa2c771d82b10d00d84c7d SHA512: f2f6ce4ad3154bb4c3758991b26119574c573650317124c380d9f1505c3b193062b00c15791702822e46e82de05258ed9e726f2ab8d79db0a111b3299efdfbfa Homepage: https://cran.r-project.org/package=EpiNow2 Description: CRAN Package 'EpiNow2' (Estimate and Forecast Real-Time Infection Dynamics) Estimates the time-varying reproduction number, rate of spread, and doubling time using a renewal equation approach combined with Bayesian inference via Stan. Supports Gaussian process and random walk priors for modelling changes in transmission over time. Accounts for delays between infection and observation (incubation period, reporting delays), right-truncation in recent data, day-of-week effects, and observation overdispersion. Can estimate relationships between primary and secondary outcomes (e.g., cases to hospitalisations or deaths) and forecast both. Runs across multiple regions in parallel. Based on Abbott et al. (2020) and Gostic et al. (2020) . Package: r-cran-epiphy Architecture: amd64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1016 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-transport, r-cran-msm, r-cran-pbapply, r-cran-rcpp Suggests: r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-spdep, r-cran-emdist, r-cran-vegan, r-cran-mass, r-cran-emdbook, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-epiphy_0.5.0-1.ca2404.1_amd64.deb Size: 680428 MD5sum: f24bc5740c082b835ba6ffa934fe832e SHA1: d509e12411a032f8bf756095fb078ea18e654ec9 SHA256: cf54f92f812896a27c48d1cd2947b181535380a01667babdb9475d3b291702fa SHA512: 4e2de5d5b49a6e78a9dd308b231fad3ce50c995ee8d3cf021f0175e8c38fff5f740ae097b093075c10594b813ef4eedb35d9973cb6a0895aa91dbf1d32e7604b Homepage: https://cran.r-project.org/package=epiphy Description: CRAN Package 'epiphy' (Analysis of Plant Disease Epidemics) A toolbox to make it easy to analyze plant disease epidemics. It provides a common framework for plant disease intensity data recorded over time and/or space. Implemented statistical methods are currently mainly focused on spatial pattern analysis (e.g., aggregation indices, Taylor and binary power laws, distribution fitting, SADIE and 'mapcomp' methods). See Laurence V. Madden, Gareth Hughes, Franck van den Bosch (2007) for further information on these methods. Several data sets that were mainly published in plant disease epidemiology literature are also included in this package. Package: r-cran-epipvr Architecture: amd64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3821 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-posterior, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-bayesplot, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-epipvr_0.0.1-1.ca2404.1_amd64.deb Size: 1731562 MD5sum: 783f9747124589838e339a917d809618 SHA1: 113d7f762bb6afdea817b50f1b7e52e859d0452e SHA256: 392cda56b9893f1586cb5fb8cee378e613a5f323b85f5d7e430bb17a90075faf SHA512: 2ef0439470f447972425c383fdff5f374bfdb32780b607fbcbcbc36871b321502d404bc6a3e7dcb89d8a350e47c3dbc35231cebde632dd5b170c7f6ad2c03a2b Homepage: https://cran.r-project.org/package=EpiPvr Description: CRAN Package 'EpiPvr' (Estimating Plant Pathogen Epidemiology Parameters fromLaboratory Assays) Provides functions for estimating plant pathogen parameters from access period (AP) experiments. Separate functions are implemented for semi-persistently transmitted (SPT) and persistently transmitted (PT) pathogens. The common AP experiment exposes insect cohorts to infected source plants, healthy test plants, and intermediate plants (for PT pathogens). The package allows estimation of acquisition and inoculation rates during feeding, recovery rates, and latent progression rates (for PT pathogens). Additional functions support inference of epidemic risk from pathogen and local parameters, and also simulate AP experiment data. The functions implement probability models for epidemiological analysis, as derived in Donnelly et al. (2025), . These models were originally implemented in the 'EpiPv' 'GitHub' package. Package: r-cran-epiworldr Architecture: amd64 Version: 0.14.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6636 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-quarto, r-cran-tinytest, r-cran-netplot, r-cran-igraph, r-cran-data.table, r-cran-diagrammer Filename: pool/dists/noble/main/r-cran-epiworldr_0.14.0.0-1.ca2404.1_amd64.deb Size: 3496042 MD5sum: e26b2c63d07747dd2a30a87a2e6e919f SHA1: d95d66f15fe3f321de2378c7f8aa96fbca5cf59c SHA256: fc46471744854c413571c5956a4d0c0f93749cae907adaa542a38934f7c9315c SHA512: 28b400a3ffc068d3f3d509eb9c0445dc9fa9c80f5ec4388f875dcbc1eb31a23016acd04fc362d9193f76a59562acce963cba47303c89944d8c8e9d87220a8582 Homepage: https://cran.r-project.org/package=epiworldR Description: CRAN Package 'epiworldR' (Fast Agent-Based Epi Models) A flexible framework for Agent-Based Models (ABM), the 'epiworldR' package provides methods for prototyping disease outbreaks and transmission models using a 'C++' backend, making it very fast. It supports multiple epidemiological models, including the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Removed (SIR), Susceptible-Exposed-Infected-Removed (SEIR), and others, involving arbitrary mitigation policies and multiple-disease models. Users can specify infectiousness/susceptibility rates as a function of agents' features, providing great complexity for the model dynamics. Furthermore, 'epiworldR' is ideal for simulation studies featuring large populations. Package: r-cran-epizootic Architecture: amd64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1296 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-dplyr, r-cran-tibble, r-cran-r6, r-cran-cli, r-cran-raster, r-cran-qs2, r-cran-poems, r-cran-doparallel, r-cran-foreach, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-geosphere, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-epizootic_2.0.0-1.ca2404.1_amd64.deb Size: 855200 MD5sum: c0acaf2f87e83e5bfc3dca799d4831ea SHA1: e107e741a37789b798fec6a6fec84f357c274931 SHA256: 6a33114466c3737345ab545d43163f19d0efd1d0a3a679da1f687784f1ea8e19 SHA512: b6c94f4f0ca4874f2ecae4a0fa738f6241a3afe4dd1dafc950a858283943577fe431ec468556912d375246b14f08c74d46a3ccc9239549a3b1ea32ec352a41cf Homepage: https://cran.r-project.org/package=epizootic Description: CRAN Package 'epizootic' (Spatially Explicit Population Models of Disease Transmission inWildlife) This extension of the pattern-oriented modeling framework of the 'poems' package provides a collection of modules and functions customized for modeling disease transmission on a population scale in a spatiotemporally explicit manner. 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Package: r-cran-epm Architecture: amd64 Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1438 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-epm_1.1.6-1.ca2404.1_amd64.deb Size: 1272592 MD5sum: 1a5411fb6d8eda5dc1e1fc9b31095a47 SHA1: 74f09b7d5712073ecc5477a819fbc0ea211a2835 SHA256: 4c5488564bd2644a6220999b3787b038e69de122224138dd63c6178583a427ea SHA512: 1e3abf48a3cc85bce777a1cf69060e29708eae202e91da0b7b40b0310a8d4403c9ac470e55713a59fb87bfc56dc0a9702b46b48d715d1524413bad46fdead11f Homepage: https://cran.r-project.org/package=epm Description: CRAN Package 'epm' (EcoPhyloMapper) Facilitates the aggregation of species' geographic ranges from vector or raster spatial data, and that enables the calculation of various morphological and phylogenetic community metrics across geography. Citation: Title, PO, DL Swiderski and ML Zelditch (2022) . 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Package: r-cran-equalden.hd Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 396 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-equalden.hd_1.2.1-1.ca2404.1_amd64.deb Size: 355712 MD5sum: ea0e894e849ddd53a094c5a6cb1af760 SHA1: 788fbf9f316f085bcb43eb9eff2f16b1378db8cd SHA256: e1168432109436d6b6f12e3ed6e99e0e9ecd803965978f635be8e0a2cdbdac7e SHA512: f0f96835706c11076f1dde920277831a7995a59c46fc9f128b9fa7c96c574a3c77b23afc546933cf276c0fdbd9cb268bda640e87a655791c2aa1f5115f27b15f 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. 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Package: r-cran-equivalence Architecture: amd64 Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-boot, r-cran-paireddata Filename: pool/dists/noble/main/r-cran-equivalence_0.8.2-1.ca2404.1_amd64.deb Size: 130968 MD5sum: f644c3feb514a7f2a209279fc5583781 SHA1: a06362ff9b96ae05f95eac091c6397c207c4a097 SHA256: aac3fafa05bce760a2951d1623d47549e020db0bc5fb8714eb1bfcd1999cd67e SHA512: e5c32cc9557f80e18079cfc2d8ca7c9cffa3f9fbcf5b7b81d9295e54170f03cf4d8081a62f13e0b9498fe9a4a9d700cdec101bd0a6f6a515d62a800b081f1cc4 Homepage: https://cran.r-project.org/package=equivalence Description: CRAN Package 'equivalence' (Provides Tests and Graphics for Assessing Tests of Equivalence) Provides statistical tests and graphics for assessing tests of equivalence. Such tests have similarity as the alternative hypothesis instead of the null. Sample data sets are included. 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Outputs a table with compound names, matching scores and the integrated area of the compound for each sample. Package implementation is described in Domingo-Almenara et al. (2016) . 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See Krivitsky (2012) and Krivitsky, Hunter, Morris, and Klumb (2023) . 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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) . 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(2013) . Package: r-cran-ergmgp Architecture: amd64 Version: 0.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-network, r-cran-ergm, r-cran-networkdynamic, r-cran-statnet.common Filename: pool/dists/noble/main/r-cran-ergmgp_0.1-2-1.ca2404.1_amd64.deb Size: 91496 MD5sum: 3bcee7464df07a2be7d4d02c6f0537e7 SHA1: b09c85f2ee74f397e79b29ab492888cafdd85a91 SHA256: ffe05c25fdb6ba36a781c0d1ac22697e50a98785a0f963770a6ead491e25a73d SHA512: 5be3ccd5e23fe7685a0d11345606d7ed8344cb1fb27d650295e80cb3897c5388f23340ee2e7be74c5b5d6ce365000af0c9863aa23f10df5b342a6f6ddaa1b44b Homepage: https://cran.r-project.org/package=ergmgp Description: CRAN Package 'ergmgp' (Tools for Modeling ERGM Generating Processes) Provides tools for simulating draws from continuous time processes with well-defined exponential family random graph (ERGM) equilibria, i.e. ERGM generating processes (EGPs). A number of EGPs are supported, including the families identified in Butts (2023) , as are functions for hazard calculation and timing calibration. 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(2020) . As a difference from the 'ergm' package, 'ergmito' circumvents using Markov-Chain Maximum Likelihood Estimator (MC-MLE) and instead uses Maximum Likelihood Estimator (MLE) to fit ERGMs for small networks. As exhaustive enumeration is computationally feasible for small networks, this R package takes advantage of this and provides tools for calculating likelihood functions, and other relevant functions, directly, meaning that in many cases both estimation and simulation of ERGMs for small networks can be faster and more accurate than simulation-based algorithms. 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Exponential-family Random Graph Models (ERGM) and Gibbs Fields are special cases of ERNMs and can also be estimated with the package. Please cite Fellows and Handcock (2012), "Exponential-family Random Network Models" available at . 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The ESS algorithm is used for model selection in decomposable graphical models. Package: r-cran-essentials Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-essentials_0.1.0-1.ca2404.1_amd64.deb Size: 42980 MD5sum: a0c05923b078b7c4cc5a18063f5aa7ca SHA1: 5e439ae0136cea8834c2afb43f68a51a2086e7af SHA256: 9c35da7f0c3af86e5b8bbf5863c5a6bde000d0d7fa6df2c5e2fa4eb4c43f5d6d SHA512: b1be3fbcdae87a7594c7fb82c7c8ffb458a4f8e5ec9572d603ca8581ecd33fc24f10ce56845aa87a975a68c48e78f7b22e87c7396378aceda147bce3a4630be3 Homepage: https://cran.r-project.org/package=essentials Description: CRAN Package 'essentials' (Essential Functions not Included in Base R) Functions for converting objects to scalars (vectors of length 1) and a more inclusive definition of data that can be interpreted as numbers (numeric and complex alike). 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In other words, the resulting histogram servers as an optimal density estimator, and meanwhile recovers the features, such as increases or modes, with both false positive and false negative controls. Moreover, it provides a parsimonious representation in terms of the number of blocks, which simplifies data interpretation. The only assumption for the method is that data points are independent and identically distributed, so it applies to fairly general situations, including continuous distributions, discrete distributions, and mixtures of both. For details see Li, Munk, Sieling and Walther (2016) . 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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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Although this approach is known to be an efficient heuristic, the results of recursive tree methods are only locally optimal, as splits are chosen to maximize homogeneity at the next step only. An alternative way to search over the parameter space of trees is to use global optimization methods like evolutionary algorithms. The 'evtree' package implements an evolutionary algorithm for learning globally optimal classification and regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while the 'partykit' package is leveraged to represent the resulting trees in R, providing unified infrastructure for summaries, visualizations, and predictions. 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Package: r-cran-exactmultinom Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-exactmultinom_0.1.3-1.ca2404.1_amd64.deb Size: 56386 MD5sum: a7ebd24489f3b6529f4dd9de93d31fcb SHA1: 5b626c43e3a0bca1a7aeb27658d3ff3c12eef80a SHA256: 5d18062dde19677b74af168e575ebf36ef8017ae60cdc4be0b6d4c6ae0bfaa89 SHA512: 404c1ffdc323962476e4645ab9e18130495dd93693f4c5898005e0856358c97a194c48a9944a2602b73f647a2f945e6a24e3020079088ede494b29876d9c82f0 Homepage: https://cran.r-project.org/package=ExactMultinom Description: CRAN Package 'ExactMultinom' (Multinomial Goodness-of-Fit Tests) Computes exact p-values for multinomial goodness-of-fit tests based on multiple test statistics, namely, Pearson's chi-square, the log-likelihood ratio and the probability mass statistic. Implements the algorithm detailed in Resin (2023) . Estimates based on the classical asymptotic chi-square approximation or Monte-Carlo simulation can also be computed. 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Package: r-cran-exactvartest Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-bench, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-ggplot2, r-cran-xts, r-cran-quantmod, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-exactvartest_0.1.3-1.ca2404.1_amd64.deb Size: 134766 MD5sum: 54bdf736c5b72a7e605292418b1e43d3 SHA1: 6c4163c3d3d4d52468c625c432ace78979f716f9 SHA256: 89bb6573584547206732f2a5ec51f01bb1e53ef5b48272271c5ca2706faf1f4c SHA512: 4c6f00bf2fbca908dc58faf487620b66fefc3c080b835b8cb5e2f13a3e3b98a780f5d17de481fd89775ff537081cf9ecae89e3feed888e300e9c095d50b16280 Homepage: https://cran.r-project.org/package=ExactVaRTest Description: CRAN Package 'ExactVaRTest' (Exact Finite-Sample Value-at-Risk Back-Testing) Provides fast dynamic-programming algorithms in 'C++'/'Rcpp' (with pure 'R' fallbacks) for the exact finite-sample distributions and p-values of Christoffersen (1998) independence (IND) and conditional-coverage (CC) VaR backtests. For completeness, it also provides the exact unconditional-coverage (UC) test following Kupiec (1995) via a closed-form binomial enumeration. See Christoffersen (1998) and Kupiec (1995) . Package: r-cran-exametrika Architecture: amd64 Version: 1.13.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3903 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm, r-cran-igraph, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-exametrika_1.13.1-1.ca2404.1_amd64.deb Size: 2742760 MD5sum: 30590a839a90ecf6db4bf573e0055253 SHA1: 782d95590745cd2b27203129dfc3112a9f272323 SHA256: 51a68239a938d10ea7ecd9369260ddfc890abb586933ece0f76518fd9963e888 SHA512: 665710624871e03ce15f75517ff2dcfa410b789fc45891891881a8377dd610cd4b7d0f7525722e6a58a3f227f5be3c5bd52600ac09393a7ec1178a37e304fbeb Homepage: https://cran.r-project.org/package=exametrika Description: CRAN Package 'exametrika' (Test Theory Analysis and Biclustering) Implements comprehensive test data engineering methods as described in Shojima (2022, ISBN:978-9811699856). Provides statistical techniques for engineering and processing test data: Classical Test Theory (CTT) with reliability coefficients for continuous ability assessment; Item Response Theory (IRT) including Rasch, 2PL, and 3PL models with item/test information functions; Latent Class Analysis (LCA) for nominal clustering; Latent Rank Analysis (LRA) for ordinal clustering with automatic determination of cluster numbers; Biclustering methods including infinite relational models for simultaneous clustering of examinees and items without predefined cluster numbers; and Bayesian Network Models (BNM) for visualizing inter-item dependencies. Features local dependence analysis through LRA and biclustering, parameter estimation, dimensionality assessment, and network structure visualization for educational, psychological, and social science research. Package: r-cran-exceedprob Architecture: amd64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/noble/main/r-cran-exceedprob_0.0.1-1.ca2404.1_amd64.deb Size: 87452 MD5sum: 911cc11beec3e43687eaf58e9c31d039 SHA1: f252842e27fe7d2ba4ee54a6bcb4f819bce16e0c SHA256: d0d109e0e82e8f5dc4079d7128e7d069861786b4e12ffcb3b471d252a904975c SHA512: c481aa2a254207e49caa1ed390d14583addfa61eafe6675635896b3bc4865a39c1b6852144a1e0e3e191a653926313f9d363dbcd7d6c89bab251877f9a35002c Homepage: https://cran.r-project.org/package=exceedProb Description: CRAN Package 'exceedProb' (Confidence Intervals for Exceedance Probability) Computes confidence intervals for the exceedance probability of normally distributed estimators. Currently only supports general linear models. Please see Segal (2019) for more information. Package: r-cran-excursions Architecture: amd64 Version: 2.5.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 813 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-fmesher, r-cran-withr, r-cran-lifecycle Suggests: r-cran-testthat, r-cran-sf, r-cran-sp, r-cran-inlabru, r-cran-rcolorbrewer, r-cran-splancs, r-cran-fields, r-cran-rspde, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-excursions_2.5.11-1.ca2404.1_amd64.deb Size: 567878 MD5sum: 13161f24ad2fef7462ee94f017b14b6b SHA1: 2f4d1dab524a3a4bebc39b9d4442413322ee7994 SHA256: f19bed0a409cf245477af6ea27a71cfc7ae4d280a78ea729e8a3e0512c27b2ed SHA512: 4478d350da4c8e938690f4f581939f5f88c83389a8086dbf0a83d3b31930f132d93902e30f511bedd404d95f37e8eea322c8f8161d78ed47273f29f7cb4ff49f Homepage: https://cran.r-project.org/package=excursions Description: CRAN Package 'excursions' (Excursion Sets and Contour Credibility Regions for Random Fields) Functions that compute probabilistic excursion sets, contour credibility regions, contour avoiding regions, and simultaneous confidence bands for latent Gaussian random processes and fields. The package also contains functions that calculate these quantities for models estimated with the INLA package. The main references for excursions are Bolin and Lindgren (2015) , Bolin and Lindgren (2017) , and Bolin and Lindgren (2018) . These can be generated by the citation function in R. Package: r-cran-exdex Architecture: amd64 Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 993 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-chandwich, r-cran-rcpp, r-cran-rcpproll, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-revdbayes, r-cran-rmarkdown, r-cran-testthat, r-cran-zoo Filename: pool/dists/noble/main/r-cran-exdex_1.2.4-1.ca2404.1_amd64.deb Size: 692602 MD5sum: dcfde2f5d7cc0a3f3625dda31ff6a347 SHA1: a66b8263d6bd0b4efd044776cd40cbeb8af951b1 SHA256: 76c8481bb96c0cd231df0444bcbc4ae9051634d184516af51a48775bb60ace76 SHA512: 8d2e07a2b640d1f3d0a39e25d67870e8884139c5ced5f17f6c17290aec6c719a8ac7b1c7a8fe858c790c276ec02a668fe7dd9857c3d7ffef00663d1d95cf24c0 Homepage: https://cran.r-project.org/package=exdex Description: CRAN Package 'exdex' (Estimation of the Extremal Index) Performs frequentist inference for the extremal index of a stationary time series. Two types of methodology are used. One type is based on a model that relates the distribution of block maxima to the marginal distribution of series and leads to the semiparametric maxima estimators described in Northrop (2015) and Berghaus and Bucher (2018) . Sliding block maxima are used to increase precision of estimation. A graphical block size diagnostic is provided. The other type of methodology uses a model for the distribution of threshold inter-exceedance times (Ferro and Segers (2003) ). Three versions of this type of approach are provided: the iterated weight least squares approach of Suveges (2007) , the K-gaps model of Suveges and Davison (2010) and a similar approach of Holesovsky and Fusek (2020) that we refer to as D-gaps. For the K-gaps and D-gaps models this package allows missing values in the data, can accommodate independent subsets of data, such as monthly or seasonal time series from different years, and can incorporate information from right-censored inter-exceedance times. Graphical diagnostics for the threshold level and the respective tuning parameters K and D are provided. Package: r-cran-exdqlm Architecture: amd64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1905 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-coda, r-cran-tictoc, r-cran-magic, r-cran-crch, r-cran-truncnorm, r-cran-fnn, r-cran-laplacesdemon, r-cran-rcpp, r-cran-matrixstats, r-cran-nimble, r-cran-numderiv, r-cran-bh, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-pkgload, r-cran-mass Filename: pool/dists/noble/main/r-cran-exdqlm_0.4.0-1.ca2404.1_amd64.deb Size: 1514042 MD5sum: bfe8fe0d0def58e54f95e377ce192c1e SHA1: 28c0271dc272135817060571ff1d9ca4d093003d SHA256: 318289b5bc13988ae3cc9698d8ebee228f250c9728f5a240a912a520471952f4 SHA512: de0ec642501f6125daace26e4d68d69ac3533f7bfd85ef75fbcc6e24d81227420997209555bc1d9a513058bc054385bdd528507aea486f94bbe1f27f8640bc3a Homepage: https://cran.r-project.org/package=exdqlm Description: CRAN Package 'exdqlm' (Extended Dynamic Quantile Linear Models) Bayesian quantile-regression routines for dynamic state-space models and static regression under the extended asymmetric Laplace (exAL) error distribution. The dynamic state-space models are extended dynamic quantile linear models (exDQLMs). The package combines dynamic exDQLM inference via LDVB, MCMC, and legacy ISVB with static exAL regression via LDVB and MCMC, reduced AL/DQLM paths through fixed skewness, component builders for trend/seasonality/regression blocks, static shrinkage priors including ridge, regularized horseshoe, and 'rhs_ns', evidence lower bound diagnostics, optional C++ accelerators, and posterior predictive synthesis across separately fitted quantiles through 'quantileSynthesis()'. Dynamic exDQLM methods are described in Barata et al. (2020) . Package: r-cran-exhaustivesearch Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-mlbench Filename: pool/dists/noble/main/r-cran-exhaustivesearch_1.0.2-1.ca2404.1_amd64.deb Size: 111326 MD5sum: 1241327189e82d3d91485e5cac24a9ac SHA1: 8d81572f78d69f30036f34566a60c87526829697 SHA256: 1b4811c405974b2a36d276f50dfe3b0ae9f3850bc14a052964ab630aa2df0d0f SHA512: ae6ec1bfc61fca47636992dd415a5e26bd311285ae5feb2dc9b92ca16cec54c60bd473f33d401d50258ade6ee5f4eea70b78354ce6e2832fe3bb31f5918e44d0 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: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2820 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-exif_0.1.1-1.ca2404.1_amd64.deb Size: 2719056 MD5sum: a9ed542a498788258859b9be154d1477 SHA1: a5b19f1f1975fe3d424c6f83399e67c4e4fa7859 SHA256: 5de39a8e34d8e9242b0a060bf408256f68aca3074ca3274fbafa2085fb5e1d88 SHA512: 75e0e53f1dc498c19221c322eebb159ddbda4c03df128b538899496b349e34d9dca4ea2f305841ea3205f03c697172457824e14550c483abff59703f274bfa16 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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It includes functions for model comparison, estimation of quantity of interest in extreme value analysis and plotting. Reference: CN Behrens, HF Lopes, D Gamerman (2004) . FF do Nascimento, D. Gamerman, HF Lopes . 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Estimation methods for univariate independent observations and temporal dependent observations are available. The methodology is extended to the case of independent multidimensional observations. The statistical inference is performed through parametric and non-parametric estimators. Inferential procedures such as confidence intervals, confidence regions and hypothesis testing are obtained by exploiting the asymptotic theory. Adapts the methodologies derived in Padoan and Stupfler (2022) , Davison et al. (2023) , Daouia et al. (2018) , Drees (2000) , Drees (2003) , de Haan and Ferreira (2006) , de Haan et al. (2016) , Padoan and Rizzelli (2024) , Daouia et al. (2024) . Package: r-cran-extremes Architecture: amd64 Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1163 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lmoments, r-cran-distillery Suggests: r-cran-fields Filename: pool/dists/noble/main/r-cran-extremes_2.2-1.ca2404.1_amd64.deb Size: 1122186 MD5sum: d050bf2ca8d6f2b70b5f3c371d182a24 SHA1: 8e609bcd0d975eb399a54a46abee7fe54d9b1787 SHA256: 9e2dbcfe167df26308451a1ec1fcc298f472e934498c35191ce13b8d0c30a110 SHA512: 8dfbfc8a86b27ca3ef9e297d016d7b63a6196737e2a221df766dbed679010d53dc824fdfe7fb78629f7b1640d68b6fd66cb9316941c1f1abefe54ab1c2dd4313 Homepage: https://cran.r-project.org/package=extRemes Description: CRAN Package 'extRemes' (Extreme Value Analysis) General functions for performing extreme value analysis. In particular, allows for inclusion of covariates into the parameters of the extreme-value distributions, as well as estimation through MLE, L-moments, generalized (penalized) MLE (GMLE), as well as Bayes. Inference methods include parametric normal approximation, profile-likelihood, Bayes, and bootstrapping. Some bivariate functionality and dependence checking (e.g., auto-tail dependence function plot, extremal index estimation) is also included. For a tutorial, see Gilleland and Katz (2016) and for bootstrapping, please see Gilleland (2020) . 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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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Empirical Bayes procedures to estimate the prior information from multi-group data are included. See, e.g.,Bersson and Hoff (2022) "Optimal Conformal Prediction for Small Areas". Package: r-cran-factoclass Architecture: amd64 Version: 1.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: libc6 (>= 2.4), libstdc++6 (>= 4.3), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ade4, r-cran-ggplot2, r-cran-ggrepel, r-cran-xtable, r-cran-scatterplot3d, r-cran-kernsmooth Filename: pool/dists/noble/main/r-cran-factoclass_1.2.9-1.ca2404.1_amd64.deb Size: 210528 MD5sum: 2e9cf02731925193ef755af22e534cdf SHA1: 22474f67ec1dcc2e1a33002217cb89f3d53599a8 SHA256: 1c972026e48bf67e7d01808d4d2df5760878173bf444379112738466e217cb05 SHA512: 4b24fb308eba99fe6717beaa4c100679da037b4131837040a6f967bf1f482bc739458a2f40269045f9a1cc689f6fabf436a841bfcd8aa84f97ca75c872f4f9bc Homepage: https://cran.r-project.org/package=FactoClass Description: CRAN Package 'FactoClass' (Combination of Factorial Methods and Cluster Analysis) Some functions of 'ade4' and 'stats' are combined in order to obtain a partition of the rows of a data table, with columns representing variables of scales: quantitative, qualitative or frequency. 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The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017). 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Useful for analysis of large datasets involving variables such as age, years, states, countries, or education levels. 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Supports linear, probit, ordered probit, and multinomial logit model components. Features include multi-stage estimation, automatic parameter initialization, analytical gradients and Hessians, and parallel estimation. Methods are described in Heckman, Humphries, and Veramendi (2016) , Heckman, Humphries, and Veramendi (2018) , and Humphries, Joensen, and Veramendi (2024) . 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Functions for computing tail-weighted dependence measures in Lee, Joe and Krupskii (2018) and estimating tail dependence parameter. 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Package: r-cran-fad Architecture: amd64 Version: 0.9-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 701 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rspectra, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-gparotation Filename: pool/dists/noble/main/r-cran-fad_0.9-3-1.ca2404.1_amd64.deb Size: 372346 MD5sum: 48d3621dc6bad0faf094b0f337fcfc4b SHA1: 28240eedb0fcb38229f7eb0cb6d64d13daf91567 SHA256: 58f94577db5e031c83c74623a395da4d776ddf394fac2165963e1671a6eda05c SHA512: e5750f784ba1661f8dc81107282b79380645d0907a05cbc48d1cf0156ede7af6e6fda3d92e096bd82a4e3c214de0c5351709458e43a01907109d9e197659480f Homepage: https://cran.r-project.org/package=fad Description: CRAN Package 'fad' (Factor Analysis for Data) Compute maximum likelihood estimators of parameters in a Gaussian factor model using the the matrix-free methodology described in Dai et al. 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Package: r-cran-falcon Architecture: amd64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-falcon_0.2-1.ca2404.1_amd64.deb Size: 148838 MD5sum: ac59ff959ce1efdabe9a1aedfe3c066d SHA1: faa165531e5d7e9486dc0e99f3a7b3cef64e049e SHA256: bd243a4e660fd71d9bbeed5033d4282acd13fc14d91e92eccd65288a4163ed61 SHA512: 151ce21cbe40e6c06bfd0a845afdc6dbbb9858381d99663c9e98b041140d642b1d9d10f467d7755e1060813c07d53d778343ea6cd442503be3a3269d0cd157a5 Homepage: https://cran.r-project.org/package=falcon Description: CRAN Package 'falcon' (Finding Allele-Specific Copy Number in Next-GenerationSequencing Data) This is a method for Allele-specific DNA Copy Number Profiling using Next-Generation Sequencing. Given the allele-specific coverage at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number. 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Package: r-cran-familias Architecture: amd64 Version: 2.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-kinship2, r-cran-rsolnp Filename: pool/dists/noble/main/r-cran-familias_2.6.4-1.ca2404.1_amd64.deb Size: 153894 MD5sum: c69c172843c2da6f2c64d06949f0ac43 SHA1: 0557bd9a709229d59478c91dec973c7909088e2d SHA256: f073e251b51c79ab71f06234c94c8c1cb63922d8eddb0d6fe6d9d501d0c9284f SHA512: fed01db7b3225ffd38666435bad19c32eaeec4b9f03d4ccb28289c898c1562fba7b1a3382745ffbf436f835f93219174fbe9134645ce1c64b72943966d80131c Homepage: https://cran.r-project.org/package=Familias Description: CRAN Package 'Familias' (Probabilities for Pedigrees Given DNA Data) An interface to the core 'Familias' functions which are programmed in C++. The implementation is described in Egeland, Mostad and Olaisen (1997) and Simonsson and Mostad (2016) . 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Package: r-cran-far Architecture: amd64 Version: 0.6-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme Filename: pool/dists/noble/main/r-cran-far_0.6-7-1.ca2404.1_amd64.deb Size: 200588 MD5sum: 550edb2c31165a1e919913db3f25a315 SHA1: 174402bea58d77030c4729d1b75c1d09ebc01e75 SHA256: a8e68e4f45b04fb107e399aac03814662c86b693705187851b25d2f9e0ae87be SHA512: b398477e37fea3cfa8ce121a0a4f93dbcc24d9834cb560741062cf0bd66338a52da754874e18788e1108926c4bb6b15da8bb3313073df648436ad60d4f59464f Homepage: https://cran.r-project.org/package=far Description: CRAN Package 'far' (Modelization for Functional AutoRegressive Processes) Modelizations and previsions functions for Functional AutoRegressive processes using nonparametric methods: functional kernel, estimation of the covariance operator in a subspace, ... Package: r-cran-farff Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bbmisc, r-cran-checkmate, r-cran-readr, r-cran-stringi Suggests: r-cran-openml, r-cran-testthat Filename: pool/dists/noble/main/r-cran-farff_1.1.1-1.ca2404.1_amd64.deb Size: 46602 MD5sum: 125bd49331a5489e9d5ff67d11cc8f46 SHA1: 5e7a2805655bb26c3b77965d161a9f452a853dca SHA256: aee389dcd684aa01ba093560662dd7ed512c264071d96de0a14d7c9971013e1c SHA512: 53a54a68eb6d693f14ff19b84c7327b8d02c68762a5ca7560b2059528c530aa251b587185fdfac84537ce95180994950896fadabdc76d78136246067ea32098e Homepage: https://cran.r-project.org/package=farff Description: CRAN Package 'farff' (A Faster 'ARFF' File Reader and Writer) Reads and writes 'ARFF' files. 'ARFF' (Attribute-Relation File Format) files are like 'CSV' files, with a little bit of added meta information in a header and standardized NA values. They are quite often used for machine learning data sets and were introduced for the 'WEKA' machine learning 'Java' toolbox. See for further info on 'ARFF' and for for more info on 'WEKA'. 'farff' gets rid of the 'Java' dependency that 'RWeka' enforces, and it is at least a faster reader (for bigger files). It uses 'readr' as parser back-end for the data section of the 'ARFF' file. Consistency with 'RWeka' is tested on 'Github' and 'Travis CI' with hundreds of 'ARFF' files from 'OpenML'. Package: r-cran-farmselect Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ncvreg, r-cran-fbasics, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-farmselect_1.0.2-1.ca2404.1_amd64.deb Size: 144232 MD5sum: 5b8b83c08ac60805335d8f8c4c8332d1 SHA1: 897f71908b61b44b4e3039fc5753186ac7772bd7 SHA256: e76d5804f3180088f52888e101b94b00b51ef6da29af0abc0706ab6e07f4d4ac SHA512: 13b55878ac4e3c349275def16178e1833c1f9f7037eb8757629b2b608870bd50307c504f73115405c00f5e5867d62c54eddddf50f34cd06910ddff88e4cf40ed Homepage: https://cran.r-project.org/package=FarmSelect Description: CRAN Package 'FarmSelect' (Factor Adjusted Robust Model Selection) Implements a consistent model selection strategy for high dimensional sparse regression when the covariate dependence can be reduced through factor models. By separating the latent factors from idiosyncratic components, the problem is transformed from model selection with highly correlated covariates to that with weakly correlated variables. It is appropriate for cases where we have many variables compared to the number of samples. Moreover, it implements a robust procedure to estimate distribution parameters wherever possible, hence being suitable for cases when the underlying distribution deviates from Gaussianity. See the paper on the 'FarmSelect' method, Fan et al.(2017) , for detailed description of methods and further references. Package: r-cran-farmtest Architecture: amd64 Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 422 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-farmtest_2.2.0-1.ca2404.1_amd64.deb Size: 180406 MD5sum: 968b7b35da8c1826363785fcd40b7fbb SHA1: d6ddec8890291c76c02da7a425661df0fcea0f82 SHA256: ed17a629fc61a887e2c8a4b867549ebb5500878f59c067c28a7c39f1a7a4ed4a SHA512: c86ff340561ba2c7bc2caf35e73514278efb09230dad675e70ccb468e271fc6d5f30647b82254bbcb6aebc235a6e5edf3651d803c68489d4988978e3d11644ad Homepage: https://cran.r-project.org/package=FarmTest Description: CRAN Package 'FarmTest' (Factor-Adjusted Robust Multiple Testing) Performs robust multiple testing for means in the presence of known and unknown latent factors presented in Fan et al.(2019) "FarmTest: Factor-Adjusted Robust Multiple Testing With Approximate False Discovery Control" . Implements a series of adaptive Huber methods combined with fast data-drive tuning schemes proposed in Ke et al.(2019) "User-Friendly Covariance Estimation for Heavy-Tailed Distributions" to estimate model parameters and construct test statistics that are robust against heavy-tailed and/or asymmetric error distributions. Extensions to two-sample simultaneous mean comparison problems are also included. As by-products, this package contains functions that compute adaptive Huber mean, covariance and regression estimators that are of independent interest. Package: r-cran-farver Architecture: amd64 Version: 2.1.2-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2511 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-farver_2.1.2-1.ca2404.2_amd64.deb Size: 1403396 MD5sum: db297a2421cba0b33ad4c0d23d8574b7 SHA1: e4f056c6294af213f2d3e6b6685f45c2c6f0f4bb SHA256: bc00f69614d40efb35befadae76820bc61e14941b534a2db1f894450a1a3f979 SHA512: d3a6528d560fbc0b7df9d82832fe7a77bfe4dc798781d71e0908439d94d8f0ce92a457ec4faf72a3b97255847d76d49b4a511d93ffb13c7f6f90b8d5759a285e Homepage: https://cran.r-project.org/package=farver Description: CRAN Package 'farver' (High Performance Colour Space Manipulation) The encoding of colour can be handled in many different ways, using different colour spaces. 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Package: r-cran-fas Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-matrix Filename: pool/dists/noble/main/r-cran-fas_1.0.0-1.ca2404.1_amd64.deb Size: 53254 MD5sum: 42c11565af88971648c05570ff685f51 SHA1: 7ad940cf43f5ce4b082175aba92f0f9e2867cdc0 SHA256: 1b28d9c7f129f764bfa337bddd221638b2edf0445a8f4cf8363d9bdd54a80022 SHA512: 3cbfe3ee03c7a103f957c67525b5f5b2454d936f82d715139a9d9d599d360b81e051dc679ebae95dd7ed047b0739692b9a2fcef6406f36cc303b87c1aa8b3a08 Homepage: https://cran.r-project.org/package=FAS Description: CRAN Package 'FAS' (Factor-Augmented Sparse Regression Tuning-Free Testing) The 'FAS' package implements the bootstrap method for the tuning parameter selection and tuning-free inference on sparse regression coefficient vectors. Currently, the test could be applied to linear and factor-augmented sparse regressions, see Lederer & Vogt (2021, JMLR) and Beyhum & Striaukas (2023) . Package: r-cran-fasano.franceschini.test Architecture: amd64 Version: 2.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fasano.franceschini.test_2.2.2-1.ca2404.1_amd64.deb Size: 150382 MD5sum: 978240fdc5db815dbb6792fc86e8fab2 SHA1: 379e78065bf55909f527ddb8e2309804394ec7e7 SHA256: ea5ea34c2b1f7080ab2d8c72e5aa47d47d3f2028b8ede5b7891cc08323ad1b7b SHA512: 75ef75eff035eb603697ad65d4039b3737771b633afa0b21bc971ac15741bb818d0d7991b54e885bc0a6801354c097267a244f35b594fcc9bac99a71375f9b24 Homepage: https://cran.r-project.org/package=fasano.franceschini.test Description: CRAN Package 'fasano.franceschini.test' (Fasano-Franceschini Test: A Multivariate Kolmogorov-SmirnovTwo-Sample Test) An implementation of the two-sample multivariate Kolmogorov-Smirnov test described by Fasano and Franceschini (1987) . This test evaluates the null hypothesis that two i.i.d. random samples were drawn from the same underlying probability distribution. The data can be of any dimension, and can be of any type (continuous, discrete, or mixed). Package: r-cran-fastadi Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lrmf3, r-cran-matrix, r-cran-glue, r-cran-logger, r-cran-rcpp, r-cran-rlang, r-cran-rspectra, r-cran-rcpparmadillo Suggests: r-cran-invertiforms, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fastadi_0.1.2-1.ca2404.1_amd64.deb Size: 183628 MD5sum: 2a4cc539acef5fbd87b1bf37c02ace23 SHA1: 63f1ca4d4c31ce1630406945432b6a2728f1654f SHA256: 7c04bf92aa992529e3ddcb75a17053eecd85ef265c2e7aa42f2b4fad94374a92 SHA512: f74ecc428aeabf4c8337edaa21c60500855313fc9a98c546844b69663eb9df4442e64395ab0b24fa9fb708c402c7fdccbb1297d1f567e013682905ffcb63c552 Homepage: https://cran.r-project.org/package=fastadi Description: CRAN Package 'fastadi' (Self-Tuning Data Adaptive Matrix Imputation) Implements the AdaptiveImpute matrix completion algorithm of 'Intelligent Initialization and Adaptive Thresholding for Iterative Matrix Completion' as well as the specialized variant of 'Co-Factor Analysis of Citation Networks' . AdaptiveImpute is useful for embedding sparsely observed matrices, often out performs competing matrix completion algorithms, and self-tunes its hyperparameter, making usage easy. Package: r-cran-fastaft Architecture: amd64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-survival Filename: pool/dists/noble/main/r-cran-fastaft_1.4-1.ca2404.1_amd64.deb Size: 34682 MD5sum: d1c145011bc800f36aee3a6936d451a2 SHA1: ace0124cf507ba0b681c56091d3ccbf3799b14e1 SHA256: 07d9f9e719789e3332d23bb7ec8f5e92af00f440f73a2709b865374a988fe90f SHA512: 669690c97ec27593f83846491c1a9a9d3209e696226ff343e9fcba867c5cfcc30299b23c1910b9b46514adb64a85ef0333f4f27108b35aa4f27e722753ec3682 Homepage: https://cran.r-project.org/package=fastAFT Description: CRAN Package 'fastAFT' (Fast Regression for the Accelerated Failure Time (AFT) Model) Fast censored linear regression for the accelerated failure time (AFT) model of Huang (2013) . Package: r-cran-fastbandchol Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-fastbandchol_0.1.1-1.ca2404.1_amd64.deb Size: 67338 MD5sum: b13999fa8051447f277641aa8f267f9b SHA1: 27857292205052a6c2fe6d640118cdca8055247c SHA256: 8151c510bfedcbade37b7a390d7c7d9aae1c15fb8134bf4e879e4a32ba399082 SHA512: 9b3da403ff4a2646cad87ea5ab6136e72016644b49a3f103766a00545da7bd299b8971195bba778198ecb3996e704675b1adfb8d147c887b5ab7dce64fa661ed Homepage: https://cran.r-project.org/package=FastBandChol Description: CRAN Package 'FastBandChol' (Fast Estimation of a Covariance Matrix by Banding the CholeskyFactor) Fast and numerically stable estimation of a covariance matrix by banding the Cholesky factor using a modified Gram-Schmidt algorithm implemented in RcppArmadilo. See for details on the algorithm. Package: r-cran-fastbeta Architecture: amd64 Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-adaptivetau, r-cran-desolve Filename: pool/dists/noble/main/r-cran-fastbeta_0.5.1-1.ca2404.1_amd64.deb Size: 237008 MD5sum: a3297fb94689bbb9b4fab32b61fcbd9e SHA1: 366684180367d920243215784acffb1b442d5f3e SHA256: 25b7c54c7bb8979e9480caa000b46e6ec81ce27162050812d607a99bc3b09bd3 SHA512: c5a7c2bf913deb93a66809f1337e28599dea1e371f6db2cae0236f668f89ee35e69e96a040468ad53ec725b4c072315adf84e754968287f93c2c91c322af9f55 Homepage: https://cran.r-project.org/package=fastbeta Description: CRAN Package 'fastbeta' (Fast Approximation of Time-Varying Infectious DiseaseTransmission Rates) A fast method for approximating time-varying infectious disease transmission rates from disease incidence time series and other data, based on a discrete time approximation of an SEIR model, as analyzed in Jagan et al. (2020) . Package: r-cran-fastcluster Architecture: amd64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fastcluster_1.3.0-1.ca2404.1_amd64.deb Size: 181896 MD5sum: cacae0f2d3de4b64b3b1188b6140b9d1 SHA1: 598b0055371bd1c862f6da3781b11d89c54963ce SHA256: 67e630786297a9f1e1acd1b040085bfc5343e6b113a055181d78e2c5417450a9 SHA512: ec6811f0dba566ed14ce796fd031025345da794abfdcf0fdfebec3d2a792cad82d3032cab789939fa2b7ee95ddca6ed1132064be59a93e002f1bfbd5bf4d0144 Homepage: https://cran.r-project.org/package=fastcluster Description: CRAN Package 'fastcluster' (Fast Hierarchical Clustering Routines for R and 'Python') This is a two-in-one package which provides interfaces to both R and 'Python'. It implements fast hierarchical, agglomerative clustering routines. Part of the functionality is designed as drop-in replacement for existing routines: linkage() in the 'SciPy' package 'scipy.cluster.hierarchy', hclust() in R's 'stats' package, and the 'flashClust' package. It provides the same functionality with the benefit of a much faster implementation. Moreover, there are memory-saving routines for clustering of vector data, which go beyond what the existing packages provide. For information on how to install the 'Python' files, see the file INSTALL in the source distribution. Based on the present package, Christoph Dalitz also wrote a pure 'C++' interface to 'fastcluster': . Package: r-cran-fastcmh Architecture: amd64 Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1099 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bindata, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fastcmh_0.2.7-1.ca2404.1_amd64.deb Size: 159180 MD5sum: 768667cf85d1e60fe0e14e9429322cab SHA1: 2afc3ea4e838821c72bf1b7c9fb878cd0328a7d7 SHA256: b7ea5736c7f42f72156e3ad021e493b971fbfbd38d1243f4fb4d3d89d9153463 SHA512: 3457d4b5984ed429457b3cd215375bb3daa04adb2c565c15906fbe3341b69cfe4f9ae7345ca8c71a8de46e7d8139b16c80163da05e8adf8dfb228f02dba2106f Homepage: https://cran.r-project.org/package=fastcmh Description: CRAN Package 'fastcmh' (Significant Interval Discovery with Categorical Covariates) A method which uses the Cochran-Mantel-Haenszel test with significant pattern mining to detect intervals in binary genotype data which are significantly associated with a particular phenotype, while accounting for categorical covariates. Package: r-cran-fastcmprsk Architecture: amd64 Version: 1.26.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dynpred, r-cran-foreach, r-cran-survival, r-cran-matrix Suggests: r-cran-testthat, r-cran-cmprsk Filename: pool/dists/noble/main/r-cran-fastcmprsk_1.26.1-1.ca2404.1_amd64.deb Size: 111064 MD5sum: b8d19fd5551842934c049e4a13261dc5 SHA1: 1f35308f1d6d185de4d74c7182db733021776d1e SHA256: a21c0c0cc838f8e502ab0344b82abf440f925dd4831f1b03fc5d2748e2a707c3 SHA512: 4244a35aed464242278e8b0ff9ea68ee47977992cdc031e0be56f3234ad8eef6f350088c667512f1720a3ca795d0681ffb72102cd0b5b3bc0e8f9406a00548a8 Homepage: https://cran.r-project.org/package=fastcmprsk Description: CRAN Package 'fastcmprsk' (Fine-Gray Regression via Forward-Backward Scan) In competing risks regression, the proportional subdistribution hazards (PSH) model is popular for its direct assessment of covariate effects on the cumulative incidence function. This package allows for both penalized and unpenalized PSH regression in linear time using a novel forward-backward scan. Penalties include Ridge, Lease Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Plus (MCP), and elastic net . Package: r-cran-fastcox Architecture: amd64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-fastcox_1.1.4-1.ca2404.1_amd64.deb Size: 128678 MD5sum: dbe64cfa3c0664c272bebce37dbb4b82 SHA1: fee598f9b0a942d4fe70055e04b29213010aac04 SHA256: 6583a3fb96525b0043afa9bbd0287a38521577a0c6a2d3a3fab9bf57945b944b SHA512: 6f99ee933efa8ff3c0001cf91625edbae1ec444874a83dcc68d846595ebc81e6c964d1eaaca9b16f75af40a35d3a2c8b992afe8cebee96958d70e79a18ea48b0 Homepage: https://cran.r-project.org/package=fastcox Description: CRAN Package 'fastcox' (Lasso and Elastic-Net Penalized Cox's Regression in HighDimensions Models using the Cocktail Algorithm) We implement a cocktail algorithm, a good mixture of coordinate decent, the majorization-minimization principle and the strong rule, for computing the solution paths of the elastic net penalized Cox's proportional hazards model. The package is an implementation of Yang, Y. and Zou, H. (2013) . Package: r-cran-fastcpd Architecture: amd64 Version: 0.16.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7046 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-matrix, r-cran-rcpp, r-cran-progress, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-testthat Suggests: r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-matrixstats, r-cran-mvtnorm, r-cran-rmarkdown, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-fastcpd_0.16.2-1.ca2404.1_amd64.deb Size: 4435214 MD5sum: 5e4c6bb9727f3ad7e32ab910f905b737 SHA1: 582d5ed8edba6ca322b813a01a0e38a6ffaa34bd SHA256: 29d48f4cbaeec988e2957f51e66d302f37388f19a067fccadc07a1344158cf27 SHA512: f81bcb960e1f41594718a19d999cd19a5699fa198616b770903925287e4fee5df86fc60070c61a6d6215b2eecd6c68e8eec01f451fb4a3f19f6d742ee5a7a148 Homepage: https://cran.r-project.org/package=fastcpd Description: CRAN Package 'fastcpd' (Fast Change Point Detection via Sequential Gradient Descent) Implements fast change point detection algorithm based on the paper "Sequential Gradient Descent and Quasi-Newton's Method for Change-Point Analysis" by Xianyang Zhang, Trisha Dawn . The algorithm is based on dynamic programming with pruning and sequential gradient descent. It is able to detect change points a magnitude faster than the vanilla Pruned Exact Linear Time(PELT). The package includes examples of linear regression, logistic regression, Poisson regression, penalized linear regression data, and whole lot more examples with custom cost function in case the user wants to use their own cost function. Package: r-cran-fastdigest Architecture: amd64 Version: 0.6-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-runit Filename: pool/dists/noble/main/r-cran-fastdigest_0.6-4-1.ca2404.1_amd64.deb Size: 16370 MD5sum: 98504a1ad60eb03987a00176fc00d618 SHA1: de27842d64d0f5917cda8c811f32f82655a89fdf SHA256: 25d9ca5ecf4d908c4425c7f1baf6e6fb70e138f4fe025124ac2eda53827b18dd SHA512: 2bd3ff1e2915039ba98bba34a0b73657c03897c5ae2b72ed48180688bff544444eaac6192f77874530d98c5478b4e1e61798805504450825dec0989f4e4609e2 Homepage: https://cran.r-project.org/package=fastdigest Description: CRAN Package 'fastdigest' (Fast, Low Memory Footprint Digests of R Objects) Provides an R interface to Bob Jenkin's streaming, non-cryptographic 'SpookyHash' hash algorithm for use in digest-based comparisons of R objects. 'fastdigest' plugs directly into R's internal serialization machinery, allowing digests of all R objects the serialize() function supports, including reference-style objects via custom hooks. Speed is high and scales linearly by object size; memory usage is constant and negligible. Package: r-cran-fastei Architecture: amd64 Version: 0.0.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1961 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-reshape2, r-cran-viridis, r-cran-dplyr, r-cran-qpdf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fastei_0.0.19-1.ca2404.1_amd64.deb Size: 1540940 MD5sum: abf61a9fa024cf1f7bf185058ad95d17 SHA1: 04d8449e3c210b0fd0072fe690a0a3c172bbeffd SHA256: 71e6d00bc6ccd3dbeedfe7d4c0f31bea7e1c76c3cf948270a5c00de7cec34b96 SHA512: d9bfb826a47d183276e9238ba93129d2ad8586ec0d55ef83c9313b7c0ea0aa36451212d05f6aaef76681177b1f5c2570dd553ddfd0925eb369799673d3d5e0cf Homepage: https://cran.r-project.org/package=fastei Description: CRAN Package 'fastei' (Methods for ''A Fast Alternative for the R x C EcologicalInference Case'') Estimates the probability matrix for the R×C Ecological Inference problem using the Expectation-Maximization Algorithm with four approximation methods for the E-Step, and an exact method as well. It also provides a bootstrap function to estimate the standard deviation of the estimated probabilities. In addition, it has functions that aggregate rows optimally to have more reliable estimates in cases of having few data points. For comparing the probability estimates of two groups, a Wald test routine is implemented. The library has data from the first round of the Chilean Presidential Election 2021 and can also generate synthetic election data. Methods described in Thraves, Charles; Ubilla, Pablo; Hermosilla, Daniel (2024) ''A Fast Ecological Inference Algorithm for the R×C case'' . Package: r-cran-fasterelasticnet Architecture: amd64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3394 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fasterelasticnet_1.1.2-1.ca2404.1_amd64.deb Size: 3330014 MD5sum: 34bc52f6c92772ca10068a17cd3b8fdc SHA1: 8d5eee8d82c910cc9855c8f58b9ec242ce2dc819 SHA256: 39835373982438160bcd133022561ad9cfd9df09b03e8026c73a5d9581bbe1e6 SHA512: 502aab32abcbbd059b3b34402513f41cc9477dbb9014ebb77e37984b6895f87fbd07c6351805872c2913049b4c8f431f2353bd9c531d23aa74c9a6e108b55a69 Homepage: https://cran.r-project.org/package=fasterElasticNet Description: CRAN Package 'fasterElasticNet' (An Amazing Fast Way to Fit Elastic Net) Fit Elastic Net, Lasso, and Ridge regression and do cross-validation in a fast way. We build the algorithm based on Least Angle Regression by Bradley Efron, Trevor Hastie, Iain Johnstone, etc. (2004)() and some algorithms like Givens rotation and Forward/Back Substitution. In this way, many matrices to be computed are retained as triangular matrices which can eventually speed up the computation. The fitting algorithm for Elastic Net is written in C++ using Armadillo linear algebra library. Package: r-cran-fasterize Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 719 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-raster, r-cran-wk, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-microbenchmark, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-geos Filename: pool/dists/noble/main/r-cran-fasterize_1.1.0-1.ca2404.1_amd64.deb Size: 437276 MD5sum: 9b063760c23acd24c7b2be441e76fca3 SHA1: 7bf538020ab382122c66057cdce158ea4b753e52 SHA256: 2f9049e50ab22bab08c0a065b56d299604bca3eff5a12a6ac63493ad23a05d75 SHA512: 6795024884ef047475cae8f3335e59c2855148ca4313b9e4f54053ff55e22779cbe0cc23cacd695d8a48133a20676b3a28e7953c86c9236c01ec98fa4a874b0e Homepage: https://cran.r-project.org/package=fasterize Description: CRAN Package 'fasterize' (Fast Polygon to Raster Conversion) Provides a drop-in replacement for rasterize() from the 'raster' package that takes polygon vector or data frame objects, and is much faster. There is support for the main options provided by the rasterize() function, including setting the field used and background value, and options for aggregating multi-layer rasters. Uses the scan line algorithm attributed to Wylie et al. (1967) . Package: r-cran-fastgasp Architecture: amd64 Version: 0.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1133 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rstiefel, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-fastgasp_0.6.4-1.ca2404.1_amd64.deb Size: 669802 MD5sum: 1e68ffc18580d67371b1e7aea34b2542 SHA1: ccd41d68ea46a43284c40b7f79c55153814e8cc6 SHA256: e3ce0f21a9f2c6182b4dc378001c480667081200d9c4aebb6318883860f24d3a SHA512: 1056f75dd9dd7c992ccd1583b914ee082619ba6043d420225dc41d9ef9435f2001a328a31bdedf298e93aa6fe5486a7156127ec0e3ed5c1f7c883106c01fa9df 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. 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Package: r-cran-fastgeojson Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1213 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-jsonlite, r-cran-sf, r-cran-leaflet Filename: pool/dists/noble/main/r-cran-fastgeojson_0.1.3-1.ca2404.1_amd64.deb Size: 855206 MD5sum: 65185d0da5532be721bd3a080347f743 SHA1: cce57f0f3a68f5c4173eb39caecf7da7cc628846 SHA256: 39b3ba44458225b4b2cfbaf5d4837444b3c95654218198e5adb7c3d4f5647cdf SHA512: 12fb97fdaf6d385359132cb895bfb4ecf21e521d0326523282422d68d37f0532570bba1191bd234fdda76f607cfdf079d4dea4820b8b9ca075bc95883d4745a7 Homepage: https://cran.r-project.org/package=fastgeojson Description: CRAN Package 'fastgeojson' (High-Performance 'GeoJSON' and 'JSON' Serialization) Converts R data frames and 'sf' spatial objects into 'JSON' and 'GeoJSON' strings. The core encoders are implemented in 'Rust' using the 'extendr' framework and are designed to efficiently serialize large tabular and spatial datasets. Returns serialized 'JSON' text, allowing applications such as 'shiny' or web APIs to transfer data to client-side 'JavaScript' libraries without additional encoding overhead. Package: r-cran-fastghquad Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-fastghquad_1.0.1-1.ca2404.1_amd64.deb Size: 50850 MD5sum: 02045baac0b05d6293dfcd027dfb34cc SHA1: c9478339e52d47215df25af60ef716749fcdf2e8 SHA256: 2b97c3bfd18d694b7cb57ebfddea7904408eb168f94107958fda949441f0716e SHA512: 4a1b93c70ecdcfbd1df92ee2ddc15b8c0986def1535263f2b29ce615172e4d60fb7432e419524117faf2e2b79d6c11bccc08cc02296a6845183b1bde3f3b33e0 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. 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The second is a fast 'GLCM' 'RcppArmadillo' implementation which is parallelized (using 'OpenMP') with the option to return all 'GLCM' features at once. For more information, see "Artifact-Free Thin Cloud Removal Using Gans" by Toizumi Takahiro, Zini Simone, Sagi Kazutoshi, Kaneko Eiji, Tsukada Masato, Schettini Raimondo (2019), IEEE International Conference on Image Processing (ICIP), pp. 3596-3600, . 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Package: r-cran-fastgp Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 565 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-mvtnorm, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-fastgp_1.3-1.ca2404.1_amd64.deb Size: 405506 MD5sum: 4c88c73c29ea27a156c9f29dd3040c74 SHA1: 23035791d7af00639ad46a7a5eaa3a0b7c9eaf4f SHA256: 2cd9c2086b107c7acdc189fa0c51b97cc29cce1f32c6e5326a49e1e3609c3b8a SHA512: c458c2c2cfedcc3ecbcf13519affade1cc0f5cd4c9e6a56870516b4bb0f4def46a18a5bb7d923ffb716af214e13dfefe8031d65351b65ebb7bc4102dda85985f Homepage: https://cran.r-project.org/package=FastGP Description: CRAN Package 'FastGP' (Efficiently Using Gaussian Processes with Rcpp and RcppEigen) Contains Rcpp and RcppEigen implementations of matrix operations useful for Gaussian process models, such as the inversion of a symmetric Toeplitz matrix, sampling from multivariate normal distributions, evaluation of the log-density of a multivariate normal vector, and Bayesian inference for latent variable Gaussian process models with elliptical slice sampling (Murray, Adams, and MacKay 2010). Package: r-cran-fasthamming Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: libc6 (>= 2.4), libgomp1 (>= 6), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fasthamming_1.2-1.ca2404.1_amd64.deb Size: 14420 MD5sum: 6e92b6399bf3531ba0d825bf17b6fd6e SHA1: 2ba1e05204f7fa8de4d7df652f02e834f9448c7a SHA256: e4f9679ae0c94213e9ec302cbfe3e6c866f302131ab324ad85fe5bdd44536bad SHA512: 9c14a6fbc6e3b0f2f207acc62c42f3745a483d1cc0f50292f28e6f8121e47b156cea0def3e9255e2fc0004baf5ed534d1d6afd0f9d2c248fc4f11fca55f6676b 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: amd64 Version: 1.2-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-fastica_1.2-7-1.ca2404.1_amd64.deb Size: 42560 MD5sum: 130983962185a52b51a2b00af4a053ff SHA1: f9bc3147c04efab7325a9219100f63f85ac46efa SHA256: 6b89d73597c302f78e9be9b4278e5d640056517f397c91b17e72e1fb094a0cde SHA512: 4186ac126136172b9e90dc97bd538d045f9fe7b2eb4a1b35dadc97a1b98e4fcd6491635125a7e9d2b10bebc6d029ddc7c88a64840c3e080dc8a4f624f90a2d70 Homepage: https://cran.r-project.org/package=fastICA Description: CRAN Package 'fastICA' (FastICA Algorithms to Perform ICA and Projection Pursuit) Implementation of FastICA algorithm to perform Independent Component Analysis (ICA) and Projection Pursuit. 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Package: r-cran-fastjt Architecture: amd64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-fastjt_1.0.8-1.ca2404.1_amd64.deb Size: 376756 MD5sum: ac7741f46397830ff305d56e2cfee022 SHA1: 65607f93ffc6edc066ab2de23ca6e35fe3b2c2f2 SHA256: f9c6e8e16cd2397613ac5347716d3a180c4da7cf3d1d547697daa4d9c990b85e SHA512: aff38df0491c933cad5b6d2abf4e9e6a0c673b013b9337beebca2c212b40de00360ec1013cbf2a2d579d4e5564b98675dc6845ac74fcf63e96e597b03a570d81 Homepage: https://cran.r-project.org/package=fastJT Description: CRAN Package 'fastJT' (Efficient Jonckheere-Terpstra Test Statistics for Robust MachineLearning and Genome-Wide Association Studies) This 'Rcpp'-based package implements highly efficient functions for the calculation of the Jonckheere-Terpstra statistic. 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Package: r-cran-fastkmedoids Architecture: amd64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fastkmedoids_1.6-1.ca2404.1_amd64.deb Size: 84746 MD5sum: cdca08ce725852868e07136043a86278 SHA1: 4e96c7dbf706276322a8198622bc502a7b0ba51d SHA256: 7cf299eedae986526d5465285f5021ec5f95966032f8c74f3dad1827153a1965 SHA512: cc8c894d59680d9e62b5c9e4fd4d7e19f2cf07b1a490076fab606b5b53c96e57c78340faba91508e69b86872529e08bca7ee8e2a5b5f55d733e19a9933a8b3ef Homepage: https://cran.r-project.org/package=fastkmedoids Description: CRAN Package 'fastkmedoids' (Faster K-Medoids Clustering Algorithms: FastPAM, FastCLARA,FastCLARANS) R wrappers of C++ implementation of Faster K-Medoids clustering algorithms (FastPAM, FastCLARA and FastCLARANS) proposed in Erich Schubert, Peter J. Rousseeuw 2019 . 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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) . 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Package: r-cran-fastliu Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-fastliu_1.0-1.ca2404.1_amd64.deb Size: 226038 MD5sum: 4af15e7d1f0dcf1e7bb5f779ed826376 SHA1: 760411704a76d1da02b0e9c9d175879de92cc701 SHA256: 71c1717852f25ad7c0f6cf1366947417f1ae37d313a2e09027a6b3d5889ee55f SHA512: 0400630361b884bf39dd4288fddad30dd9b1df70b20e8595ef83d106eae1efad035c5937344bd6cdf83edc76b32e93f50af4d02fdff029e2d85660f632508001 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-fastvoter Architecture: amd64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fastvoter_0.0.3-1.ca2404.1_amd64.deb Size: 101906 MD5sum: 76e06aadb1b5c34ccb952966620edb34 SHA1: 02f6d2c74aae9937219378552599bd2c2807fe94 SHA256: 172b393d96d58835e42fdabe8d6017edb48a3650c3143459a21609e4ca808faf SHA512: f2d7827fbb7e5244c330820fefc14a31c617d04172ec5c39fe7d7d02e11a334b5a174fa194ae169a1ca1b447dd0b492a2ebae56e73f86f7a0aefc45d487b0d66 Homepage: https://cran.r-project.org/package=fastVoteR Description: CRAN Package 'fastVoteR' (Efficient Voting Methods for Committee Selection) A fast 'Rcpp'-based implementation of polynomially-computable voting theory methods for committee ranking and scoring. 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Package: r-cran-fastwavelets Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-fastwavelets_1.0.1-1.ca2404.1_amd64.deb Size: 98544 MD5sum: ab9ade8033493688d4a1bbd94f51f234 SHA1: 56b080c2e0977b469a3a4c4ff231a8ed30d6cc0e SHA256: 92afc67f854d483c60e3318393f040fb8a212be6a689db452fde7e209c764a5b SHA512: 1cb7f9bb789bb02c0f926f65fa9a8f83d89af3cbf4df9fc711561dadf1f82414a9de7a0253e5cb8bf611fee1c913fac5d1cba9e54e05fc9df77c81ebdd5b7c5f Homepage: https://cran.r-project.org/package=fastWavelets Description: CRAN Package 'fastWavelets' (Compute Maximal Overlap Discrete Wavelet Transform (MODWT) and ÀTrous Discrete Wavelet Transform) A lightweight package to compute Maximal Overlap Discrete Wavelet Transform (MODWT) and À Trous Discrete Wavelet Transform by leveraging the power of 'Rcpp' to make these operations fast. This package was designed for use in forecasting, and allows users avoid the inclusion of future data when performing wavelet decomposition of time series. See Quilty and Adamowski (2018) . Package: r-cran-fastymd Architecture: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-fasttime, r-cran-lubridate, r-cran-microbenchmark, r-cran-tinytest, r-cran-ymd, r-cran-litedown Filename: pool/dists/noble/main/r-cran-fastymd_0.1.5-1.ca2404.1_amd64.deb Size: 37862 MD5sum: a11f67feda47fd6c4cc64eb94d9f2ecd SHA1: 3682586417e169f44cde8bc1afbbf58d8891e23f SHA256: 583c55e7da140d1495698ecc73ba2b35a7f2b8e667c17a64c32a3a81131ab4b8 SHA512: c9ba5bbfd1a9ac5d431f0a883a038a17faf8a53c874d7b50d6899c864a82997cf9fce1917babea60b11b3603d1b7b8df48d44019aeccf7fa5988c41eb7586079 Homepage: https://cran.r-project.org/package=fastymd Description: CRAN Package 'fastymd' (Fast Utilities for Year Month Day Objects) A collection of utility functions for working with Year Month Day objects. 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Package: r-cran-faulttree Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-magrittr Filename: pool/dists/noble/main/r-cran-faulttree_1.0.1-1.ca2404.1_amd64.deb Size: 261350 MD5sum: c73d28ec9e996471b8308a6f692cdf21 SHA1: 517656dd0242a5a02efebdab6ec8296af29bb330 SHA256: cf4fb22ecedc6796ab8e481fa49427af97db775d6ef069638dbadfc1f55e375c SHA512: d6ccc2bd306a8d439670ed4970caa7c15c757140828c340f5b6be92cf00b0076ad5084d82684c5829632515f7be60773a9240c71e6bea1375c86bf6319258b1b Homepage: https://cran.r-project.org/package=FaultTree Description: CRAN Package 'FaultTree' (Fault Trees for Risk and Reliability Analysis) Construction, calculation and display of fault trees. Methods derived from Clifton A. 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(2017) . Provides automated parameter optimization, multi-prey diet modeling, and comprehensive energy budget simulations for fisheries research and aquaculture applications. An optional 'TMB' (Template Model Builder) backend delivers 10-50x speedup in maximum likelihood estimation while maintaining full backward compatibility. Includes species-specific parameter databases and tools for modeling fish growth, consumption, and metabolism under varying environmental conditions. Package: r-cran-fbasics Architecture: amd64 Version: 4052.98-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2841 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-timedate, r-cran-timeseries, r-cran-mass, r-cran-spatial, r-cran-gss, r-cran-stabledist Suggests: r-cran-interp, r-cran-runit Filename: pool/dists/noble/main/r-cran-fbasics_4052.98-1.ca2404.1_amd64.deb Size: 2473944 MD5sum: b8503ebf005224da6bcdb07b6074abe6 SHA1: c15f1e032ef99cc693b2842600509e398bb3eea7 SHA256: 76e7cfd98ecde488c7db3524218903fbf37dad3cd7e4625c9295b6599d05deed SHA512: 76142cb8f9298a9097bd3143c8466921b0595de6b34f74fcce726704797a91ec4345bb83df4f1e7a177e62c159a33e718132f90605ff74af3f85a8bd7007a64d Homepage: https://cran.r-project.org/package=fBasics Description: CRAN Package 'fBasics' (Rmetrics - Markets and Basic Statistics) Provides a collection of functions to explore and to investigate basic properties of financial returns and related quantities. 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Package: r-cran-fbati Architecture: amd64 Version: 1.0-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 658 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-pbatr, r-cran-fgui, r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-fbati_1.0-11-1.ca2404.1_amd64.deb Size: 462734 MD5sum: 0e942d7014c26d8b81f8f4cab6aa5e3e SHA1: 6abec9527767f04711679071bf75cb6374168e02 SHA256: 647d2031d7624154142335d0f3e18da2b5fdf648cf2cb8d1a4f21ef84bef6b84 SHA512: 692ed54e36b008a666d3ea2294265b2ead65bbe3fe9b598349cefe886169bbf4fb3f845de1bfa16e4998dee65db771d1fc1a9c588da474bccea9f7be5351901c Homepage: https://cran.r-project.org/package=fbati Description: CRAN Package 'fbati' (Gene by Environment Interaction and Conditional Gene Tests forNuclear Families) Does family-based gene by environment interaction tests, joint gene, gene-environment interaction test, and a test of a set of genes conditional on another set of genes. Package: r-cran-fbcrm Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-fbcrm_1.1-1.ca2404.1_amd64.deb Size: 144814 MD5sum: f4e799e02d43aed53025b909d9df7327 SHA1: 120c6f5152e1419778ab586570422c07b783440c SHA256: eed6079c38831b38ebff3d911fa44b958e9f7e92182ef9715fa3307c6ddf8e12 SHA512: 2d252817304606c5db97cf38468ce93741bcceed64f3a2eaea4e16ed60f8e993dd997b8b3b6750be960321a3954b774bf4cd59432ad4ba897b783cf83d2eda4f Homepage: https://cran.r-project.org/package=FBCRM Description: CRAN Package 'FBCRM' (Phase I Optimal Dose Assignment using the FBCRM and MFBCRMMethods) Performs dose assignment and trial simulation for the FBCRM (Fully Bayesian Continual Reassessment Method) and MFBCRM (Mixture Fully Bayesian Continual Reassessment Method) phase I clinical trial designs. 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Package: r-cran-fbfsearch Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3841 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-fbfsearch_1.3-1.ca2404.1_amd64.deb Size: 3763098 MD5sum: ca7e9031b0e86744ec658f9f8d0eaf79 SHA1: 88fd437a087097575eaa4d804352aaa518a2a659 SHA256: 9b3e94d5031ecd568508136b0481687d809492c9523963a0733daba22ee9c2f6 SHA512: 719930d9a4f9479717bcf864c858f3fba6be352eed89e2fe13d18a9145d456dcb766a43fffc0a0d167f3048526117ec09776b115c32602083530feae9ae1653d Homepage: https://cran.r-project.org/package=FBFsearch Description: CRAN Package 'FBFsearch' (Algorithm for Searching the Space of Gaussian Directed AcyclicGraph Models Through Moment Fractional Bayes Factors) We propose an objective Bayesian algorithm for searching the space of Gaussian directed acyclic graph (DAG) models. 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Package: r-cran-fbms Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5164 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-fastglm, r-cran-gensa, r-cran-r2r, r-cran-bas, r-cran-tolerance Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-lme4, r-cran-kernlab, r-cran-mvtnorm, r-cran-caic4 Filename: pool/dists/noble/main/r-cran-fbms_1.3-1.ca2404.1_amd64.deb Size: 4980766 MD5sum: e10c1a4f828e63235f7b2d2de9d04779 SHA1: 8a470e63c7da451d9ee32cbc9675638669d80e72 SHA256: c5b20290d947be14f4bfe5c0e5c6f4c36bc3f9e3d67858af05ff814c624ee795 SHA512: 960d21653138b6837308df598a1789321bb2b2267cb66f2e5fd9c1282143a9785f300f096b64646a41c913cabad8e1bf2d06c7013d423134026a5367499bcbc6 Homepage: https://cran.r-project.org/package=FBMS Description: CRAN Package 'FBMS' (Flexible Bayesian Model Selection and Model Averaging) Implements the Mode Jumping Markov Chain Monte Carlo algorithm described in and its Genetically Modified counterpart described in as well as the sub-sampling versions described in for flexible Bayesian model selection and model averaging. 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The analysis of paired receiver operating curves is supported as well, so that a comparison of two predictors is possible. You can also plot the results and calculate confidence intervals. On a typical desktop computer the time needed for the calculation of 100000 bootstrap replicates given 500 observations requires time on the order of magnitude of one second. Package: r-cran-fcar Architecture: amd64 Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3246 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-matrix, r-cran-r6, r-cran-rlang, r-cran-rcpp, r-cran-registry, r-cran-settings, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-purrr, r-cran-cli, r-cran-bh Suggests: r-cran-arules, r-cran-covr, r-cran-dt, r-cran-fractional, r-cran-knitr, r-cran-markdown, r-cran-miniui, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat, r-cran-tictoc, r-cran-tikzdevice, r-cran-tinytex, r-cran-ggplot2, r-cran-ggraph, r-cran-igraph, r-cran-rstudioapi, r-cran-yaml, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-fcar_1.5.0-1.ca2404.1_amd64.deb Size: 1817964 MD5sum: 3c205d28cd4caeb63a78f9e4c723c849 SHA1: ba20c444672ae81e31aa950f7b078db196d4edc0 SHA256: 26dc37f4d842d666260db2821ff5c631b1e4d19ce75b1153701876401aa170c2 SHA512: 31af922a110b6bc202c6708d8ecd7890dc8aa86e02a0111d0651bf1c18351ea89555d1da5e10a4c6fa054562098758f19a5948da767ab52b9a8c2c9adbb5bc0b Homepage: https://cran.r-project.org/package=fcaR Description: CRAN Package 'fcaR' (Formal Concept Analysis) Provides tools to perform fuzzy formal concept analysis, presented in Wille (1982) and in Ganter and Obiedkov (2016) . 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Package: r-cran-fchange Architecture: amd64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1846 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fastmatrix, r-cran-fda, r-cran-ftsa, r-cran-ggplot2, r-cran-ggpubr, r-cran-mass, r-cran-plot3d, r-cran-plotly, r-cran-rainbow, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rfast, r-cran-sandwich, r-cran-scales, r-cran-tensora, r-cran-tidyr, r-cran-vars Suggests: r-cran-compquadform, r-cran-fda.usc, r-cran-forecast, r-cran-fundata, r-cran-jmuoutlier, r-cran-knitr, r-cran-lattice, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fchange_2.1.0-1.ca2404.1_amd64.deb Size: 1670326 MD5sum: 7513af24d056572a6437bbfe3457db10 SHA1: 52a4a6ea798bc54294f5372db4ed7e824858c774 SHA256: 217ee86f6d44186be96b7021617a84e65e53e41ecfbed83ccb2204683ba9ee74 SHA512: 81357ef7d4c54268eef29c2f1c4f75abb240983900ab17a159b4c104937a3ba88ce55b5e31bdbec314d461bd3b4e4ab2a59adf286044e41e604c91fde19c332f Homepage: https://cran.r-project.org/package=fChange Description: CRAN Package 'fChange' (Functional Change Point Detection and Analysis) Analyze functional data and its change points. Includes functionality to store and process data, summarize and validate assumptions, characterize and perform inference of change points, and provide visualizations. Data is stored as discretely collected observations without requiring the selection of basis functions. For more details see chapter 8 of Horvath and Rice (2024) . Additional papers are forthcoming. Focused works are also included in the documentation of corresponding functions. 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It includes functions for bond-related indicators, such as yield to maturity ('YTM'), modified duration, and Macaulay duration, as well as functions for calculating time-weighted and money-weighted rates of return (using 'Modified Dietz' method) for multiple portfolios, given their market values and profit and loss ('PnL') data. 'fcl' is designed to be efficient and accurate for financial analysis and computation. The methods used in this package are based on the following references: , . 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Package: r-cran-fctbases Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-fctbases_1.1.1-1.ca2404.1_amd64.deb Size: 101548 MD5sum: ab4ef0a1d8149b8f07292396631922a2 SHA1: fb9c9832ea797f942bf1e00a091830120b652f83 SHA256: 3d47a4e1a9b59422fe3e8a12f70563e5307bec32b0a7ca34062f9e8d2654d25d SHA512: 473cfee5bc4b134bb0d68ea5f94cbf8c7a6978e1d42533dce53e24e4fc3ee62b09fd3c6b93a2b785705b45fe2ad5f5522fdaf0242f93aca9b612f988fd1e544e Homepage: https://cran.r-project.org/package=fctbases Description: CRAN Package 'fctbases' (Functional Bases) Easy-to-use, very fast implementation of various functional bases. Easily used together with other packages. A functional basis is a collection of basis functions [\phi_1, ..., \phi_n] that can represent a smooth function, i.e. $f(t) = \sum c_k \phi_k(t)$. First- and second-order derivatives are also included. These are the mathematically correct ones, no approximations applied. As of version 1.1, this package includes B-splines, Fourier bases and polynomials. Package: r-cran-fcwtr Architecture: amd64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 885 Depends: libc6 (>= 2.27), libfftw3-single3 (>= 3.3.10), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-ggplot2, r-cran-hms, r-cran-viridis, r-cran-rlang, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fcwtr_0.2.1-1.ca2404.1_amd64.deb Size: 771612 MD5sum: d8254b1bc0b198bcdd2af64dabea9ab5 SHA1: 6400e4973856dbdceabfba5ef2186be7052790f2 SHA256: 6d2493407c2c9141f228ec3ac14ac61b7179b3e270e3a80a259fd07604846a63 SHA512: 42c3eb6da28e707f164b99e969805594571f950db43db15781c05f033df76276f65d2bf762696b3b27d70f210915feef21c19a6a7672c154aebe6f58e6354f43 Homepage: https://cran.r-project.org/package=fCWTr Description: CRAN Package 'fCWTr' (Fast Continuous Wavelet Transform) Enables the usage of the fast continuous wavelet transform, originally implemented in the 'C++' library 'fCWT' by Lukas Arts. See Arts, P.A. and Van den Broek, E.L. (2022) for details. The package includes simple helpers such as a plotting function. Package: r-cran-fd Architecture: amd64 Version: 1.0-12.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libc6 (>= 2.29), r-base-core (>= 4.6.0), r-api-4.0, r-cran-ade4, r-cran-ape, r-cran-geometry, r-cran-vegan Filename: pool/dists/noble/main/r-cran-fd_1.0-12.5-1.ca2404.1_amd64.deb Size: 178274 MD5sum: 30c211c7c73940718032563776e6319c SHA1: e3909ae562d913417d232076757fd8f9974f9693 SHA256: 9831bb88723b36166e974ed204a78be0b2d96dce54823e4b2aad2d819f80d60e SHA512: 30f1f1196ba87e721e37caf7b3d5429f138e64c1d1a60e5f0402e4e8597add981574b8883217aaa67e7266bd1710e67247c4b65440b71f5ba90e828d3381c6bf Homepage: https://cran.r-project.org/package=FD Description: CRAN Package 'FD' (Measuring Functional Diversity (FD) from Multiple Traits, andOther Tools for Functional Ecology) Computes different multidimensional FD indices. Implements a distance-based framework to measure FD that allows any number and type of functional traits, and can also consider species relative abundances. Also contains other useful tools for functional ecology. Package: r-cran-fda.usc Architecture: amd64 Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3210 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda, r-cran-mass, r-cran-mgcv, r-cran-knitr, r-cran-nlme, r-cran-doparallel, r-cran-iterators, r-cran-foreach, r-cran-ksamples Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fda.usc_2.2.0-1.ca2404.1_amd64.deb Size: 2969184 MD5sum: 9c59918feb93f00cb59de680fde6e000 SHA1: d71d722116845051c11cc1ade481ed33ab3bbb17 SHA256: d2f2ebc8bc375ba70c96ea1ed40f71c7c1d1088405a547afa3e964ab844a60d0 SHA512: 304dc077df9f7fec0403d50c58ca6a05e8b91a027b7e3ddbf2e76db065fa4794665edde59e280a8572e01e82b1c1365d85b98240cde03835969f9789debef183 Homepage: https://cran.r-project.org/package=fda.usc Description: CRAN Package 'fda.usc' (Functional Data Analysis and Utilities for Statistical Computing) Routines for exploratory and descriptive analysis of functional data such as depth measurements, atypical curves detection, regression models, supervised classification, unsupervised classification and functional analysis of variance. Package: r-cran-fda Architecture: amd64 Version: 6.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4989 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fds, r-cran-desolve Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-lattice Filename: pool/dists/noble/main/r-cran-fda_6.2.0-1.ca2404.1_amd64.deb Size: 2677744 MD5sum: 95f78f20f130bb54da1e6eb497e2ea80 SHA1: acd34745047d02210ea6a64f2449fd7764a742fc SHA256: 96493947781403099a1c19512eec717f0c3d274b304905ff41d99471d2fd1c03 SHA512: 2ef391c8e4f69fcc1bf9365988244b9090ba63d5335795a08baba054f4526d243b63f9abcd00c45a7e3f687f00273948f36c91d36a4e111aba3c5accead861d9 Homepage: https://cran.r-project.org/package=fda Description: CRAN Package 'fda' (Functional Data Analysis) These functions were developed to support functional data analysis as described in Ramsay, J. O. and Silverman, B. W. (2005) Functional Data Analysis. New York: Springer and in Ramsay, J. O., Hooker, Giles, and Graves, Spencer (2009). Functional Data Analysis with R and Matlab (Springer). The package includes data sets and script files working many examples including all but one of the 76 figures in this latter book. Matlab versions are available by ftp from . Package: r-cran-fdacluster Architecture: amd64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6584 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-cluster, r-cran-dbscan, r-cran-fdasrvf, r-cran-future.apply, r-cran-ggplot2, r-cran-lpsolve, r-cran-nloptr, r-cran-progressr, r-cran-rcpp, r-cran-rlang, r-cran-tibble, r-cran-rcpparmadillo Suggests: r-cran-fda, r-cran-fundata, r-cran-future, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-fdacluster_0.4.2-1.ca2404.1_amd64.deb Size: 5360858 MD5sum: 50d3c0621683cadc753bad7b637c3f76 SHA1: adf1da54a72fb570f7f7949170fa84e78dd81772 SHA256: 01d11c6714efaba298a02154972cac5fc8e660d5c4475a3a3f5f157bf02c0e85 SHA512: eee3b716332dd732c9f971bb803f7fd7ba0842f47d5759b2bdbe9ca2e20a640ecb722871b61dc7518ce91dfb9e312bc47dfc2f348abc50fbad2feeabbc012f07 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" . 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The implementations are done for both dense and sparsely observed functional data. Pointwise confidence bands can be constructed for each case. Further, the influence of past predictor values are modeled by a smooth history index function, while the effects on the response are described by smooth varying coefficient functions, which are very useful in analyzing real data such as COVID data. References: Yao, F., Müller, H.G., Wang, J.L. (2005) . Sentürk, D., Müller, H.G. (2010) . Package: r-cran-fdadensity Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3605 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-fdapace Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fdadensity_0.1.4-1.ca2404.1_amd64.deb Size: 3600522 MD5sum: b6ed96150737105d2261b94d165844e3 SHA1: 52c01dc9bbf9cc8962dcb9066d53a1de3aaa1339 SHA256: fbe0c4cfde3323d482e3cdc042101d9d672eaed8fc10c1700a4e13d38021af4d SHA512: 1847023864e5523f56f617a351d898591d237fdef4345ec214f5805594d5ba1c504266792c18895931f9392b6660d4b8a969a9263594090d18b979dda4337d14 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. 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Matrix computation are approximated by semi-explicit operator equivalents with linear computational complexity. Markussen (2013) . Package: r-cran-fdaoutlier Architecture: amd64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 790 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat, r-cran-covr, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fdaoutlier_0.2.1-1.ca2404.1_amd64.deb Size: 678870 MD5sum: b55dd421b81e0189b255ea891507a6e8 SHA1: 1abd80123276b3d202ec57fda5ef58bfbb9b1762 SHA256: 93e46c08fa878503bbc76af887e359fcc19a7adf516fec1efdadb4124356deb4 SHA512: 7b617c3cdb6596c34891a7c5d01ad11d65b1af5657a956e335c9062154f93a7af03069431434883096d77fbc473c0d7b497d6c506a9d26810805c018fe8d3d02 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: 2228 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-hmisc, r-cran-mass, r-cran-matrix, r-cran-pracma, r-cran-numderiv, r-cran-rcppeigen Suggests: r-cran-plot3d, r-cran-rgl, r-cran-aplpack, r-cran-mgcv, r-cran-ks, r-cran-gtools, r-cran-knitr, r-cran-rmarkdown, r-cran-emcluster, r-cran-minqa, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fdapace_0.6.0-1.ca2404.1_amd64.deb Size: 1583158 MD5sum: 75c9d0c0fc9b67d1d0d2005880b3c38a SHA1: 1e6e2e6339b5a05bff2e722c3e3f7efff6c8eda2 SHA256: baae6ee67f1b82e548e29ad7559fda86b1cc48bada4f89575a2738132fc0f567 SHA512: 35194a3c978f06fadf6341a2589016160f5d376f2b5afaf57a9d41ea83f2a0f3a8d277a215f4be221aaacbc08604b5aff8f0c91474740f3202ce37d3e6db7e36 Homepage: https://cran.r-project.org/package=fdapace Description: CRAN Package 'fdapace' (Functional Data Analysis and Empirical Dynamics) A versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. This core algorithm yields covariance and mean functions, eigenfunctions and principal component (scores), for both functional data and derivatives, for both dense (functional) and sparse (longitudinal) sampling designs. For sparse designs, it provides fitted continuous trajectories with confidence bands, even for subjects with very few longitudinal observations. PACE is a viable and flexible alternative to random effects modeling of longitudinal data. There is also a Matlab version (PACE) that contains some methods not available on fdapace and vice versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry). References: Wang, J.L., Chiou, J., Müller, H.G. (2016) ; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) . Package: r-cran-fdapde Architecture: amd64 Version: 1.1-21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9508 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl, r-cran-matrix, r-cran-plot3d, r-cran-rcppeigen, r-cran-rcpp Suggests: r-cran-mass, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fdapde_1.1-21-1.ca2404.1_amd64.deb Size: 2621440 MD5sum: 375a6fb4990014c705bf74108fc914ef SHA1: 15d130a1d84ded109c1f07dc6ad23e10fc5333d4 SHA256: 31efbaf35dbc27d975d8572486cc44d3fa1ad8f82c4830ad1e1ac21235d82b17 SHA512: ba801adb76c597da6ae87b4a019848cc4f5f71542d36ef4d0d1b1469ca3fd0b57b1950a881898e14b63cb7cdbb28c612a6b40e3f792d01360a4e653f6858fef0 Homepage: https://cran.r-project.org/package=fdaPDE Description: CRAN Package 'fdaPDE' (Physics-Informed Spatial and Functional Data Analysis) An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization. See Sangalli, L. M. (2021) "Spatial Regression With Partial Differential Equation Regularisation" for an overview. The release 1.1-9 requires R (>= 4.2.0) to be installed on windows machines. Package: r-cran-fdarep Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-fdapace, r-cran-hmisc, r-cran-mass, r-cran-matrix, r-cran-pracma, r-cran-numderiv, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fdarep_0.1.1-1.ca2404.1_amd64.deb Size: 197566 MD5sum: bedfa0eaa82ad61ea2b4047e6eed0a3e SHA1: f45bb3ecb4e7dad3945d4eb3a5997b378bbab4e6 SHA256: 91be7738c43385688460ae8ca9e4f4ed3714ff003f5fd2e888f246106a7bec08 SHA512: b1102b19d73f8ffdcab14cd317161dd43f6e66ed0aea7b9851f7b9ae5990f23e44528e17da0bfe80e98b90c08c5f982684b6024569d36a7be36c3ed35c109f04 Homepage: https://cran.r-project.org/package=fdarep Description: CRAN Package 'fdarep' (Two-Dimensional FPCA, Marginal FPCA, and Product FPCA forRepeated Functional Data) Provides an implementation of two-dimensional functional principal component analysis (FPCA), Marginal FPCA, and Product FPCA for repeated functional data. Marginal and Product FPCA implementations are done for both dense and sparsely observed functional data. References: Chen, K., Delicado, P., & Müller, H. G. (2017) . Chen, K., & Müller, H. G. (2012) . Hall, P., Müller, H.G. and Wang, J.L. (2006) . Yao, F., Müller, H. G., & Wang, J. L. (2005) . Package: r-cran-fdars Architecture: amd64 Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8993 Depends: libc6 (>= 2.39), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-fda.usc, r-cran-fda, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-ggforce, r-cran-gridextra, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-fdars_0.3.3-1.ca2404.1_amd64.deb Size: 3095962 MD5sum: dc60d490199bbc6052d5658958f59a39 SHA1: 5b55f94f10d6712385b6b57bdafb0e7e58463dca SHA256: eb5dec2d5ad3266d4b32a316e09c99735843c095d13a93282404343c5a5f68dc SHA512: 2bcd514a4daccd97c98894885c968d0f88c84f425a6c2353e35a81586fa6a29717caabf60d84909390d784b5fd14434a263fe9ffb3084bfe5668caea9323ca1b Homepage: https://cran.r-project.org/package=fdars Description: CRAN Package 'fdars' (Functional Data Analysis in 'Rust') Functional data analysis tools with a high-performance 'Rust' backend. Provides methods for functional data manipulation, depth computation, distance metrics, regression, and statistical testing. Supports both 1D functional data (curves) and 2D functional data (surfaces). Methods are described in Ramsay and Silverman (2005, ISBN:978-0-387-40080-8) "Functional Data Analysis" and Ferraty and Vieu (2006, ISBN:978-0-387-30369-7) "Nonparametric Functional Data Analysis". Package: r-cran-fdasp Architecture: amd64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2753 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-doparallel, r-cran-foreach, r-cran-ks, r-cran-pracma, r-cran-cvxr, r-cran-rcpparmadillo Suggests: r-cran-rcolorbrewer, r-cran-gglasso, r-cran-glmnet, r-cran-latex2exp Filename: pool/dists/noble/main/r-cran-fdasp_1.1.2-1.ca2404.1_amd64.deb Size: 1110548 MD5sum: 7fe87dfc7ea2ddd01fccfde9d3c1a283 SHA1: 37053c885d1b67b104551a33638fefecb614cd3a SHA256: 06e58c95cd35de003b57ff4f348a2b3c0508bac15272fa449bd42148fc1baca7 SHA512: f86655fcde16acacc1bc024598577bb398922a439c0773698bf5f2945a86d4f9079c27ee7365bfd0aa23fc5eb690b5707bad3d1c262a27f673099bbe35b8977b Homepage: https://cran.r-project.org/package=fdaSP Description: CRAN Package 'fdaSP' (Sparse Functional Data Analysis Methods) Provides algorithms to fit linear regression models under several popular penalization techniques and functional linear regression models based on Majorizing-Minimizing (MM) and Alternating Direction Method of Multipliers (ADMM) techniques. See Boyd et al (2010) for complete introduction to the method. Package: r-cran-fdasrvf Architecture: amd64 Version: 2.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4285 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-coda, r-cran-doparallel, r-cran-fields, r-cran-foreach, r-cran-lpsolve, r-cran-matrix, r-cran-mvtnorm, r-cran-rcpp, r-cran-rlang, r-cran-minpack.lm, r-cran-tolerance, r-cran-viridislite, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-interp, r-cran-plot3d, r-cran-plot3drgl, r-cran-rgl, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-fdasrvf_2.4.4-1.ca2404.1_amd64.deb Size: 3898054 MD5sum: 48286b97f3ce5ca71677f924f02a2abe SHA1: 949ef9d67a81a06a2117bf85b46dcf205c717d8a SHA256: 146adaf7035468a811e3d775f12a0a7ba616a9c7a68ce97105a128f054a9b5b3 SHA512: 078ffafa3b07444038cd55e3cb9e7e9e763a70b72eb0edcfe254cf640d10c271d110034d6cd4660e323bd9bc646cc605db8b92670e2f01ca2f04ece8a1d71b9e Homepage: https://cran.r-project.org/package=fdasrvf Description: CRAN Package 'fdasrvf' (Elastic Functional Data Analysis) Performs alignment, PCA, and modeling of multidimensional and unidimensional functions using the square-root velocity framework (Srivastava et al., 2011 and Tucker et al., 2014 ). This framework allows for elastic analysis of functional data through phase and amplitude separation. Package: r-cran-fddm Architecture: amd64 Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2950 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-formula, r-cran-rcppeigen Suggests: r-cran-rtdists, r-cran-rwiener, r-cran-ggplot2, r-cran-reshape2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark, r-cran-ggnewscale, r-cran-ggforce, r-cran-wienr, r-cran-emmeans, r-cran-estimability, r-cran-lmtest, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-fddm_1.0-2-1.ca2404.1_amd64.deb Size: 1512406 MD5sum: 5159a0ceb8cbabf48da51506116dedc2 SHA1: 35157a74fb928259e2c15057cd0b4e6772bc0560 SHA256: 0a47855590cadf24a01a40f1f4dc64111cc26e46b94c37cd587df91d5fb009a7 SHA512: fb3c4e5cced0dfcc0e87e8bb795218ec8d4b06b5a403c15d038b2ea9f7db639ef3f8f4538a4f734bc55b2bee2b29d5d52e5bd7e76417ae1be88b2284db99641f Homepage: https://cran.r-project.org/package=fddm Description: CRAN Package 'fddm' (Fast Implementation of the Diffusion Decision Model) Provides the probability density function (PDF), cumulative distribution function (CDF), the first-order and second-order partial derivatives of the PDF, and a fitting function for the diffusion decision model (DDM; e.g., Ratcliff & McKoon, 2008, ) with across-trial variability in the drift rate. Because the PDF, its partial derivatives, and the CDF of the DDM both contain an infinite sum, they need to be approximated. 'fddm' implements all published approximations (Navarro & Fuss, 2009, ; Gondan, Blurton, & Kesselmeier, 2014, ; Blurton, Kesselmeier, & Gondan, 2017, ; Hartmann & Klauer, 2021, ) plus new approximations. All approximations are implemented purely in 'C++' providing faster speed than existing packages. Package: r-cran-fdesigns Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 718 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-mvquad, r-cran-mvtnorm, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fdesigns_1.2-1.ca2404.1_amd64.deb Size: 513162 MD5sum: f312613c738a1cb5023634d4d98da9ee SHA1: 958ed8dee668d02ed9de4d6661f9375de512135f SHA256: 5ab74331605f46ebb60161bfc7e14775a93af391d38864542a73b8532c7aad18 SHA512: 2b381c8446ab4418cdd9bea3d044bab9092d51693c6b76b2a22eea614be05d24b280376ae68e7b97c882833d7ac89f074c71d48597aa8583b90b9464ff25be69 Homepage: https://cran.r-project.org/package=fdesigns Description: CRAN Package 'fdesigns' (Optimal Experimental Designs for Dynamic/Functional Models) Optimal experimental designs for functional linear and functional generalised linear models, for scalar responses and profile/dynamic factors. The designs are optimised using the coordinate exchange algorithm. The methods are discussed by Michaelides (2023) . Package: r-cran-fdma Architecture: amd64 Version: 2.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 720 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-forecast, r-cran-foreach, r-cran-gplots, r-cran-iterators, r-cran-itertools, r-cran-psych, r-cran-png, r-cran-rcpp, r-cran-tseries, r-cran-xts, r-cran-zoo, r-cran-rcpparmadillo Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-fdma_2.2.9-1.ca2404.1_amd64.deb Size: 599082 MD5sum: baf00a1992fe179eb19107b83464ea2b SHA1: 65bafa88c12994f4b366c47d82c49cee1fa0d433 SHA256: a480056c1b17cbe7e31c1cccfa062ade616f33f12c4b2f97181b47c3e627c6cf SHA512: d51419c4084dad46740f1a4b2ab15078b7d1fd64270afc0c99c781ad7a5136e891f8f84f640e834fbb3dc5e20adb73e52255a8902cb2fb2d40913e66c1541aae Homepage: https://cran.r-project.org/package=fDMA Description: CRAN Package 'fDMA' (Dynamic Model Averaging and Dynamic Model Selection forContinuous Outcomes) Allows to estimate dynamic model averaging, dynamic model selection and median probability model. The original methods are implemented, as well as, selected further modifications of these methods. In particular the user might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using 'Google Trends'. The code is written in a way which minimises the computational burden (which is quite an obstacle for dynamic model averaging if many variables are used). For example, this package allows for parallel computations and Occam's window approach. The package is designed in a way that is hoped to be especially useful in economics and finance. Main reference: Raftery, A.E., Karny, M., Ettler, P. (2010) . Package: r-cran-fdott Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-roi, r-cran-future.apply, r-cran-progressr, r-cran-transport, r-cran-slam, r-cran-rrapply, r-cran-rcpparmadillo Suggests: r-cran-roi.plugin.glpk, r-cran-future Filename: pool/dists/noble/main/r-cran-fdott_0.1.0-1.ca2404.1_amd64.deb Size: 190306 MD5sum: d5ba6b87f284e860823b15e7e5c69c6a SHA1: 29e36e6e83b5265f14fcb11e9ddc5f04caf49b20 SHA256: a657ab13a78a7b75577156f970a57cd957f9a65ab3c7197e6e4ec25cd537fe3e SHA512: cc7bbc20f943dd8fed8e70b9628c4259bb747774c3fc2cdf8ba2cec6c70be01e50d4e69eec7ea2220512b60bb682c1a043488ea2f3caf13710ccdfc3dd93cde0 Homepage: https://cran.r-project.org/package=FDOTT Description: CRAN Package 'FDOTT' (Optimal Transport Based Testing in Factorial Design) Perform optimal transport based tests in factorial designs as introduced in Groppe et al. (2025) via the FDOTT() function. These tests are inspired by ANOVA and its nonparametric counterparts. They allow for testing linear relationships in factorial designs between finitely supported probability measures on a metric space. Such relationships include equality of all measures (no treatment effect), interaction effects between a number of factors, as well as main and simple factor effects. Package: r-cran-fdrtool Architecture: amd64 Version: 1.2.18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fdrtool_1.2.18-1.ca2404.1_amd64.deb Size: 139106 MD5sum: 8e92c0319c2643b0a4288d9d13cc1239 SHA1: e1c58058700afc8a4a8477af050c0f51665fc4fc SHA256: 0198c4780bcb54137af0f318dc913175b461f80e4ed6c727b425a44399306f3c SHA512: fe7226d212f54420a8e34c8cb2c926a01c74a47007113988895ef3c69c6ee27c521e2afa16bea4f1653d682d636c821e28f9ac5cda228bc58cd6d7010019d3be Homepage: https://cran.r-project.org/package=fdrtool Description: CRAN Package 'fdrtool' (Estimation of (Local) False Discovery Rates and Higher Criticism) Estimates both tail area-based false discovery rates (Fdr) as well as local false discovery rates (fdr) for a variety of null models (p-values, z-scores, correlation coefficients, t-scores). The proportion of null values and the parameters of the null distribution are adaptively estimated from the data. In addition, the package contains functions for non-parametric density estimation (Grenander estimator), for monotone regression (isotonic regression and antitonic regression with weights), for computing the greatest convex minorant (GCM) and the least concave majorant (LCM), for the half-normal and correlation distributions, and for computing empirical higher criticism (HC) scores and the corresponding decision threshold. Package: r-cran-fdx Architecture: amd64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 709 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-poissonbinomial, r-cran-pracma, r-cran-discretefdr, r-cran-checkmate, r-cran-lifecycle, r-cran-rcpparmadillo Suggests: r-cran-discretetests Filename: pool/dists/noble/main/r-cran-fdx_2.0.2-1.ca2404.1_amd64.deb Size: 399354 MD5sum: 758325bf8fd5568cc8babd22f39e7df0 SHA1: d886613c1d029de9af2542b3405d28877a09fcd6 SHA256: b8b0fbea335d101449c5f0fbcbcb4fe108fcac25df76741e9230558d41e5e4b6 SHA512: f9f017d6e0528350f36042a622f6db2b53a5aaa2eca973a4feff57e3432b9126e929f0aaf9413b8cead1a503bea98abe699204295d400db1550477f7a5fa15c2 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. 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Package: r-cran-fhmm Architecture: amd64 Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4282 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-curl, r-cran-foreach, r-cran-httr, r-cran-jsonlite, r-cran-mass, r-cran-oeli, r-cran-padr, r-cran-pracma, r-cran-progress, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-devtools, r-cran-dosnow, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tseries Filename: pool/dists/noble/main/r-cran-fhmm_1.4.3-1.ca2404.1_amd64.deb Size: 3297612 MD5sum: 65c79ddae9c2f46db2072cd49ec06dba SHA1: 837f9a9129d8da97b25a28a1c6477b507ef840cb SHA256: acf57fb63b4746d305396dd41e1a7d830c456600d0078db98b92019249d31ba6 SHA512: f4c83ec23624dcac9112af5abd26c701aaadce1fb3f1624f951c5825f4ab2bf7c197a97e813e6c96e1fed3fb5d5ac37de485370ddeacd3be2c497e901c2a4ffb Homepage: https://cran.r-project.org/package=fHMM Description: CRAN Package 'fHMM' (Fitting Hidden Markov Models to Financial Data) Fitting (hierarchical) hidden Markov models to financial data via maximum likelihood estimation. See Oelschläger, L. and Adam, T. "Detecting Bearish and Bullish Markets in Financial Time Series Using Hierarchical Hidden Markov Models" (2021, Statistical Modelling) for a reference on the method. A user guide is provided by the accompanying software paper "fHMM: Hidden Markov Models for Financial Time Series in R", Oelschläger, L., Adam, T., and Michels, R. (2024, Journal of Statistical Software) . Package: r-cran-fiberld Architecture: amd64 Version: 0.1-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-vgam, r-cran-foreach, r-cran-doparallel, r-cran-rcpp, r-cran-bh, r-cran-rcpparmadillo Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-fiberld_0.1-8-1.ca2404.1_amd64.deb Size: 262026 MD5sum: 89676faa9609720943b7706b924869b5 SHA1: 638925abf392a44b9c32650eb853ec9f3822430c SHA256: ba8c1372432310e71dfcf4a2fb4554d12ac2d5e027e5554b9ccbdb40f686dfdd SHA512: fffbc70c3f5a5fcccac1bcd6c8f05b3e2c406e0a44b39c0c4e1bf436ec601c58431f7d3761b22bd7857292256a2c58289631896a715808fdba43499de264502b Homepage: https://cran.r-project.org/package=fiberLD Description: CRAN Package 'fiberLD' (Fiber Length Determination) Routines for estimating tree fiber (tracheid) length distributions in the standing tree based on increment core samples. Two types of data can be used with the package, increment core data measured by means of an optical fiber analyzer (OFA), e.g. such as the Kajaani Fiber Lab, or measured by microscopy. Increment core data analyzed by OFAs consist of the cell lengths of both cut and uncut fibres (tracheids) and fines (such as ray parenchyma cells) without being able to identify which cells are cut or if they are fines or fibres. The microscopy measured data consist of the observed lengths of the uncut fibres in the increment core. A censored version of a mixture of the fine and fiber length distributions is proposed to fit the OFA data, under distributional assumptions (Svensson et al., 2006) . The package offers two choices for the assumptions of the underlying density functions of the true fiber (fine) lenghts of those fibers (fines) that at least partially appear in the increment core, being the generalized gamma and the log normal densities. Package: r-cran-fibos Architecture: amd64 Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fs, r-cran-dplyr, r-cran-readr, r-cran-stringr, r-cran-tidyr, r-cran-reticulate, r-cran-glue Filename: pool/dists/noble/main/r-cran-fibos_1.2.3-1.ca2404.1_amd64.deb Size: 33648 MD5sum: ac76ef09303500bfd39d6be73b122b5a SHA1: c7f6f4762b10f733d25dfd399e336e4dadee2f95 SHA256: 83050ded853ce01bbc59d009be7a4c55807cfb829bd01179fd7058d43bc661bb SHA512: d1761e0a1301eaa6e46125701aed6595213675f5abe191e7bb9126e95368d1d3d3693008f5bf996bfca37a5a1b4c25de259824ddde4055264b19d4e0493d785f Homepage: https://cran.r-project.org/package=fibos Description: CRAN Package 'fibos' (Occlusion Surface Using the Occluded Surface and FibonacciOccluded Surface) The Occluded Surface (OS) algorithm is a widely used approach for analyzing atomic packing in biomolecules as described by Pattabiraman N, Ward KB, Fleming PJ (1995) . Here, we introduce 'fibos', an 'R' and 'Python' package that extends the 'OS''' methodology, as presented in Soares HHM, Romanelli JPR, Fleming PJ, da Silveira CH (2024) . Package: r-cran-fica Architecture: amd64 Version: 1.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 396 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-jade, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-bssasymp Filename: pool/dists/noble/main/r-cran-fica_1.1-3-1.ca2404.1_amd64.deb Size: 175376 MD5sum: 5792ed45ed8f419092ce2e0ececde865 SHA1: a540551a1424bacd4a8fc685fc22cca97900cd52 SHA256: 79ad4b81a8e29148aa64cb5bf978f839f0d48b9ab486e3e6f93d1576524ac499 SHA512: 03e866d5ca6b1419d8d7933b12693dc1ecb939c96d8d8d3af2b5069e40f915eafba7494882683fc710884a99a8d7bb761da896e88320b903c7a15fb94f71cbd6 Homepage: https://cran.r-project.org/package=fICA Description: CRAN Package 'fICA' (Classical, Reloaded and Adaptive FastICA Algorithms) Algorithms for classical symmetric and deflation-based FastICA, reloaded deflation-based FastICA algorithm and an algorithm for adaptive deflation-based FastICA using multiple nonlinearities. For details, see Miettinen et al. (2014) and Miettinen et al. (2017) . The package is described in Miettinen, Nordhausen and Taskinen (2018) . Package: r-cran-fido Architecture: amd64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4961 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-tidybayes, r-cran-rlang, r-cran-tidyr, r-cran-rcppeigen, r-cran-rcppnumerical, r-cran-rcppziggurat, r-cran-bh Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ape, r-cran-numderiv, r-cran-laplacesdemon, r-cran-mcmcpack, r-bioc-phyloseq Filename: pool/dists/noble/main/r-cran-fido_1.1.2-1.ca2404.1_amd64.deb Size: 3567930 MD5sum: 977b6243265da9d3a5c3f86a9ca9e12f SHA1: 5d518f7e8945b71fee41742cc164175296abd825 SHA256: 5f1b52840f043506daa274d669c9cf1c0c670e2f68fc0c3ed64da0d9862e7b80 SHA512: 0bd15a899c5afb1dd2684ad9aed8197fe58ac2e1d226c059904def44f10d67de144fe419c63141e55c7aa4e116b5d10a4c57f5531a7ea257e77f9401046b6014 Homepage: https://cran.r-project.org/package=fido Description: CRAN Package 'fido' (Bayesian Multinomial Logistic Normal Regression) Provides methods for fitting and inspection of Bayesian Multinomial Logistic Normal Models using MAP estimation and Laplace Approximation as developed in Silverman et. Al. (2022) . Key functionality is implemented in C++ for scalability. 'fido' replaces the previous package 'stray'. Package: r-cran-fields Architecture: amd64 Version: 17.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4846 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0, r-cran-spam, r-cran-viridislite, r-cran-rcolorbrewer, r-cran-maps Suggests: r-cran-mapproj Filename: pool/dists/noble/main/r-cran-fields_17.3-1.ca2404.1_amd64.deb Size: 4787614 MD5sum: bce13ce0dd660356e823771b54e47c86 SHA1: 2e35b9abc404af6f71bfc3d16993eac1c1164e85 SHA256: 20df7a050996ce463356b5c0241b976c2319c85109ec65db802159cf8f3c14ac SHA512: f7ff54f69fa0679823b3229e5b4d3f481c89b8698d015ac5649f5a21e2bb772507bd3bc04c37095fc266ea00470cb922ea3abf02ae20a0f3d352d07c5921a59d Homepage: https://cran.r-project.org/package=fields Description: CRAN Package 'fields' (Tools for Spatial Data) For curve, surface and function fitting with an emphasis on splines, spatial data, geostatistics, and spatial statistics. The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter). A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview. All graphics functions focus on using base R graphics. The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding the source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI . Development of this package was supported in part by the National Science Foundation Grant 1417857, the National Center for Atmospheric Research, and Colorado School of Mines. See the Fields URL for a vignette on using this package and some background on spatial statistics. Package: r-cran-fiestautils Architecture: amd64 Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4125 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dbi, r-cran-gdalraster, r-cran-hbsae, r-cran-josae, r-cran-mase, r-cran-nlme, r-cran-rcpp, r-cran-rcolorbrewer, r-cran-rpostgres, r-cran-rsqlite, r-cran-sae, r-cran-sf, r-cran-sqldf, r-cran-terra, r-cran-units Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-fiestautils_1.3.2-1.ca2404.1_amd64.deb Size: 4105918 MD5sum: a4708e903aa896f47da1833ed2d5c478 SHA1: fc630141b0a3fc4e1acf36d858b03632db07e454 SHA256: 82fb1e5491e743edd90a783957e456d691b45f3d47b5f3aa6113256d42af370d SHA512: ca7701995bbd4364ca6c589708da528e22dfbfd55ca8a38ed5efb769f47fe2923487139e9af38c556733813832a5cfbcaf3a84bc652a7a5db3ef77070c1ed5f4 Homepage: https://cran.r-project.org/package=FIESTAutils Description: CRAN Package 'FIESTAutils' (Utility Functions for Forest Inventory Estimation and Analysis) A set of tools for data wrangling, spatial data analysis, statistical modeling (including direct, model-assisted, photo-based, and small area tools), and USDA Forest Service data base tools. These tools are aimed to help Foresters, Analysts, and Scientists extract and perform analyses on USDA Forest Service data. Package: r-cran-filearray Architecture: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1114 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-digest, r-cran-fastmap, r-cran-rcpp, r-cran-uuid, r-cran-bh Suggests: r-cran-bit64, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-filearray_0.2.2-1.ca2404.1_amd64.deb Size: 592012 MD5sum: 20ff29b355d3a5619a9be8fc8c493365 SHA1: 3e176a7c3dc55e6424343b6b43ead95cd4d3c6dd SHA256: 037b894a439ac66722511669d9deac54195336de2b8fec826e45a8fedb211508 SHA512: b165d6ead5fcbd54446fc3be6130f4adff79fd76b6a0465682ed92ecacca791c4cdcffded841a6280195211238f9a83dba9fa79dcae6270dde75dcd57592749d Homepage: https://cran.r-project.org/package=filearray Description: CRAN Package 'filearray' (File-Backed Array for Out-of-Memory Computation) Stores large arrays in files to avoid occupying large memories. Implemented with super fast gigabyte-level multi-threaded reading/writing via 'OpenMP'. Supports multiple non-character data types (double, float, complex, integer, logical, and raw). Package: r-cran-filehash Architecture: amd64 Version: 2.4-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest Filename: pool/dists/noble/main/r-cran-filehash_2.4-6-1.ca2404.1_amd64.deb Size: 357230 MD5sum: b68ba8822055f1d9cd36551a9e9b58b0 SHA1: 5dd2fe041bf1c55bece33b830ec45972db008884 SHA256: 2b4fd87c89f04362b1a4dcc92095ec2fcd8caa78f8a5f70df89ef0c33e3c424b SHA512: c1c47f3e1424dddb309cd1ac7dc9e2d1a75127c56fa8b91f6438c02baf25a9edbad481487a2362b60647ee760acf13ccd89323f1e161e55259a05cc4ca7abd64 Homepage: https://cran.r-project.org/package=filehash Description: CRAN Package 'filehash' (Simple Key-Value Database) Implements a simple key-value style database where character string keys are associated with data values that are stored on the disk. A simple interface is provided for inserting, retrieving, and deleting data from the database. Utilities are provided that allow 'filehash' databases to be treated much like environments and lists are already used in R. These utilities are provided to encourage interactive and exploratory analysis on large datasets. Three different file formats for representing the database are currently available and new formats can easily be incorporated by third parties for use in the 'filehash' framework. Package: r-cran-filelock Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-callr, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-filelock_1.0.3-1.ca2404.1_amd64.deb Size: 25534 MD5sum: a446ee68e07caae3005f57d21c0b5feb SHA1: 8cca51ea23a0491ddf3fd0b071a471c1c03a7aa8 SHA256: 1fee6f9316ea91b33763bb7b34a2be46062c23eb04f7e8e589229ceee57c9d1b SHA512: fc6453b9ad66696533a5d38f0ec0ebd88c349bc07f4621e6a21b52ff554fc17657bf554dab9f9c390046f0d22a4320121f6eb0b4ad1d3f79097e69395baa3781 Homepage: https://cran.r-project.org/package=filelock Description: CRAN Package 'filelock' (Portable File Locking) Place an exclusive or shared lock on a file. It uses 'LockFile' on Windows and 'fcntl' locks on Unix-like systems. Package: r-cran-filling Architecture: amd64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 861 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cvxr, r-cran-rcpp, r-cran-rdpack, r-cran-roptspace, r-cran-rspectra, r-cran-nabor, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-filling_0.2.4-1.ca2404.1_amd64.deb Size: 724756 MD5sum: 497357650924537bd98326d0eb042967 SHA1: b56567261ee52e5ef049ef232ed036741cae2c3b SHA256: 884773a1fa9813fd090520211191e5a63687bec86b50d8ed2bed71c57197e252 SHA512: df367d098fe7436ec89eb32d9c46b899a068b113a3e7a485b39b1402b07d991dcb03ba3da6f2ab753e2e656ee75b1e48d8d179a8e41569d7b497a59a744f86ec Homepage: https://cran.r-project.org/package=filling Description: CRAN Package 'filling' (Matrix Completion, Imputation, and Inpainting Methods) Filling in the missing entries of a partially observed data is one of fundamental problems in various disciplines of mathematical science. For many cases, data at our interests have canonical form of matrix in that the problem is posed upon a matrix with missing values to fill in the entries under preset assumptions and models. We provide a collection of methods from multiple disciplines under Matrix Completion, Imputation, and Inpainting. See Davenport and Romberg (2016) for an overview of the topic. Package: r-cran-finalsize Architecture: amd64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1378 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-bookdown, r-cran-colorspace, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-ggtext, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-scales, r-cran-socialmixr, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-finalsize_0.2.1-1.ca2404.1_amd64.deb Size: 496370 MD5sum: 2dd57d657f020603479734158b02aa08 SHA1: b8e32e0b60035735e4dd6461daf5251de317cded SHA256: f3592cf165c53b7a7590fe80c38c7c0e697466d0e4048063e267c982f744203e SHA512: a954902ef9518ae4ecd57a004aa5efa234d8b0bb0aead05eb23e745fc4f3a1f670c2a02459bbd13fdcb5ecc362945919845e7c41ad1d4fcbfc1592cbd4d7a135 Homepage: https://cran.r-project.org/package=finalsize Description: CRAN Package 'finalsize' (Calculate the Final Size of an Epidemic) Calculate the final size of a susceptible-infectious-recovered epidemic in a population with demographic variation in contact patterns and susceptibility to disease, as discussed in Miller (2012) . Package: r-cran-fingerprint Architecture: amd64 Version: 3.5.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 369 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-runit, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fingerprint_3.5.10-1.ca2404.1_amd64.deb Size: 244130 MD5sum: b7bb2c949213ba15d277d0fdf9e7e18f SHA1: 5ffdeead1545f38646b358996f7ca4ff55ed14ba SHA256: 7bd33a56c1781e1923239c54155693a57955fb9eeb56beab0a0205cf4504d555 SHA512: 9a2fd418f92c6e9c6077af68fb02ac4525d32a1e8ef92f83d9f51c1e484033fb9478341c5a7d1c380707a6733e50267acf718efb96145ae4d4d71a54be95f88d Homepage: https://cran.r-project.org/package=fingerprint Description: CRAN Package 'fingerprint' (Functions to Operate on Binary Fingerprint Data) Functions to manipulate binary fingerprints of arbitrary length. A fingerprint is represented by an object of S4 class 'fingerprint' which is internally represented a vector of integers, such that each element represents the position in the fingerprint that is set to 1. The bitwise logical functions in R are overridden so that they can be used directly with 'fingerprint' objects. A number of distance metrics are also available (many contributed by Michael Fadock). Fingerprints can be converted to Euclidean vectors (i.e., points on the unit hypersphere) and can also be folded using OR. Arbitrary fingerprint formats can be handled via line handlers. Currently handlers are provided for CDK, MOE and BCI fingerprint data. Package: r-cran-fingerpro Architecture: amd64 Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3799 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-klar, r-cran-ggplot2, r-cran-ggally, r-cran-plyr, r-cran-mass, r-cran-reshape, r-cran-gridextra, r-cran-scales, r-cran-car, r-cran-rcppprogress, r-cran-ternary, r-cran-dplyr, r-cran-crayon, r-cran-plotly, r-cran-rcppgsl Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fingerpro_2.1-1.ca2404.1_amd64.deb Size: 2468110 MD5sum: 2698ec1667e97bc4db223eb7db711175 SHA1: 97831e19012bea5728626e82461bffc60fee5878 SHA256: 6d0a3f0070b34adaae4a997338936ada27088de5df7223d7e1c2f02688bec7f7 SHA512: b4c4cb91715749d5bbf2f693703f77526fd4d86c3783b0a9ba11beabf74a3137e133cfa20c541282267a69f17ea29a828f51429617bf06bf5c4cd53496ae16c2 Homepage: https://cran.r-project.org/package=fingerPro Description: CRAN Package 'fingerPro' (Unmixing Model Framework) Quantifies the provenance of sediments by applying a mixing model algorithm to end sediment mixtures based on a comprehensive characterization of the sediment sources. The 'fingerPro' model builds upon the foundational concept of using mass balance linear equations for sediment source quantification by incorporating several distinct technical advancements. It employs an optimization approach to normalize discrepancies in tracer ranges and minimize the objective function. Latin hypercube sampling is used to explore all possible combinations of source contributions (0-100%), mitigating the risk of local minima. Uncertainty in source estimates is quantified through a Monte Carlo routine, and the model includes additional metrics, such as the normalized error of the virtual mixture, to detect mathematical inconsistencies, non-physical solutions, and biases. A new linear variability propagation (LVP) method is also included to address and quantify potential bias in model outcomes, particularly when dealing with dominant or non-contributing sources and high source variability, offering a significant advancement for field studies where direct comparison with theoretical apportionments is not feasible. In addition to the unmixing model, a complete framework for tracer selection is included. Several methods are implemented to evaluate tracer behaviour by considering both source and mixture information. These include the Consistent Tracer Selection (CTS) method to explore all tracer combinations and select the optimal ones improving the robustness and interpretability of the model results. A Conservative Balance (CB) method is also incorporated to enable the use of isotopic tracers. The package also provides several graphical tools to support data exploration and interpretation, including box plots, correlation plots, Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA). Package: r-cran-finity Architecture: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-stabledist, r-cran-rcpparmadillo, r-cran-bh Filename: pool/dists/noble/main/r-cran-finity_0.1.5-1.ca2404.1_amd64.deb Size: 96288 MD5sum: 14a62fc29a9e0843d837b441dd5b3836 SHA1: e5fbc5896134b543be8388d3c27fd94a282b4f7c SHA256: bc09598f92dab7ca0b94e3ffe47a227da91dcd28c5fece87e9f16ecef1840da1 SHA512: d8a30f2282108bb22e83f2b5d66798e84caec6a5b143aa438a7fc7642889bfe63de8573fbc43575b504e2a39b876a52769a4aea40f889d88c67bc7a0c63b94d3 Homepage: https://cran.r-project.org/package=finity Description: CRAN Package 'finity' (Test for Finiteness of Moments in a Distribution) The purpose of this package is to tests whether a given moment of the distribution of a given sample is finite or not. For heavy-tailed distributions with tail exponent b, only moments of order smaller than b are finite. Tail exponent and heavy- tailedness are notoriously difficult to ascertain. But the finiteness of moments (including fractional moments) can be tested directly. This package does that following the test suggested by Trapani (2016) . Package: r-cran-finnet Architecture: amd64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 943 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp Suggests: r-cran-knitr, r-cran-igraph, r-cran-network, r-cran-markdown, r-cran-spb, r-cran-yahoofinancer Filename: pool/dists/noble/main/r-cran-finnet_0.2.1-1.ca2404.1_amd64.deb Size: 708596 MD5sum: e142283619a4a5f1af2d0b8bfdb4ab48 SHA1: 6222570a68b56408b0bf63023bdd4434b1d2a01c SHA256: bc4b24ca359e380e19c4b35d9cb2b94fee9736e359e6e1c7a14a73388781bd4c SHA512: 1bd4d6b2a98acdbb68a098475b33207bded0325e3c9574e63a1709cddcf665b421f2cbf32d0168224f5297e89a08b50175fae68e387d3f7060dfc72bc7e23a4b Homepage: https://cran.r-project.org/package=FinNet Description: CRAN Package 'FinNet' (Quickly Build and Manipulate Financial Networks) Providing classes, methods, and functions to deal with financial networks. Users can easily store information about both physical and legal persons by using pre-made classes that are studied for integration with scraping packages such as 'rvest' and 'RSelenium'. Moreover, the package assists in creating various types of financial networks depending on the type of relation between its units depending on the relation under scrutiny (ownership, board interlocks, etc.), the desired tie type (valued or binary), and renders them in the most common formats (adjacency matrix, incidence matrix, edge list, 'igraph', 'network'). There are also ad-hoc functions for the Fiedler value, global network efficiency, and cascade-failure analysis. Package: r-cran-fio Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1393 Depends: libc6 (>= 2.39), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clipr, r-cran-emoji, r-cran-fs, r-cran-miniui, r-cran-readxl, r-cran-rlang, r-cran-shiny, r-cran-rdpack, r-cran-r6 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-bench, r-cran-leontief, r-cran-ggplot2, r-cran-writexl, r-cran-callr, r-cran-testthat, r-cran-fiodata Filename: pool/dists/noble/main/r-cran-fio_1.0.0-1.ca2404.1_amd64.deb Size: 735960 MD5sum: f615d34c89105538c6c864ede58f8b53 SHA1: 167fef8e90d3d83ccb628d9e8e42a08576588f5d SHA256: 8e434711b3a1e1b5ccdeb968f9a21a0875a8f2756352967a89008ab9984272e1 SHA512: 050ff03487a1e9d4ef7788ef348adcb123b78d699f08418c07c3d202a6b084e66329e51f6c7365b37c9af67996d518020877b7d0e9239a7984b109f81cb23caa Homepage: https://cran.r-project.org/package=fio Description: CRAN Package 'fio' (Friendly Input-Output Analysis) Simplifies the process of economic input-output analysis by combining user-friendly interfaces with high-performance computation. It provides tools for analyzing both single-region and multi-regional economic systems through a hybrid architecture that pairs R's accessibility with Rust's computational efficiency. Package: r-cran-fipp Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrixstats, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fipp_1.0.0-1.ca2404.1_amd64.deb Size: 140716 MD5sum: 5b87422126e4fdbfc1a587b4aab32c49 SHA1: 4c591ea388a8c97bb96ad55cdf9064d19671c451 SHA256: 5d9e48f86dbd35330b7924bc25ce767647a520b966805882f67e37cd71f80688 SHA512: a477752217ddfd31a24e256f7bc265df04054c4017cf94d0f23ed6464c345ba527e4a98a87c5c5fa163087f7200585496bb10355130b847174ebddd563b76d8e Homepage: https://cran.r-project.org/package=fipp Description: CRAN Package 'fipp' (Induced Priors in Bayesian Mixture Models) Computes implicitly induced quantities from prior/hyperparameter specifications of three Mixtures of Finite Mixtures models: Dirichlet Process Mixtures (DPMs; Escobar and West (1995) ), Static Mixtures of Finite Mixtures (Static MFMs; Miller and Harrison (2018) ), and Dynamic Mixtures of Finite Mixtures (Dynamic MFMs; Frühwirth-Schnatter, Malsiner-Walli and Grün (2020) ). For methodological details, please refer to Greve, Grün, Malsiner-Walli and Frühwirth-Schnatter (2020) ) as well as the package vignette. Package: r-cran-fire Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1868 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/noble/main/r-cran-fire_1.0.1-1.ca2404.1_amd64.deb Size: 1745518 MD5sum: 0dc1dca1a298f57bd78efc67f445634e SHA1: 92e13ab5f5fe95f8a444dccd80d7a1d13cc2978f SHA256: e3898b2956fa196040402a6b43e9350120415672be11a6d77bd77106a190a805 SHA512: 58e382ba4e93cae407f4f8e857bd7af872010e53a2d3a4c2160b79133ed9d431a5a5260091287cd6043950d5617d0f1886880229c0b8cc82e46f51aa9b711acd Homepage: https://cran.r-project.org/package=FiRE Description: CRAN Package 'FiRE' (Finder of Rare Entities (FiRE)) The algorithm assigns rareness/ outlierness score to every sample in voluminous datasets. The algorithm makes multiple estimations of the proximity between a pair of samples, in low-dimensional spaces. To compute proximity, FiRE uses Sketching, a variant of locality sensitive hashing. For more details: Jindal, A., Gupta, P., Jayadeva and Sengupta, D., 2018. Discovery of rare cells from voluminous single cell expression data. Nature Communications, 9(1), p.4719. . Package: r-cran-firm Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1502 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-seurat, r-cran-rann, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-firm_0.1.2-1.ca2404.1_amd64.deb Size: 705364 MD5sum: 391635f40cf390f748b7eb9ae735fb83 SHA1: ee88b83af54b169543e12f3b4994505cea1f3637 SHA256: b8f609ced05c6136d3c48e0c115096016819565d811bbd10e29117b8d548e228 SHA512: 4c49df36a1d7550de7ffbedd99973fa0ac7c75f71b73a28a769fddf85968c03bf976e94bdfaa97f50baf4471981c10a322aad3d6d0d54612b4aab9227ce8355f 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. 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Package: r-cran-fit Architecture: amd64 Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1149 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-xml, r-cran-gglasso, r-cran-mass, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fit_0.0.6-1.ca2404.1_amd64.deb Size: 570416 MD5sum: 6cb4c9de5cc1d4fe8b27b81f9055edf3 SHA1: eea674d0c79218cf0477b2b2dae5e66a4d822921 SHA256: 9f8a500e41552040975dd2d866cc1de09594ca1b35a5e162be0104750586c309 SHA512: cc2ef91e5bdb311b02fde4c0d9179135160ad3c19eefdbc9007df58780fa52d5b73aad228f15ff72ff5f592627e083555b4363cc1522c1dbc45ed22f7baa1539 Homepage: https://cran.r-project.org/package=FIT Description: CRAN Package 'FIT' (Transcriptomic Dynamics Models in Field Conditions) Provides functionality for constructing statistical models of transcriptomic dynamics in field conditions. It further offers the function to predict expression of a gene given the attributes of samples and meteorological data. Nagano, A. J., Sato, Y., Mihara, M., Antonio, B. A., Motoyama, R., Itoh, H., Naganuma, Y., and Izawa, T. (2012). . Iwayama, K., Aisaka, Y., Kutsuna, N., and Nagano, A. J. (2017). . Package: r-cran-fixes Architecture: amd64 Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1223 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-fixest, r-cran-broom, r-cran-tibble, r-cran-rlang, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-haven, r-cran-testthat, r-cran-plotly, r-cran-tidyr, r-cran-did, r-cran-didimputation Filename: pool/dists/noble/main/r-cran-fixes_0.8.1-1.ca2404.1_amd64.deb Size: 811358 MD5sum: ef294c248b6fc1bcaf55cc51ad61e492 SHA1: 85fdc6ef38cd56c6b49805ebbd9b69b6b67e2882 SHA256: 51ea2d4d6f9646b53573d515fb53d13197a626fa73bf3e39cfaa53ec9987de0c SHA512: 665deb6112ee11f8efc5a89891407c615889553ab481514630a4d8be33f58b0abc3c8f320286177a32709b2aced0641757b34f7c0ffca62fe1385527d451e104 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. 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Package: r-cran-fkf.sp Architecture: amd64 Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mathjaxr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-fkf, r-cran-nfcp Filename: pool/dists/noble/main/r-cran-fkf.sp_0.3.4-1.ca2404.1_amd64.deb Size: 77318 MD5sum: 6c37700b7d6856f5671dbe5962de69de SHA1: 395a2ac12b8f63c0526c87c958164d8a47865bdd SHA256: 6ffd8776eab011d3a6a5e873803577043200fcd1cb3a22c5a40edc8ddf5b64c2 SHA512: 5cd1282bf8c6e2dc41101a7e72fcfdc0e3a144a0d10dc245a010d3f2d761a2bbaf421d7429d01e601ef7ac78c860a1a5ff8fc1f25a18374a3107c047a2b44599 Homepage: https://cran.r-project.org/package=FKF.SP Description: CRAN Package 'FKF.SP' (Fast Kalman Filtering Through Sequential Processing) Fast and flexible Kalman filtering and smoothing implementation utilizing sequential processing, designed for efficient parameter estimation through maximum likelihood estimation. Sequential processing is a univariate treatment of a multivariate series of observations and can benefit from computational efficiency over traditional Kalman filtering when independence is assumed in the variance of the disturbances of the measurement equation. Sequential processing is described in the textbook of Durbin and Koopman (2001, ISBN:978-0-19-964117-8). 'FKF.SP' was built upon the existing 'FKF' package and is, in general, a faster Kalman filter/smoother. Package: r-cran-fkf Architecture: amd64 Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fkf_0.2.6-1.ca2404.1_amd64.deb Size: 124344 MD5sum: f9bb1b617bedd04b26daec46484c614b SHA1: 1a196ecf84e6cb9bb0fe63bd551cd65553dad22e SHA256: a4a9e8b0d53c8267311132dd774daa72b0b1d20a9dc674edb884b93792a2c278 SHA512: 5c1c7b4b7b436faae89c5cf5aeb0292bf0810991331de3dce6190db32eb5df5db2e65c977b5d5262e9a1225ed0b56507d426522f55dd998b69a72ee3345ca0e1 Homepage: https://cran.r-project.org/package=FKF Description: CRAN Package 'FKF' (Fast Kalman Filter) This is a fast and flexible implementation of the Kalman filter and smoother, which can deal with NAs. It is entirely written in C and relies fully on linear algebra subroutines contained in BLAS and LAPACK. Due to the speed of the filter, the fitting of high-dimensional linear state space models to large datasets becomes possible. This package also contains a plot function for the visualization of the state vector and graphical diagnostics of the residuals. Package: r-cran-fksum Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rarpack, r-cran-mass, r-cran-matrix, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-fksum_1.0.1-1.ca2404.1_amd64.deb Size: 206396 MD5sum: 29a8c3360b9e73f9831a2f265b8efbce SHA1: 88ff56df6157bd6d7c0e423b1ff7abde77ddbd4b SHA256: f82383acadc39e06e5ef50d652e0ee4f7a6009467b7ef26060e166b670082e74 SHA512: 209887581c4c9524c1e69b58137f35a7be9736b963e3ba364b8b54fa021b78cc64d424974f2ff168b487e97e876b53150f49c389a8a55888073ba09907c47b3f Homepage: https://cran.r-project.org/package=FKSUM Description: CRAN Package 'FKSUM' (Fast Kernel Sums) Implements the method of Hofmeyr, D.P. (2021) for fast evaluation of univariate kernel smoothers based on recursive computations. Applications to the basic problems of density and regression function estimation are provided, as well as some projection pursuit methods for which the objective is based on non-parametric functionals of the projected density, or conditional density of a response given projected covariates. The package is accompanied by an instructive paper in the Journal of Statistical Software . Package: r-cran-flam Architecture: amd64 Version: 3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass Filename: pool/dists/noble/main/r-cran-flam_3.2-1.ca2404.1_amd64.deb Size: 164492 MD5sum: 722cff3bd528f7f86d9f90b91ba2d1c7 SHA1: 768e8ea8005fb18a6d37392cb473ad8fe0b55a9f SHA256: 7f768aa16422c8eaff61159368ccc7e44aca2305e739f79817f69cf5a82b7dcd SHA512: 3e92b57dcc51ed49950a3d9cd1dee4b037aafb2b3ca813c746d5fa369b8248eabb37232b6d11616995d351e365983ab7a78d2a4957f84357b380752114f92752 Homepage: https://cran.r-project.org/package=flam Description: CRAN Package 'flam' (Fits Piecewise Constant Models with Data-Adaptive Knots) Implements the fused lasso additive model as proposed in Petersen, A., Witten, D., and Simon, N. (2016). Fused Lasso Additive Model. Journal of Computational and Graphical Statistics, 25(4): 1005-1025. Package: r-cran-flamingos Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4851 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-flamingos_0.1.0-1.ca2404.1_amd64.deb Size: 3789672 MD5sum: d0c4c8cff8a267b9fa01e3fc559b8ae6 SHA1: d3cc2b0346a401c537758e55e81f3bda3b3a60ea SHA256: 37f681d01aabcf8629c5a0b3cfde26df551a1e50405226ba6c6b66c50486b639 SHA512: f5dab809482270967d367705bb0bf3bb9e63faebfdc8171d45e43808484487afd27c8b46ac1cb76c5ea77b9571d8813a81c2ec2b1e01c4b0fb3d3d4ba70e2f20 Homepage: https://cran.r-project.org/package=flamingos Description: CRAN Package 'flamingos' (Functional Latent Data Models for Clustering HeterogeneousCurves ('FLaMingos')) Provides a variety of original and flexible user-friendly statistical latent variable models for the simultaneous clustering and segmentation of heterogeneous functional data (i.e time series, or more generally longitudinal data, fitted by unsupervised algorithms, including EM algorithms. Functional Latent Data Models for Clustering heterogeneous curves ('FLaMingos') are originally introduced and written in 'Matlab' by Faicel Chamroukhi . The references are mainly the following ones. Chamroukhi F. (2010) . Chamroukhi F., Same A., Govaert, G. and Aknin P. (2010) . Chamroukhi F., Same A., Aknin P. and Govaert G. (2011). . Same A., Chamroukhi F., Govaert G. and Aknin, P. (2011) . Chamroukhi F., and Glotin H. (2012) . Chamroukhi F., Glotin H. and Same A. (2013) . Chamroukhi F. (2015) . Chamroukhi F. and Nguyen H-D. (2019) . Package: r-cran-flan Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 829 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppgsl Filename: pool/dists/noble/main/r-cran-flan_1.0-1.ca2404.1_amd64.deb Size: 321704 MD5sum: 66fe675f15c488d388ccbcd69be8b174 SHA1: ae62bc46251d907885414728a16da58c8569a6d8 SHA256: a45a3a3b6dc75a72738d0edec07bcc7f4af6346b1757799632a719d13facb77f SHA512: b1dfab41d92c0823d7462c089f71a7de7f0375639a59e55e90a8581d9a09e7b923b1b9b3564707fd028539b3b9f9c7c73f40b911656732c514e554ee94bf04cd Homepage: https://cran.r-project.org/package=flan Description: CRAN Package 'flan' (FLuctuation ANalysis on Mutation Models) Tools for fluctuations analysis of mutant cells counts. Main reference is A. Mazoyer, R. Drouilhet, S. Despreaux and B. Ycart (2017) . 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Package: r-cran-flexbcf Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-flexbcf_1.0.2-1.ca2404.1_amd64.deb Size: 165614 MD5sum: f56f6dd88a51e50c4e26d7b5d485a3bf SHA1: 56b2c52f60fe6aabd30f6b1778bb1d479bde6788 SHA256: 134e5b8e0194a2a218af563e6a8e72c1f6c068967c3b30cecf2ec36ff99bde0c SHA512: 01976a642597a4fa46ff39a25df699021ccb191d3891411ecbbafebcb1d3cf5274bd35937901f8aedf5a6502fa79643cba239ae899bfedd23d6ff9a79d2c1621 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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In case of bounded continuous responses (e.g., proportions and rates), available models are the flexible beta (Migliorati, S., Di Brisco, A. M., Ongaro, A. (2018) ), the variance-inflated beta (Di Brisco, A. M., Migliorati, S., Ongaro, A. (2020) ), the beta (Ferrari, S.L.P., Cribari-Neto, F. (2004) ), and their augmented versions to handle the presence of zero/one values (Di Brisco, A. M., Migliorati, S. (2020) ) are implemented. In case of bounded discrete responses (e.g., bounded counts, such as the number of successes in n trials), available models are the flexible beta-binomial (Ascari, R., Migliorati, S. (2021) ), the beta-binomial, and the binomial are implemented. Inference is dealt with a Bayesian approach based on the Hamiltonian Monte Carlo (HMC) algorithm (Gelman, A., Carlin, J. B., Stern, H. S., Rubin, D. B. (2014) ). Besides, functions to compute residuals, posterior predictives, goodness of fit measures, convergence diagnostics, and graphical representations are provided. Package: r-cran-flexrl Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 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-progress, r-cran-testit, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-flexrl_0.1.1-1.ca2404.1_amd64.deb Size: 196880 MD5sum: 60244aa01d6606b66ae41e8a42ed2c81 SHA1: 67620896f1a9b419f47c65e4794c74e3b26c27cd SHA256: ec67abf3862431270f9561ab06a30132cb0f486d09803f8b62f85165e61b81b7 SHA512: ab218b377ce5a7a7eba4a3397ee387cdc42a476cf145feb959e8081778f9e3a27dac06401075480ec64a816007361747433fe1e2914fb625a39bfde1cb9de0e0 Homepage: https://cran.r-project.org/package=FlexRL Description: CRAN Package 'FlexRL' (A Flexible Model for Record Linkage) Implementation of the Stochastic Expectation Maximisation (StEM) approach to Record Linkage described in the paper by K. Robach, S. L. van der Pas, M. A. van de Wiel and M. H. Hof (2024, ); see citation("FlexRL") for details. This is a record linkage method, for finding the common set of records among 2 data sources based on Partially Identifying Variables (PIVs) available in both sources. It includes modelling of dynamic Partially Identifying Variables (e.g. postal code) that may evolve over time and registration errors (missing values and mistakes in the registration). Low memory footprint. 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It allows to model non-linear and non-proportional effects and both non proportional and non linear effects, using splines (B-spline and truncated power basis), Weighted Cumulative Index of Exposure effect, with correction model for the life table. Both non proportional and non linear effects are described in Remontet, L. et al. (2007) and Mahboubi, A. et al. (2011) . Package: r-cran-flexsurv Architecture: amd64 Version: 2.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3176 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-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/noble/main/r-cran-flexsurv_2.3.2-1.ca2404.1_amd64.deb Size: 2517156 MD5sum: 36deba674fe751e3d72b881d50aa60cf SHA1: b2db2d3df582ca862b9b4c82ea5ad7d31966f33e SHA256: e87ea8ce998a646d8bb7d81aee03c8f3543e40cb611cc26f4b3b3fdee6885249 SHA512: 4513331920ad6604e64f1fe30a33059a89d1fefeeed798b87d9f809565ab54572d15256db4a6ab872b37b4a0b06443b89f52c23293cf3dcd37c485e52013e321 Homepage: https://cran.r-project.org/package=flexsurv Description: CRAN Package 'flexsurv' (Flexible Parametric Survival and Multi-State Models) Flexible parametric models for time-to-event data, including the Royston-Parmar spline model, generalized gamma and generalized F distributions. 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(2024) and Van Migerode et al. (2025) . Originally developed to flexibly reconstruct the Degree of Urbanisation classification of cities, towns and rural areas developed by Dijkstra et al. (2021) . Now it also support a broader range of delineation approaches, using multiple datasets – including population, built-up area, and night-time light grids – and different thresholding methods. Package: r-cran-flexvarjm Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-lcmm, r-cran-marqlevalg, r-cran-mvtnorm, r-cran-randtoolbox, r-cran-rcpp, r-cran-survminer, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-flexvarjm_0.1.0-1.ca2404.1_amd64.deb Size: 333978 MD5sum: 160d9fea18fa6342b2fbbade39245e5e SHA1: e6c9d8cb43fe6b04986fa9b8b53677051a55c926 SHA256: efb0db6fd037c9e151176538a8a92ea637d2af426e149c508f42e9adfe731a81 SHA512: 8e572945d3ad2d704675b6fc79b6188248bf7d66973124216780beee592c77b3a47debeeaa3d6c59bdc304bac4d22c3ad83d2f438ca3e3d0108258109067c530 Homepage: https://cran.r-project.org/package=FlexVarJM Description: CRAN Package 'FlexVarJM' (Estimate Joint Models with Subject-Specific Variance) Estimation of mixed models including a subject-specific variance which can be time and covariate dependent. 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'FLINT' extends GNU 'MPFR' and GNU 'MP' with support for operations on standard rings (the integers, the integers modulo n, finite fields, the rational, p-adic, real, and complex numbers) as well as matrices and polynomials over rings. 'FLINT' implements midpoint-radius interval arithmetic, also known as ball arithmetic, in the real and complex numbers, enabling computation in arbitrary precision with rigorous propagation of rounding and other errors; see Johansson (2017) . Finally, 'FLINT' provides ball arithmetic implementations of many special mathematical functions, with high coverage of reference works such as the NIST Digital Library of Mathematical Functions . The R interface defines S4 classes, generic functions, and methods for representation and basic operations as well as plain R functions mirroring and vectorizing entry points in the C library. Package: r-cran-flintyr Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.4), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-assertthat, r-cran-testthat, r-cran-rcpparmadillo Suggests: r-cran-devtools Filename: pool/dists/noble/main/r-cran-flintyr_0.1.0-1.ca2404.1_amd64.deb Size: 150038 MD5sum: 029f6c705c8cc98ca86aa406449f9381 SHA1: 2fbaff50dfc6f52a5d8494259091576446b49958 SHA256: 2f93dd8663982d1db1ecacbc76ae7dc27db542834b9d498eeafd279b64ac3015 SHA512: 3bdcdf614fa09c34b89c7ccf374fcf7699d90c0cfd5e010985adf508ddd99f18db6dd8a158e4396521c17251c89574bf59508f1edb02a39b073e751dcd2c283a Homepage: https://cran.r-project.org/package=flintyR Description: CRAN Package 'flintyR' (Simple and Flexible Tests of Sample Exchangeability) Given a multivariate dataset and some knowledge about the dependencies between its features, it is customary to fit a statistical model to the features to infer parameters of interest. 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Package: r-cran-flip Architecture: amd64 Version: 2.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 510 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cherry, r-cran-e1071, r-cran-plyr, r-cran-somemtp Filename: pool/dists/noble/main/r-cran-flip_2.5.1-1.ca2404.1_amd64.deb Size: 421410 MD5sum: 60101eca94406e0d50cd791a2bcd9063 SHA1: 4c2a5cfbf7d2879ac5f439af23c0eb18b671bf61 SHA256: 9387f2c69c6f78f24b7e1294ac1982951aaec7e075fcd0c50fabac1bdc9c4a92 SHA512: b1f6b1e1f46ca280e545b87d6db31f0b851bcef95248f1a76006f572fd390d2e5ec21bf642f37765d423fab13d406293f1d0b256f434151a31b71072ef23e8a5 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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The package implements the Path-Sized Logit model for traffic assignment - Ben-Akiva and Bierlaire (1999) - an efficient route enumeration algorithm, and provides powerful utility functions for (multimodal) network generation, consolidation/contraction, and/or simplification. The user is expected to provide a transport network (either a graph or collection of linestrings) and an origin-destination (OD) matrix of trade/traffic flows. Maintained by transport consultants at CPCS (cpcs.ca). Package: r-cran-flsa Architecture: amd64 Version: 1.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-flsa_1.5.5-1.ca2404.1_amd64.deb Size: 79674 MD5sum: 23877baef0859ba354776a182d0c751b SHA1: d2b0a14b0afb90f2016e0dfe37ba2e8e81cfcea5 SHA256: de4aaa25f907d4f7a0afdbee8003b66062639f88477487db0e8e1feb6790657a SHA512: c7410e9d9cfd00ebd2caa67dec1478063d54ed5ce4754add969d37d6fcec4f76d04b560ee10249e821988469e93597f08265ec5aa31316c978874676d87c63f5 Homepage: https://cran.r-project.org/package=flsa Description: CRAN Package 'flsa' (Path Algorithm for the General Fused Lasso Signal Approximator) Implements a path algorithm for the Fused Lasso Signal Approximator. For more details see the help files or the article by Hoefling (2009) . Package: r-cran-flsss Architecture: amd64 Version: 9.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2740 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Filename: pool/dists/noble/main/r-cran-flsss_9.2.8-1.ca2404.1_amd64.deb Size: 908358 MD5sum: fa1fe47c413aace52cbd0bb56fa68a29 SHA1: 9c5d837422b474552e54358db2c49ffad52a8964 SHA256: fb09819cfde1907b104b707ff4155380e915e86bce66f7294d3e70517e4fc3df SHA512: a0935b45e85685a46f89243b4dfc8f771edb26391fee430fa4573043dc1fa7457282907b4161703c4299cd23c34153d2c1d094bc62269edf8f1f5b03454b3ee2 Homepage: https://cran.r-project.org/package=FLSSS Description: CRAN Package 'FLSSS' (Mining Rigs for Problems in the Subset Sum Family) Specialized solvers for combinatorial optimization problems in the Subset Sum family. The solvers differ from the mainstream in the options of (i) restricting subset size, (ii) bounding subset elements, (iii) mining real-value multisets with predefined subset sum errors, (iv) finding one or more subsets in limited time. A novel algorithm for mining the one-dimensional Subset Sum induced algorithms for the multi-Subset Sum and the multidimensional Subset Sum. The multi-threaded framework for the latter offers exact algorithms to the multidimensional Knapsack and the Generalized Assignment problems. Historical updates include (a) renewed implementation of the multi-Subset Sum, multidimensional Knapsack and Generalized Assignment solvers; (b) availability of bounding solution space in the multidimensional Subset Sum; (c) fundamental data structure and architectural changes for enhanced cache locality and better chance of SIMD vectorization; (d) option of mapping floating-point instance to compressed 64-bit integer instance with user-controlled precision loss, which could yield substantial speedup due to the dimension reduction and efficient compressed integer arithmetic via bit-manipulations; (e) distributed computing infrastructure for multidimensional subset sum; (f) arbitrary-precision zero-margin-of-error multidimensional Subset Sum accelerated by a simplified Bloom filter. The package contains a copy of 'xxHash' from . Package vignette () detailed a few historical updates. Functions prefixed with 'aux' (auxiliary) are independent implementations of published algorithms for solving optimization problems less relevant to Subset Sum. Package: r-cran-fluidsynth Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: libc6 (>= 2.4), libfluidsynth3 (>= 2.0.5), libsdl2-2.0-0 (>= 2.0.12), r-base-core (>= 4.4.0), r-api-4.0, r-cran-av, r-cran-rappdirs Filename: pool/dists/noble/main/r-cran-fluidsynth_1.0.2-1.ca2404.1_amd64.deb Size: 53940 MD5sum: 9f94472fa5f7d71652e93b74a83f3454 SHA1: c1f5e32f8b6ac5e431029fb6a5284d024b346650 SHA256: a1243b1eb3fab11c8e7bb60666c387fa73ccb2b968541bf643fb13d1ed490987 SHA512: 15900bf2491c4558759694b536deeaa34aa3c9539886fcb962d4fff40ff5d1297a7d0e547e6a0020a67064f5d181750c5fe7b1b470288373bb49bc2fce779cf8 Homepage: https://cran.r-project.org/package=fluidsynth Description: CRAN Package 'fluidsynth' (Read and Play Digital Music (MIDI)) Bindings to 'libfluidsynth' to parse and synthesize MIDI files. It can read MIDI into a data frame, play it on the local audio device, or convert into an audio file. Package: r-cran-fluxpoint Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-blockmatrix, r-cran-corpcor, r-cran-doparallel, r-cran-ggplot2, r-cran-glmnet, r-cran-mass, r-cran-matrix, r-cran-nnls, r-cran-pracma, r-cran-simdesign, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-fluxpoint_0.1.1-1.ca2404.1_amd64.deb Size: 174622 MD5sum: d0248f2c46f3ba1498a0218565714b78 SHA1: 70b03364aad186e1beb9aba2b91f6480dd305311 SHA256: a1d452c245cbb1986bac1324278f2228df561fb46d7a79f119f60f8b0498f159 SHA512: 41519e9e757bc915b1b9c3750a410cc47b080f21c0d68371e4c2e5090ea6c1533598b0b486b50e69e509d7ce2958ec35a0bda9162d77111c37f6cf9b4ffdc3a4 Homepage: https://cran.r-project.org/package=FluxPoint Description: CRAN Package 'FluxPoint' (Change Point Detection for Non-Stationary and Cross-CorrelatedTime Series) Implements methods for multiple change point detection in multivariate time series with non-stationary dynamics and cross-correlations. 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Package: r-cran-flying Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 928 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-knitr, r-cran-kableextra, r-cran-rmarkdown Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-flying_0.1.3-1.ca2404.1_amd64.deb Size: 345456 MD5sum: 6ed95f626eff9868d56f8fa6a103b8b5 SHA1: cf411c26f89cba6fe34afc6388a27097dac0b47a SHA256: 3d41edf61270a10e78ff67495efd71bb554ae2efa2a1bad4714aa72d54a6332b SHA512: cf5ec37ab0500abee5c06580782c8319b2f5fb16274893775f6552fa3bbd32eaaecfbb21c99cbf45033afdce8373b3a7e9f1c57f330ceb4ec69d0cea564cd251 Homepage: https://cran.r-project.org/package=flying Description: CRAN Package 'flying' (Simulation of Bird Flight Range) Functions for range estimation in birds based on Pennycuick (2008) and Pennycuick (1975), 'Flight' program which compliments Pennycuick (2008) requires manual entry of birds which can be tedious when there are thousands of birds to estimate. 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Implemented are two ODE methods discussed in Pennycuick (1975) and time-marching computation methods as in Pennycuick (1998) and Pennycuick (2008). See Pennycuick (1975, ISBN:978-0-12-249405-5), Pennycuick (1998) , and Pennycuick (2008, ISBN:9780080557816). 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Package: r-cran-fmds Architecture: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 704 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-fmds_0.1.5-1.ca2404.1_amd64.deb Size: 355510 MD5sum: 5656defe7257e8a3de852b63472de433 SHA1: 6ffc65d98548cc8bf09de5793a14ec5f9dcdf804 SHA256: c8b5ed66e0e34c1d2e063114f3ee92c551f8d7d4b647c95a86598d00b7b317b7 SHA512: 5d28835ce1e096aac21523adbe2c56f402332982c685f370b9e06b5e48dc378022c205a2dbd6150d8bcae42ab16bb975a64e517b3f582f110d0ebc71e9a4d2c4 Homepage: https://cran.r-project.org/package=fmds Description: CRAN Package 'fmds' (Multidimensional Scaling Development Kit) Multidimensional scaling (MDS) functions for various tasks that are beyond the beta stage and way past the alpha stage. Currently, options are available for weights, restrictions, classical scaling or principal coordinate analysis, transformations (linear, power, Box-Cox, spline, ordinal), outlier mitigation (rdop), out-of-sample estimation (predict), negative dissimilarities, fast and faster executions with low memory footprints, penalized restrictions, cross-validation-based penalty selection, supplementary variable estimation (explain), additive constant estimation, mixed measurement level distance calculation, restricted classical scaling, etc. More will come in the future. References. Busing (2024) "A Simple Population Size Estimator for Local Minima Applied to Multidimensional Scaling". Manuscript submitted for publication. Busing (2025) "Node Localization by Multidimensional Scaling with Iterative Majorization". Manuscript submitted for publication. Busing (2025) "Faster Multidimensional Scaling". Manuscript in preparation. Barroso and Busing (2025) "e-RDOP, Relative Density-Based Outlier Probabilities, Extended to Proximity Mapping". Manuscript submitted for publication. Package: r-cran-fmdu Architecture: amd64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-smacof Filename: pool/dists/noble/main/r-cran-fmdu_0.2.1-1.ca2404.1_amd64.deb Size: 142576 MD5sum: 322b83d08b69dea64cd18ce638a9d600 SHA1: 94a9fb4a395df8d001177f9bea6a85617adc79de SHA256: cb803bfaf54aa823a948eb956d57a11935b903732cd76c815dc951fb38b6e0c1 SHA512: 5af831dd6b04c70f8b143a8825ab1f76150bc1a0f574d1a10fb68948fae0475b5b3a4ba53de96a872cfbe13301d5c8aa2ecb9142f0977a7ad937e5f4c5b5d760 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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Reading and writing data stored by some versions of 'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', and for reading and writing some 'dBase' files. 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Scenario-neutral approaches 'stress-test' the performance of a modelled system by applying a wide range of plausible hydroclimate conditions (see Brown & Wilby (2012) and Prudhomme et al. (2010) ). These approaches allow the identification of hydroclimatic variables that affect the vulnerability of a system to hydroclimate variation and change. This tool enables the generation of perturbed time series using a range of approaches including simple scaling of observed time series (e.g. Culley et al. (2016) ) and stochastic simulation of perturbed time series via an inverse approach (see Guo et al. (2018) ). It incorporates 'Richardson-type' weather generator model configurations documented in Richardson (1981) , Richardson and Wright (1984), as well as latent variable type model configurations documented in Bennett et al. (2018) , Rasmussen (2013) , Bennett et al. 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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. 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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: amd64 Version: 2019.08.26-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fpop_2019.08.26-1.ca2404.1_amd64.deb Size: 29842 MD5sum: 8ca51e5e813c4aa12aeefbfc49396233 SHA1: 6671c552c069241b51003a9ccea014e5cc80f910 SHA256: 82337243ebbb1e724af8cab51caebaff37e14ab9cf2d12f0c2f36bc5be1eeb0f SHA512: 83250dd5d148d7f50c105c86bed76025983f96552e516103059fa0c36a8e4ec1db40c3b9f007c6bb661ab90c19bb3b8fd2c259c082b59c03a9bb61693a77fcb4 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: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fpopw_1.1-1.ca2404.1_amd64.deb Size: 61594 MD5sum: 52e6f71dd0b85db27871df9e32f6f418 SHA1: eea0152559ec890dccc5b8a7c5ae4b73925d541c SHA256: 3fbba869119db1c564f01b30a77eb25596bc4cbeb0f5b9ccb5a52a0b553606d8 SHA512: 7b2008342bc2fd722b9d66e6a82ee57fd22ccdcaa288257f2531b753038c530c206aa80974503bea95dd8a963503d2112c5d328fec0116e32747ac83eaba7139 Homepage: https://cran.r-project.org/package=fpopw Description: CRAN Package 'fpopw' (Weighted Segmentation using Functional Pruning and OptimalPartioning) Weighted-L2 FPOP Maidstone et al. (2017) and pDPA/FPSN Rigaill (2010) algorithm for detecting multiple changepoints in the mean of a vector. Also includes a few model selection functions using Lebarbier (2005) and the 'capsushe' package. Package: r-cran-fpow Architecture: amd64 Version: 0.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fpow_0.0-3-1.ca2404.1_amd64.deb Size: 13230 MD5sum: 76b812277c4e608c2736bdf43ed83637 SHA1: dc90366ba976a04f50a9a83d12ed551e096ee1df SHA256: 516e3d81507a6f3126d92fe4afcdfcbeff2150b1b1d44253cc0158fad630db50 SHA512: f62363dcacb8833baff8ce44d4bc71e050af5abd47bacf4be040cbff71c5d62bf171f80ecf0f8bee603b288c1f189494c8c7b88d0aff9683857fdc760c89886f Homepage: https://cran.r-project.org/package=fpow Description: CRAN Package 'fpow' (Computing the Noncentrality Parameter of the Noncentral FDistribution) Returns the noncentrality parameter of the noncentral F distribution if probability of type I and type II error, degrees of freedom of the numerator and the denominator are given. It may be useful for computing minimal detectable differences for general ANOVA models. This program is documented in the paper of A. Baharev, S. Kemeny, On the computation of the noncentral F and noncentral beta distribution; Statistics and Computing, 2008, 18 (3), 333-340. Package: r-cran-fproc Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: libc6 (>= 2.14), 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-terra, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fproc_0.1.0-1.ca2404.1_amd64.deb Size: 203124 MD5sum: 2f0391bae74ad70752c7e8ecc83c8b9e SHA1: 26117d2ff3c09cb8257122394b4798c671bfe9be SHA256: 7fdcd847bde5c28f657851dcda5963611590b2e951a9e6354c04cecdcd816bb9 SHA512: f0c2faf31d29472afae0eec32f41d8f861957e6e8f414d6c727a7c065de4ee00e3e329fed689c4f18560eb096b599c3c0c722ca5926c4043ffb25ec0c7cfa14c Homepage: https://cran.r-project.org/package=fpROC Description: CRAN Package 'fpROC' (Fast Partial Receiver Operating Characteristic (ROC) Test forEcological Niche Modeling) Provides optimized 'C++' code for computing the partial Receiver Operating Characteristic (ROC) test used in niche and species distribution modeling. The implementation follows Peterson et al. (2008) . Parallelization via 'OpenMP' was implemented with assistance from the 'DeepSeek' Artificial Intelligence Assistant (). Package: r-cran-frab Architecture: amd64 Version: 0.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1342 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-disordr Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat, r-cran-mvtnorm, r-cran-covr Filename: pool/dists/noble/main/r-cran-frab_0.0-6-1.ca2404.1_amd64.deb Size: 746816 MD5sum: 83f3c34c1f05c50993c0842ad940e1af SHA1: 64a792677f4ce47a4a2a390e4792e560a9be5a1a SHA256: f4f0243b478383cdf929f5bd9292d064e64abce4c478ae3d7c19a886012e6bfc SHA512: 76d5fe9735a60a2e9490b9f5b53d17a13ea9d1cb22ff91009d0077d08a6deabfcd4e8810480995f635ed5a683ba744f0fe882ca6d276021aaad5af67f4958402 Homepage: https://cran.r-project.org/package=frab Description: CRAN Package 'frab' (How to Add Two R Tables) Methods to "add" two R tables; also an alternative interpretation of named vectors as generalized R tables, so that c(a=1,b=2,c=3) + c(b=3,a=-1) will return c(b=5,c=3). Uses 'disordR' discipline (Hankin, 2022, ). Extraction and replacement methods are provided. The underlying mathematical structure is the Free Abelian group, hence the name. To cite in publications please use Hankin (2023) . Package: r-cran-fracdiff Architecture: amd64 Version: 1.5-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-longmemo, r-cran-forecast, r-cran-urca Filename: pool/dists/noble/main/r-cran-fracdiff_1.5-4-1.ca2404.1_amd64.deb Size: 98368 MD5sum: 7f95dc4165f1133b83a21ce16f94c341 SHA1: 385683312d3860a7649144b150279286c9f4a049 SHA256: 9dc7d69165db7878b8689d8a5fab8701eb79cfa09b04fa4cde7f3c0b04249e51 SHA512: 253ef428e03cdd75747c408574510a35162acd0e4adcc9427a231d3ce0eb49de6957636a11e68be21f833471fce43ad232d04137f3a823d6473fec4d9b5acef0 Homepage: https://cran.r-project.org/package=fracdiff Description: CRAN Package 'fracdiff' (Fractionally Differenced ARIMA aka ARFIMA(P,d,q) Models) Maximum likelihood estimation of the parameters of a fractionally differenced ARIMA(p,d,q) model (Haslett and Raftery, Appl.Statistics, 1989); including inference and basic methods. Some alternative algorithms to estimate "H". Package: r-cran-fractalregression Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 719 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-colorramps, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-ggplot2, r-cran-crqa, r-cran-mfdfa, r-cran-fracdiff, r-cran-tseries, r-cran-fields, r-cran-gridextra, r-cran-qpdf Filename: pool/dists/noble/main/r-cran-fractalregression_1.2-1.ca2404.1_amd64.deb Size: 423004 MD5sum: 945d7d42aa20cdfdf7e7d44f639b9788 SHA1: 4ba4ec63ca8dfd920e37bb2c2d368a3473a900ef SHA256: fe3717dd1a8303b295708105b444e855231ee888435c81adc6854755dff08578 SHA512: 497ff802338be6b83b292a8070bed6e8af8cb4bd28a46d75cdde3fc4ff66ff0db211db0fe41e26a42da15f1fc3f1657a6fd0981b4d267dd406be03bd1147f10b Homepage: https://cran.r-project.org/package=fractalRegression Description: CRAN Package 'fractalRegression' (Performs Fractal Analysis and Fractal Regression) Various functions for performing fractal and multifractal analysis including performing fractal regression. Please refer to Peng and colleagues (1994) , Kantelhardt and colleagues (2002), and Likens and colleagues (2019) . Package: r-cran-fractional Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-fractional_0.1.3-1.ca2404.1_amd64.deb Size: 149536 MD5sum: 45756457ff20e940868cc63498399727 SHA1: 7be6f39fa1cf7f45b7638021c4e38c751acbb900 SHA256: b44f35ef40d8e68550be3fde72861c838da5de752d7f90e6feb702dc58450c6d SHA512: 118cc712690682d51f16d3ed289e76306aafa1ff35128a1db87bbc7dc45e2361665c5bf97034730e7c25c00bc0c8ec4e77cbeb4b338f30711b520560cfa85d45 Homepage: https://cran.r-project.org/package=fractional Description: CRAN Package 'fractional' (Vulgar Fractions in R) The main function of this package allows numerical vector objects to be displayed with their values in vulgar fractional form. This is convenient if patterns can then be more easily detected. In some cases replacing the components of a numeric vector by a rational approximation can also be expected to remove some component of round-off error. The main functions form a re-implementation of the functions 'fractions' and 'rational' of the MASS package, but using a radically improved programming strategy. Package: r-cran-fracture Architecture: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-fracture_0.2.2-1.ca2404.1_amd64.deb Size: 115304 MD5sum: c42a097cd9b572dbac8acb3fc4f27b2b SHA1: 8629387909aadb5a11b0fadde332787d052d32bc SHA256: 63f077bc510ff9bd5f46604b4c210cfae7894339f8bf48cc69f8d15a2ec527bb SHA512: 1a5468a2ae60cd19f7c40aee67f228d2db85d1e3c2fbe99b2c6480c9bdcebec48623c60b31f381975a88caae5434a1ef15e6322f7dc1facf6af02680379620fe Homepage: https://cran.r-project.org/package=fracture Description: CRAN Package 'fracture' (Convert Decimals to Fractions) Provides functions for converting decimals to a matrix of numerators and denominators or a character vector of fractions. Supports mixed or improper fractions, finding common denominators for vectors of fractions, limiting denominators to powers of ten, and limiting denominators to a maximum value. Also includes helper functions for finding the least common multiple and greatest common divisor for a vector of integers. Implemented using C++ for maximum speed. Package: r-cran-frailtyem Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 856 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-magrittr, r-cran-msm, r-cran-ggplot2, r-cran-expint, r-cran-tibble, r-cran-matrix, r-cran-numderiv Suggests: r-cran-dplyr, r-cran-plotly, r-cran-gridextra, r-cran-egg Filename: pool/dists/noble/main/r-cran-frailtyem_1.0.1-1.ca2404.1_amd64.deb Size: 690678 MD5sum: dd58c6603b3da5281da400865c4ebd68 SHA1: 6ec646f0980a21abaf3e0d6eaa50ed27a783d8fc SHA256: a7a1be46c5c73862d48076ffc4c7f4b6d2363213e90865e32314327afc1502b6 SHA512: d87e9fd03264e73c97b91d26920f9d1e9f7a1ee5348497030d0b0098bcf0fe620af6009c6e77483756e6c902acfc23ce8607595fbe7c7e49eb821811da98de4a Homepage: https://cran.r-project.org/package=frailtyEM Description: CRAN Package 'frailtyEM' (Fitting Frailty Models with the EM Algorithm) Contains functions for fitting shared frailty models with a semi-parametric baseline hazard with the Expectation-Maximization algorithm. Supported data formats include clustered failures with left truncation and recurrent events in gap-time or Andersen-Gill format. Several frailty distributions, such as the the gamma, positive stable and the Power Variance Family are supported. Package: r-cran-frailtymmpen Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1499 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-numderiv, r-cran-mgcv, r-cran-rcpp, r-cran-rcppgsl Filename: pool/dists/noble/main/r-cran-frailtymmpen_1.2.1-1.ca2404.1_amd64.deb Size: 1311180 MD5sum: 836bc673ecba6469c1cc3ad2220557e1 SHA1: ce4bfedf38039b3179fab6b43932679ca16dd3be SHA256: e2214a457775a9d9544968a1e54590877d8c160ba6cf5ef51bd8c158014a3219 SHA512: c6ee54a9bc851945a847fd1e54a9d7cc2fa5d43825c3c95317eda3725204b1fda43f6817dfe0dfe1caade6df51b9f78b63b432dfc49484e93fe0d6cc570a44fa Homepage: https://cran.r-project.org/package=frailtyMMpen Description: CRAN Package 'frailtyMMpen' (Efficient Algorithm for High-Dimensional Frailty Model) The penalized and non-penalized Minorize-Maximization (MM) method for frailty models to fit the clustered data, multi-event data and recurrent data. Least absolute shrinkage and selection operator (LASSO), minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalized functions are implemented. All the methods are computationally efficient. These general methods are proposed based on the following papers, Huang, Xu and Zhou (2022) , Huang, Xu and Zhou (2023) . Package: r-cran-frailtypack Architecture: amd64 Version: 3.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9188 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), libgomp1 (>= 6), r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-doby, r-cran-mass, r-cran-survc1, r-cran-survival, r-cran-matrixcalc, r-cran-nlme, r-cran-rootsolve, r-cran-shiny, r-cran-statmod, r-cran-dplyr, r-cran-marqlevalg, r-cran-tidyr Suggests: r-cran-knitr, r-cran-timereg, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-frailtypack_3.8.0-1.ca2404.1_amd64.deb Size: 5635662 MD5sum: 1c03ddc08d56019377da99c5eb82d462 SHA1: 29111cb078440e73083aa21d4d2734e8727a5d2e SHA256: ec1b68d45b3f24dd8782564c40e15eefc6e66319638d8bfb5b8c2ec19ca914b3 SHA512: f08aa121a7dce9cfe9f95a62eb754b1bf4f5d36f0ab691c0347984cdc322c38295cc7248cc17ce89f02c97c860b917cfdc4670a273a93070217155540227dfe7 Homepage: https://cran.r-project.org/package=frailtypack Description: CRAN Package 'frailtypack' (Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints) The following several classes of frailty models using a penalized likelihood estimation on the hazard function but also a parametric estimation can be fit using this R package: 1) A shared frailty model (with gamma or log-normal frailty distribution) and Cox proportional hazard model. Clustered and recurrent survival times can be studied. 2) Additive frailty models for proportional hazard models with two correlated random effects (intercept random effect with random slope). 3) Nested frailty models for hierarchically clustered data (with 2 levels of clustering) by including two iid gamma random effects. 4) Joint frailty models in the context of the joint modelling for recurrent events with terminal event for clustered data or not. A joint frailty model for two semi-competing risks and clustered data is also proposed. 5) Joint general frailty models in the context of the joint modelling for recurrent events with terminal event data with two independent frailty terms. 6) Joint Nested frailty models in the context of the joint modelling for recurrent events with terminal event, for hierarchically clustered data (with two levels of clustering) by including two iid gamma random effects. 7) Multivariate joint frailty models for two types of recurrent events and a terminal event. 8) Joint models for longitudinal data and a terminal event. 9) Trivariate joint models for longitudinal data, recurrent events and a terminal event. 10) Joint frailty models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time and/or longitudinal endpoints with the possibility to use a mediation analysis model. 11) Conditional and Marginal two-part joint models for longitudinal semicontinuous data and a terminal event. 12) Joint frailty-copula models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time endpoints. 13) Generalized shared and joint frailty models for recurrent and terminal events. Proportional hazards (PH), additive hazard (AH), proportional odds (PO) and probit models are available in a fully parametric framework. For PH and AH models, it is possible to consider type-varying coefficients and flexible semiparametric hazard function. Prediction values are available (for a terminal event or for a new recurrent event). Left-truncated (not for Joint model), right-censored data, interval-censored data (only for Cox proportional hazard and shared frailty model) and strata are allowed. In each model, the random effects have the gamma or normal distribution. Now, you can also consider time-varying covariates effects in Cox, shared and joint frailty models (1-5). The package includes concordance measures for Cox proportional hazards models and for shared frailty models. 14) Competing Joint Frailty Model: A single type of recurrent event and two terminal events. 15) functions to compute power and sample size for four Gamma-frailty-based designs: Shared Frailty Models, Nested Frailty Models, Joint Frailty Models, and General Joint Frailty Models. Each design includes two primary functions: a power function, which computes power given a specified sample size; and a sample size function, which computes the required sample size to achieve a specified power. 16) Weibull Illness-Death model with or without shared frailty between transitions. Left-truncated and right-censored data are allowed. 17) Weibull Competing risks model with or without shared frailty between the transitions. Left-truncated and right-censored data are allowed. Moreover, the package can be used with its shiny application, in a local mode or by following the link below. Package: r-cran-frailtysurv Architecture: amd64 Version: 1.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 946 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-nleqslv, r-cran-reshape2, r-cran-ggplot2, r-cran-numderiv, r-cran-rcpp Suggests: r-cran-knitr, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-frailtysurv_1.3.8-1.ca2404.1_amd64.deb Size: 665940 MD5sum: 1554a58a9dd005d8e7521c5582d4838f SHA1: 9a33156e31306f252aaff948f059091de4be288b SHA256: f1936b88514e5d951987f2cefa2e1b7b966fe0dfe26ef7c56d6e4ca9dc42466a SHA512: 99746a044d5b4c021152238ead654a56c8a752cbe91f6d43634c139b8d0c6147b8d64c57747d25c3a04ed2bdb6cecfb2fd1c5f0ba87f7237b8867752a92c9bc4 Homepage: https://cran.r-project.org/package=frailtySurv Description: CRAN Package 'frailtySurv' (General Semiparametric Shared Frailty Model) Simulates and fits semiparametric shared frailty models under a wide range of frailty distributions using a consistent and asymptotically-normal estimator. Currently supports: gamma, power variance function, log-normal, and inverse Gaussian frailty models. Package: r-cran-free Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-free_1.0.2-1.ca2404.1_amd64.deb Size: 46358 MD5sum: 9bf7cfcd8cd38bf3a3329aa9239af1bf SHA1: a057cddc6516230c5365bac2e8888362ec8520ff SHA256: 48de30f07fac8f791acd0941d2f6e80921f67bf0903ec21e95941e5c8603755b SHA512: 74878bf4754076b7ade00d897746a90f12a87030e20f9e385775d8a62c233ec56922050d824d8788e687f19439d59e2fdead087563c50504c8807b65a0cbadbc Homepage: https://cran.r-project.org/package=free Description: CRAN Package 'free' (Flexible Regularized Estimating Equations) Unified regularized estimating equation solver. Currently the package includes one solver with the l1 penalty only. More solvers and penalties are under development. Reference: Yi Yang, Yuwen Gu, Yue Zhao, Jun Fan (2021) . 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Uses 'disordR' discipline (Hankin, 2022, ). To cite the package in publications please use Hankin (2022) . Package: r-cran-freebird Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-scalreg, r-cran-rmosek, r-cran-matrix, r-cran-mass Filename: pool/dists/noble/main/r-cran-freebird_1.0-1.ca2404.1_amd64.deb Size: 54882 MD5sum: 85327901c033b11f26d9a2aad0e9f529 SHA1: 387e13ee8e7f53160df8eb1163b3da633356f6c6 SHA256: 35e373ee3fc826ec76caf3e231751a11acb93216a74fbfa223140908d1fdcfe7 SHA512: cf5e2612b77d7c4c15dd6902181bbf07280b24aa71883aae8e5b7b867c44aa9dbea316673c8551b7f4532d0e00357c257a7d07ef36411f13dd44b2da4df3c70c Homepage: https://cran.r-project.org/package=freebird Description: CRAN Package 'freebird' (Estimation and Inference for High Dimensional Mediation andSurrogate Analysis) Estimates and provides inference for quantities that assess high dimensional mediation and potential surrogate markers including the direct effect of treatment, indirect effect of treatment, and the proportion of treatment effect explained by a surrogate/mediator; details are described in Zhou et al (2022) and Zhou et al (2020) . This package relies on the optimization software 'MOSEK', . Package: r-cran-freestiler Architecture: amd64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4632 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf Suggests: r-cran-arrow, r-cran-dbi, r-cran-duckdb, r-cran-httpuv, r-cran-jsonlite, r-cran-mapgl, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-freestiler_0.1.7-1.ca2404.1_amd64.deb Size: 2381214 MD5sum: 6785daf77045b63c147e231f50d23489 SHA1: 1ab0c80b344f6b5add67450749201c086444dc8b SHA256: 47217a79af21025735a981a84997ccb39a3a773d0942c1bc349f6fb3674a22c2 SHA512: 010364dca6e28df20ff035cd11641b9f23e83b151d09a7c8f0330cf8ea278f244512ee07c594e895eba6bd5253662eac6c02dba7ce6c0d21e44b112e1f7b347c Homepage: https://cran.r-project.org/package=freestiler Description: CRAN Package 'freestiler' (Create Vector Tiles from Spatial Data) Create vector tile archives in 'PMTiles' format from 'sf' spatial data frames. 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Package: r-cran-fresa.cad Architecture: amd64 Version: 3.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3469 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-stringr, r-cran-misctools, r-cran-hmisc, r-cran-proc, r-cran-rcpparmadillo Suggests: r-cran-nlme, r-cran-rpart, r-cran-gplots, r-cran-rcolorbrewer, r-cran-class, r-cran-cvtools, r-cran-glmnet, r-cran-randomforest, r-cran-survival, r-cran-e1071, r-cran-mass, r-cran-naivebayes, r-cran-mrmre, r-cran-epir, r-cran-desctools, r-cran-irr, r-cran-survminer, r-cran-bess, r-cran-ggplot2, r-cran-robustbase, r-cran-mda, r-cran-twosamples, r-cran-rfast, r-cran-whitening, r-cran-corrplot Filename: pool/dists/noble/main/r-cran-fresa.cad_3.4.8-1.ca2404.1_amd64.deb Size: 2959226 MD5sum: 3e6ea271cd28ef9222b305b05a6d8272 SHA1: 9854833c207b5fc21efb41350022a8bdf4f0c8b3 SHA256: a5681d9a29e743bfb12b6bf1d5f6e2bfd9b6473b2c4cd1385e9b81b96df1a7e8 SHA512: f444472fd6274eaf3e595f8a063fbb150c457bb608f7dd21304c3f208e1a8de1fb276cebabb7fd79340902639565093f9962d0acdc22f6dd819803f344e7e55d Homepage: https://cran.r-project.org/package=FRESA.CAD Description: CRAN Package 'FRESA.CAD' (Feature Selection Algorithms for Computer Aided Diagnosis) Contains a set of utilities for building and testing statistical models (linear, logistic,ordinal or COX) for Computer Aided Diagnosis/Prognosis applications. Utilities include data adjustment, univariate analysis, model building, model-validation, longitudinal analysis, reporting and visualization. Package: r-cran-freshd Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-glamlasso, r-cran-rcpparmadillo, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-freshd_1.0-1.ca2404.1_amd64.deb Size: 180198 MD5sum: b847914624455671b33ccb9ae7ee10c5 SHA1: 73461b3a3422e75ae19c5050d9076575ce2576b8 SHA256: d66abafbfb771c60c32b9b6e7c8e6e37520ae7019cb7b050551362bc42ecac66 SHA512: 28ebc95a7fce710b7de047e4d187b171f7d32a10a6eafa6f25fd640bd493747aa2fbdc2724a46a8dd166392d6ffe7a166b060e1de08ca5ccb1b9f6a68c7148a7 Homepage: https://cran.r-project.org/package=FRESHD Description: CRAN Package 'FRESHD' (Fast Robust Estimation of Signals in Heterogeneous Data) Procedure for solving the maximin problem for identical design across heterogeneous data groups. Particularly efficient when the design matrix is either orthogonal or has tensor structure. Orthogonal wavelets can be specified for 1d, 2d or 3d data simply by name. For tensor structured design the tensor components (two or three) may be supplied. The package also provides an efficient implementation of the generic magging estimator. Package: r-cran-frk Architecture: amd64 Version: 2.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9212 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-dplyr, r-cran-fmesher, r-cran-ggplot2, r-cran-hmisc, r-cran-matrix, r-cran-plyr, r-cran-rcpp, r-cran-sp, r-cran-spacetime, r-cran-sparseinv, r-cran-statmod, r-cran-tmb, r-cran-ggpubr, r-cran-reshape2, r-cran-scales, r-cran-rcppeigen Suggests: r-cran-covr, r-cran-gridextra, r-cran-gstat, r-cran-knitr, r-cran-lme4, r-cran-mapproj, r-cran-sf, r-cran-spdep, r-cran-splancs, r-cran-testthat, r-cran-verification Filename: pool/dists/noble/main/r-cran-frk_2.3.2-1.ca2404.1_amd64.deb Size: 7534962 MD5sum: 68fef13eb3a9ba715f66df98f0e5383a SHA1: 0b7a4ee13769bf2c957f7c50d4e53138502ac981 SHA256: 9d6c967e9c5d5b945527caf118c649169f3e4822bf9826a1c4a5241188b9b392 SHA512: 7ff07b543a66a3adee06cfa7616fea6c8cd88e28ba15fd4c4a6bf786f888ec7a8ee257f026cd7a8cb60a5a7f6ed07a652ec05a1e128a9b61f27e12d63fe41de2 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. 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Handles both classification and regression, as well as provides permutation feature importance via a novel, highly optimised algorithm. 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The algorithm finds potentially high-order interactions in high-dimensional binary two-class classification data, without requiring lower order interactions to be informative. The search is particularly fast when the matrices of predictors are sparse. It can also be used to perform market basket analysis when supplied with a single binary data matrix. Here it will find collections of columns which for many rows contain all 1's. 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Package: r-cran-func2vis Architecture: amd64 Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-devtools, r-cran-ggrepel, r-cran-randomcolor Filename: pool/dists/noble/main/r-cran-func2vis_1.0-3-1.ca2404.1_amd64.deb Size: 293400 MD5sum: 560397f1203c54e7f0271cc98fbcca90 SHA1: 85a133e53f40175b38b06f49bdf2947c9f282ed8 SHA256: 2fb0e30624e76dfaa9aaf6cbeec5da94888826d6ae396810cb46f7c69e92ff0a SHA512: 1eb3b8ebf51326a52ae7da52187a95e6afbf942707aa1f8878ba48e25f7f9df4a2322358513f22a8811d2fb4ba025974f07549e6f97ff6a97177f45d4045ecaf Homepage: https://cran.r-project.org/package=func2vis Description: CRAN Package 'func2vis' (Clean and Visualize Over Expression Results from'ConsensusPathDB') Provides functions to have visualization and clean-up of enriched gene ontologies (GO) terms, protein complexes and pathways (obtained from multiple databases) using 'ConsensusPathDB' from gene set over-expression analysis. Performs clustering of pathway based on similarity of over-expressed gene sets and visualizations similar to Ingenuity Pathway Analysis (IPA) when up and down regulated genes are known. The methods are described in a paper currently submitted by Orecchioni et al, 2020 in Nanoscale. 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See 'GitHub' repository for the tutorial: . Citation: Gavin M. Douglas, Sunu Kim, Morgan G. I. Langille, B. Jesse Shapiro (2023) . Package: r-cran-funcharts Architecture: amd64 Version: 1.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1852 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase, r-cran-dplyr, r-cran-ggplot2, r-cran-patchwork, r-cran-tidyr, r-cran-rcpp, r-cran-fda, r-cran-fda.usc, r-cran-roahd, r-cran-rrcov, r-cran-rfast, r-cran-mgcv, r-cran-scam, r-cran-fdapace, r-cran-rspectra, r-cran-mass, r-cran-rofanova, r-cran-spatstat.univar, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-sn Filename: pool/dists/noble/main/r-cran-funcharts_1.8.1-1.ca2404.1_amd64.deb Size: 1503168 MD5sum: 7b92764cf22533ed8d2e1a824fc3cc84 SHA1: 2f0373657aad88b8636e0fdd2cb2eb8c26e1a624 SHA256: 7a1ad4f4dae37d279320a6ead63db525fc9aab3088410893ed488452960b5302 SHA512: 29059c504845b33f419f1ad2812c5901f57df2bc232bacc1aabeec2077c0c26c99cf576ec49252a9c63b1fb752d236124e55d0b847ac9ff76eff4f3b1a02bb6c 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) . 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Functional test statistics are asymmetric and functionally optimal, unique from other related statistics. Tests in this package reveal evidence for causality based on the causality-by- functionality principle. They include asymptotic functional chi-squared tests (Zhang & Song 2013) , an adapted functional chi-squared test (Kumar & Song 2022) , and an exact functional test (Zhong & Song 2019) (Nguyen et al. 2020) . The normalized functional chi-squared test was used by Best Performer 'NMSUSongLab' in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges (Hill et al. 2016) . A function index (Zhong & Song 2019) (Kumar et al. 2018) derived from the functional test statistic offers a new effect size measure for the strength of functional dependency, a better alternative to conditional entropy in many aspects. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependency not possible with symmetrical Pearson's chi-squared or Fisher's exact tests. Package: r-cran-funitroots Architecture: amd64 Version: 4052.82-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-timeseries, r-cran-fbasics, r-cran-urca Suggests: r-cran-runit, r-cran-interp Filename: pool/dists/noble/main/r-cran-funitroots_4052.82-1.ca2404.1_amd64.deb Size: 603362 MD5sum: 68374f0e4c6ffe958ebeb6068dd5a907 SHA1: 8d933b0298ed4e6ba03f31164d58ac158246ebe9 SHA256: 8b4fdc75da005c5da5c5ce88054e05fdb2e23bff691e79349fd3de18861dc8d2 SHA512: cf0fd4d80e97e8f1dbd7752b65665fe506cdf39a02074e47d25adfdf10c65feec7dae2f9c234ac783f81f5db7d8bd782b70a6b21a317d8c5c5e467d994bb16b5 Homepage: https://cran.r-project.org/package=fUnitRoots Description: CRAN Package 'fUnitRoots' (Rmetrics - Modelling Trends and Unit Roots) Provides four addons for analyzing trends and unit roots in financial time series: (i) functions for the density and probability of the augmented Dickey-Fuller Test, (ii) functions for the density and probability of MacKinnon's unit root test statistics, (iii) reimplementations for the ADF and MacKinnon Test, and (iv) an 'urca' Unit Root Test Interface for Pfaff's unit root test suite. Package: r-cran-funmodisco Architecture: amd64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5897 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-progress, r-cran-rcpp, r-cran-dendextend, r-cran-fastcluster, r-cran-fda, r-cran-ggtext, r-cran-purrr, r-cran-scales, r-cran-class, r-cran-combinat, r-cran-data.table, r-cran-stringr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-kableextra, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-funmodisco_1.1.5-1.ca2404.1_amd64.deb Size: 5471276 MD5sum: 9f219df6bb28d1d1bbe3b3deb95a596a SHA1: 3c01ae60bcf8c1d53db09d5807051a013856dbb7 SHA256: c56215e9055ded7d8eebea4e1eaa27d8ce6d22ab29b0ec9d7d9ebee565ca34eb SHA512: daaf263f25db29b059a1e64ffbe8d61c3dd49dbe8e289373d478853c43783b95b230a50e64ee35d3661c784775d4d62be4e8878c601e8ef695fc7de74647a447 Homepage: https://cran.r-project.org/package=funMoDisco Description: CRAN Package 'funMoDisco' (Motif Discovery in Functional Data) Efficiently implementing two complementary methodologies for discovering motifs in functional data: ProbKMA and FunBIalign. Cremona and Chiaromonte (2023) "Probabilistic K-means with Local Alignment for Clustering and Motif Discovery in Functional Data" is a probabilistic K-means algorithm that leverages local alignment and fuzzy clustering to identify recurring patterns (candidate functional motifs) across and within curves, allowing different portions of the same curve to belong to different clusters. It includes a family of distances and a normalization to discover various motif types and learns motif lengths in a data-driven manner. It can also be used for local clustering of misaligned data. Di Iorio, Cremona, and Chiaromonte (2023) "funBIalign: A Hierarchical Algorithm for Functional Motif Discovery Based on Mean Squared Residue Scores" applies hierarchical agglomerative clustering with a functional generalization of the Mean Squared Residue Score to identify motifs of a specified length in curves. This deterministic method includes a small set of user-tunable parameters. Both algorithms are suitable for single curves or sets of curves. The package also includes a flexible function to simulate functional data with embedded motifs, allowing users to generate benchmark datasets for validating and comparing motif discovery methods. Package: r-cran-fuser Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-irlba, r-cran-rcpp, r-cran-glmnet, r-cran-rspectra, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fuser_1.0.1-1.ca2404.1_amd64.deb Size: 168428 MD5sum: 6bbf5ebc1a312d6e625338954f474d80 SHA1: b4340576c8b568f78769a8aa83086e1ed8344af1 SHA256: 59f97bdbd677eea2882053244bbbfcb15daaa9fd3bb44972578ed3cf67381566 SHA512: 5f613a825a4f33bb97f12cff5ec2a1555a520130b17e5a43d84bcfaec4bc3ec70a0ed05ad854695013671709ae4a06add630ac02c9d9bca03735f5dae0c836ca Homepage: https://cran.r-project.org/package=fuser Description: CRAN Package 'fuser' (Fused Lasso for High-Dimensional Regression over Groups) Enables high-dimensional penalized regression across heterogeneous subgroups. Fusion penalties are used to share information about the linear parameters across subgroups. The underlying model is described in detail in Dondelinger and Mukherjee (2017) . Package: r-cran-fuzzyimputationtest Architecture: amd64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fuzzysimres, r-cran-fuzzynumbers, r-cran-missforest, r-cran-miceranger, r-cran-vim, r-cran-fuzzyresampling, r-cran-mice Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fuzzyimputationtest_0.5.0-1.ca2404.1_amd64.deb Size: 141538 MD5sum: 1657909bc12459022fefb18463f4f257 SHA1: 31ab05663ffc297a9dc5b4bd0aa32e6a0db22a85 SHA256: 84926d24547ae4ab9b146d7a9005d33158a5e879916de96b847865e634f159d5 SHA512: 707cab88bcfd234e64058f3576f8715e37ea2131f0e98072cd07e8d17c9dc0d1011b9290e2a02a5d6b741374e24dc6873271b4764b5af1dfd0a27e226c0074c8 Homepage: https://cran.r-project.org/package=FuzzyImputationTest Description: CRAN Package 'FuzzyImputationTest' (Imputation Procedures and Quality Tests for Fuzzy Data) Special procedures for the imputation of missing fuzzy numbers are still underdeveloped. The goal of the package is to provide the new d-imputation method (DIMP for short, Romaniuk, M. and Grzegorzewski, P. (2023) "Fuzzy Data Imputation with DIMP and FGAIN" RB/23/2023) and covert some classical ones applied in R packages ('missForest','miceRanger','knn') for use with fuzzy datasets. Additionally, specially tailored benchmarking tests are provided to check and compare these imputation procedures with fuzzy datasets. Package: r-cran-fuzzyranktests Architecture: amd64 Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 657 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-fuzzyranktests_0.5-1.ca2404.1_amd64.deb Size: 520968 MD5sum: f49689dc8100bf90bccf768773995a60 SHA1: 83b38fb6be42bd38c4c220976b995d08e7cdbcaf SHA256: a9c840d9eaf60bd9064278a1ffbe1e3e764ce26357be0948ef15d58b85b36191 SHA512: 01b71ece95c010e3b4260749e5149346bb43b992e43bb062488027921014342b0ea050a86aec5aefc98fcf0dc6fffd84f12256b168a5574b0da2ff030481df8d Homepage: https://cran.r-project.org/package=fuzzyRankTests Description: CRAN Package 'fuzzyRankTests' (Fuzzy Rank Tests and Confidence Intervals) Does fuzzy tests and confidence intervals (following Geyer and Meeden, Statistical Science, 2005, ) for sign test and Wilcoxon signed rank and rank sum tests. Package: r-cran-fuzzysimres Architecture: amd64 Version: 0.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 503 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fuzzynumbers, r-cran-palasso Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-fuzzysimres_0.4.8-1.ca2404.1_amd64.deb Size: 434788 MD5sum: c068e82ed8bee7cee732fbb2c1126dbc SHA1: ee9f2980c912e45546cdbaf1d7552508b2a65c4c SHA256: bef8a7a60faac05706c42ec61f86aacb63157388cf847f27fb61939007b2e96f SHA512: 91abfcd1db134dc4c5e7d52525c20e63a570d045c6f03432b2e56330e70b6aa9f1b121afeb2686de6117b16f8c753a2414598e261ac45a2cf85bf7144c75c09d Homepage: https://cran.r-project.org/package=FuzzySimRes Description: CRAN Package 'FuzzySimRes' (Simulation and Resampling Methods for Epistemic Fuzzy Data) Random simulations of fuzzy numbers are still a challenging problem. The aim of this package is to provide the respective procedures to simulate fuzzy random variables, especially in the case of the piecewise linear fuzzy numbers (PLFNs, see Coroianua et al. (2013) for the further details). Additionally, the special resampling algorithms known as the epistemic bootstrap are provided (see Grzegorzewski and Romaniuk (2022) , Grzegorzewski and Romaniuk (2022) , Romaniuk et al. (2024) ) together with the functions to apply statistical tests and estimate various characteristics based on the epistemic bootstrap. The package also includes real-life datasets of epistemic fuzzy triangular and trapezoidal numbers. The fuzzy numbers used in this package are consistent with the 'FuzzyNumbers' package. Package: r-cran-fuzzystring Architecture: amd64 Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 636 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rcpp, r-cran-stringdist Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-qdapdictionaries, r-cran-readr, r-cran-rmarkdown, r-cran-rvest, r-cran-stringr, r-cran-testthat, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-fuzzystring_0.0.5-1.ca2404.1_amd64.deb Size: 349696 MD5sum: fa1504e45a9cab54c0d317614ec1ab58 SHA1: 137b7fd1a9583b92a4f44c2e92fca89658423066 SHA256: a394ccb1ec8df11ba629becab21be07caa2b0da4dc8a3ecd82fa8f3a9fdedaad SHA512: 8048499c8ab151e5260ba176fdbd571c7d74fa0f52ccb41043e693798206ea408523b92dcc54b0aef5eaf1f6ac61131953e91e0024d0939d53e377a92da7ea81 Homepage: https://cran.r-project.org/package=fuzzystring Description: CRAN Package 'fuzzystring' (Fast Fuzzy String Joins for Data Frames) Perform fuzzy joins on data frames using approximate string matching. Implements inner, left, right, full, semi, and anti joins with string distance metrics from the 'stringdist' package, including Optimal String Alignment, Levenshtein, Damerau-Levenshtein, Jaro-Winkler, q-gram, cosine, Jaccard, and Soundex. Uses a 'data.table' backend plus compiled 'C++' result assembly to reduce overhead in large joins, while adaptive candidate planning avoids unnecessary distance evaluations in single-column string joins. Suitable for reconciling misspellings, inconsistent labels, and other near-match identifiers while optionally returning the computed distance for each match. Package: r-cran-fvddppkg Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-fvddppkg_0.1.2-1.ca2404.1_amd64.deb Size: 165714 MD5sum: 34e26b6ecd8c178777d0d7bc7e4801bd SHA1: 4029786267c92d6a7d47b4f1537bfdf4c78f18cd SHA256: 0bd92bceb75e1c7471cdba493e16dfcfb3feff45824ffd2b15e00a051e8fa2d7 SHA512: 9e8916deadcc22f223bda5947a6ed9b4a50988f7ac4598a906480f0444b4fe78773fb27f528afe739464454570cb449b3b8fe919f58bb15f0b5312daadb33cf4 Homepage: https://cran.r-project.org/package=FVDDPpkg Description: CRAN Package 'FVDDPpkg' (Implement Fleming-Viot-Dependent Dirichlet Processes) A Bayesian Nonparametric model for the study of time-evolving frequencies, which has become renowned in the study of population genetics. The model consists of a Hidden Markov Model (HMM) in which the latent signal is a distribution-valued stochastic process that takes the form of a finite mixture of Dirichlet Processes, indexed by vectors that count how many times each value is observed in the population. The package implements methodologies presented in Ascolani, Lijoi and Ruggiero (2021) and Ascolani, Lijoi and Ruggiero (2023) that make it possible to study the process at the time of data collection or to predict its evolution in future or in the past. Package: r-cran-fwildclusterboot Architecture: amd64 Version: 0.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1281 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-collapse, r-cran-dreamerr, r-cran-formula, r-cran-generics, r-cran-dqrng, r-cran-gtools, r-cran-matrix, r-cran-juliaconnector, r-cran-mass, r-cran-rcpp, r-cran-summclust, r-cran-rlang, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-fixest, r-cran-lfe, r-cran-ivreg, r-cran-clubsandwich, r-cran-lmtest, r-cran-data.table, r-cran-fabricatr, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-broom, r-cran-modelsummary, r-cran-bench, r-cran-testthat, r-cran-tibble, r-cran-sandwich Filename: pool/dists/noble/main/r-cran-fwildclusterboot_0.13.0-1.ca2404.1_amd64.deb Size: 932002 MD5sum: bfc46d3cd772784277ccf8e4933cb01f SHA1: ec8ea575e1d6a77a1b437920733b61b9bc6f31f1 SHA256: a6c98cbf134b51240606fe7a8cb5b7f302498b4121f56b20d1b59131d5857896 SHA512: a9c5575f9c68d250ce6fd613de511baf860b74bda9352d7d282c00bd9f430eacf557b2565820a9fab5805e6279f21e016158dc2131344d05e65327f90745f23e Homepage: https://cran.r-project.org/package=fwildclusterboot Description: CRAN Package 'fwildclusterboot' (Fast Wild Cluster Bootstrap Inference for Linear Models) Implementation of fast algorithms for wild cluster bootstrap inference developed in 'Roodman et al' (2019, 'STATA' Journal, ) and 'MacKinnon et al' (2022), which makes it feasible to quickly calculate bootstrap test statistics based on a large number of bootstrap draws even for large samples. Multiple bootstrap types as described in 'MacKinnon, Nielsen & Webb' (2022) are supported. Further, 'multiway' clustering, regression weights, bootstrap weights, fixed effects and 'subcluster' bootstrapping are supported. Further, both restricted ('WCR') and unrestricted ('WCU') bootstrap are supported. Methods are provided for a variety of fitted models, including 'lm()', 'feols()' (from package 'fixest') and 'felm()' (from package 'lfe'). Additionally implements a 'heteroskedasticity-robust' ('HC1') wild bootstrap. Last, the package provides an R binding to 'WildBootTests.jl', which provides additional speed gains and functionality, including the 'WRE' bootstrap for instrumental variable models (based on models of type 'ivreg()' from package 'ivreg') and hypotheses with q > 1. Package: r-cran-fwsim Architecture: amd64 Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-fwsim_0.3.4-1.ca2404.1_amd64.deb Size: 130014 MD5sum: d7fc95a2e6104484da777d860691bed6 SHA1: 606ad1b19dd081b9a4d63ec8e2375d90662f0d6d SHA256: 177f59046ebed3badfff17c2b55ad3517703c51275767797d21f417c90d38367 SHA512: 5092e4c765989628a8d1025ad032e19d8629e81d23e2d1990cf02075c5ee1e745c7b9df4a5968dc9100a3429b5ee5c2fb84bae5942712dc7e5d4846efde2c096 Homepage: https://cran.r-project.org/package=fwsim Description: CRAN Package 'fwsim' (Fisher-Wright Population Simulation) Simulates a population under the Fisher-Wright model (fixed or stochastic population size) with a one-step neutral mutation process (stepwise mutation model, logistic mutation model and exponential mutation model supported). The stochastic population sizes are random Poisson distributed and different kinds of population growth are supported. For the stepwise mutation model, it is possible to specify locus and direction specific mutation rate (in terms of upwards and downwards mutation rate). Intermediate generations can be saved in order to study e.g. drift. Package: r-cran-ga Architecture: amd64 Version: 3.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43898 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-iterators, r-cran-cli, r-cran-crayon, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-doparallel, r-cran-dorng, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ga_3.2.5-1.ca2404.1_amd64.deb Size: 2514856 MD5sum: 6dd12d0ffc9aea656d1b5648b8673b0d SHA1: 264ee08195bd7ff3b8d3b58bc03b1450fb0cfe34 SHA256: 87165a32910ed84867fa11fffce43b50c5e856061553a5db700995a94a8dcf9e SHA512: c4a8b5a5c265b6e5c46bf5a16a57bd28d00c36b1e80257ea64cc7b4d9d8a4bb5917957e3bfb3fea57bc7d27f37e7692934edd784df7d5d0ff31443d5e380066b Homepage: https://cran.r-project.org/package=GA Description: CRAN Package 'GA' (Genetic Algorithms) Flexible general-purpose toolbox implementing genetic algorithms (GAs) for stochastic optimisation. Binary, real-valued, and permutation representations are available to optimize a fitness function, i.e. a function provided by users depending on their objective function. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. Local search using general-purpose optimisation algorithms can be applied stochastically to exploit interesting regions. GAs can be run sequentially or in parallel, using an explicit master-slave parallelisation or a coarse-grain islands approach. For more details see Scrucca (2013) and Scrucca (2017) . Package: r-cran-gadag Architecture: amd64 Version: 0.99.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-gadag_0.99.0-1.ca2404.1_amd64.deb Size: 120620 MD5sum: 6116b01b21d5b2b29eb6273844395359 SHA1: 9197c1987f3443e494db1fd1e33b68efdba44674 SHA256: 351db07d777e6c169ded0b5c4c5c987e31cc28476fdbc350cce89ffef10b2b9c SHA512: 8d1ad5f88a60264c82282aa853cb3eeef25f7bb31a6c20858718b6a3ca9daf7be7c93971cbdb4d0e99ba41b8ca1439431845cee63128cf5506051b44f18514de Homepage: https://cran.r-project.org/package=GADAG Description: CRAN Package 'GADAG' (A Genetic Algorithm for Learning Directed Acyclic Graphs) Sparse large Directed Acyclic Graphs learning with a combination of a convex program and a tailored genetic algorithm (see Champion et al. (2017) ). Package: r-cran-gadget2 Architecture: amd64 Version: 2.3.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1010 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-unittest Filename: pool/dists/noble/main/r-cran-gadget2_2.3.11-1.ca2404.1_amd64.deb Size: 349828 MD5sum: 94678e40e7f1ddba6d06e7c27f9f8ba0 SHA1: f38682957fdd7c3d65a504e352604311afe12d65 SHA256: e59196a1af8f308da1340b5303bc0624450b7634741544b5d8043d695543ee46 SHA512: 576ae2949f76ee6e255a757bd8137f76713970299d75a9259976916c04966b3412c43d91e4685d6c962b1d7788f25ca88b2910fc4384ed57f0d70eb16732dcb4 Homepage: https://cran.r-project.org/package=gadget2 Description: CRAN Package 'gadget2' (Gadget is the Globally-Applicable Area Disaggregated GeneralEcosystem Toolbox) A statistical ecosystem modelling package, taking many features of the ecosystem into account. Gadget works by running an internal model based on many parameters, and then comparing the data from the output of this model to real data to get a goodness-of-fit likelihood score. These parameters can then be adjusted, and the model re-run, until an optimum is found, which corresponds to the model with the lowest likelihood score. Gadget allows the user to include a number of features into an ecosystem model: One or more species, each of which may be split into multiple stocks; multiple areas with migration between areas; predation between and within species; maturation; reproduction and recruitment; multiple commercial and survey fleets taking catches from the populations. For more details see . This is the C++ Gadget2 runtime, making it available for R. Package: r-cran-gadjid Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 750 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gadjid_0.1.0-1.ca2404.1_amd64.deb Size: 314160 MD5sum: d7cd8a92603793a4c0177fb5d11490a8 SHA1: fc0984bf89f0b30bf5d16167ea56b2a217c78033 SHA256: 4dcacdd1c0ef557bce15cb162e853162b2f4ee2b0c9c3e4ef1b1d00d9cc04169 SHA512: d196c62157d982b0fa3b91bbe3c67672a32ce861733dd59c1c902b74c6942dd5a9f16624b5196798b74d6b4d9a09e405a35746d5d572db7337903bb007dc0f9c 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) . 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The genetic algorithm attempts to make the result of the expression as low as possible (usually this would be the sum of residuals squared). Package: r-cran-gagas Architecture: amd64 Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 727 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-rcppeigen Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-gagas_0.6.2-1.ca2404.1_amd64.deb Size: 283008 MD5sum: ba3a887db200d8c32548a8340dd1c51b SHA1: 24284c3804691b6a4b35c0c9ac7f7e155d9fe9d5 SHA256: ef5944f85c078ad3006bc37f2c53cbf71ec619de7a76ce64012bf6d55cfe9ab9 SHA512: 214f39be4593cb80d7418fd8e31a623478593684c0a83ba66f0716bf9271629878370f917246b1b74eaee8c0f32b294f7fabd8f3511be0277cec91bb577e3199 Homepage: https://cran.r-project.org/package=GAGAs Description: CRAN Package 'GAGAs' (Global Adaptive Generative Adjustment Algorithm for GeneralizedLinear Models) Fits linear regression, logistic and multinomial regression models, Poisson regression, Cox model via Global Adaptive Generative Adjustment Algorithm. For more detailed information, see Bin Wang, Xiaofei Wang and Jianhua Guo (2022) . This paper provides the theoretical properties of Gaga linear model when the load matrix is orthogonal. Further study is going on for the nonorthogonal cases and generalized linear models. These works are in part supported by the National Natural Foundation of China (No.12171076). Package: r-cran-galamm Architecture: amd64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5021 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gratia, r-cran-lattice, r-cran-lme4, r-cran-matrix, r-cran-memoise, r-cran-mgcv, r-cran-nlme, r-cran-rcpp, r-cran-rdpack, r-cran-reformulas, r-cran-rcppeigen Suggests: r-cran-covr, r-cran-gamm4, r-cran-knitr, r-cran-plmixed, r-cran-rlang, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-galamm_0.4.0-1.ca2404.1_amd64.deb Size: 2991048 MD5sum: 2d77ff236276c442efde1a8ab0ed6888 SHA1: 87264e9d00fba97f7eaada3e7b2a45b40b3a5c8b SHA256: 81af62d4cece658c039ce274d665989f098753186beebfc203a4778cc3e5be58 SHA512: fe995285879401fb37a61305dd4040070f370f1e1b43ea9ee8cf3b8cc9c9be2f84b063a9bb56d90935101721da2fab223275ce20ef15e718a49b8585756ae5f8 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) ). 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Currently, any symmetric two by two matrix is allowed, and strategies can remember the history of an opponent's play from the previous three rounds of moves in iterated interactions between players. The genetic algorithm returns a list of adaptive strategies given payoffs, and the mean fitness of strategies in each generation. 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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 . 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Package: r-cran-gamsel Architecture: amd64 Version: 1.8-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 826 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-mda Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gamsel_1.8-5-1.ca2404.1_amd64.deb Size: 356948 MD5sum: 2491cc61e363378194c2ac499a5cb813 SHA1: 95c717b7dd34e36ffde1ca482dcc2bce2eda41e7 SHA256: c3ef2c99f33cd66fb686ebf6b43a1d4eaaaf3dd2e024bfb2be04756ac7629c30 SHA512: 6770132807a160791f8496462e8bc2f1a5e8607113623f1c30d51d39e8d6d86a667dfeee5f510ba1f38df354347e1db5988fa3c97796585c2b025e457bc13918 Homepage: https://cran.r-project.org/package=gamsel Description: CRAN Package 'gamsel' (Fit Regularization Path for Generalized Additive Models) Using overlap grouped-lasso penalties, 'gamsel' selects whether a term in a 'gam' is nonzero, linear, or a non-linear spline (up to a specified max df per variable). 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Package: r-cran-gamselbayes Architecture: amd64 Version: 2.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1253 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ecdat Filename: pool/dists/noble/main/r-cran-gamselbayes_2.0-3-1.ca2404.1_amd64.deb Size: 915414 MD5sum: 627e71caa58d1cc96731a420a1ad1bca SHA1: a8f3186c6aab7eee74a9cd327b36de739b302ae2 SHA256: 7854527066b4cf6e85f046a1bd8ba4560c621e11ff1bc5075d0ca3886490f2fe SHA512: 6a0b0f9bd183056f9ff41bc2dad99f284f2c45fe438011febdc73fff3ab5d75ca8b859df3a0e3051c45bf6a79ed751972b1e46684b963733230d443d32e34b84 Homepage: https://cran.r-project.org/package=gamselBayes Description: CRAN Package 'gamselBayes' (Bayesian Generalized Additive Model Selection) Generalized additive model selection via approximate Bayesian inference is provided. 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Package: r-cran-gamstransfer Architecture: amd64 Version: 3.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1262 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-r.utils, r-cran-collections Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gamstransfer_3.0.8-1.ca2404.1_amd64.deb Size: 782518 MD5sum: 36def62f7217da161b2e8fc431de8c5f SHA1: a8d7ab0026d8d2e72f84544d9bf5ab0bfdc0bcd2 SHA256: 497aa0b61c26b0e624b501e8b574d92926b0db3aebb827d4ddfcabc531f074cd SHA512: 0f857b3e7278724a7307d479a7770b66faf7748074d56e961d98f3ebea0064ac9e9a20c9814cc5dfc8acccf9210926f8de2604540c233749dd4049ade3ac65e4 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. 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Package: r-cran-gandatamodel Architecture: amd64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1005 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-tensorflow Filename: pool/dists/noble/main/r-cran-gandatamodel_2.0.1-1.ca2404.1_amd64.deb Size: 702052 MD5sum: 4ab16d48f1d6e45ee84a55a01645d7ac SHA1: 0cc2fa4945869664e3c4337b244a3ee91d7dd0d8 SHA256: fbf1a7a3a10107bf1022a377db543a9886f1c29047ec55544ab2cd8a7a1eeb6b SHA512: 78d9b34c8ddf25ab2312e65052d591c30c45491c67f33344d827e41dc33c5fd268064945d4d35e34eefce5fcb843e756c176884edb1b6bde6b1c615ea69a014a Homepage: https://cran.r-project.org/package=ganDataModel Description: CRAN Package 'ganDataModel' (Build a Metric Subspaces Data Model for a Data Source) Neural networks are applied to create a density value function which approximates density values for a data source. 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Package: r-cran-gangenerativedata Architecture: amd64 Version: 2.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1317 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-tensorflow, r-cran-httr Filename: pool/dists/noble/main/r-cran-gangenerativedata_2.1.6-1.ca2404.1_amd64.deb Size: 1012748 MD5sum: 33a98640a93c282c8b886908a8ded8d7 SHA1: e0bef48e79cf5a5bef78b74ea0bd05bd72741573 SHA256: b3a5a6b63ac5d6222bc1badd18913a75da0639f36ae8a1011fe253e7595d068a SHA512: f0c98ed962279df7a9e8d994e53109e5a38b8804e5f0c9fae9c00db582fef20396700621579e9fce9095b292f073c0a62a1a6baa36feece86019ee141c91c557 Homepage: https://cran.r-project.org/package=ganGenerativeData Description: CRAN Package 'ganGenerativeData' (Generate Generative Data for a Data Source) Generative Adversarial Networks are applied to generate generative data for a data source. A generative model consisting of a generator and a discriminator network is trained. During iterative training the distribution of generated data is converging to that of the data source. Direct applications of generative data are the created functions for data evaluation, missing data completion and data classification. A software service for accelerated training of generative models on graphics processing units is available. Reference: Goodfellow et al. (2014) . Package: r-cran-gap Architecture: amd64 Version: 1.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2061 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gap.datasets, r-cran-dplyr, r-cran-ggplot2, r-cran-plotly, r-cran-rdpack Suggests: r-cran-bradleyterry2, r-cran-diagrammer, r-cran-dot, r-cran-mass, r-cran-matrix, r-cran-mcmcglmm, r-cran-r2jags, r-cran-bdsmatrix, r-cran-bookdown, r-cran-calibrate, r-cran-circlize, r-cran-coda, r-cran-cowplot, r-cran-coxme, r-cran-foreign, r-cran-genetics, r-cran-haplo.stats, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-kinship2, r-cran-knitr, r-cran-lattice, r-cran-magic, r-cran-matrixstats, r-cran-meta, r-cran-metafor, r-cran-nlme, r-cran-pedigree, r-cran-pedigreemm, r-cran-plotrix, r-cran-readr, r-cran-reshape, r-cran-rmarkdown, r-cran-rms, r-cran-survival, r-cran-valr Filename: pool/dists/noble/main/r-cran-gap_1.14-1.ca2404.1_amd64.deb Size: 1155260 MD5sum: 928ae643a3d637ac79508a5ad4693610 SHA1: 45db863c4da763ec146902a5fa4d0b99f7c7cc80 SHA256: 3540af127094d7fd089d2c7dff0d272d5d4d6413d93c25b037eab6b05948d2f4 SHA512: 64bd2a9cfc95dc892ba6c9a7968852b526b6fd35b9963f9f151a6f7fec87e64fcc72f87af08bd816d29d00884552bde7a55853729282d34f369c69c9339d4061 Homepage: https://cran.r-project.org/package=gap Description: CRAN Package 'gap' (Genetic Analysis Package) As first reported [Zhao, J. H. 2007. "gap: Genetic Analysis Package". J Stat Soft 23(8):1-18. ], it is designed as an integrated package for genetic data analysis of both population and family data. Currently, it contains functions for sample size calculations of both population-based and family-based designs, probability of familial disease aggregation, kinship calculation, statistics in linkage analysis, and association analysis involving genetic markers including haplotype analysis with or without environmental covariates. Over years, the package has been developed in-between many projects hence also in line with the name (gap). Package: r-cran-gapfill Architecture: amd64 Version: 0.9.6-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-fields, r-cran-foreach, r-cran-rcpp, r-cran-quantreg Suggests: r-cran-roxygen2, r-cran-spam, r-cran-testthat, r-cran-abind Filename: pool/dists/noble/main/r-cran-gapfill_0.9.6-1-1.ca2404.1_amd64.deb Size: 137060 MD5sum: fae6226bf1affce6570bf7a4c2343ea5 SHA1: 0550142b9ddf0a73414de9f99aaa17c87d312063 SHA256: 252ad89acfe68d291602ae48027ea06f1bca428de388de0efe71886f6b0d7f08 SHA512: 8263f472b03d4584a94013ffb5f759808406fdcf91bf5ca820bffacfa49e4cfda5d5a1c22caea3a7a4e00acc8d81d958d36e556fc3421e7f543a5be49276bd40 Homepage: https://cran.r-project.org/package=gapfill Description: CRAN Package 'gapfill' (Fill Missing Values in Satellite Data) Tools to fill missing values in satellite data and to develop new gap-fill algorithms. The methods are tailored to data (images) observed at equally-spaced points in time. The package is illustrated with MODIS NDVI data. Package: r-cran-gapr Architecture: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-bioc-complexheatmap, r-cran-rcolorbrewer, r-cran-gridextra, r-cran-dendextend, r-cran-circlize, r-cran-seriation, r-cran-magick Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-gapr_0.1.5-1.ca2404.1_amd64.deb Size: 166894 MD5sum: d08e5a222e49a398c86ec85d13fb2a76 SHA1: 294fada4d5e28663405ac471f30861f9cccb6fbd SHA256: e2482e36b9a2ea46202cf1d504fdfd2287ee9c7635296d5c798eecc0231f271e SHA512: 5335f424d16079b411ed24646942daf8e4e6e03e6793df66c51dd54acb170df73fbbaa7786c8180db9d6fc0561823d55821e009df6538de80df9713009a1b67c Homepage: https://cran.r-project.org/package=GAPR Description: CRAN Package 'GAPR' (Generalized Association Plots) Provides a comprehensive framework for visualizing associations and interaction structures in matrix-formatted data using Generalized Association Plots (GAP). The package implements multiple proximity computation methods (e.g., correlation, distance metrics), ordering techniques including hierarchical clustering (HCT) and Rank-2-Ellipse (R2E) seriation, and optional flipping strategies to enhance visual symmetry. It supports a variety of covariate-based color annotations, allows flexible customization of layout and output, and is suitable for analyzing multivariate data across domains such as social sciences, genomics, and medical research. The method is based on Generalized Association Plots introduced by Chen (2002) and further extended by Wu, Tien, and Chen (2010) . Package: r-cran-garchx Architecture: amd64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo Suggests: r-cran-tvgarch, r-cran-lgarch Filename: pool/dists/noble/main/r-cran-garchx_1.6-1.ca2404.1_amd64.deb Size: 121920 MD5sum: 39308fb9a270302ea698a414aff7406c SHA1: 63e53f31db56e90461dfd182b78f8fbb3f3cd2c2 SHA256: 78d63ff33220c9d7c59f16f32bcb22a64822affe9cb98e94564365051081914c SHA512: db30a30b1dbe0e403c9e7427f281e948feaffa34bdf1bfe000da08d97a560cf3eecb991bf82f6494117c303a118faa8699def492cb4ea4d4c7a5a50920efe3b0 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. 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(2019) . 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Package: r-cran-gasp Architecture: amd64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1043 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-markdown, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gasp_1.0.6-1.ca2404.1_amd64.deb Size: 874150 MD5sum: f3f59e81e71defe338afa2d75d0f6a26 SHA1: 07c5f27ebc936a5d8a03c0a7e87235e035ffa2d8 SHA256: e43d3334e857cb87c8e07773a09806d2c9faa31c14b6e46026118bac65597b52 SHA512: e5a117a55faa4655958105a2912cb4c028a9946c60c9140ba0c1600df58cc3a1ddd83ffaaccdc55c5726e813863492fcec55684b7072c46ea56fa71a3a253ffe 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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Package: r-cran-gaston Architecture: amd64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5548 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcppeigen Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-gaston_1.6-1.ca2404.1_amd64.deb Size: 3075980 MD5sum: 95a8135b22f06592a6bb0f16732be49d SHA1: f06137866f91559e0bfffe7f9b69340f811acc2c SHA256: 01784eb9ed33f0c7aa05a489a86b8bee248f7d5db4b6909901b8967504937387 SHA512: f302d7e90a8f5a8642d7c9e1669e59510988739a7a06600a53f7d44fdab7fe2459773e9ffda19a1502969e237a66676f44d16315d2b75697c2f4390e1762b2d8 Homepage: https://cran.r-project.org/package=gaston Description: CRAN Package 'gaston' (Genetic Data Handling (QC, GRM, LD, PCA) & Linear Mixed Models) Manipulation of genetic data (SNPs). Computation of GRM and dominance matrix, LD, heritability with efficient algorithms for linear mixed model (AIREML). Dandine et al . Package: r-cran-gaupro Architecture: amd64 Version: 0.2.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2475 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mixopt, r-cran-numderiv, r-cran-rmarkdown, r-cran-tidyr, r-cran-ggplot2, r-cran-rcpp, r-cran-r6, r-cran-lbfgs, r-cran-rcpparmadillo Suggests: r-cran-contourfunctions, r-cran-dplyr, r-cran-ggrepel, r-cran-gridextra, r-cran-knitr, r-cran-lhs, r-cran-mass, r-cran-microbenchmark, r-cran-rlang, r-cran-splitfngr, r-cran-testthat, r-cran-testthatmulti Filename: pool/dists/noble/main/r-cran-gaupro_0.2.17-1.ca2404.1_amd64.deb Size: 1761468 MD5sum: 0f999c74da6c4a467b4f49b224cdedf8 SHA1: 5a5af00975c5170952a927a4e4acc10fd852415f SHA256: 57a35c8c2c8b3f033fff8e7482958dca460b7ddb4f9575204d439260f84ff9f8 SHA512: db1133056e273c4b4a4f1a818136500c666accccf26ea5d876400f48d6887007c42d43d90749c363ba5476daf4b6ca6553575d83cbb7ccc0dac06711783cab30 Homepage: https://cran.r-project.org/package=GauPro Description: CRAN Package 'GauPro' (Gaussian Process Fitting) Fits a Gaussian process model to data. Gaussian processes are commonly used in computer experiments to fit an interpolating model. The model is stored as an 'R6' object and can be easily updated with new data. There are options to run in parallel, and 'Rcpp' has been used to speed up calculations. For more info about Gaussian process software, see Erickson et al. (2018) . Package: r-cran-gausscov Architecture: amd64 Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3499 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gausscov_1.1.8-1.ca2404.1_amd64.deb Size: 3447932 MD5sum: 45db74ddc64a6d71ecfe7358b2a4968a SHA1: 0616f13b4987f7523fc6f512b51e1fa7ec4c616d SHA256: 4889c72623d211770e928286b6346b472f51d93c2bdb3c2b7f3a5ac39d2c1cf0 SHA512: 52fa8d3e3a09ca3b1fb048d67d069eb02d3b393d8f487faf635c6c96a3b53e6fa93c040d1b5b4db86565a504a642f06274cb8b6bca83d885c4115cd520b03dd1 Homepage: https://cran.r-project.org/package=gausscov Description: CRAN Package 'gausscov' (The Gaussian Covariate Method for Variable Selection) The standard linear regression theory whether frequentist or Bayesian is based on an 'assumed (revealed?) truth' (John Tukey) attitude to models. This is reflected in the language of statistical inference which involves a concept of truth, for example confidence intervals, hypothesis testing and consistency. The motivation behind this package was to remove the word true from the theory and practice of linear regression and to replace it by approximation. The approximations considered are the least squares approximations. An approximation is called valid if it contains no irrelevant covariates. This is operationalized using the concept of a Gaussian P-value which is the probability that pure Gaussian noise is better in term of least squares than the covariate. The precise definition given in the paper "An Approximation Based Theory of Linear Regression". Only four simple equations are required. Moreover the Gaussian P-values can be simply derived from standard F P-values. Furthermore they are exact and valid whatever the data in contrast F P-values are only valid for specially designed simulations. A valid approximation is one where all the Gaussian P-values are less than a threshold p0 specified by the statistician, in this package with the default value 0.01. This approximations approach is not only much simpler it is overwhelmingly better than the standard model based approach. The will be demonstrated using high dimensional regression and vector autoregression real data sets. The goal is to find valid approximations. The search function is f1st which is a greedy forward selection procedure which results in either just one or no approximations which may however not be valid. If the size is less than than a threshold with default value 21 then an all subset procedure is called which returns the best valid subset. A good default start is f1st(y,x,kmn=15) The best function for returning multiple approximations is f3st which repeatedly calls f1st. For more information see the papers: L. Davies and L. Duembgen, "Covariate Selection Based on a Model-free Approach to Linear Regression with Exact Probabilities", , L. Davies, "An Approximation Based Theory of Linear Regression", 2024, . Package: r-cran-gaussianhmm1d Architecture: amd64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach Filename: pool/dists/noble/main/r-cran-gaussianhmm1d_1.1.2-1.ca2404.1_amd64.deb Size: 58706 MD5sum: 20171bf6347ce8e69aecd348aa0decbb SHA1: 7ff4b5d7d8a0df2290af3b70215306114419cd0f SHA256: 28debb0e5a85f3f3b29754b8d60b3462cfae1459888fd4431e07cd1db54acbf3 SHA512: e86d4d2d90e282f15a3ec0f3e061213d7b34728b36d661fe30b3f9da0619a7c839b3c85f21a0f145b9527d6dd95fe72efccc746289edccc592a91ee1563b1e8a Homepage: https://cran.r-project.org/package=GaussianHMM1d Description: CRAN Package 'GaussianHMM1d' (Inference, Goodness-of-Fit and Forecast for Univariate GaussianHidden Markov Models) Inference, goodness-of-fit test, and prediction densities and intervals for univariate Gaussian Hidden Markov Models (HMM). The goodness-of-fit is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Chapter 10.2 of Remillard (2013) . Package: r-cran-gb Architecture: amd64 Version: 2.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-kernsmooth Filename: pool/dists/noble/main/r-cran-gb_2.3.3-1.ca2404.1_amd64.deb Size: 82540 MD5sum: 67f20b4ddcd86bdcba025c2d265c313e SHA1: 78a47a4b75bb08aa7a9b5c3a250c86bf6a00380c SHA256: 01bf53fa97a47d469b92ff8866a73adccd9bd23303f18c101331a49495bedd7e SHA512: c53ce5f29645d6ebc22bcd3e9375d392d6a3a3197b8e48c5ba2c209aa21edab2857777d8fa05562951d817f649e401b44c46d7e420a7959334418eb18b70e994 Homepage: https://cran.r-project.org/package=gb Description: CRAN Package 'gb' (Generalize Lambda Distribution and Generalized Bootstrapping) A collection of algorithms and functions for fitting data to a generalized lambda distribution via moment matching methods, and generalized bootstrapping. Package: r-cran-gbeta Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-gsl, r-cran-runuran, r-cran-rcppnumerical, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gbeta_0.1.0-1.ca2404.1_amd64.deb Size: 82252 MD5sum: d7628739e7f67ed2469810e692d363a9 SHA1: 5bc89752b1e8d3c22380407256df37fa2f0368c6 SHA256: 624d2e0ac3d8172225a0184569ea8dd594593333160b85df95b74fde07dce68d SHA512: 5e84d13fb67af9c72293db3b54bbac4fe427bdfd913cb4c1d6d75e6803270e539a2a1c4fe723e7c5522465ed8b450bc7f23b2978c490bba7ca3951a690025eea Homepage: https://cran.r-project.org/package=gbeta Description: CRAN Package 'gbeta' (Generalized Beta and Beta Prime Distributions) Density, distribution function, quantile function, and random generation for the generalized Beta and Beta prime distributions. The family of generalized Beta distributions is conjugate for the Bayesian binomial model, and the generalized Beta prime distribution is the posterior distribution of the relative risk in the Bayesian 'two Poisson samples' model when a Gamma prior is assigned to the Poisson rate of the reference group and a Beta prime prior is assigned to the relative risk. References: Laurent (2012) , Hamza & Vallois (2016) , Chen & Novick (1984) . Package: r-cran-gbj Architecture: amd64 Version: 0.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-skat, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bindata, r-cran-rje, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gbj_0.5.4-1.ca2404.1_amd64.deb Size: 142278 MD5sum: df41d36eeeb3a0c66bee6d9090090f43 SHA1: ec0b8b44b1becaac75e76b1d8aff241b7281cc7b SHA256: a9cb9992d126cd076e33154264d4805ec2e4b4b91ac79cf169df54a90c7c415e SHA512: 42a10d13165ffd8770209d0deae0900c24a7048afa65a4d1d8abb2490a2e724e032c5e308ff2fcde46b5706d479c06061cb0f80a80b7a71544db3e344ef322e6 Homepage: https://cran.r-project.org/package=GBJ Description: CRAN Package 'GBJ' (Generalized Berk-Jones Test for Set-Based Inference in GeneticAssociation Studies) Offers the Generalized Berk-Jones (GBJ) test for set-based inference in genetic association studies. The GBJ is designed as an alternative to tests such as Berk-Jones (BJ), Higher Criticism (HC), Generalized Higher Criticism (GHC), Minimum p-value (minP), and Sequence Kernel Association Test (SKAT). All of these other methods (except for SKAT) are also implemented in this package, and we additionally provide an omnibus test (OMNI) which integrates information from each of the tests. The GBJ has been shown to outperform other tests in genetic association studies when signals are correlated and moderately sparse. Please see the vignette for a quickstart guide or Sun and Lin (2017) for more details. Package: r-cran-gbm3 Architecture: amd64 Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1111 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-lattice, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass Filename: pool/dists/noble/main/r-cran-gbm3_3.0.1-1.ca2404.1_amd64.deb Size: 533114 MD5sum: 189ea01d517eb76f0de74661f2af4a81 SHA1: c0efb563ee854d0d64dcc27abc3e3fc49ea11a70 SHA256: 0b9ea9413113db9ecf178548f3cbb23e4556794acd8dc09b43aa67793374823e SHA512: 73f231b0d6ddab2bc4e52ac4ca17d2061133d03ace337d6d737f4e9c118e50463a998efc6fd44598d432ec4343995bfe4319986a5a57582cdca74931802978e9 Homepage: https://cran.r-project.org/package=gbm3 Description: CRAN Package 'gbm3' (Generalized Boosted Regression Models) Extensions to Freund and Schapire's AdaBoost algorithm, Y. Freund and R. 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Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart). Originally developed by Greg Ridgeway. Newer version available at github.com/gbm-developers/gbm3. Package: r-cran-gbop2 Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2014 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-r6, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-dplyr, r-cran-globpso, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-gbop2_0.1.4-1.ca2404.1_amd64.deb Size: 841458 MD5sum: 92612c5edd84f07154027a42b832b04e SHA1: 036937ba53de7039d8048349bfd0cb6ac19759a0 SHA256: 9b45d3f175a25ac2f3fe30e788e28e4750bcfc6900a77f16a96e809bedbd26ca SHA512: db233b860e3400cdb2a1314845cc7fc1a98b78fc69bb2e1325c9627d6b58c14189ddee06419acc703a3e000138dae6b30b48ef95fca51ed71013ce9291578b87 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. 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Package: r-cran-gcat Architecture: amd64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gcat_0.2-1.ca2404.1_amd64.deb Size: 76820 MD5sum: ef4dff296046aa136ba2c8151c79653d SHA1: 1493e8af16ffa2a01c065db8eaf018d0aa280458 SHA256: b6f8d3fd1952ef66e74f75633739e5db2caa4256dedabeca3aa80beef44d8f19 SHA512: 998b1c47f6db9d98d0556806e829f12c9a7f4822b005b7f4b7117301c71d7f126b25857d32d42c8d7204749affe3d442f354df5f62ee1e113a170c7c73227180 Homepage: https://cran.r-project.org/package=gCat Description: CRAN Package 'gCat' (Graph-Based Two-Sample Tests for Categorical Data) These are two-sample tests for categorical data utilizing similarity information among the categories. They are useful when there is underlying structure on the categories. Package: r-cran-gcdnet Architecture: amd64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gcdnet_1.0.6-1.ca2404.1_amd64.deb Size: 173344 MD5sum: 26c8e7c645de632d2b64926f5861df82 SHA1: 954996b47d8a8a1fbf74bc8f2ca806ca1666a484 SHA256: 421dcbaa4c486764000f6108229bc662cc63852cd9a0b837e077f5e524c13c49 SHA512: 7f97c0cdbdd37038d8e417d6104ef463d6c172d698eb73b21925ac640269db5bc128cd7cf8092f80f336dce27e4010a3bdea9e8d3dab35591eac6061b0b26eed Homepage: https://cran.r-project.org/package=gcdnet Description: CRAN Package 'gcdnet' (The (Adaptive) LASSO and Elastic Net Penalized Least Squares,Logistic Regression, Hybrid Huberized Support Vector Machines,Squared Hinge Loss Support Vector Machines and ExpectileRegression using a Fast Generalized Coordinate DescentAlgorithm) Implements a generalized coordinate descent (GCD) algorithm for computing the solution paths of the hybrid Huberized support vector machine (HHSVM) and its generalizations. Supported models include the (adaptive) LASSO and elastic net penalized least squares, logistic regression, HHSVM, squared hinge loss SVM and expectile regression. 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Description of the method is available from: Han and DeOliveira (2018) . Package: r-cran-gclm Architecture: amd64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gclm_0.0.1-1.ca2404.1_amd64.deb Size: 37498 MD5sum: 37a8be59578678fdb1d245b429e28b8d SHA1: 3a64dc473a89f4165e907445cbde39ccb36593b0 SHA256: 0d9655e62ce22542a4aa7a89760aea0513475a904cac2fffd1c2b3da0d29859d SHA512: 918b6419b7ebe736dcbca7bd72d737bcd2a4bc6a80e928bf9bbf566d4cf5253e6a7d427b92851c23bdc1a25bb74df3a2e52a955947051bc5113a7c9b52eb79e6 Homepage: https://cran.r-project.org/package=gclm Description: CRAN Package 'gclm' (Graphical Continuous Lyapunov Models) Estimation of covariance matrices as solutions of continuous time Lyapunov equations. Sparse coefficient matrix and diagonal noise are estimated with a proximal gradient method for an l1-penalized loss minimization problem. Varando G, Hansen NR (2020) . Package: r-cran-gcmr Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-betareg, r-cran-car, r-cran-formula, r-cran-lmtest, r-cran-nlme, r-cran-sandwich, r-cran-sp Filename: pool/dists/noble/main/r-cran-gcmr_1.0.4-1.ca2404.1_amd64.deb Size: 167054 MD5sum: b55980628d9187a33352f3bc32e83918 SHA1: 1dba894d98b7370d68715cbc3d99694a2c03fb5d SHA256: 4feac582348a59148bad132fe44cfaf908118a47277cf4056c69ebcf1fdeb3e4 SHA512: 5270d10114dfcd2d51d3538cd50342b6a166ca0c5cb10e71d3782f62b8f17d5f2c3c7d33210f836828eb3a48d4561880bba2a1ae080ac982e519fc452b127ea7 Homepage: https://cran.r-project.org/package=gcmr Description: CRAN Package 'gcmr' (Gaussian Copula Marginal Regression) Likelihood inference in Gaussian copula marginal regression models. 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This package is designed for summary statistics containing estimated regression coefficients and its estimated covariance matrix. The methodology is available from: Baghfalaki, T., Sugier, P. E., Truong, T., Pettitt, A. N., Mengersen, K., & Liquet, B. (2021) . Package: r-cran-gcpm Architecture: amd64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 921 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppprogress Filename: pool/dists/noble/main/r-cran-gcpm_1.2.2-1.ca2404.1_amd64.deb Size: 619910 MD5sum: 3c4cb21a6f827d29d755c4d1256d0fba SHA1: b4cbd3b6fae2083393c54fd5e6c237d8407de891 SHA256: b8e6bf4a3840aeefae7216a277243d1da00d94ede5895235163cf23bd0930321 SHA512: 19d76b4166e6c69cff3be48b5687a00be531ef6a10be13594402a9f65a1552b7471fda242b1e3f65e146c6a2b3c1fb091126b0e70ed474f43907e4000fcb9d5f Homepage: https://cran.r-project.org/package=GCPM Description: CRAN Package 'GCPM' (Generalized Credit Portfolio Model) Analyze the default risk of credit portfolios. Commonly known models, like CreditRisk+ or the CreditMetrics model are implemented in their very basic settings. The portfolio loss distribution can be achieved either by simulation or analytically in case of the classic CreditRisk+ model. Models are only implemented to respect losses caused by defaults, i.e. migration risk is not included. The package structure is kept flexible especially with respect to distributional assumptions in order to quantify the sensitivity of risk figures with respect to several assumptions. Therefore the package can be used to determine the credit risk of a given portfolio as well as to quantify model sensitivities. Package: r-cran-gcsm Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 418 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gcsm_0.2.0-1.ca2404.1_amd64.deb Size: 130876 MD5sum: ac6306842c7c8daf7389e7a06ac1d045 SHA1: bbd2fab51c416560b4075973ec95564988248b22 SHA256: 1855439189da342a2a476b8d99ae682287abcd0a3279b9b2a716767c2e91a1bb SHA512: 3edfdccbd43899402eabc5aaa7ca2dec88aa0b14cd15de44c2a42d2ec12fdadd9cd1c808de9c77012ce838cd9e72d6c65acf7283fdc012780b291713342714d7 Homepage: https://cran.r-project.org/package=GCSM Description: CRAN Package 'GCSM' (Implements Generic Composite Similarity Measure) Provides implementation of the generic composite similarity measure (GCSM) described in Liu et al. (2020) . The implementation is in C++ and uses 'RcppArmadillo'. Additionally, implementations of the structural similarity (SSIM) and the composite similarity measure based on means, standard deviations, and correlation coefficient (CMSC), are included. Package: r-cran-gctsc Architecture: amd64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1203 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-truncatednormal, r-cran-vgam, r-cran-truncnorm, r-cran-nlme, r-cran-car, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gctsc_0.2.4-1.ca2404.1_amd64.deb Size: 807292 MD5sum: 019f8110c4a257acff4f01e5409116b0 SHA1: e87113d678b825d93376319160ee0655248e78bc SHA256: 0218627b4657aad67da27632600da7cb40f774bf5e0d162abaa43b7d6dce5b41 SHA512: f85bd6ce5e596dfc99b05ec4e0bcc5f73242cdf895aac4f90956985230c0ee188888f0e75eb8e0871d6ba6a92a857d9afa27e674716bd84d30bd0d05dfc06c30 Homepage: https://cran.r-project.org/package=gctsc Description: CRAN Package 'gctsc' (Gaussian and Student-t Copula Models for Count Time Series) Provides likelihood-based inference for Gaussian and Student-t copula models for univariate count time series. Supports Poisson, negative binomial, binomial, beta-binomial, and zero-inflated marginals with ARMA dependence structures. Includes simulation, maximum-likelihood estimation, residual diagnostics, and predictive inference. Implements Time Series Minimax Exponential Tilting (TMET) , an adaptation of minimax exponential tilting of Botev (2017) . Also provides a linear-cost implementation of the Geweke–Hajivassiliou–Keane (GHK) simulator following Masarotto and Varin (2012) , and the Continuous Extension (CE) approximation of Nguyen and De Oliveira (2025) . The package follows the S3 design philosophy of 'gcmr' but is developed independently. Package: r-cran-gdalcubes Architecture: amd64 Version: 0.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6262 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgdal34t64 (>= 3.7.0), libnetcdf19t64 (>= 4.0.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-jsonlite, r-cran-ncdf4, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stars, r-cran-av, r-cran-gifski, r-cran-sf, r-cran-tinytest, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-gdalcubes_0.7.3-1.ca2404.1_amd64.deb Size: 3542968 MD5sum: 8395a7a0ec39a772b549cc1a6d6c7608 SHA1: 4bf1d6926ff14e6dcfb54a0c8b54bb3ce5979d19 SHA256: dc4c982efce1d8d3c3026871b38dc4d73281ddf800b4554bd4db172c5c3753eb SHA512: bf064acf323032ffcddf44747deceee24b93d640ebcbc37d0687b841f5f648ca12fb5f7656591156695b8dfe5c53c8f62caee4b7574992a913ca12109469da85 Homepage: https://cran.r-project.org/package=gdalcubes Description: CRAN Package 'gdalcubes' (Earth Observation Data Cubes from Satellite Image Collections) Processing collections of Earth observation images as on-demand multispectral, multitemporal raster data cubes. Users define cubes by spatiotemporal extent, resolution, and spatial reference system and let 'gdalcubes' automatically apply cropping, reprojection, and resampling using the 'Geospatial Data Abstraction Library' ('GDAL'). Implemented functions on data cubes include reduction over space and time, applying arithmetic expressions on pixel band values, moving window aggregates over time, filtering by space, time, bands, and predicates on pixel values, exporting data cubes as 'netCDF' or 'GeoTIFF' files, plotting, and extraction from spatial and or spatiotemporal features. All computational parts are implemented in C++, linking to the 'GDAL', 'netCDF', 'CURL', and 'SQLite' libraries. See Appel and Pebesma (2019) for further details. Package: r-cran-gdalraster Architecture: amd64 Version: 2.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6937 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgdal34t64 (>= 3.8.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-bit64, r-cran-cli, r-cran-nanoarrow, r-cran-rcpp, r-cran-wk, r-cran-xml2, r-cran-yyjsonr, r-cran-rcppint64 Suggests: r-cran-glue, r-cran-gt, r-cran-knitr, r-cran-ltc, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-vctrs Filename: pool/dists/noble/main/r-cran-gdalraster_2.6.1-1.ca2404.1_amd64.deb Size: 3832704 MD5sum: 3aa530635dc8dc6b0c1ef9ea5c7d57bc SHA1: 6c15895f5f280e7bd364e25c90b25a0c9869918e SHA256: 66b23e3d6c14a4e6da039edd83adb7d2876128bcbe4ecadcb936309597481916 SHA512: 1c021b7ad2f2b6dd833e844117e889fa9024efc2c9af2ea3723d66378e543bcec2a759448f3b4905499a6b9fc7ebd88036fe17eb99ecbb505905c4c3caf28c1a Homepage: https://cran.r-project.org/package=gdalraster Description: CRAN Package 'gdalraster' (Bindings to 'GDAL') API bindings to the Geospatial Data Abstraction Library ('GDAL', ). Implements the 'GDAL' Raster and Vector Data Models. Bindings are implemented with 'Rcpp' modules. Exposed C++ classes and stand-alone functions wrap much of the 'GDAL' API and provide additional functionality. Calling signatures resemble the native C, C++ and Python APIs provided by the 'GDAL' project. Class 'GDALRaster' encapsulates a 'GDALDataset' and its raster band objects. Class 'GDALVector' encapsulates an 'OGRLayer' and the 'GDALDataset' that contains it. Initial bindings are provided to the unified 'gdal' command line interface added in 'GDAL' 3.11. C++ stand-alone functions provide bindings to most 'GDAL' "traditional" raster and vector utilities, including 'OGR' facilities for vector geoprocessing, several algorithms, as well as the Geometry API ('GEOS' via 'GDAL' headers), the Spatial Reference Systems API, and methods for coordinate transformation. Bindings to the Virtual Systems Interface ('VSI') API implement standard file system operations abstracted for URLs, cloud storage services, 'Zip'/'GZip'/'7z'/'RAR', in-memory files, as well as regular local file systems. This provides a single interface for operating on file system objects that works the same for any storage backend. A custom raster calculator evaluates a user-defined R expression on a layer or stack of layers, with pixel x/y available as variables in the expression. Raster 'combine()' identifies and counts unique pixel combinations across multiple input layers, with optional raster output of the pixel-level combination IDs. Basic plotting capability is provided for raster and vector display. 'gdalraster' leans toward minimalism and the use of simple, lightweight objects for holding raw data. Currently, only minimal S3 class interfaces have been implemented for selected R objects that contain spatial data. 'gdalraster' may be useful in applications that need scalable, low-level I/O, or prefer a direct 'GDAL' API. Package: r-cran-gdina Architecture: amd64 Version: 2.9.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1811 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-alabama, r-cran-foreach, r-cran-ggplot2, r-cran-mass, r-cran-numderiv, r-cran-nloptr, r-cran-rcpp, r-cran-rsolnp, r-cran-shiny, r-cran-shinydashboard, r-cran-rcpparmadillo Suggests: r-cran-cdm, r-cran-cdmtools, r-cran-dorng, r-cran-doparallel, r-cran-matrix, r-cran-testthat, r-cran-polca, r-cran-stringr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gdina_2.9.12-1.ca2404.1_amd64.deb Size: 1157536 MD5sum: 3c4608bc12d768a0ba954da70596104a SHA1: 8a7c9e9d5f5b8f71b91e207075f290c00ef2bbc4 SHA256: fcaa5e8e4c31e577a342230c85dca87e850492c1b5cf1bf245205b8ef1cd0acf SHA512: 1782b0badff07eea9201cc98f7045d36be920d27e340c706a9ddc9e439d8f726665446b34654244af1e86ecdff9cb7c3d5c41944ef2e64d80cc3e621b5d66cae Homepage: https://cran.r-project.org/package=GDINA Description: CRAN Package 'GDINA' (The Generalized DINA Model Framework) A set of psychometric tools for cognitive diagnosis modeling based on the generalized deterministic inputs, noisy and gate (G-DINA) model by de la Torre (2011) and its extensions, including the sequential G-DINA model by Ma and de la Torre (2016) for polytomous responses, and the polytomous G-DINA model by Chen and de la Torre for polytomous attributes. Joint attribute distribution can be independent, saturated, higher-order, loglinear smoothed or structured. Q-matrix validation, item and model fit statistics, model comparison at test and item level and differential item functioning can also be conducted. A graphical user interface is also provided. For tutorials, please check Ma and de la Torre (2020) , Ma and de la Torre (2019) , Ma (2019) and de la Torre and Akbay (2019). Package: r-cran-gdm Architecture: amd64 Version: 1.6.0-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4995 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-reshape2, r-cran-vegan, r-cran-doparallel, r-cran-foreach, r-cran-pbapply Suggests: r-cran-tinytest, r-cran-scales, r-cran-terra Filename: pool/dists/noble/main/r-cran-gdm_1.6.0-7-1.ca2404.1_amd64.deb Size: 1275364 MD5sum: 46cfb999cb1d3ea259beef458b9a8dfd SHA1: 06586377f2fcd99a2baee74652d664aef6b292cf SHA256: 53a70b61359b3fb9439a3b0c64a989214d6f9657db8ce616c21b6c67de363ed9 SHA512: 3f6e5ab32aeec381d78993f54fb97708da38f29d8cd0bf68353a873f618715ef5260fffd943d13bc85c362e0e65482a24b3617898a84b0ea1b0839f38e72a0a0 Homepage: https://cran.r-project.org/package=gdm Description: CRAN Package 'gdm' (Generalized Dissimilarity Modeling) A toolkit with functions to fit, plot, summarize, and apply Generalized Dissimilarity Models. Mokany K, Ware C, Woolley SNC, Ferrier S, Fitzpatrick MC (2022) Ferrier S, Manion G, Elith J, Richardson K (2007) . Package: r-cran-gdpc Architecture: amd64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 781 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-xts, r-cran-zoo, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-gdpc_1.1.4-1.ca2404.1_amd64.deb Size: 584896 MD5sum: 5ed284eee91086d9c1790d7dfb91d6d9 SHA1: 2ad3a3297b2d8f2838970e188d061a3d88dd414b SHA256: 2594fd5d411dc20ed820d96ba3baf2b1b3ed2d4029ae00ace391e6a5cf24b53d SHA512: 35a0d4d19fa5adf4f11542d3afe5513d6ee56e9157cb18e24b7369d58e4742091f315ebf0a5dcf1328127f40882997199c16fe0b0a51028a2b1f4883b39d3bb7 Homepage: https://cran.r-project.org/package=gdpc Description: CRAN Package 'gdpc' (Generalized Dynamic Principal Components) Functions to compute the Generalized Dynamic Principal Components introduced in Peña and Yohai (2016) . The implementation includes an automatic procedure proposed in Peña, Smucler and Yohai (2020) for the identification of both the number of lags to be used in the generalized dynamic principal components as well as the number of components required for a given reconstruction accuracy. Package: r-cran-gdsarm Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gdsarm_0.1.1-1.ca2404.1_amd64.deb Size: 61334 MD5sum: a788c6da23cb922fb178526e99515c9e SHA1: 2af1ec1ff21a4305d34d8e531912f8247261990b SHA256: 98b32fe3ac4013cf4db6237fce96e96ed87c86f4a71ba27fb91521e2da419339 SHA512: a9d2a3f42cab3b186c40f9e1a1ce62b68aec17fd4465c9acc8dd16d80f744932a0c80315663649ac642dd7a20fc2954257368c0770dfacbf0c6924e59e45062a Homepage: https://cran.r-project.org/package=GDSARM Description: CRAN Package 'GDSARM' (Gauss - Dantzig Selector: Aggregation over Random Models) The method aims to identify important factors in screening experiments by aggregation over random models as studied in Singh and Stufken (2022) . This package provides functions to run the Gauss-Dantzig selector on screening experiments when interactions may be affecting the response. Currently, all functions require each factor to be at two levels coded as +1 and -1. Package: r-cran-gdtools Architecture: amd64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: libc6 (>= 2.14), libcairo2 (>= 1.2.4), libfreetype6 (>= 2.2.1), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fontquiver, r-cran-htmltools, r-cran-rcpp, r-cran-systemfonts Suggests: r-cran-curl, r-cran-gfonts, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gdtools_0.5.0-1.ca2404.1_amd64.deb Size: 215592 MD5sum: c48acf3c05d5db4a39d99efe34e60b92 SHA1: 881c7cf93a4b7ae2b8a79408b1de0dae635b2022 SHA256: d117ff82c9f499948db4189f89cdebaf260f7b67be11cb5fe004015b880ddec7 SHA512: 4a27fabba5e4ef9f8f45f37e4e199ee821a5167dc601fff231f57edfef6be0ab7c1ef5bc38548dc9d44485201731a2e20907b2862e340e1f1ade07c4cb5bb67e Homepage: https://cran.r-project.org/package=gdtools Description: CRAN Package 'gdtools' (Font Metrics and Font Management Utilities for R Graphics) Compute text metrics (width, ascent, descent) using 'Cairo' and 'FreeType', independently of the active graphic device. Font lookup is delegated to 'systemfonts'. Additional utilities let users register 'Google Fonts' or bundled 'Liberation' fonts, check font availability, and assemble 'htmlDependency' objects so that fonts are correctly embedded in 'Shiny' applications, 'R Markdown' documents, and 'htmlwidgets' outputs such as 'ggiraph'. Package: r-cran-gdverse Architecture: amd64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1651 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-forcats, r-cran-ggplot2, r-cran-magrittr, r-cran-patchwork, r-cran-purrr, r-cran-reticulate, r-cran-rpart, r-cran-scatterpie, r-cran-sdsfun, r-cran-sf, r-cran-tibble, r-cran-tidyr, r-cran-rcpp Suggests: r-cran-cowplot, r-cran-itmsa, r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-spedm, r-cran-terra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gdverse_1.6-1.ca2404.1_amd64.deb Size: 791214 MD5sum: e8f9968556af95bf9da58721c3cb4ee8 SHA1: d8d8a9b14c94da161aec02070d7a61662f73522c SHA256: 2195f88a688dafdc18777cdb464d320935003c27288b55f08d4eeeb47acef3fd SHA512: 769e26609e117937deb4652a1561b8cac8499e66c2b1807a27b3be4b6de6ec3ca59f83e617bc2b8fa7b398a15f915f65a7ee6dae9b5b7e5ea4ec502729677e28 Homepage: https://cran.r-project.org/package=gdverse Description: CRAN Package 'gdverse' (Analysis of Spatial Stratified Heterogeneity) Detecting spatial associations via spatial stratified heterogeneity, accounting for spatial dependencies, interpretability, complex interactions, and robust stratification. In addition, it supports the spatial stratified heterogeneity family described in Lv et al. (2025). Package: r-cran-gear Architecture: amd64 Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1967 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-autoimage, r-cran-optimx, r-cran-rcpp Suggests: r-cran-sp, r-cran-sf, r-cran-testthat, r-cran-matrix, r-cran-geor, r-cran-gstat, r-cran-spam, r-cran-ggplot2, r-cran-lattice Filename: pool/dists/noble/main/r-cran-gear_0.3.4-1.ca2404.1_amd64.deb Size: 1818114 MD5sum: a33771818613bdead558bf02bfa0ea26 SHA1: 29949396a7f08cf3c2ccb03fcd4795823f65351b SHA256: 1db42c62d0a1890ff164fb789a4d3ed4f2d43224c68dcb894caba43dec512406 SHA512: 17190adf140e8709a4773e594b50f2fa1ff959cd8c523cd5b6bd4337d7bd5af07afbc3c4880db99e4b28581f707f00988b180471513041d5a3adbb3acaa1974f Homepage: https://cran.r-project.org/package=gear Description: CRAN Package 'gear' (Geostatistical Analysis in R) Implements common geostatistical methods in a clean, straightforward, efficient manner. The methods are discussed in Schabenberger and Gotway (2004, ) and Waller and Gotway (2004, ). Package: r-cran-gedi2 Architecture: amd64 Version: 2.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1156 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-matrix, r-cran-ggplot2, r-cran-scales, r-cran-rcppeigen Suggests: r-cran-hdf5r, r-cran-uwot, r-cran-digest, r-cran-glmnet, r-cran-seurat, r-cran-seuratobject, r-bioc-singlecellexperiment, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gedi2_2.3.4-1.ca2404.1_amd64.deb Size: 635576 MD5sum: fda829f906c843dd0ccf2d537b4ad60c SHA1: db20544e619111b19f65f896dd39785537e76756 SHA256: 9dda5585ea494918dbd80257dad3282cdf23bbef98e1d773890a3464dd490dcf SHA512: 60e1ef99204f0b71364e342fac1631e3d0d27042f8eab8b14c05ac6c63032aeda245f798718ecadea0be101b4c92594a409fcdb5f5fef728a8ec81c962bf83f2 Homepage: https://cran.r-project.org/package=gedi2 Description: CRAN Package 'gedi2' (Gene Expression Decomposition and Integration) A memory-efficient implementation for integrating gene expression data from single-cell RNA sequencing experiments. Uses a C++ backend with thin R wrappers to enable analysis of large-scale single-cell datasets. The package supports multiple data modalities including count matrices, paired data (splicing, RNA velocity, CITE-seq), and binary indicators. It implements a latent variable model with block coordinate descent optimization for dimensionality reduction and batch effect correction. Core algorithms are described in Madrigal et al. (2024) . 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Package: r-cran-gee Architecture: amd64 Version: 4.13-29-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-gee_4.13-29-1.ca2404.1_amd64.deb Size: 48776 MD5sum: 614b5a6f35fd16a5096e1a5608a4aba3 SHA1: d00cfda96590b31bb399a849c116fb8fbebc8d94 SHA256: 2ce62f2c2aa8e3871cf022f6f4e3b056d38702078c0561c6ea16cd1928ca3ce1 SHA512: c5de1143bd7c036db4e255ca9a5afb9ad85d9275a672af7bb503fca7b54543d270620523ce006a484b5485d68076354d12922437b5f226b3d373903b53dfd25b Homepage: https://cran.r-project.org/package=gee Description: CRAN Package 'gee' (Generalized Estimation Equation Solver) Generalized Estimation Equation solver. 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Package: r-cran-geneaclassify Architecture: amd64 Version: 1.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1865 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-genearead, r-cran-changepoint, r-cran-signal, r-cran-rpart Suggests: r-cran-waveslim, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-geneaclassify_1.5.5-1.ca2404.1_amd64.deb Size: 1456338 MD5sum: 8119990cc9e88bfd5818b79713626505 SHA1: 8e0c51ed2cd32f2f8d3e7c5e7be5ba3ad4a0ce12 SHA256: 8c81656c3a25f7822f91b94ef89e81342f1d854cef8c0ed563dd0b850548bdb9 SHA512: 9dbea6c42111462dbe82264d2c0f0d50fcbb222d2d7cf020fff7a73028a99e299d21deaa786e38a6d8d6a07a349275df71bdb5fb99fa5bd631eb95b412c05e39 Homepage: https://cran.r-project.org/package=GENEAclassify Description: CRAN Package 'GENEAclassify' (Segmentation and Classification of Accelerometer Data) Segmentation and classification procedures for data from the 'Activinsights GENEActiv' accelerometer that provides the user with a model to guess behaviour from test data where behaviour is missing. 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Package: r-cran-genepop Architecture: amd64 Version: 1.2.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3131 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-stringr, r-cran-rcppprogress Suggests: r-cran-testthat, r-cran-knitr, r-cran-shiny Filename: pool/dists/noble/main/r-cran-genepop_1.2.14-1.ca2404.1_amd64.deb Size: 826644 MD5sum: 2efd39971538fce0d4159bf916dc98df SHA1: 52bf47bfbe01d05e8bc49ecdabc57c1b635f4aa1 SHA256: 08dfbed040948972fbdc7349597a26f8295e6b0281071ce2049401c55e4de3bf SHA512: b30d9b97338764bb1fdf2ceae4e469ba24d2affa49ec1300e683a4160446d57216ef900f50ed9b111588c3485e068abb0985198bc23707252f43c422f4f1d3de Homepage: https://cran.r-project.org/package=genepop Description: CRAN Package 'genepop' (Population Genetic Data Analysis Using Genepop) Makes the Genepop software available in R. This software implements a mixture of traditional population genetic methods and some more focused developments: it computes exact tests for Hardy-Weinberg equilibrium, for population differentiation and for genotypic disequilibrium among pairs of loci; it computes estimates of F-statistics, null allele frequencies, allele size-based statistics for microsatellites, etc.; and it performs analyses of isolation by distance from pairwise comparisons of individuals or population samples. Package: r-cran-generalizedumatrix Architecture: amd64 Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2313 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-ggplot2, r-cran-rcpparmadillo Suggests: r-cran-datavisualizations, r-cran-rgl, r-cran-mgcv, r-cran-png, r-cran-reshape2, r-cran-fields, r-cran-abcanalysis, r-cran-plotly, r-cran-deldir, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-generalizedumatrix_1.3.1-1.ca2404.1_amd64.deb Size: 837462 MD5sum: 76aacc00f28cfb75156a7521c7c61fa0 SHA1: bcc0da8e0f5da80498948fb4e55e86c668506ff4 SHA256: ca8699c7fd342a6b280d064a99579bd70d6b6ee6cfa688615972d1ad2a050e9d SHA512: 7b2f90a9a555077da947eeb9467a0fa819a83e699f9899cc44f2f077d64b5afac02ef6f923e829dc55b9e3b86707229b89b86847193fbe764048a08b7cd4ac19 Homepage: https://cran.r-project.org/package=GeneralizedUmatrix Description: CRAN Package 'GeneralizedUmatrix' (Credible Visualization for Two-Dimensional Projections of Data) Projections are common dimensionality reduction methods, which represent high-dimensional data in a two-dimensional space. However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018] . This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is derived from the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) and the main algorithm called simplified self-organizing map for dimensionality reduction methods is published in . Package: r-cran-generalizedumatrixgpu Architecture: amd64 Version: 0.1.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 625 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), ocl-icd-libopencl1 | libopencl1, r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-ggplot2, r-cran-generalizedumatrix, r-cran-rcpparmadillo Suggests: r-cran-datavisualizations, r-cran-rgl, r-cran-mgcv, r-cran-png, r-cran-reshape2, r-cran-fields, r-cran-abcanalysis, r-cran-plotly, r-cran-deldir, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-generalizedumatrixgpu_0.1.14-1.ca2404.1_amd64.deb Size: 136144 MD5sum: 3e3f51bf2bcb76f801f62bd0044a8ad5 SHA1: 90c5cd7d9aab64e583e2d16c9203929a272aedd6 SHA256: 53a9c957f86166334ca60471a223d5ce0c747b30df916944f44af7fc7b250a65 SHA512: b40518c43626a358d2338e4ab89bf4eabf7cce6391f01dbb072a11a970f8a24cfae989f4c4664be93d4c3c6ed5d040be641ebf2085d29ca9d42b869befca0897 Homepage: https://cran.r-project.org/package=GeneralizedUmatrixGPU Description: CRAN Package 'GeneralizedUmatrixGPU' (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 Thrun, M.C. and Ultsch, A.: "Uncovering High-dimensional Structures of Projections from Dimensionality Reduction Methods" (2020) . Package: r-cran-generalizedwendland Architecture: amd64 Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1794 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-spam, r-cran-spam64, r-cran-optimparallel, r-cran-fields, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-knitr, r-cran-r.rsp, r-cran-testthat, r-cran-mvtnorm, r-cran-ggplot2, r-cran-gridextra, r-cran-dplyr, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-generalizedwendland_0.6.1-1.ca2404.1_amd64.deb Size: 1419738 MD5sum: 3afcf272ea947d8d150d651eb2d79f6d SHA1: 824e651e804bc0a9f52355c039aa42b0f98a0be8 SHA256: 8867c1575a673c83114df1ec72c0687179ae84ca3607662272ca9160bc5ab7e8 SHA512: 1c239102cf986a84cb8a32e5cce122b6345489fee65eefeecc7bedf0679ac150c39c7f55dd1b8121303c5506293857385edd68543e561f75d3a239e6858220b0 Homepage: https://cran.r-project.org/package=GeneralizedWendland Description: CRAN Package 'GeneralizedWendland' (Fully Parameterized Generalized Wendland Covariance Function) A fully parameterized Generalized Wendland covariance function for use in Gaussian process models, as well as multiple methods for approximating it via covariance interpolation. The available methods are linear interpolation, polynomial interpolation, and cubic spline interpolation. Moreno Bevilacqua and Reinhard Furrer and Tarik Faouzi and Emilio Porcu (2019) >. Moreno Bevilacqua and Christian Caamaño-Carrillo and Emilio Porcu (2022) . Reinhard Furrer and Roman Flury and Florian Gerber (2022) >. Package: r-cran-genest Architecture: amd64 Version: 1.4.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2392 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dt, r-cran-gsl, r-cran-gtools, r-cran-hellno, r-cran-htmltools, r-cran-lubridate, r-cran-mass, r-cran-matrixstats, r-cran-mvtnorm, r-cran-rcpp, r-cran-shiny, r-cran-shinyjs, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-genest_1.4.9-1.ca2404.1_amd64.deb Size: 1672272 MD5sum: 79fd689f6d5eb8dc1f826444d7c2c7f0 SHA1: a1f4a01670ddddaf850f09e175e5192332493092 SHA256: 40b06aab5ac8bd4d7524971933e9790a50e005a85377c65e8fa66d911ab944bf SHA512: 977562d035e8d1e63e20f23ebc1a1af7787b9e67242ebb77fdd455715ab35d4a1e973b418dc96dcfe25ef8d9b0d6e9a2b624b552494a9a0bee351ef037e09219 Homepage: https://cran.r-project.org/package=GenEst Description: CRAN Package 'GenEst' (Generalized Mortality Estimator) Command-line and 'shiny' GUI implementation of the GenEst models for estimating bird and bat mortality at wind and solar power facilities, following Dalthorp, et al. (2018) . Package: r-cran-genie Architecture: amd64 Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-genieclust, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-genie_1.0.7-1.ca2404.1_amd64.deb Size: 92388 MD5sum: 1d093f51495227d892e05d73e0010c60 SHA1: 64fe7a2558c42dd166992aca5c6603253dda2d1b SHA256: 63e7a5b3214f91aa4d8421f5d8a025672ff77386520c676504ad3e03841ff0d2 SHA512: 7187d8c1b51f7db24e8e7df2c32c79df9fbe4f421085a3321da4762059c14562d594e22ff85cdc6031e52ad92a0d710843b81bca0415211d393ef9a4d76357d9 Homepage: https://cran.r-project.org/package=genie Description: CRAN Package 'genie' (Fast, Robust, and Outlier Resistant Hierarchical Clustering) Implements a basic version of the hierarchical clustering algorithm 'Genie' which links two point groups in such a way that an inequity measure (namely, the Gini index) of the cluster sizes does not significantly increase above a given threshold. This method most often outperforms many other data segmentation approaches in terms of clustering quality as tested on a wide range of benchmark datasets. At the same time, Genie retains the high speed of the single linkage approach, therefore it is also suitable for analysing larger data sets. For more details see (Gagolewski et al. 2016 ). For a faster and more feature-rich implementation, see the 'genieclust' package (Gagolewski, 2021 ). Package: r-cran-genieclust Architecture: amd64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-deadwood Filename: pool/dists/noble/main/r-cran-genieclust_1.3.0-1.ca2404.1_amd64.deb Size: 188272 MD5sum: acc50c2f41e276cbc5dd84c578e01551 SHA1: 8cfc26f89cd128ca5d37de2379ff85020f06e6e8 SHA256: d61ce1eba32be89d804f7ccedf9ccc14657e7fbb894d81b62b438ddf54f47e93 SHA512: aab2c9da244ea4713ba4baa5766c28ad0255dab87020d1ee9db3473c520fa9c0fa8b0186e31b60fb23a914de064af6438db188092ab6ea26eeebf7dcdc5d177f Homepage: https://cran.r-project.org/package=genieclust Description: CRAN Package 'genieclust' (Genie: Fast and Robust Hierarchical Clustering) Genie is a robust hierarchical clustering algorithm (Gagolewski, Bartoszuk, Cena, 2016 ). 'genieclust' is its faster, more capable implementation (Gagolewski, 2021 ). It enables clustering with respect to mutual reachability distances, allowing it to act as an alternative to 'HDBSCAN*' that can identify any number of clusters or their entire hierarchy. When combined with the 'deadwood' package, it can act as an outlier detector. Additional package features include the Gini and Bonferroni inequality indices, external cluster validity measures (e.g., the normalised clustering accuracy, the adjusted Rand index, the Fowlkes-Mallows index, and normalised mutual information), and internal cluster validity indices (e.g., the Calinski-Harabasz, Davies-Bouldin, Ball-Hall, Silhouette, and generalised Dunn indices). The 'Python' version of 'genieclust' is available via 'PyPI'. Package: r-cran-genio Architecture: amd64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 530 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-readr, r-cran-tibble, r-cran-dplyr, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bedmatrix, r-bioc-snpstats, r-cran-lobstr Filename: pool/dists/noble/main/r-cran-genio_1.1.2-1.ca2404.1_amd64.deb Size: 269148 MD5sum: a8086b5a033c3f5efbfb0335ffdf9eb0 SHA1: 1b4e54c7210a319226b8613c933abf0091ec5cd2 SHA256: d2f4d0f4605a5c80bd3f6c0c887a535ab5048b26709c0e35b998a8daa7d4b22f SHA512: e954a5f9ebae87af3c970b0af5d9d8a1f252af2e5937fb8d9ecb44ace771b62c74b81f8794d0ff8defc6046916e25c1b245e26a3f5116807ee0efee8acbeeb6e Homepage: https://cran.r-project.org/package=genio Description: CRAN Package 'genio' (Genetics Input/Output Functions) Implements readers and writers for file formats associated with genetics data. Reading and writing Plink BED/BIM/FAM and GCTA binary GRM formats is fully supported, including a lightning-fast BED reader and writer implementations. Other functions are 'readr' wrappers that are more constrained, user-friendly, and efficient for these particular applications; handles Plink and Eigenstrat tables (FAM, BIM, IND, and SNP files). There are also make functions for FAM and BIM tables with default values to go with simulated genotype data. Package: r-cran-genlasso Architecture: amd64 Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-igraph Filename: pool/dists/noble/main/r-cran-genlasso_1.6.1-1.ca2404.1_amd64.deb Size: 284368 MD5sum: 916ec939426c2557a7978364e0fd16b5 SHA1: 97b24b6aa459f5633b2e819a0cef862eb441003d SHA256: 3244f2f59adcc59d565ed9d1b4e1f1cf9863ceab4d79b6374354c841d303e201 SHA512: bd0e19be8ba22d3ce86e909ed54c3f849d1fcd5c0e886fd2046a8b9e847b099a3cc1c214244c912eb97117a376935854dab4beffebfb8018fbb06ae2ffecdd66 Homepage: https://cran.r-project.org/package=genlasso Description: CRAN Package 'genlasso' (Path Algorithm for Generalized Lasso Problems) Computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. 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See: "GENLIB: an R package for the analysis of genealogical data" Gauvin et al. (2015) . Package: r-cran-genodds Architecture: amd64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-genodds_1.1.2-1.ca2404.1_amd64.deb Size: 71450 MD5sum: 682fdcee8d40e8a94c6e1b0ab6ae4f7f SHA1: 6b767d41503131ac102e904e540a143292a2a5d3 SHA256: 6db2007f00be90290f8b830f6f1e73c414fc9f894bcb4ef8307c7e972b7edbe2 SHA512: e2de9d6766c60de441fa9b755708d68aca5c5e78108d8fb6ae413c4c18cd4c60e8f4bf2b3ed0fc93a7f35a7f7a16722a0bc681bb5891cb416ba4ae70f205b4c3 Homepage: https://cran.r-project.org/package=genodds Description: CRAN Package 'genodds' (Generalised Odds Ratios) Calculates Agresti's generalized odds ratios. 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Full description can be found in Janzen (2021) . Package: r-cran-gensa Architecture: amd64 Version: 1.1.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gensa_1.1.15-1.ca2404.1_amd64.deb Size: 64122 MD5sum: d4fd158d263bdd571832ad2e6a000421 SHA1: 603d4e040cba1c3487dae3b07eb82b71624ad29c SHA256: d00f7befb0e8b4fe33dbf82effbddd864d29e913f6fee2a0a5f3d522b96172c4 SHA512: 67a5be7e36e64ad8c8b62b854621fd78b86f52732ab766b07f0d457ad9f49f9b5762800bc909e40e17a1e56056682f9e8f311a31dd5d33749ca7fdaae6a92416 Homepage: https://cran.r-project.org/package=GenSA Description: CRAN Package 'GenSA' (R Functions for Generalized Simulated Annealing) Performs search for global minimum of a very complex non-linear objective function with a very large number of optima. 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(2019) for non-negative graphical models (truncated Gaussian, exponential square-root, gamma, a-b models) and univariate truncated Gaussian distributions. Also includes the original estimator for untruncated Gaussian graphical models from Lin et al. (2016) , with the addition of a diagonal multiplier. Package: r-cran-gensurv Architecture: amd64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-survival Filename: pool/dists/noble/main/r-cran-gensurv_1.0.6-1.ca2404.1_amd64.deb Size: 87778 MD5sum: 02de45b273f3061993a56e36f0a1dbef SHA1: 7c8898139c0d681bc81019f0eae8f156b10531f2 SHA256: 7ac14f6e903d9caa8e9d6dece18361b4d2684b999d95ccf57535c7db04568c53 SHA512: 50f9aed879da49d2b6e315c097ee754a05f194d54f3358db43855903c9335e639abc8c5ae44cace7ae405acc7e7ecaac10a4c9a4e8ed86ba5fa89845a848eb20 Homepage: https://cran.r-project.org/package=genSurv Description: CRAN Package 'genSurv' (Generating Multi-State Survival Data) Generation of survival data with one (binary) time-dependent covariate. Generation of survival data arising from a progressive illness-death model. Package: r-cran-gensvm Architecture: amd64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gensvm_0.1.7-1.ca2404.1_amd64.deb Size: 168322 MD5sum: 60ba3ce414b32c250c2fc5330e7ebebf SHA1: 789fc54c5cd8ddbd2fa10aeca0a0c8a3fcf77321 SHA256: 08ce9cb1c1a70c0e05dbb16a9703d2cd7cd8a9605851554ccd1f297306b24f32 SHA512: 07e63ecbc3ca5d57eab6a2e66ded08a70f137a6af17643eded5720f9578c2fb54d1ec07efe725f414874a60b576df86acb56e63034426049e95eb851eae05e6a Homepage: https://cran.r-project.org/package=gensvm Description: CRAN Package 'gensvm' (A Generalized Multiclass Support Vector Machine) The GenSVM classifier is a generalized multiclass support vector machine (SVM). This classifier aims to find decision boundaries that separate the classes with as wide a margin as possible. In GenSVM, the loss function is very flexible in the way that misclassifications are penalized. This allows the user to tune the classifier to the dataset at hand and potentially obtain higher classification accuracy than alternative multiclass SVMs. Moreover, this flexibility means that GenSVM has a number of other multiclass SVMs as special cases. One of the other advantages of GenSVM is that it is trained in the primal space, allowing the use of warm starts during optimization. This means that for common tasks such as cross validation or repeated model fitting, GenSVM can be trained very quickly. Based on: G.J.J. van den Burg and P.J.F. Groenen (2018) . Package: r-cran-geoadjust Architecture: amd64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1831 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fmesher, r-cran-terra, r-cran-sf, r-cran-summer, r-cran-matrix, r-cran-ggplot2, r-cran-fields, r-cran-tmb, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geoadjust_2.0.1-1.ca2404.1_amd64.deb Size: 1039696 MD5sum: b3b5ed151ab58cd389f8af6129309c57 SHA1: dff423ff335b8772212c2c232fb133b5c04d4844 SHA256: 7d001d175ec37f7adc86a656ccf1a69fe1f08ffd1421e925abaafc00172f1581 SHA512: 5ee21a2416fbe23ae17556643e9a99243104ede718f0525855f5f800d636e2744d6703e2c840f6897fd24f50cf26d2edb011a465daf7cc2fc4e8b403062be102 Homepage: https://cran.r-project.org/package=GeoAdjust Description: CRAN Package 'GeoAdjust' (Accounting for Random Displacements of True GPS Coordinates ofData) The purpose is to account for the random displacements (jittering) of true survey household cluster center coordinates in geostatistical analyses of Demographic and Health Surveys program (DHS) data. Adjustment for jittering can be implemented either in the spatial random effect, or in the raster/distance based covariates, or in both. Detailed information about the methods behind the package functionality can be found in our two papers. Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2024) . Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2023) . Package: r-cran-geoarrow Architecture: amd64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nanoarrow, r-cran-wk Suggests: r-cran-arrow, r-cran-r6, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geoarrow_0.4.2-1.ca2404.1_amd64.deb Size: 216094 MD5sum: 5901508a72c5559afc0451b648326328 SHA1: 12fa77da20a0ea352a2ea561bbc904946d05d8ca SHA256: b9956c761c0298c35046754b83fc5f7c37c16621aaa0fb37c9634c069a9f199e SHA512: 8ca66f0fceea67808d75f34f23dc995626c44b2b989421bfae671c98046854d71214cb6fba272fbcbb2463d3ffbd85e703e9135be741961afe9d45def488979a Homepage: https://cran.r-project.org/package=geoarrow Description: CRAN Package 'geoarrow' (Extension Types for Spatial Data for Use with 'Arrow') Provides extension types and conversions to between R-native object types and 'Arrow' columnar types. This includes integration among the 'arrow', 'nanoarrow', 'sf', and 'wk' packages such that spatial metadata is preserved wherever possible. Extension type implementations ensure first-class geometry data type support in the 'arrow' and 'nanoarrow' packages. 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The data can be continuous, binary or count data and the models implemented are flexible. Conjugate priors are assumed on some parameters while inference on the other parameters can be done through a full Bayesian analysis of by empirical Bayes methods. Package: r-cran-geocmeans Architecture: amd64 Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5775 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-tmap, r-cran-spdep, r-cran-reldist, r-cran-dplyr, r-cran-fclust, r-cran-fmsb, r-cran-future.apply, r-cran-progressr, r-cran-reshape2, r-cran-shiny, r-cran-sf, r-cran-leaflet, r-cran-plotly, r-cran-rdpack, r-cran-matrixstats, r-cran-terra, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-future, r-cran-ppclust, r-cran-clustgeo, r-cran-car, r-cran-rgl, r-cran-ggpubr, r-cran-rcolorbrewer, r-cran-kableextra, r-cran-viridis, r-cran-testthat, r-cran-bslib, r-cran-shinywidgets, r-cran-shinyhelper, r-cran-waiter, r-cran-classint, r-cran-covr Filename: pool/dists/noble/main/r-cran-geocmeans_0.3.4-1.ca2404.1_amd64.deb Size: 4245960 MD5sum: 7be094a3fa62a2323ef66b8994114c0d SHA1: 5bce255e79e5e932dc0af10c52baff5bba42fa57 SHA256: 8753bc02289f1738ceae518f88d4535f0754af7c4a7a3dd316d8dc43ead098d1 SHA512: faa249134d2440492e6f956bd992e17a25098b8dddfa6c74a5865a1cdd262e112438582f5ed2ef730f9e459a2ac69ff0b7901c935247e27b95fa0560af6af67b Homepage: https://cran.r-project.org/package=geocmeans Description: CRAN Package 'geocmeans' (Implementing Methods for Spatial Fuzzy UnsupervisedClassification) Provides functions to apply spatial fuzzy unsupervised classification, visualize and interpret results. This method is well suited when the user wants to analyze data with a fuzzy clustering algorithm and to account for the spatial dimension of the dataset. In addition, indexes for estimating the spatial consistency and classification quality are proposed. The methods were originally proposed in the field of brain imagery (seed Cai and al. 2007 and Zaho and al. 2013 ) and recently applied in geography (see Gelb and Apparicio ). 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O pacote é baseado em conjuntos de dados espaciais abertos de endereços brasileiros, utilizando como fonte principal o Cadastro Nacional de Endereços para Fins Estatísticos (CNEFE). O CNEFE é publicado pelo Instituto Brasileiro de Geografia e Estatística (IBGE), órgão oficial de estatísticas e geografia do Brasil. (A simple and efficient method for geolocating data in Brazil. The package is based on open spatial datasets of Brazilian addresses, primarily using the Cadastro Nacional de Endereços para Fins Estatísticos (CNEFE), published by the Instituto Brasileiro de Geografia e Estatística (IBGE), Brazil's official statistics and geography agency.) 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In spatial regression tasks, the goodness of fit can be improved by incorporating a geographical complexity representation vector during modeling, using a geographical complexity-weighted spatial weight matrix, or employing local geographical complexity kernel density. Similarly, in spatial sampling tasks, samples can be selected more effectively by using a method that weights based on geographical complexity. By optimizing performance in spatial regression and spatial sampling tasks, the spatial bias of the model can be effectively reduced. Package: r-cran-geodist Architecture: amd64 Version: 0.1.1-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1460 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geodist_0.1.1-1.ca2404.2_amd64.deb Size: 490232 MD5sum: 512377273e21bb8d4a7fb77008819f8e SHA1: 41638abcca44df24856c33f1a0c99510c0d2b14c SHA256: b6b6cc29a253863e2cf1280bd5c88d8cffba0e9b10a23c3703c5be3233360fd8 SHA512: 31962909a3aac17d6b0380824d01186604003eb54993609e41ad681a57e299bf7fdcd787d2e0d4f18744ecb379a6cf5b92f1e5d29f332663e67b1dc30d86b9fc Homepage: https://cran.r-project.org/package=geodist Description: CRAN Package 'geodist' (Fast, Dependency-Free Geodesic Distance Calculations) Dependency-free, ultra fast calculation of geodesic distances. Includes the reference nanometre-accuracy geodesic distances of Karney (2013) , as used by the 'sf' package, as well as Haversine and Vincenty distances. Default distance measure is the "Mapbox cheap ruler" which is generally more accurate than Haversine or Vincenty for distances out to a few hundred kilometres, and is considerably faster. The main function accepts one or two inputs in almost any generic rectangular form, and returns either matrices of pairwise distances, or vectors of sequential distances. Package: r-cran-geodiv Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2237 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-pracma, r-cran-spatial, r-cran-e1071, r-cran-sf, r-cran-zoo, r-cran-rcpp, r-cran-terra, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-geodiv_1.1.0-1.ca2404.1_amd64.deb Size: 1943154 MD5sum: a8fa4ec389ef318593ff0e7426417e63 SHA1: 34d68deb2b8d98faf1c55809fb383dc4b9f22690 SHA256: d937a494c063a3287ba019d15b4e727ca07bf7c2d072cd05d32b7d6ce1685cdc SHA512: a814414226f42b84841b9d2a884bf98a0d00933eeda07ad7840e9beee8da334222e566b91ad0c6965b13ae935cbef22234063a0a789718b7bbdced8e0f7737af Homepage: https://cran.r-project.org/package=geodiv Description: CRAN Package 'geodiv' (Methods for Calculating Gradient Surface Metrics) Methods for calculating gradient surface metrics for continuous analysis of landscape features. 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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-geofkf Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-geofkf_0.1.1-1.ca2404.1_amd64.deb Size: 126632 MD5sum: f08a304921a98e00fae7df10320c10e4 SHA1: ed207f923e7cfc3eb70a4081dfee6e934504bf75 SHA256: 42eddc267f4e7afdc22a15d038a0f527d9c152ef476553f19a0957bba45748b0 SHA512: 239b063eff60a6644d0ea4296589ab61595af52285e735d2433c647dade3b12e813c2cfda6ff02db414c418af0e2e3bd31549bde2e315c458a5faf0391590e2c Homepage: https://cran.r-project.org/package=geoFKF Description: CRAN Package 'geoFKF' (Kriging Method for Spatial Functional Data) A Kriging method for functional datasets with spatial dependency. This functional Kriging method avoids the need to estimate the trace-variogram, and the curve is estimated by minimizing a quadratic form. The curves in the functional dataset are smoothed using Fourier series. The functional Kriging of this package is a modification of the method proposed by Giraldo (2011) . Package: r-cran-geofourierfda Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-magrittr, r-cran-orthopolynom, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-geofourierfda_0.1.0-1.ca2404.1_amd64.deb Size: 94708 MD5sum: b0ae380a01e35e70ffd39f522edff665 SHA1: 6e627f5240e201458f67d65463a2d0ca64600536 SHA256: 25765384633f582ffaef5afe1aad893eb66d2f97c5c7c1073d50bad7e17df5d1 SHA512: 0bde6c5bf0faa8f5efaf56c65271c7515f3cad88c45d251b126f2eb31c9f031809161a83c4a3ecd2b03dd239fef5fdbf036f7229211619677ff16fdbf1d93b90 Homepage: https://cran.r-project.org/package=geoFourierFDA Description: CRAN Package 'geoFourierFDA' (Ordinary Functional Kriging Using Fourier Smoothing and GaussianQuadrature) Implementation of the ordinary functional kriging method proposed by Giraldo (2011) . This implements an alternative method to estimate the trace-variogram using Fourier Smoothing and Gaussian Quadrature. Package: r-cran-geographiclib Architecture: amd64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2143 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geographiclib_0.4.2-1.ca2404.1_amd64.deb Size: 711176 MD5sum: 41bb3bf6e7bf87bd3580e2669aa9d89c SHA1: 989b5df17a93d763bd39423d56aca97b56c63692 SHA256: 37e9ce33ca161f90dba225e48e257c69e59150b9a6adf0ecb2d51f579f3e1aa3 SHA512: a7ae445542a16a1a7e80a18b0b4bb058dc1fc5218aa4f8446b8adf504fb601301b8ce3408150d29b1bc40e3a717478ec46987731511875cd94bf8450cf776126 Homepage: https://cran.r-project.org/package=geographiclib Description: CRAN Package 'geographiclib' (Access to 'GeographicLib') Bindings to the 'GeographicLib' C++ library for precise geodetic calculations including geodesic computations (distance, bearing, paths, intersections), map projections (UTM/UPS, Transverse Mercator, Lambert Conformal Conic, and more), grid reference systems (MGRS, Geohash, GARS, Georef), coordinate conversions (geocentric, local Cartesian), and polygon area on the WGS84 ellipsoid. All functions are fully vectorized. 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Package: r-cran-geohashtools Architecture: amd64 Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-sf, r-cran-sp, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-geohashtools_0.3.3-1.ca2404.1_amd64.deb Size: 104572 MD5sum: 8e3c90c3807d103dfbd30d2cc61cbe05 SHA1: e50a4c60fbb28c8b3a9ede6aa8a089e82c78c361 SHA256: 5a590d1daa610957f5ddca13cff2f4266f02107390ea54ed04de041b5bb64b87 SHA512: 32bd89e407562eccd92ff3ad9ba8d0112752a88c825a92ab38d45bf780784351f3fcf662e54c59599c116790eb86fe0dd1ab5eeaa0caaaf2a940ba8a721e14ca 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-geomap Architecture: amd64 Version: 2.5-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3415 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rpmg, r-cran-fields, r-cran-sf, r-cran-mba Suggests: r-cran-geomapdata, r-cran-maps Filename: pool/dists/noble/main/r-cran-geomap_2.5-11-1.ca2404.1_amd64.deb Size: 3175360 MD5sum: 4dfba79081e559f27fb0e4eb690357b8 SHA1: 5176d7b27fde2048b33a9fd26e48a537e9f85610 SHA256: 34c5dfafda722a376efe8cb3481c54f56381635a995a8f23bfed73df0ec1bf14 SHA512: 3f538684ee6a762406717cd8db050ba3016b6e44c33df99a9052cc66d059c6706447deef3dd6837dc4c37b86ad160491437436c55d73ed9727714957fd760c0a Homepage: https://cran.r-project.org/package=GEOmap Description: CRAN Package 'GEOmap' (Topographic and Geologic Mapping) Set of routines for making map projections (forward and inverse), topographic maps, perspective plots, geological maps, geological map symbols, geological databases, interactive plotting and selection of focus regions. Package: r-cran-geometries Architecture: amd64 Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 855 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-geometries_0.2.5-1.ca2404.1_amd64.deb Size: 247674 MD5sum: bd5e9f6b60ee85c6e4c66cbb07a7bd7d SHA1: 4213b26bd1986cbf11c01539ddbca08cbfdb5243 SHA256: 887980be291552957cfb8870ee940f3c451dda028b98d119a7da0ab6d91e4cd2 SHA512: f0e9bf26a22fdf1b139188ed3c7fdd3e32143fc4fea9c84c38692de4c436ead8d6f98421abf31e9a90f6964ea69561e1313d8f4bfe128facc298e41c317e21a0 Homepage: https://cran.r-project.org/package=geometries Description: CRAN Package 'geometries' (Convert Between R Objects and Geometric Structures) Geometry shapes in 'R' are typically represented by matrices (points, lines), with more complex shapes being lists of matrices (polygons). 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Package: r-cran-geometry Architecture: amd64 Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1927 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-magic, r-cran-rcpp, r-cran-lpsolve, r-cran-linprog, r-cran-rcppprogress Suggests: r-cran-spelling, r-cran-testthat, r-cran-rgl, r-cran-r.matlab, r-cran-interp Filename: pool/dists/noble/main/r-cran-geometry_0.5.2-1.ca2404.1_amd64.deb Size: 883672 MD5sum: 46b93953d0e5854fd8c5b9104373c1bf SHA1: c2bd17ad207d42f90998674ccf7d7963a15e5d81 SHA256: 9e8dd490b71131fb880d993e887208680ee516267b152309ed886a98c9bd6c70 SHA512: 8969dc193c8114c25fac744db4cef920d68968d75a9670ba291e5ef6dc06bf8395f4110f1228e20914981154ad9d1da8d69097772135ea7e9a0f18dbe5162104 Homepage: https://cran.r-project.org/package=geometry Description: CRAN Package 'geometry' (Mesh Generation and Surface Tessellation) Makes the 'Qhull' library available in R, in a similar manner as in Octave and MATLAB. Qhull computes convex hulls, Delaunay triangulations, halfspace intersections about a point, Voronoi diagrams, furthest-site Delaunay triangulations, and furthest-site Voronoi diagrams. It runs in 2D, 3D, 4D, and higher dimensions. It implements the Quickhull algorithm for computing the convex hull. Qhull does not support constrained Delaunay triangulations, or mesh generation of non-convex objects, but the package does include some R functions that allow for this. Package: r-cran-geommc Architecture: amd64 Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1571 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-cubature, r-cran-matrix, r-cran-numderiv, r-cran-progress, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sass, r-cran-bslib Filename: pool/dists/noble/main/r-cran-geommc_1.3.2-1.ca2404.1_amd64.deb Size: 988434 MD5sum: ce94b7d0eb67fa39797c0450175bfb8a SHA1: 6eb33031643188a4273702ba8ec11abdf79898ad SHA256: 17080dbf0a1925832e54a7f2a54c1034afc32035ba81d5b77600e84e62315419 SHA512: 074741743b04514f5fbf88b74a92b8f84ee2aee08daaf0474c82f776e72bed42bc962c0903624f470ca6c910ddfc6b6543eb30332f3a164b3d418856cd6afd0a Homepage: https://cran.r-project.org/package=geommc Description: CRAN Package 'geommc' (Geometric Markov Chain Sampling) Simulates from discrete and continuous target distributions using geometric Metropolis-Hastings (MH) algorithms. 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Package: r-cran-geomodels Architecture: amd64 Version: 2.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4068 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields, r-cran-mapproj, r-cran-shape, r-cran-progressr, r-cran-future.apply, r-cran-spam, r-cran-scatterplot3d, r-cran-dotcall64, r-cran-fastgp, r-cran-plotrix, r-cran-pracma, r-cran-pbivnorm, r-cran-sn, r-cran-sp, r-cran-nabor, r-cran-hypergeo, r-cran-vgam, r-cran-foreach, r-cran-future, r-cran-dofuture, r-cran-minqa, r-cran-withr Suggests: r-cran-numderiv, r-cran-memuse Filename: pool/dists/noble/main/r-cran-geomodels_2.2.3-1.ca2404.1_amd64.deb Size: 3727404 MD5sum: 1b2310947b21d860ef53f823e4e733fa SHA1: b1a9f071607ffd995a8d24b838aad8bdf138b77e SHA256: b1e78a817c68dd59e1d9875d4a3e07abb11891b86394a140b31344da5590ee4b SHA512: 5f46ce8adcfb0ffa2f8b2738f28f8bce9ee7e80d40571376d4d4b7d533be921d5dbcbe1fb4dc1c6d32be7257bf79443a75925e094293c8cdcd1b5cf8b8a9e640 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: amd64 Version: 1.9-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1658 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-sp, r-cran-splancs Suggests: r-cran-scatterplot3d, r-cran-lattice Filename: pool/dists/noble/main/r-cran-geor_1.9-6-1.ca2404.1_amd64.deb Size: 1501398 MD5sum: bf64d2c4c9fdad1df104fea9d86760c9 SHA1: b64d3af7911485faede4e03de2bc9f36b98c9533 SHA256: 2a247e255193afd21f89a64ad37192a02719e4de267c119babeb978df2bae650 SHA512: 5e15eb80c8f61da8d104234d2d605abc55a33ed7d7f88d5fc8f02cab40dccd61e3fec888ee54219ca58be086a876b424ef7efe1e0058b9bc2f7f382e6f650a33 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: amd64 Version: 1.6-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1369 Depends: libc6 (>= 2.35), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-geosphere_1.6-8-1.ca2404.1_amd64.deb Size: 1049352 MD5sum: 619f51886d30c1e32a42c9158b5b7dbe SHA1: bdffcfdc7d99ed38ad6518a0f375c841644e89d1 SHA256: ed7d0876a87be6d0cc877a19af8161df4bf786dfea1939bec6c45bec8d1aeea7 SHA512: a0fc6264e4b6cbacb21d6777f6f896f4cd2bb0add7a64a2975351d509c848be8127efceda65ed0b9c02fd2ba82310207ea7cac3661f5684d405957fb03533f4b 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. 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Reference: Damian V. Wandler & Jan Hannig (2012) . 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Fiducial inference is somehow similar to Bayesian inference, in the sense that it is based on a distribution that represents the uncertainty about the parameters, like the posterior distribution in Bayesian statistics. It does not require a prior distribution, and it yields results close to frequentist results. Reference: Cisewski and Hannig (2012) . 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Package: r-cran-ggclassification Architecture: amd64 Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-ggclassification_0.1-1.ca2404.1_amd64.deb Size: 67776 MD5sum: 6b61e82de8eab78e9683c63debf12e57 SHA1: 5bf944202da9d36a5f33f1035a64495ee8d8fb1c SHA256: 923ca409c66d4e36a28d01183a0cc30597308541813716fd8fd1f9ddebdaa16e SHA512: 22320a85822573227f0c6fe2b979311f92e81b2f672a96eaa61f9fe6aec538cf1a999d46f4dcdf8fbf8c978e99df1ff58596cf6d97b0e507f29e1f0a82b8cb49 Homepage: https://cran.r-project.org/package=GGClassification Description: CRAN Package 'GGClassification' (Gabriel Graph Based Large-Margin Classifiers) Contains the implementation of a binary large margin classifier based on Gabriel Graph. References for this method can be found in L.C.B. Torres et al. (2015) . Package: r-cran-ggdist Architecture: amd64 Version: 3.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3523 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-rlang, r-cran-cli, r-cran-tibble, r-cran-vctrs, r-cran-withr, r-cran-glue, r-cran-gtable, r-cran-distributional, r-cran-numderiv, r-cran-quadprog, r-cran-rcpp Suggests: r-cran-tidyselect, r-cran-dplyr, r-cran-fda, r-cran-posterior, r-cran-beeswarm, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-vdiffr, r-cran-svglite, r-cran-fontquiver, r-cran-sysfonts, r-cran-showtext, r-cran-mvtnorm, r-cran-covr, r-cran-broom, r-cran-patchwork, r-cran-tidyr, r-cran-ragg, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-ggdist_3.3.3-1.ca2404.1_amd64.deb Size: 2701114 MD5sum: fcd0ea0c57fb0f37de6b7383ebb51a59 SHA1: 26a6f253762c632bc8725ce79421ae02621a7ebd SHA256: fd7d9c98b46bbbd7c6e099d89607a44de696fdc5b2f1b748c64bdb085f526d49 SHA512: 587062604be6ed9357b8f4bed78e2b063f03188ecc36a15412030499cd55b43980616186354294389ce33658ae39899b4294417551dd1865ce877decbd3676df Homepage: https://cran.r-project.org/package=ggdist Description: CRAN Package 'ggdist' (Visualizations of Distributions and Uncertainty) Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include but are not limited to: points with multiple uncertainty intervals, eye plots (Spiegelhalter D., 1999) , density plots, gradient plots, dot plots (Wilkinson L., 1999) , quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) , complementary cumulative distribution function barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) , and fit curves with multiple uncertainty ribbons. Package: r-cran-ggdmc Architecture: amd64 Version: 0.2.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 728 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-coda, r-cran-ggplot2, r-cran-matrixstats, r-cran-data.table, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggdmc_0.2.6.2-1.ca2404.1_amd64.deb Size: 415202 MD5sum: 5f15d69a885ef07aeda50aea3f24753a SHA1: 02f021ab6739ca5ea1b60f3ad86a6f5072030710 SHA256: c7079ece52944a2b520428be840494b41194cbd5a32b44cecb93fa201666f25a SHA512: 1a4d131e87841344ad6a046d7cb1b1f250195c21da9fe7459b065badc6284ef32b0c7b6a29553a5627c218cf39cf0d6aa013173a8e4a708987b21f937faa3a63 Homepage: https://cran.r-project.org/package=ggdmc Description: CRAN Package 'ggdmc' (Cognitive Models) Hierarchical Bayesian models. The package provides tools to fit two response time models, using the population-based Markov Chain Monte Carlo. Package: r-cran-ggdmcheaders Architecture: amd64 Version: 0.2.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ggdmcheaders_0.2.9.1-1.ca2404.1_amd64.deb Size: 46578 MD5sum: 7ed6e2d8b7913fbb6f5c18d97762cad2 SHA1: d01d792bc1079de5b78dfe990a662a4896cd9a84 SHA256: 2f32a96c1b7a4e798c3e35062cb757163bac680c67784b58925137a5bf723f7f SHA512: 6dfc5c12dec8dee46d2a821803cf68e63d826e0b5321f084c2e4881cade50402d67bdcbc75309ab71afc0c8e69cd2569d2ec6c3fb5e62c0550a0a6869e04c271 Homepage: https://cran.r-project.org/package=ggdmcHeaders Description: CRAN Package 'ggdmcHeaders' ('C++' Headers for 'ggdmc' Package) A fast 'C++' implementation of the design-based, Diffusion Decision Model (DDM) and the Linear Ballistic Accumulation (LBA) model. 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Package: r-cran-ggdmclikelihood Architecture: amd64 Version: 0.2.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-ggdmcheaders Suggests: r-cran-testthat, r-cran-ggdmcmodel Filename: pool/dists/noble/main/r-cran-ggdmclikelihood_0.2.9.0-1.ca2404.1_amd64.deb Size: 96482 MD5sum: fc56e957a7eddfd584af7ec29b7d28ab SHA1: 75d3c1e106baba23cbe3ea81e18b08f357178d04 SHA256: 94846740630d8b3db6790944bd2c7fb84bd610e4ed7c0e76a169b08ecf1f3002 SHA512: 9a920d79b46e369fae7b78de69f152591a606a21dd0c5b728e30b2cbedf2b5f091d794df7e7936eda491d2253b91f0e70a2c5cb01b17d3187e7050bd1855cfac Homepage: https://cran.r-project.org/package=ggdmcLikelihood Description: CRAN Package 'ggdmcLikelihood' (Likelihood Computation for 'ggdmc' Package) Efficient computation of likelihoods in design-based choice response time models, including the Decision Diffusion Model, is supported. 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Package: r-cran-gglinedensity Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2546 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-ggplot2, r-cran-lifecycle, r-cran-rlang, r-cran-scales, r-cran-vctrs, r-cran-vdiffr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gglinedensity_0.2.0-1.ca2404.1_amd64.deb Size: 1516704 MD5sum: abc092e022b67b7b192acd0691c003da SHA1: 9c22b477a35ceb268f0befc3116094db5feaf70c SHA256: f05f49082515101532bcde0ebec283f31c1f13de4f5cc84df6e6acd545bda883 SHA512: e552997e170f0ee383cc34ee78fc7da633205827a55ed3679073dcb3bd3977ad4991f83a9b0c89d0d3b76bb31505ea5d51ba127525910c425a40e6a1ebfee2ab Homepage: https://cran.r-project.org/package=gglinedensity Description: CRAN Package 'gglinedensity' (Make DenseLines Heatmaps with 'ggplot2') Visualise overlapping time series lines as a heatmap of line density. 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Package: r-cran-ggmlr Architecture: amd64 Version: 0.7.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6034 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-generics, r-cran-r6 Suggests: r-cran-testthat, r-cran-mlr3, r-cran-paradox, r-cran-digest, r-cran-parsnip, r-cran-tibble, r-cran-rlang, r-cran-dials, r-cran-lgr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggmlr_0.7.6-1.ca2404.1_amd64.deb Size: 2339936 MD5sum: d5fbd6875fbe29a217a9cfe0d41092aa SHA1: 3eec4ce778eeeb64363ccf76d82efb9390f62154 SHA256: cd50995531868f3d5a86cdc2e32eee6e23ae75247e1a541ff13f52e76167812e SHA512: 5d0b7fd9951ce5725aa7137f63cabb3b327cf7418d918adf39698bed21fc3778c847ebf07d51a93ca673c80f1a24c67d9b76ce7b78e09f2645b8213788866e7f Homepage: https://cran.r-project.org/package=ggmlR Description: CRAN Package 'ggmlR' ('GGML' Tensor Operations for Machine Learning) Provides 'R' bindings to the 'GGML' tensor library for machine learning, optimized for 'Vulkan' GPU acceleration with a transparent CPU fallback. The package features a 'Keras'-like sequential API and a 'PyTorch'-style 'autograd' engine for building, training, and deploying neural networks. Key capabilities include high-performance 5D tensor operations, 'f16' precision, and efficient quantization. It supports native 'ONNX' model import (50+ operators) and 'GGUF' weight loading from the 'llama.cpp' and 'Hugging Face' ecosystems. Designed for zero-overhead inference via dedicated weight buffering, it integrates seamlessly as a 'parsnip' engine for 'tidymodels' and provides first-class learners for the 'mlr3' framework. See for more information about the underlying library. Package: r-cran-ggmncv Architecture: amd64 Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1707 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-reshape, r-cran-ggally, r-cran-ggplot2, r-cran-glassofast, r-cran-network, r-cran-numderiv, r-cran-mathjaxr, r-cran-mass, r-cran-pbapply, r-cran-sna, r-cran-rcpparmadillo Suggests: r-cran-car, r-cran-corpcor, r-cran-corrplot, r-cran-dplyr, r-cran-networktoolbox, r-cran-networkcomparisontest, r-cran-nlshrink, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-ggmncv_2.1.2-1.ca2404.1_amd64.deb Size: 1240562 MD5sum: 01826996259a6935975f584c943fffa4 SHA1: 7f6fa1b3601cf468d392c06cd3689d630bc7143d SHA256: 7bfd49ad08d869c0ade092485c7063849aae417488f1a17a0ac471ee75b6e696 SHA512: 12b5589309bca5b0fa2da1410ff03c2ff788ea0341db1c01c881aaf075043d98aa059dd0ecd75474d7d1dca561be85dc1a0e245f5e95493929021587d448274b Homepage: https://cran.r-project.org/package=GGMncv Description: CRAN Package 'GGMncv' (Gaussian Graphical Models with Nonconvex Regularization) Estimate Gaussian graphical models with nonconvex penalties, including methods described by Williams (2020) . Penalties include atan (Wang and Zhu, 2016) , seamless L0 (Dicker, Huang and Lin, 2013) , exponential (Wang, Fan and Zhu, 2018) , smooth integration of counting and absolute deviation (Lv and Fan, 2009) , logarithm (Mazumder, Friedman and Hastie, 2011) , Lq, smoothly clipped absolute deviation (Fan and Li, 2001) , and minimax concave penalty (Zhang, 2010) . The package also provides extensions for variable inclusion probabilities, multiple regression coefficients, and statistical inference (Janková and van de Geer, 2015) . Package: r-cran-ggmselect Architecture: amd64 Version: 0.1-12.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-lars, r-cran-gtools Suggests: r-cran-network, r-cran-glasso Filename: pool/dists/noble/main/r-cran-ggmselect_0.1-12.7.1-1.ca2404.1_amd64.deb Size: 366976 MD5sum: 427de6d0c78dbf46a567079f499610c3 SHA1: 7bcc6683692cfdbceb7bb11316259a9111a8eb29 SHA256: f40644b14b4622d80b103686bb2ec70299779706c4bed17c64be5536e24a7bc5 SHA512: a0bd6d1de3d6b3323ff0dcd6206e507fa4cafb707cd95e96065a39a83caf701260c83af76e29e8c4a587f9afbb92006ff4d9c4fc84fa0477847e535fbd469675 Homepage: https://cran.r-project.org/package=GGMselect Description: CRAN Package 'GGMselect' (Gaussian Graphs Models Selection) Graph estimation in Gaussian Graphical Models, following the method developed by C. Giraud, S. Huet and N. 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Package: r-cran-ggpointdensity Architecture: amd64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5889 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass Suggests: r-cran-viridis, r-cran-dplyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggpointdensity_0.2.1-1.ca2404.1_amd64.deb Size: 5884114 MD5sum: d4daf12cf47873216b15468179e9b6d3 SHA1: 99c2e82593408f100d5ba316b412e1bb8f976995 SHA256: 78e69f18c4d7d21af2d8aca98dc54483dcc12cf00c746d120aa32eae5b7be9a3 SHA512: 3fe1653e8dcdfdb7fa3fe255e9f4cea13f39416c4520abcb164e627dcba325300ea5a5b0a7e269a3f67e6cfe1dfb0283d7a97d84f31127677ec845c31ea39783 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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Especially, it contains fitting procedures, an AIC-based model selection routine, and functions for the computation of density, quantile, probability, random variates, expected shortfall and some portfolio optimization and plotting routines as well as the likelihood ratio test. In addition, it contains the Generalized Inverse Gaussian distribution. See Chapter 3 of A. J. McNeil, R. Frey, and P. Embrechts. Quantitative risk management: Concepts, techniques and tools. Princeton University Press, Princeton (2005). Package: r-cran-gibasa Architecture: amd64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 979 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-matrix, r-cran-rcpp, r-cran-rcppparallel, r-cran-readr, r-cran-rlang, r-cran-stringi Suggests: r-cran-roxygen2, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-gibasa_1.1.3-1.ca2404.1_amd64.deb Size: 478960 MD5sum: 06b69b0364a7dee426b1f29811953249 SHA1: e0d499835326305224b3aedf2972d95d4f18d449 SHA256: a68ef6eb0456a50150a6707f204f98a51ec8a1d627869defc518c984600d0525 SHA512: 2191c9623d45982d249074f21c5e7abc5393c7f5da6ff2a11111e3347aa593693a2b026a45c7b9df7723c1180b02cfc1d836dceebcde838edece0df096b86517 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: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-gicf_1.0.1-1.ca2404.1_amd64.deb Size: 88886 MD5sum: 2a5fcdfda0637109777606d528704049 SHA1: ae8ed1011d7fe1329acb5c0cd6d56a43df485349 SHA256: 1a062b5e24437fa327724b279c06728b86eb60d397770fa0fb91d0ecec9f3064 SHA512: 4bbf368e331a905b7a07b2120abdb2cbaa4390deb79297d71557ee5a52ede07cec5a249ce54f4f2c579e968ade7650a9a801c543012026271dd3e66f6445cff8 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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Our approach could be divided into three categories. First of all, we use Hard Graphical Thresholding for best subset selection problem of Gaussian graphical model, and the core concept of this method was proposed by Luo et al. (2014) . Secondly, a closed form solution for graphical lasso under acyclic graph structure is implemented in our package (Fattahi and Sojoudi (2019) ). Furthermore, we implement block coordinate descent algorithm to efficiently solve the covariance selection problem (Dempster (1972) ). Our package is computationally efficient and can solve ultra-high-dimensional problems, e.g. p > 10,000, in a few minutes. 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Supports modeling all distribution parameters (alpha, beta, gamma, delta, lambda) as functions of predictors through various link functions. Provides efficient maximum likelihood estimation via Template Model Builder ('TMB'), offering comprehensive diagnostics, model comparison tools, and simulation methods. Particularly useful for analyzing proportions, rates, indices, and other bounded response data with complex distributional features not adequately captured by simpler models. Package: r-cran-glamlasso Architecture: amd64 Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 783 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-glamlasso_3.0.1-1.ca2404.1_amd64.deb Size: 286714 MD5sum: 04aed7cb8b4fb625c130e3cbfc9d512e SHA1: a5a1cc9365b607469ed527c9c7f65bf8f688b874 SHA256: e6d6a8db5a709fdcf537ecfa7dcda5e63b820f22567fd67961fd4b8d20a11764 SHA512: 2e478d13c2bd4d130a0ef0c8d0e7bd7d558dc5e22794bc8588b6fcb8cd7edb5e714e52dd3efbeb94d440401c2b51ab972b0e5f70cc7e541c478ea6da2a973bd0 Homepage: https://cran.r-project.org/package=glamlasso Description: CRAN Package 'glamlasso' (Penalization in Large Scale Generalized Linear Array Models) Efficient design matrix free lasso penalized estimation in large scale 2 and 3-dimensional generalized linear array model framework. The procedure is based on the gdpg algorithm from Lund et al. (2017) . Currently Lasso or Smoothly Clipped Absolute Deviation (SCAD) penalized estimation is possible for the following models: The Gaussian model with identity link, the Binomial model with logit link, the Poisson model with log link and the Gamma model with log link. It is also possible to include a component in the model with non-tensor design e.g an intercept. Also provided are functions, glamlassoRR() and glamlassoS(), fitting special cases of GLAMs. Package: r-cran-glarmadillo Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-glarmadillo_1.1.1-1.ca2404.1_amd64.deb Size: 82632 MD5sum: 23cffa40cd46cf8156b1be53149568c8 SHA1: d8b72b74a4cbe7dbcda5cd59d5e3d585306915a8 SHA256: 6829c1c1e0c63d25f8630c2b2172903de2012e2f0fbc9a0279fada1e1072b594 SHA512: 18fe5e333ce72b15f6a4283e126641d0243fc3ef2f087028087f28dbae532a5ebe784e26c4ed3d5a34231c1abbe1efe306fcecfb18a9a4a5459f7365677435f9 Homepage: https://cran.r-project.org/package=Glarmadillo Description: CRAN Package 'Glarmadillo' (Solve the Graphical Lasso Problem with 'Armadillo') Efficiently implements the Graphical Lasso algorithm, utilizing the 'Armadillo' 'C++' library for rapid computation. This algorithm introduces an L1 penalty to derive sparse inverse covariance matrices from observations of multivariate normal distributions. Features include the generation of random and structured sparse covariance matrices, beneficial for simulations, statistical method testing, and educational purposes in graphical modeling. A unique function for regularization parameter selection based on predefined sparsity levels is also offered, catering to users with specific sparsity requirements in their models. The methodology for sparse inverse covariance estimation implemented in this package is based on the work of Friedman, Hastie, and Tibshirani (2008) . Package: r-cran-glasso Architecture: amd64 Version: 1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-glasso_1.11-1.ca2404.1_amd64.deb Size: 32594 MD5sum: d0e957e7d68f3b6cf75acf414754ed80 SHA1: 0c9343e30a9ed0f5e67d5b4c5f8b572f3de55c68 SHA256: fc3ac0bbe92f31342c4855342ca956f79976c57255518dee8b48ae31038d50b4 SHA512: dfd9abae67da88104b9fc3e0c7fa3cb079a1f6a1233a040823ee528b4bfdafeb62a376696ee1dc70ad04e4fa394a768fee1aee43eb293e7445adf954ba042a3a Homepage: https://cran.r-project.org/package=glasso Description: CRAN Package 'glasso' (Graphical Lasso: Estimation of Gaussian Graphical Models) Estimation of a sparse inverse covariance matrix using a lasso (L1) penalty. Facilities are provided for estimates along a path of values for the regularization parameter. Package: r-cran-glassofast Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-glasso, r-cran-rbenchmark Filename: pool/dists/noble/main/r-cran-glassofast_1.0.1-1.ca2404.1_amd64.deb Size: 19628 MD5sum: ecd56927ee51c2b4ff2647ae2bc91826 SHA1: 8bb8a22be75817a293758507991aaedaa51e0574 SHA256: 7046288e32e93ec89afca0165a53f3c026ebb03e298e73d258267d826cd07e5e SHA512: 9e4a8330f1c0ede325d583217c1532b99dd07f3dbb62e8dea5d52a6c9d35fe249c015e89dedf00ce4108257e4376d155885a960e8add82825ffd67a35ef8dab5 Homepage: https://cran.r-project.org/package=glassoFast Description: CRAN Package 'glassoFast' (Fast Graphical LASSO) A fast and improved implementation of the graphical LASSO. Package: r-cran-glca Architecture: amd64 Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2021 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-glca_1.4.2-1.ca2404.1_amd64.deb Size: 998292 MD5sum: 9979a90b28209fca60e00536c50a27a6 SHA1: d94a3c837f91d1c3755819fb18ba2437c6e83b50 SHA256: ed0d417654c554fdcc4e9cab69920d6b7d40a4503846b5e166f5e98210b9ff4e SHA512: c3c9eb3d6d6ab67dc6aa77c03b3e0cf5267823f0e8811720f2501a569903ddb8613d14e49a80e44b5c8345953b2843a2a4ec0548d52a1fe8cfd56e4d064840b9 Homepage: https://cran.r-project.org/package=glca Description: CRAN Package 'glca' (An R Package for Multiple-Group Latent Class Analysis) Fits multiple-group latent class analysis (LCA) for exploring differences between populations in the data with a multilevel structure. There are two approaches to reflect group differences in glca: fixed-effect LCA (Bandeen-Roche et al (1997) ; Clogg and Goodman (1985) ) and nonparametric random-effect LCA (Vermunt (2003) ). Package: r-cran-glcm Architecture: amd64 Version: 1.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-glcm_1.6.6-1.ca2404.1_amd64.deb Size: 270230 MD5sum: 0e18bca9b1dc5699a642360472b29eac SHA1: 89a2a20c31d734bf04521152eb22018815835845 SHA256: 866fbb05af8b98ac7599197a5b7907d91dd99decdd9fd0e16c7490ee74689a8b SHA512: 4e2a9e7399221554fc136d4e4ff8771245d74c1b94fa9bdb086ffaec404055af3ed7692c4f26123fb9a10deabfc01dade7bbc7aba4f34b0d902fce2fe0dc5ff1 Homepage: https://cran.r-project.org/package=glcm Description: CRAN Package 'glcm' (Calculate Textures from Grey-Level Co-Occurrence Matrices(GLCMs)) Enables calculation of image textures (Haralick 1973) from grey-level co-occurrence matrices (GLCMs). Supports processing images that cannot fit in memory. Package: r-cran-glcmtextures Architecture: amd64 Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 752 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-rcpp, r-cran-raster, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-glcmtextures_0.6.3-1.ca2404.1_amd64.deb Size: 580514 MD5sum: 73fb57b01f86f3615097f4b4c552c1f2 SHA1: 875865eb25034839d6cac174bcd88ae87b28fabb SHA256: c95f87217deba8299592a1c4c7f1a3718566d122e68fc92e3cbb0209b1acc3ee SHA512: 3a4fec96997f6001c4dd770e6a2e68b9ed291ede5b22538131c67799025d81dc9dfe71d43a90fd0b3816b3bec29cc2be9f22055b0f1adf1d543a0f154feeeef2 Homepage: https://cran.r-project.org/package=GLCMTextures Description: CRAN Package 'GLCMTextures' (GLCM Textures of Raster Layers) Calculates grey level co-occurrence matrix (GLCM) based texture measures (Hall-Beyer (2017) ; Haralick et al. (1973) ) of raster layers using a sliding rectangular window. It also includes functions to quantize a raster into grey levels as well as tabulate a glcm and calculate glcm texture metrics for a matrix. Package: r-cran-gld Architecture: amd64 Version: 2.6.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-lmom Filename: pool/dists/noble/main/r-cran-gld_2.6.8-1.ca2404.1_amd64.deb Size: 237088 MD5sum: bad57b55455bf22744880d5bed92f890 SHA1: 6185ffce21d32267afe6e31ddbae58df2e1acf21 SHA256: d0b00b43ae28d17fee81adf78be46d10fce0aa5b78c501defdb369550f6d1963 SHA512: a2cd86be294fd8c808ac08d33b493384a6dbfe01928d9d81948cfdc7712c401dad0126b59a98459c06825699f9b27de5e522617abf9a8f2d8c3a77a90e8813fc Homepage: https://cran.r-project.org/package=gld Description: CRAN Package 'gld' (Estimation and Use of the Generalised (Tukey) LambdaDistribution) The generalised lambda distribution, or Tukey lambda distribution, provides a wide variety of shapes with one functional form. This package provides random numbers, quantiles, probabilities, densities and density quantiles for four different types of the distribution, the FKML (Freimer et al 1988), RS (Ramberg and Schmeiser 1974), GPD (van Staden and Loots 2009) and FM5 - see documentation for details. It provides the density function, distribution function, and Quantile-Quantile plots. It implements a variety of estimation methods for the distribution, including diagnostic plots. Estimation methods include the starship (all 4 types), method of L-Moments for the GPD and FKML types, and a number of methods for only the FKML type. These include maximum likelihood, maximum product of spacings, Titterington's method, Moments, Trimmed L-Moments and Distributional Least Absolutes. Package: r-cran-gldex Architecture: amd64 Version: 2.0.0.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 573 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-spacefillr Filename: pool/dists/noble/main/r-cran-gldex_2.0.0.9.4-1.ca2404.1_amd64.deb Size: 505348 MD5sum: a86f82e881c90558aefe22c24ae23bfd SHA1: db4d75d6900dbfa7049988beb81d5acbb93d75fa SHA256: d6f817a851b1750592c2d0249be7fbb41335c6646732a9050bef485ba8d1b93d SHA512: b63c280fe77a81b0e94158ec2c91e0066911cc9465d4d92c8fd2288bf1fcbacae416c9193bf7233a55e18e540da2cf124477e4fa79fd066e2242597ebf549016 Homepage: https://cran.r-project.org/package=GLDEX Description: CRAN Package 'GLDEX' (Fitting Single and Mixture of Generalised Lambda Distributions) The fitting algorithms considered in this package have two major objectives. One is to provide a smoothing device to fit distributions to data using the weight and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution. References include the following: Karvanen and Nuutinen (2008) "Characterizing the generalized lambda distribution by L-moments" , King and MacGillivray (1999) "A starship method for fitting the generalised lambda distributions" , Su (2005) "A Discretized Approach to Flexibly Fit Generalized Lambda Distributions to Data" , Su (2007) "Nmerical Maximum Log Likelihood Estimation for Generalized Lambda Distributions" , Su (2007) "Fitting Single and Mixture of Generalized Lambda Distributions to Data via Discretized and Maximum Likelihood Methods: GLDEX in R" , Su (2009) "Confidence Intervals for Quantiles Using Generalized Lambda Distributions" , Su (2010) "Chapter 14: Fitting GLDs and Mixture of GLDs to Data using Quantile Matching Method" , Su (2010) "Chapter 15: Fitting GLD to data using GLDEX 1.0.4 in R" , Su (2015) "Flexible Parametric Quantile Regression Model" , Su (2021) "Flexible parametric accelerated failure time model". Package: r-cran-glide Architecture: amd64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1692 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-foreach, r-cran-doparallel Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-glide_1.0.5-1.ca2404.1_amd64.deb Size: 1661942 MD5sum: 9356304039c2f39f011cf7fdc4a4e2f0 SHA1: 6212046a4533b47bcac8c6021fab0a775b95345d SHA256: 902215561b2efc5a263a7b3553e016808539e5842d30bd776b947cbb0e9d9d02 SHA512: 40527430e08d252b00c5d063e2485c7f2625ec8f5ec9e6d763e535c6ebee2081ef2bcfd472bd9825a57a1675eaa52f374d9163eea45927913effc4de6f355485 Homepage: https://cran.r-project.org/package=GLIDE Description: CRAN Package 'GLIDE' (Global and Individual Tests for Direct Effects) Global and individual tests for pleiotropy and direct effects in Mendelian randomization studies. Refer to J. Y. Dai, U. Peters, X. Wang, J. Kocarnik et al. AJE (2018) . Package: r-cran-glinternet Architecture: amd64 Version: 1.0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: libc6 (>= 2.14), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-glinternet_1.0.12-1.ca2404.1_amd64.deb Size: 108640 MD5sum: d1b66b1e70502fe04d4bbf774bd1978e SHA1: a3049c9d076726db97752fdcadb25e8b52007a3b SHA256: f30be9efecf1889a59ae1356a600a3396a634af4471338e52590a29e30243ad1 SHA512: e2df11e5395ce7aeae76ec8eb41c9cf48cca4982409c71f2618c047746356428f6f1b13f4cb9dbfc692f2142ec9450379c35fe7b1992eb5ef6ca9386bc3e3023 Homepage: https://cran.r-project.org/package=glinternet Description: CRAN Package 'glinternet' (Learning Interactions via Hierarchical Group-LassoRegularization) Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015) . Package: r-cran-glinvci Architecture: amd64 Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1178 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-optimx, r-cran-lbfgsb3c, r-cran-bb, r-cran-ape, r-cran-numderiv, r-cran-plyr, r-cran-rlang, r-cran-generics Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-glinvci_1.2.4-1.ca2404.1_amd64.deb Size: 661354 MD5sum: c4897c95368b6c0c13d2a91da97a29aa SHA1: f83dfffcfe608001c192be4af1ac27d9e6c59a75 SHA256: ba077b094cf32602ab3b9b227880f15084fd832c4b23000167def0599e2f2167 SHA512: e85a64ea7688f8fba092e00ee7151474f2f063a3e247b867c89c55b35d847eeff1d459b1587980ec482d2e3eca9eb349bb0f0c43c4476b9a527fc775b33b3a49 Homepage: https://cran.r-project.org/package=glinvci Description: CRAN Package 'glinvci' (Phylogenetic Comparative Methods with Uncertainty Estimates) A framework for analytically computing the asymptotic confidence intervals and maximum-likelihood estimates of a class of continuous-time Gaussian branching processes defined by Mitov V, Bartoszek K, Asimomitis G, Stadler T (2019) . The class of model includes the widely used Ornstein-Uhlenbeck and Brownian motion branching processes. The framework is designed to be flexible enough so that the users can easily specify their own sub-models, or re-parameterizations, and obtain the maximum-likelihood estimates and confidence intervals of their own custom models. Package: r-cran-gllm Architecture: amd64 Version: 0.38-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gllm_0.38-1.ca2404.1_amd64.deb Size: 79560 MD5sum: 854dd377e278f9c78674ddf3bd2aeae9 SHA1: a68f3f4d000e4099b70d494fa813e7f9412589bc SHA256: 3265fee1c1a805ccc485ca8c89b5819552d6eac130a058775f6b481032e434a3 SHA512: 9896074c074f51868cc7da0f1ba8f3e759c1ace61f98297ba9c7628bda430b93aecbde7b29ae4aa3651f47e016b017c9b7688548f2e85540b1263927faf8c586 Homepage: https://cran.r-project.org/package=gllm Description: CRAN Package 'gllm' (Generalised log-Linear Model) Routines for log-linear models of incomplete contingency tables, including some latent class models, via EM and Fisher scoring approaches. Allows bootstrapping. See Espeland and Hui (1987) for general approach. Package: r-cran-gllvm Architecture: amd64 Version: 2.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8853 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-tmb, r-cran-mass, r-cran-matrix, r-cran-fishmod, r-cran-mgcv, r-cran-alabama, r-cran-nloptr, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-gclus, r-cran-corrplot, r-cran-lattice, r-cran-mvabund, r-cran-ape Filename: pool/dists/noble/main/r-cran-gllvm_2.0.10-1.ca2404.1_amd64.deb Size: 3782026 MD5sum: ef98c2754c11e99b8fbb53845de844d0 SHA1: 716356e20290ce8e1a2f1ce28ff67eb126fec0a4 SHA256: 2faaff13a07709ee4b089f7d79220b628b2e7525e3979190bd46d7c23e4dae00 SHA512: df8bfd98dc21a5d293e4421ffbae7158259112fc7d0f195d4588246c517d63be9e6c869b6e8e2cdbbb4c20fc983981e103ff7b6e7ce0a041f2a5fffe5971314a Homepage: https://cran.r-project.org/package=gllvm Description: CRAN Package 'gllvm' (Generalized Linear Latent Variable Models) Analysis of multivariate data using generalized linear latent variable models (gllvm). Estimation is performed using either the Laplace method, variational approximations, or extended variational approximations, implemented via TMB (Kristensen et al. (2016), ). Package: r-cran-glm.deploy Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-glm.deploy_1.0.4-1.ca2404.1_amd64.deb Size: 82654 MD5sum: bb31adab7b1383ca8a03bb4a49afbf1a SHA1: 31be2b1acf1ef8aa4a427a75487d5a914f1a8273 SHA256: 68529e6dd8c36e356037338fc03746fd722f8bca55c8c3c9ff3cd095cce72998 SHA512: 854663663c97ff573dff5ed7ccb6577c2fd9b9fed8513a2f609c71b95f3f7a363686b340f05b84a3d9b38e0f01c1fa8bab22863e80a34c9835c0e257496f81e3 Homepage: https://cran.r-project.org/package=glm.deploy Description: CRAN Package 'glm.deploy' ('C' and 'Java' Source Code Generator for Fitted Glm Objects) Provides two functions that generate source code implementing the predict function of fitted glm objects. In this version, code can be generated for either 'C' or 'Java'. The idea is to provide a tool for the easy and fast deployment of glm predictive models into production. The source code generated by this package implements two function/methods. One of such functions implements the equivalent to predict(type="response"), while the second implements predict(type="link"). Source code is written to disk as a .c or .java file in the specified path. In the case of c, an .h file is also generated. Package: r-cran-glmaspu Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-mass, r-cran-mnormt, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-glmaspu_1.0-1.ca2404.1_amd64.deb Size: 94576 MD5sum: 7af5b80f95d093c403a8ff777cab75ae SHA1: 533ef816ef3fb691c733a3976e1d3850d52018ed SHA256: af004fc854cb0ac1caef129da850e3624f5f27dbc1dd013b972f93b77ac4bc11 SHA512: 54d9fff4e11ceb288e74358b025cf32c630f415f58c211ca28d44c0ec85935d53f6aa4788970e91cb462b281d0153848a9f999268faebe37d2ee9929e44bffd7 Homepage: https://cran.r-project.org/package=GLMaSPU Description: CRAN Package 'GLMaSPU' (An Adaptive Test on High Dimensional Parameters in GeneralizedLinear Models) Several tests for high dimensional generalized linear models have been proposed recently. In this package, we implemented a new test called adaptive sum of powered score (aSPU) for high dimensional generalized linear models, which is often more powerful than the existing methods in a wide scenarios. We also implemented permutation based version of several existing methods for research purpose. We recommend users use the aSPU test for their real testing problem. You can learn more about the tests implemented in the package via the following papers: 1. Pan, W., Kim, J., Zhang, Y., Shen, X. and Wei, P. (2014) A powerful and adaptive association test for rare variants, Genetics, 197(4). 2. Guo, B., and Chen, S. X. (2016) . Tests for high dimensional generalized linear models. Journal of the Royal Statistical Society: Series B. 3. Goeman, J. J., Van Houwelingen, H. C., and Finos, L. (2011) . Testing against a high-dimensional alternative in the generalized linear model: asymptotic type I error control. Biometrika, 98(2). Package: r-cran-glmbayes Architecture: amd64 Version: 0.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6526 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-coda, r-cran-rcpp, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-glmbayes_0.9.5-1.ca2404.1_amd64.deb Size: 2877784 MD5sum: d001afe6730c44cc0704489e9b6ffc05 SHA1: 4c8f31e9e4221f99f6e53048e5a0a77393a1fdb5 SHA256: 6a26387c5a2254870f313a87c5cc04231ba97421358837add56ab9b98cdf972c SHA512: 4f9a5d4a316dc2d8d740dd48ad0c6d1986b2bd904f50a4cc3474837d2562d9c1fe9bd832d57c683b5a5d223e964eb81b9bead80a5ddbe1c488b5d8ca762bc754 Homepage: https://cran.r-project.org/package=glmbayes Description: CRAN Package 'glmbayes' (Bayesian Generalized Linear Models (IID Samples)) Provides Bayesian linear and generalized linear model fitting with independent and identically distributed (iid) posterior samples. The main functions mirror R's lm() and glm() interfaces while adding prior family specifications for Gaussian, Poisson, binomial, and Gamma models with log-concave likelihoods. Sampling for supported non-conjugate models uses accept-reject methods based on likelihood subgradients as in Nygren and Nygren (2006) . The package also includes tools for prior setup, posterior summaries, prediction, diagnostics, simulation, vignettes, and optional 'OpenCL' acceleration for larger models. Package: r-cran-glmcat Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1848 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-stringr, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-gtools, r-cran-tidyr, r-cran-ordinal Filename: pool/dists/noble/main/r-cran-glmcat_1.0.0-1.ca2404.1_amd64.deb Size: 1058830 MD5sum: 437db7ff95589b5951c53ba28ef39109 SHA1: 0c504183e204efebb5704e94e5688f7e6231c166 SHA256: 14a31a7d68307e475c9320c0b53e8d03bbd30930592b7c9cc96af32f76793fe2 SHA512: dfaebd8e7f3f59cc519387deedf5d698aa8a6c21508a079b2f8c0752980da91bdca9ceb8933eddd39bc7bafb313efc4911800ee676df30cafae6689e6aee46ac Homepage: https://cran.r-project.org/package=GLMcat Description: CRAN Package 'GLMcat' (Generalized Linear Models for Categorical Responses) In statistical modeling, there is a wide variety of regression models for categorical dependent variables (nominal or ordinal data); yet, there is no software embracing all these models together in a uniform and generalized format. Following the methodology proposed by Peyhardi, Trottier, and Guédon (2015) , we introduce 'GLMcat', an R package to estimate generalized linear models implemented under the unified specification (r, F, Z). Where r represents the ratio of probabilities (reference, cumulative, adjacent, or sequential), F the cumulative cdf function for the linkage, and Z, the design matrix. The package accompanies the paper "GLMcat: An R Package for Generalized Linear Models for Categorical Responses" in the Journal of Statistical Software, Volume 114, Issue 9 (see ). Package: r-cran-glmlep Architecture: amd64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-glmlep_0.2-1.ca2404.1_amd64.deb Size: 41950 MD5sum: c379ea1f83a87b606d49c30607d05f55 SHA1: 50762d4f1ebf41844a05cf199b9036bfcd00c9d6 SHA256: 88e324f3bdf585ba4cac148ac1b424d6ed8d48954755a9d9501f8e7722fce0bf SHA512: a9a66b338d354172a304b44825ca3519be86f251c2c862e1ac88400f77d73c7de9f30387028e05777e7fc76f019b5dc460a167d19cec45ce55926512eef375af Homepage: https://cran.r-project.org/package=glmlep Description: CRAN Package 'glmlep' (Fit GLM with LEP-Based Penalized Maximum Likelihood) Efficient algorithms for fitting regularization paths for linear or logistic regression models penalized by LEP. Package: r-cran-glmm Architecture: amd64 Version: 1.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-trust, r-cran-mvtnorm, r-cran-matrix, r-cran-doparallel, r-cran-foreach, r-cran-itertools Suggests: r-cran-knitr, r-cran-v8 Filename: pool/dists/noble/main/r-cran-glmm_1.4.5-1.ca2404.1_amd64.deb Size: 371074 MD5sum: 176bfaef27204465106108617c966807 SHA1: de8cd48b916539f171333cb7147d62286ccecb0e SHA256: ea904e953ea4aaedae13be231cd95245dd0d3238f4ba06f9a9210139ccebaf76 SHA512: b69696e476bc09792478b13680c8b2b66963e4226cbb97e187218702f44d2e3eba9efc9e43af92c1006e058097173737808740d0bd0bf593250ae86243ef1e65 Homepage: https://cran.r-project.org/package=glmm Description: CRAN Package 'glmm' (Generalized Linear Mixed Models via Monte Carlo LikelihoodApproximation) Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information. Package: r-cran-glmmep Architecture: amd64 Version: 1.0-3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-matrixcalc Suggests: r-cran-mlmrev Filename: pool/dists/noble/main/r-cran-glmmep_1.0-3.1-1.ca2404.1_amd64.deb Size: 228208 MD5sum: 5b45d74736a03b3cd8ab07b061bb0fd2 SHA1: e012fe17af97e42bbec6dd7d26168903d5b39958 SHA256: 39163c45d815ac7349d71c9237b46f0d177ebb9c218dd37bb1e82cc2792af3ed SHA512: 82e44f0688e664e518d115e3099684e4fc00f92f8f6448b20024d7f1d50bdfd7ad2bbdd719d201b1aac43ed128d514d4c08146005efc8cffb6a34108a0fb0f12 Homepage: https://cran.r-project.org/package=glmmEP Description: CRAN Package 'glmmEP' (Generalized Linear Mixed Model Analysis via ExpectationPropagation) Approximate frequentist inference for generalized linear mixed model analysis with expectation propagation used to circumvent the need for multivariate integration. In this version, the random effects can be any reasonable dimension. However, only probit mixed models with one level of nesting are supported. The methodology is described in Hall, Johnstone, Ormerod, Wand and Yu (2018) . Package: r-cran-glmmfields Architecture: amd64 Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2907 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-assertthat, r-cran-broom, r-cran-broom.mixed, r-cran-cluster, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-loo, r-cran-mvtnorm, r-cran-nlme, r-cran-reshape2, r-cran-rstan, r-cran-rstantools, r-cran-tibble, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-bayesplot, r-cran-coda, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-viridis Filename: pool/dists/noble/main/r-cran-glmmfields_0.1.8-1.ca2404.1_amd64.deb Size: 1175950 MD5sum: 11e1c3978d3e48392207428be6ee30bb SHA1: cb9b53dcc9c0a594203d6c60df17073ef91af35a SHA256: 264d98938cfd01e078f564f728126d6698427d9f80266ef7193242d1925730b4 SHA512: 2884fc195aafc1d99f4cb20a7d655a4b98c8dd13e276afdc87a1de89198286c7c9374e7a4057ab456244523cae5af17e3a233506b672df78d57d0663af1c7fa0 Homepage: https://cran.r-project.org/package=glmmfields Description: CRAN Package 'glmmfields' (Generalized Linear Mixed Models with Robust Random Fields forSpatiotemporal Modeling) Implements Bayesian spatial and spatiotemporal models that optionally allow for extreme spatial deviations through time. 'glmmfields' uses a predictive process approach with random fields implemented through a multivariate-t distribution instead of the usual multivariate normal. Sampling is conducted with 'Stan'. References: Anderson and Ward (2019) . Package: r-cran-glmmlasso Architecture: amd64 Version: 1.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 712 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-minqa, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-mass, r-cran-nlme Filename: pool/dists/noble/main/r-cran-glmmlasso_1.6.4-1.ca2404.1_amd64.deb Size: 543182 MD5sum: 8fc99471aac30449a219ddcdb8675cb9 SHA1: e2ef73924ef7d4b246e88c13d9f975c9a64fc701 SHA256: ab5a2471603882045d973096dda2a0f5c7473d3c66c72a9ee81da2d5d70d080a SHA512: fc56b697d9ebfd2c9f61fb66f3dfd226d6939301da554c3700cc2a300690bc19b7535ce272cb63edbd844d6746b0bdec89493f8250cbf61dd957afb589f649d3 Homepage: https://cran.r-project.org/package=glmmLasso Description: CRAN Package 'glmmLasso' (Variable Selection for Generalized Linear Mixed Models byL1-Penalized Estimation) A variable selection approach for generalized linear mixed models by L1-penalized estimation is provided, see Groll and Tutz (2014) . See also Groll and Tutz (2017) for discrete survival models including heterogeneity. Package: r-cran-glmmml Architecture: amd64 Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-glmmml_1.1.7-1.ca2404.1_amd64.deb Size: 256430 MD5sum: 6feb3c29296d5635dbd7426e3025a3f2 SHA1: 306197efae14a23fbe83152fa497f4dc8a9123a0 SHA256: 1c8e1d0c7654828d3b3196a2edcbe3a835ccf7456a9c57794d9f32fcff1a6bfb SHA512: 2542163384ac049e523313bc8c3ccd6cd896b3eeafff779cd9a8e521ce3a3c9caf0b6719f2a1e34bb8f3b11a2259957e163e01af70779bba853fbc6384035e33 Homepage: https://cran.r-project.org/package=glmmML Description: CRAN Package 'glmmML' (Generalized Linear Models with Clustering) Binomial and Poisson regression for clustered data, fixed and random effects with bootstrapping. Package: r-cran-glmmpen Architecture: amd64 Version: 1.5.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4119 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-bigmemory, r-cran-rcpp, r-cran-ggplot2, r-cran-matrix, r-cran-ncvreg, r-cran-reshape2, r-cran-rstan, r-cran-stringr, r-cran-mvtnorm, r-cran-mass, r-cran-survival, r-cran-rstantools, r-cran-bh, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-glmmpen_1.5.4.8-1.ca2404.1_amd64.deb Size: 1660976 MD5sum: d139defb9f9bf6c49603746e183473d4 SHA1: a93dbc7f088ac76f5f2c7eba101457bdc4be6153 SHA256: 93beef47c4fc0c7b81e5c9e154292cbb0a51d973821cb6b869a62d4cd49dde3e SHA512: d6a7067cf595926a9fbf83dadd99dab1d2709e56dd86a1b3cf125f57fd27759d842497d6848e3e4c766aa85c94922b9f8a600114d142c9c7e5ae32395ba978bd Homepage: https://cran.r-project.org/package=glmmPen Description: CRAN Package 'glmmPen' (High Dimensional Penalized Generalized Linear Mixed Models(pGLMM)) Fits high dimensional penalized generalized linear mixed models using the Monte Carlo Expectation Conditional Minimization (MCECM) algorithm. The purpose of the package is to perform variable selection on both the fixed and random effects simultaneously for generalized linear mixed models. The package supports fitting of Binomial, Gaussian, and Poisson data with canonical links, and supports penalization using the MCP, SCAD, or LASSO penalties. The MCECM algorithm is described in Rashid et al. (2020) . The techniques used in the minimization portion of the procedure (the M-step) are derived from the procedures of the 'ncvreg' package (Breheny and Huang (2011) ) and 'grpreg' package (Breheny and Huang (2015) ), with appropriate modifications to account for the estimation and penalization of the random effects. The 'ncvreg' and 'grpreg' packages also describe the MCP, SCAD, and LASSO penalties. Package: r-cran-glmmrbase Architecture: amd64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4227 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-r6, r-cran-rcppeigen, r-cran-bh, r-cran-rcppparallel Suggests: r-cran-fmesher, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-glmmrbase_1.4.0-1.ca2404.1_amd64.deb Size: 1390478 MD5sum: bbdc706fa7d74563bd42a392aa8b2d0b SHA1: ff1ef58bee33009a3c984550935a2de5a134a73a SHA256: 15b6e2793ffb32b03a4e8ddfb2dd2638af777ab16a02bbab20947eaaa838708c SHA512: 00f9d24fbc51ccea5880e35d911236a4e4113c13519e69e0dacec8aa9a93da7657b937f3df1b89f153b721046bf88411441b24ce125ae5dfe69917c8d3f96538 Homepage: https://cran.r-project.org/package=glmmrBase Description: CRAN Package 'glmmrBase' (Generalised Linear Mixed Models in R) Specification, analysis, simulation, and fitting of generalised linear mixed models. Includes Markov Chain Monte Carlo Maximum likelihood model fitting for a range of models, non-linear fixed effect specifications, a wide range of flexible covariance functions that can be combined arbitrarily, robust and bias-corrected standard error estimation, power calculation, data simulation, and more. Package: r-cran-glmmroptim Architecture: amd64 Version: 0.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1040 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-glmmrbase, r-cran-rcpp, r-cran-digest, r-cran-rcppeigen, r-cran-rcppprogress, r-cran-sparsechol, r-cran-bh, r-cran-rminqa Suggests: r-cran-testthat, r-cran-cvxr Filename: pool/dists/noble/main/r-cran-glmmroptim_0.3.7-1.ca2404.1_amd64.deb Size: 419628 MD5sum: 5e7a7f8c52970b767f3a3712828d9b5e SHA1: 71ed82b3597d78043955413409358a2c17609fc0 SHA256: 3518182337f907da15ab3f8a911bd172bf340f4681c25e9368cbde93c181509d SHA512: b50acc0f9d633dee2ac189e0f7de961fc70a0ecdecaf5f312943f991147167555889a12bd6671e0586bb1a237626c9cfe0cdf1a06775f7fff73a1dc6cbe0c781 Homepage: https://cran.r-project.org/package=glmmrOptim Description: CRAN Package 'glmmrOptim' (Approximate Optimal Experimental Designs Using GeneralisedLinear Mixed Models) Optimal design analysis algorithms for any study design that can be represented or modelled as a generalised linear mixed model including cluster randomised trials, cohort studies, spatial and temporal epidemiological studies, and split-plot designs. See for a detailed manual on model specification. A detailed discussion of the methods in this package can be found in Watson, Hemming, and Girling (2023) . Package: r-cran-glmmsel Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-lme4, r-cran-mass, r-cran-nlme Filename: pool/dists/noble/main/r-cran-glmmsel_1.0.3-1.ca2404.1_amd64.deb Size: 173020 MD5sum: 9c4342c883eb62708e53fae497702335 SHA1: 45bfb97930744bc4dfce18dc37a33e97bc649305 SHA256: 7bd69fbd1382a5b825cf8c7395a0f4fef3dd253e541e33bc3f82bf20c3318380 SHA512: 31f9efeadeb0bd745b07fa902544ecdc67fe4344956d6b1a5a2d52eb20131c7f52119da5eaafb6ecfc4efbd3f842f7b5455944678cae199738c1cb23bca2281c Homepage: https://cran.r-project.org/package=glmmsel Description: CRAN Package 'glmmsel' (Generalised Linear Mixed Model Selection) Provides tools for fitting sparse generalised linear mixed models with l0 regularisation. Selects fixed and random effects under the hierarchy constraint that fixed effects must precede random effects. Uses coordinate descent and local search algorithms to rapidly deliver near-optimal estimates. Gaussian and binomial response families are currently supported. For more details see Thompson, Wand, and Wang (2025) . Package: r-cran-glmmtmb Architecture: amd64 Version: 1.1.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10748 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tmb, r-cran-lme4, r-cran-matrix, r-cran-nlme, r-cran-numderiv, r-cran-mgcv, r-cran-reformulas, r-cran-pbkrtest, r-cran-sandwich, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mass, r-cran-lattice, r-cran-ggplot2, r-cran-mlmrev, r-cran-bbmle, r-cran-pscl, r-cran-coda, r-cran-reshape2, r-cran-car, r-cran-emmeans, r-cran-estimability, r-cran-dharma, r-cran-multcomp, r-cran-mumin, r-cran-effects, r-cran-dotwhisker, r-cran-broom, r-cran-broom.mixed, r-cran-plyr, r-cran-png, r-cran-boot, r-cran-texreg, r-cran-xtable, r-cran-huxtable, r-cran-blme, r-cran-purrr, r-cran-dplyr, r-cran-ade4, r-cran-ape, r-cran-gsl, r-cran-lmertest, r-cran-metafor Filename: pool/dists/noble/main/r-cran-glmmtmb_1.1.14-1.ca2404.1_amd64.deb Size: 6288522 MD5sum: be7b6b1a5320ca44bad170c492febf73 SHA1: 2a3984756591e87e709084253a2139fca755b211 SHA256: cbcb14bde205bbd340b11322eb2e5c7e551d170fa88a768d0fbccde924982ceb SHA512: 1e5c75247fdbdfccb89e8135b6cbe8958c4b499829dd90500d0915a7ec227f14f15020bf4611e6155bb1879b53811e69703f5f78b024d93bec939a774eb62aa8 Homepage: https://cran.r-project.org/package=glmmTMB Description: CRAN Package 'glmmTMB' (Generalized Linear Mixed Models using Template Model Builder) Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via 'TMB' (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation. Package: r-cran-glmnet Architecture: amd64 Version: 5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3548 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-foreach, r-cran-shape, r-cran-survival, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-lars, r-cran-nnet, r-cran-testthat, r-cran-xfun, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-glmnet_5.0-1.ca2404.1_amd64.deb Size: 2368814 MD5sum: 31d98d4b3699ec75186b6d5ac0b1e452 SHA1: 69244eb1a5e4168834fbdcd918725481c5acd446 SHA256: 6e619f23ca838eaafee85731d679f11cd457ba9d30db05830f900950b2e38648 SHA512: be25c75d8418a6f37feb5ed454402bbba20d11a1d1ec9f25c0766740948b88fa794b27c60ed28fd739b72407f7105199e46bc5065b5626418d40bcfe036d3a65 Homepage: https://cran.r-project.org/package=glmnet Description: CRAN Package 'glmnet' (Lasso and Elastic-Net Regularized Generalized Linear Models) Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression; see and . There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family (). This comes with a modest computational cost, so when the built-in families suffice, they should be used instead. The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers cited. 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Package: r-cran-gowersom Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-statmatch, r-cran-dplyr, r-cran-gower, r-cran-ggplot2, r-cran-cluster, r-cran-reshape2, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gowersom_0.1.0-1.ca2404.1_amd64.deb Size: 40380 MD5sum: 13b488808b4fa7b9f1a8bd9180926734 SHA1: ed61bc0bd7e2d61c77b14e5c45c5760a6c44494d SHA256: de300f78edd0672a18e3c6916fbbeb08e690d6ebed6e01b2818dcba5609ac4ef SHA512: efa23d4f9081ea17ee6c65b08b8d10f07c0a728355c74e5fd0414b9c935058dd668cc7d00f1c7cb0b39061d13b449ab352ed0ddbeea3d6519f0f0477eb151d4b Homepage: https://cran.r-project.org/package=GowerSom Description: CRAN Package 'GowerSom' (Self-Organizing Maps for Mixed-Attribute Data Using GowerDistance) Implements a variant of the Self-Organizing Map (SOM) algorithm designed for mixed-attribute datasets. Similarity between observations is computed using the Gower distance, and categorical prototypes are updated via heuristic strategies (weighted mode and multinomial sampling). Provides functions for model fitting, mapping, visualization (U-Matrix and component planes), and evaluation, making SOM applicable to heterogeneous real-world data. For methodological details see Sáez and Salas (2026) . Package: r-cran-gpareto Architecture: amd64 Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1567 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dicekriging, r-cran-emoa, r-cran-rcpp, r-cran-rgenoud, r-cran-pbivnorm, r-cran-pso, r-cran-randtoolbox, r-cran-kriginv, r-cran-mass, r-cran-dicedesign, r-cran-ks, r-cran-rgl Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-gpareto_1.1.9-1.ca2404.1_amd64.deb Size: 1303998 MD5sum: 61d61d860891b82486909d3c96499610 SHA1: e7a949bb1962dafc90c50734a5042cdbfb502c2b SHA256: 6f0dcfaf3b78261b8e2e4c0979793500f4cf49b12eea32f40ba73635ffa41568 SHA512: eadbd1fa3a23913dc38160e07832a3775e8bb445894e30c002600ccaa137dacdb84264d4cac3d3201af19afb1639b55e6eb55d246e894dd5de0939df628efd4d Homepage: https://cran.r-project.org/package=GPareto Description: CRAN Package 'GPareto' (Gaussian Processes for Pareto Front Estimation and Optimization) Gaussian process regression models, a.k.a. Kriging models, are applied to global multi-objective optimization of black-box functions. Multi-objective Expected Improvement and Step-wise Uncertainty Reduction sequential infill criteria are available. A quantification of uncertainty on Pareto fronts is provided using conditional simulations. Package: r-cran-gpbayes Architecture: amd64 Version: 0.1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1646 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppprogress Filename: pool/dists/noble/main/r-cran-gpbayes_0.1.0-6-1.ca2404.1_amd64.deb Size: 750108 MD5sum: 6abb4851649c8027824c99af2e6d290c SHA1: d44e3b12cfdb8aa04f75b1f899c01861f25092a0 SHA256: 3e706f4a8b51566ef79b9768db5d30bf6c081175064f9368b115becd74ebebd5 SHA512: 10cfe3cffb96bad4cf7587603293dfb64ed18d283d1c581458a9faa643f24b2071752b5ac4679a9520cc89e75b8e9f215821e3643284e084628f83ade5f4dd0a Homepage: https://cran.r-project.org/package=GPBayes Description: CRAN Package 'GPBayes' (Tools for Gaussian Process Modeling in UncertaintyQuantification) Gaussian processes ('GPs') have been widely used to model spatial data, 'spatio'-temporal data, and computer experiments in diverse areas of statistics including spatial statistics, 'spatio'-temporal statistics, uncertainty quantification, and machine learning. This package creates basic tools for fitting and prediction based on 'GPs' with spatial data, 'spatio'-temporal data, and computer experiments. Key characteristics for this GP tool include: (1) the comprehensive implementation of various covariance functions including the 'Matérn' family and the Confluent 'Hypergeometric' family with isotropic form, tensor form, and automatic relevance determination form, where the isotropic form is widely used in spatial statistics, the tensor form is widely used in design and analysis of computer experiments and uncertainty quantification, and the automatic relevance determination form is widely used in machine learning; (2) implementations via Markov chain Monte Carlo ('MCMC') algorithms and optimization algorithms for GP models with all the implemented covariance functions. The methods for fitting and prediction are mainly implemented in a Bayesian framework; (3) model evaluation via Fisher information and predictive metrics such as predictive scores; (4) built-in functionality for simulating 'GPs' with all the implemented covariance functions; (5) unified implementation to allow easy specification of various 'GPs'. Package: r-cran-gpboost Architecture: amd64 Version: 1.6.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10426 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-data.table, r-cran-rjsonio, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gpboost_1.6.7-1.ca2404.1_amd64.deb Size: 3247404 MD5sum: 89669262476eab8ad9496091ee4a1c9c SHA1: 63518f0404bd2204b3dad89adab241b65803df8a SHA256: 1607c6cf7ed691440fb34fae0f17c2012ee50ae7f65ebfabd305e3f99b3644f8 SHA512: 88184196ddd1433819a544b1ef2aa2bf0b0e8440d50ebc76922419bbb98c6cce590fe71eeac17aa56c5f2932f297c6b78bd4367c1c37382592414567338b4461 Homepage: https://cran.r-project.org/package=gpboost Description: CRAN Package 'gpboost' (Combining Tree-Boosting with Gaussian Process and Mixed EffectsModels) An R package that allows for combining tree-boosting with Gaussian process and mixed effects models. 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Package: r-cran-gpcerf Architecture: amd64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-xgboost, r-cran-mass, r-cran-spatstat.geom, r-cran-logger, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-ggplot2, r-cran-cowplot, r-cran-rlang, r-cran-rfast, r-cran-superlearner, r-cran-wcorr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gpcerf_0.2.4-1.ca2404.1_amd64.deb Size: 250004 MD5sum: 841270f60d293375c49f27e2c0e56b4e SHA1: 9a25b7f7eae53a6d6392b5bc590c75f437093ae4 SHA256: cdb54be54f4db5160a6711476f080db80845bb7dd527706e13bfa2d99ccfffbf SHA512: dab22ea66bace17dded0033113db8a3616e48c8eb1489b181a80ae326374f03054c5adca8a6fb6fc1898c396456677ea44eae9874ef5281767ddb7615cbae6d9 Homepage: https://cran.r-project.org/package=GPCERF Description: CRAN Package 'GPCERF' (Gaussian Processes for Estimating Causal Exposure ResponseCurves) Provides a non-parametric Bayesian framework based on Gaussian process priors for estimating causal effects of a continuous exposure and detecting change points in the causal exposure response curves using observational data. Ren, B., Wu, X., Braun, D., Pillai, N., & Dominici, F.(2021). "Bayesian modeling for exposure response curve via gaussian processes: Causal effects of exposure to air pollution on health outcomes." arXiv preprint . Package: r-cran-gpcmlasso Architecture: amd64 Version: 0.1-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 445 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-ltm, r-cran-rcpp, r-cran-teachingdemos, r-cran-cubature, r-cran-caret, r-cran-statmod, r-cran-mvtnorm, r-cran-mirt, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-gpcmlasso_0.1-9-1.ca2404.1_amd64.deb Size: 252106 MD5sum: 6c780f5759875dfd1a345da8a43203b0 SHA1: be9fce95894b11e8dd68b3701157b00fa231b706 SHA256: 531fd1caf19c8ab2e5566a7d4315483e3a6dc37991cec7e74046c6874b4c01fe SHA512: e71e9edd7c54fb017d8e8e0513471a7ebe485ac967d62820fc6ca3c8910784654a1dee3f8aca9445b1f213ea14d3b97c2b4dc0ee498bb66f142ce9b29b8963c9 Homepage: https://cran.r-project.org/package=GPCMlasso Description: CRAN Package 'GPCMlasso' (Differential Item Functioning in Generalized Partial CreditModels) Provides a framework to detect Differential Item Functioning (DIF) in Generalized Partial Credit Models (GPCM) and special cases of the GPCM as proposed by Schauberger and Mair (2019) . A joint model is set up where DIF is explicitly parametrized and penalized likelihood estimation is used for parameter selection. The big advantage of the method called GPCMlasso is that several variables can be treated simultaneously and that both continuous and categorical variables can be used to detect DIF. 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Package: r-cran-gpg Architecture: amd64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2027 Depends: libc6 (>= 2.14), libgpgme11t64 (>= 1.10.0), r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-askpass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gpg_1.3.0-1.ca2404.1_amd64.deb Size: 1067970 MD5sum: f1b6dc000894a58b577121ebf9b01dc5 SHA1: fa10ea22050bd0985ffdca11485518df12d85245 SHA256: f05a28c84943d68116b5332ca669d2ec6c47ab44e37ff42e2fbf2560cbf3802c SHA512: a409812463f4dfeab8c65622429f2726e7ce22a0b6a501d582b0c47bec1174f264546604830e7cf0a5e782221d3ab6ef51fe729103303df4488c1a9231d12b8c Homepage: https://cran.r-project.org/package=gpg Description: CRAN Package 'gpg' (GNU Privacy Guard for R) Bindings to GnuPG for working with OpenGPG (RFC4880) cryptographic methods. Includes utilities for public key encryption, creating and verifying digital signatures, and managing your local keyring. 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Package: r-cran-gpgame Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-dicekriging, r-cran-gpareto, r-cran-kriginv, r-cran-dicedesign, r-cran-mass, r-cran-mnormt, r-cran-mvtnorm, r-cran-matrixstats Suggests: r-cran-diceoptim, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gpgame_1.2.1-1.ca2404.1_amd64.deb Size: 212120 MD5sum: d4da216832cfc3111772796a952b1fa1 SHA1: 775a156b1a0a03611a571fc9523adedccf45cc88 SHA256: 209bbe42eb9d041f24e2fc2d16879d01a9033ee39f97759b735621ab10390753 SHA512: 259959b009eda6148978b9682eeef9fab542d2a5045794db17774cc159ccefda19a74249953e784da2721e3706ceaba2c3fd0fbeb56d82370adca9be874a61f7 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-gplite Architecture: amd64 Version: 0.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3551 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-gplite_0.13.0-1.ca2404.1_amd64.deb Size: 2189602 MD5sum: 4e66ff051ecfd78bb771785b8888287f SHA1: 0f4d7768634acc62387fe4b950cf8fcf0342a96c SHA256: b8bfc90f871ca283808e44f39c76bce98b05a43df9a9528dc54171b721d577b9 SHA512: 534bde94636330f08a68434c2f33eb092100dc14f2d12ba0f54526ea9fba4549aafcc1df25e5a01939c6c2ef4e39d9e9d36a2c25ff00b4a4447b5a93dcb909e5 Homepage: https://cran.r-project.org/package=gplite Description: CRAN Package 'gplite' (General Purpose Gaussian Process Modelling) Implements the most common Gaussian process (GP) models using Laplace and expectation propagation (EP) approximations, maximum marginal likelihood (or posterior) inference for the hyperparameters, and sparse approximations for larger datasets. 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Package: r-cran-gpm Architecture: amd64 Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-lhs, r-cran-randtoolbox, r-cran-lattice, r-cran-pracma, r-cran-foreach, r-cran-doparallel, r-cran-iterators, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-gpm_3.0.1-1.ca2404.1_amd64.deb Size: 170620 MD5sum: 1bcac7932380a5ba3e20c2972ae14f4b SHA1: c2cf3faddbf1267334da0d7614cb3a1eb5dd1a22 SHA256: 80dfd56935f4b79513bc2168130e3a0876fb4ca49f52fd6c7cb4aae2e9438453 SHA512: 6e54c25135d9e4c69bc37015e91f4ce5a8c9a6b282a39f8bf4f7086ba7cc74dae19c7d621e6e0c212a0cf6b115c08778cc29381e9d67c9ce0a6968af12367ec8 Homepage: https://cran.r-project.org/package=GPM Description: CRAN Package 'GPM' (Gaussian Process Modeling of Multi-Response and Possibly NoisyDatasets) Provides a general and efficient tool for fitting a response surface to a dataset via Gaussian processes. The dataset can have multiple responses and be noisy (with stationary variance). The fitted GP model can predict the gradient as well. The package is based on the work of Bostanabad, R., Kearney, T., Tao, S. Y., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. International Journal for Numerical Methods in Engineering, 114, 501-516. Package: r-cran-gppenalty Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gppenalty_1.0.1-1.ca2404.1_amd64.deb Size: 160126 MD5sum: a9df46697b2108d0013a108a22597801 SHA1: 368170763d48eeff96c4302544ba233d88cf0f69 SHA256: 61f068d5816687d9373e7e4a74a51b4013adba77adf24703838c809393181f62 SHA512: 903a1cf9a7735622acf0a79cd7b6eeeba136e878feba7d8ae5244c405c7cb5f5d225342ce115d7ea1c41f5012a67a937eb4306feb6d812aea25ef99e3f4e01a9 Homepage: https://cran.r-project.org/package=GPpenalty Description: CRAN Package 'GPpenalty' (Penalized Likelihood in Gaussian Processes) Implements maximum likelihood estimation for Gaussian processes, supporting both isotropic and separable models with predictive capabilities. Includes penalized likelihood estimation following Li and Sudjianto (2005, ), with cross-validation guided by decorrelated prediction error (DPE) metric. DPE metric, motivated by Mahalanobis distance, serves as evaluation criteria that accounts for predictive uncertainty in tuning parameter selection (Mutoh, Booth, and Stallrich, 2025, ). Designed specifically for small datasets. Package: r-cran-gps Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-gps_1.2-1.ca2404.1_amd64.deb Size: 207728 MD5sum: 192b436e5afe4a8110da6ea65b4d165f SHA1: e50a5835c4787fdd3f604db262dfaf99526cea77 SHA256: 22bbcd61db793c09f9d88ccdb616a020d687f50f71ae508bc68831c44a5e128c SHA512: 744f7950525921166836da98c0f2a494911264fb558a54403182d621d1b607b711f34e2af524c24ac3f7d4b4a0c83533da606f6fc9ca290bee9b2e16a670eafe Homepage: https://cran.r-project.org/package=gps Description: CRAN Package 'gps' (General P-Splines) General P-splines are non-uniform B-splines penalized by a general difference penalty, proposed by Li and Cao (2022) . Constructible on arbitrary knots, they extend the standard P-splines of Eilers and Marx (1996) . They are also related to the O-splines of O'Sullivan (1986) via a sandwich formula that links a general difference penalty to a derivative penalty. The package includes routines for setting up and handling difference and derivative penalties. It also fits P-splines and O-splines to (x, y) data (optionally weighted) for a grid of smoothing parameter values in the automatic search intervals of Li and Cao (2023) . It aims to facilitate other packages to implement P-splines or O-splines as a smoothing tool in their model estimation framework. Package: r-cran-gpss Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 402 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-ggplot2, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-mass, r-cran-posterior, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gpss_1.0.3-1.ca2404.1_amd64.deb Size: 248318 MD5sum: 37ed714ddc49fd85acf0e1193e5548b6 SHA1: a5b8bfe42afef8c0a0487483a43af1db5f12731c SHA256: 228812afeb1711a17b4cfb603b53e26dfa4c512140ee3d9b505e43966743306f SHA512: b561f6c3d2a651da3160d654b64babcfd1f15ec1499576ca45d70700039c21487ae8249459eb18dd8c6b43425765d0920d80449ccd58cfaf26ea4a12342d3871 Homepage: https://cran.r-project.org/package=gpss Description: CRAN Package 'gpss' (Gaussian Processes for Social Science) Provides Gaussian process (GP) regression tools for social science inference problems. GPs combine flexible nonparametric regression with principled uncertainty quantification: rather than committing to a single model fit, the posterior reflects lesser knowledge at the edge of or beyond the observed data, where other approaches become highly model-dependent. The package reduces user-chosen hyperparameters from three to zero and supplies convenience functions for regression discontinuity (gp_rdd()), interrupted time-series (gp_its()), and general GP fitting (gpss(), gp_train(), gp_predict()). Methods are described in Cho, Kim, and Hazlett (2026) . Package: r-cran-gptcm Architecture: amd64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1518 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-riskregression, r-cran-ggplot2, r-cran-ggridges, r-cran-micoptcm, r-cran-loo, r-cran-mvnfast, r-cran-matrix, r-cran-scales, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-survminer Filename: pool/dists/noble/main/r-cran-gptcm_1.1.3-1.ca2404.1_amd64.deb Size: 942306 MD5sum: 350c68afb3e784ffb8cac621ec014bb0 SHA1: 05dae3aec47c2bdaa169e318394ec5ad141665eb SHA256: 2c26599a02e7a062977e8905bab17dd1455ccf34e93bc4af151f12537817f4ee SHA512: a1ebb929091f0f222a46dc7d3a864fc11ea85726c510f93f6cae2d985eed235741bd7b28640a39236da5264e39728d32ffa78ff499632636f60fe2c94b13fb7f Homepage: https://cran.r-project.org/package=GPTCM Description: CRAN Package 'GPTCM' (Generalized Promotion Time Cure Model with Bayesian ShrinkagePriors) Generalized promotion time cure model (GPTCM) via Bayesian hierarchical modeling for multiscale data integration (Zhao et al. (2025) ). The Bayesian GPTCMs are applicable for both low- and high-dimensional data. Package: r-cran-gpvam Architecture: amd64 Version: 3.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-numderiv, r-cran-rlang, r-cran-rcpp, r-cran-ggplot2, r-cran-patchwork, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-gpvam_3.2-0-1.ca2404.1_amd64.deb Size: 346136 MD5sum: 82fdcbb1b0a0290512eb84a0d93eaa2c SHA1: 61264eceb552dc89bac249e08af0e24e555bcb48 SHA256: fe2543fd228a71c672048c94f2e99271a0148f9f01c03ace210f3986ebbe8f65 SHA512: 670f7389168d4938777e17f8f13c55919aba376b187f75554e03d2a05cc867ea26721810e495b3479ef3d81272a022637950624994edde475bb87b51075872d9 Homepage: https://cran.r-project.org/package=GPvam Description: CRAN Package 'GPvam' (Maximum Likelihood Estimation of Multiple Membership MixedModels Used in Value-Added Modeling) An EM algorithm, Karl et al. 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Package: r-cran-gpvecchia Architecture: amd64 Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 874 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-sparseinv, r-cran-fields, r-cran-matrix, r-cran-gpgp, r-cran-fnn, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-mvtnorm, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gpvecchia_0.1.8-1.ca2404.1_amd64.deb Size: 450454 MD5sum: 4952569fffe6e36509bb0857202a8a7c SHA1: 269825564e84ca942b9cfa8c93128ece242ef2fa SHA256: 459e7a6c72af4b86fd32565cceba6e6680e10dc3eaa19ef3c13a97f71ae798a1 SHA512: 6ec5225794bdcf9e591f4d1f6a562b35afda733bc5bd7e078d441fe7428071a4186a8a2868ce727c054a2329cd080c884f6a3b603c2c82ad56e395ff1e9a2c21 Homepage: https://cran.r-project.org/package=GPvecchia Description: CRAN Package 'GPvecchia' (Scalable Gaussian-Process Approximations) Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017) . 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The package also offers general frameworks for GWAS of any trait type (Bi et al., 2020 ), while accounting for sample relatedness (Xu et al., 2025 ) or population structure (Ma et al., 2025 ). By accurately approximating score statistic distributions using saddlepoint approximation (SPA), these methods can effectively control type I error rates for rare variants and in the presence of unbalanced phenotype distributions. Additionally, the package includes functions for simulating genotype and phenotype data to support research and method development. 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Documentation of the package is provided in vignettes included in the package and in the paper by Højsgaard (2012, ). See 'citation("gRain")' for details. Package: r-cran-grainscape Architecture: amd64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2971 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-raster, r-cran-rcpp, r-cran-sf, r-cran-sp Suggests: r-cran-covr, r-cran-cowplot, r-cran-diagrammer, r-cran-dplyr, r-cran-ggthemes, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-webshot2, r-cran-withr Filename: pool/dists/noble/main/r-cran-grainscape_0.5.0-1.ca2404.1_amd64.deb Size: 1617850 MD5sum: 062c11c652a34380d86fe92a709e76cf SHA1: 82e752fc19340556382e4f11a6ab6bb562d17f17 SHA256: 52bb873646311eaa7eee931fb4e629f3eb72415fc65151101bbafa3e167d158c SHA512: e1d75362ad5a12059068ef9c346f1205a091f431a90b7fe5440de1949b69ae6607562bf645cd2c41d58d4a0a94169ac79f2e7870bd72f00818477b74ef30668e Homepage: https://cran.r-project.org/package=grainscape Description: CRAN Package 'grainscape' (Landscape Connectivity, Habitat, and Protected Area Networks) Given a landscape resistance surface, creates minimum planar graph (Fall et al. (2007) ) and grains of connectivity (Galpern et al. (2012) ) models that can be used to calculate effective distances for landscape connectivity at multiple scales. Documentation is provided by several vignettes, and a paper (Chubaty, Galpern & Doctolero (2020) ). Package: r-cran-grandr Architecture: amd64 Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1605 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rlang, r-cran-ggplot2, r-cran-patchwork, r-cran-rcurl, r-cran-plyr, r-cran-reshape2, r-cran-mass, r-cran-scales, r-cran-cowplot, r-cran-minpack.lm, r-cran-lfc, r-cran-labeling, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-circlize, r-cran-seurat, r-bioc-complexheatmap, r-cran-ggrepel, r-bioc-deseq2, r-bioc-s4vectors, r-cran-data.table, r-bioc-clusterprofiler, r-bioc-biomart, r-cran-msigdbr, r-bioc-fgsea, r-cran-rclipboard, r-cran-cubature, r-cran-dt, r-cran-shinyjs, r-cran-shinyjqui, r-cran-rcolorbrewer, r-cran-gsl, r-cran-htmltools, r-cran-matrixstats, r-cran-vgam, r-cran-quantreg, r-cran-shiny, r-cran-ggrastr, r-cran-viridislite, r-cran-desolve Filename: pool/dists/noble/main/r-cran-grandr_0.2.7-1.ca2404.1_amd64.deb Size: 1487794 MD5sum: 0ab9494eb8dd4ec524e25b9620013da7 SHA1: fc81e56b4ad8cca457f90750fbd4d7a3c2e69213 SHA256: 8899f3672238a52e67463732f0ab06e088ab378fa2620e338181c3cf71b111da SHA512: 4fc2036a6c7a3631067501f5bea6f9f28e55efb6a207ef3b96d8210263b4da36bf977c01fd5e84ee9ee99644fb19aa7abd851431def7f020223a4f0592e722ac Homepage: https://cran.r-project.org/package=grandR Description: CRAN Package 'grandR' (Comprehensive Analysis of Nucleotide Conversion Sequencing Data) Nucleotide conversion sequencing experiments have been developed to add a temporal dimension to RNA-seq and single-cell RNA-seq. 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Package: r-cran-graphicalevidence Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-mvtnorm, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-graphicalevidence_1.1-1.ca2404.1_amd64.deb Size: 200570 MD5sum: 6eb2ce007e7e6bb8d7bc8a9f2fd7b534 SHA1: 4f9cee60aef2caad4080ce83969bb390a03131c5 SHA256: 593ec33c99f5723b871debae9c2592618fd3930e33715ef41d56826cadc5e23c SHA512: 8786403168045261ef87d0de166ea45dfd5bf618194a54e61595c4976567082305debcd5e7ae810e1676db4ff24544e50715b7dd8978205eeb4e36991f376172 Homepage: https://cran.r-project.org/package=graphicalEvidence Description: CRAN Package 'graphicalEvidence' (Graphical Evidence) Computes marginal likelihood in Gaussian graphical models through a novel telescoping block decomposition of the precision matrix which allows estimation of model evidence. 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See also Epskamp, Waldorp, Mottus & Borsboom (2018) . 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Most are based on the concept of stress majorization by Gansner et al. (2004) . Some more specific algorithms allow the user to emphasize hidden group structures in networks or focus on specific nodes. Package: r-cran-graphpcor Architecture: amd64 Version: 0.1.25-1.ca2404.2 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/noble/main/r-cran-graphpcor_0.1.25-1.ca2404.2_amd64.deb Size: 698908 MD5sum: 24e99d138ff05ed664eb5195b196368b SHA1: a0d87d00a622edd87243e2f8f1878bb8d16941c0 SHA256: b357df3ccb921b6767ecab914bd3f0a52a65f775860bbb43b04f166e791b0333 SHA512: cb4c4e8eec70c47d489f70a4832f4439bf53833afe6ae6a24de145257dc69a8180d651a45d39063ced5d3e6c866ce2d9daf3c9f42466bab1cd0601a47f9f6688 Homepage: https://cran.r-project.org/package=graphpcor Description: CRAN Package 'graphpcor' (Models for Correlation Matrices Based on Graphs) Implement some models for correlation/covariance matrices including two approaches to model correlation matrices from a graphical structure. One use latent parent variables as proposed in Sterrantino et. al. (2024) . The other uses a graph to specify conditional relations between the variables. The graphical structure makes correlation matrices interpretable and avoids the quadratic increase of parameters as a function of the dimension. In the first approach a natural sequence of simpler models along with a complexity penalization is used. The second penalizes deviations from a base model. These can be used as prior for model parameters, considering C code through the 'cgeneric' interface for the 'INLA' package (). This allows one to use these models as building blocks combined and to other latent Gaussian models in order to build complex data models. Package: r-cran-graphql Architecture: amd64 Version: 1.5.3-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-graphql_1.5.3-1.ca2404.2_amd64.deb Size: 80076 MD5sum: fde7de26f919c9937e3735c0017f1940 SHA1: b08dbd158006ae81f2c93ff4b025394719509d85 SHA256: ffa9a0a868dffbca1d9199dde29403138295a1b04d52df11b614e606e20e9195 SHA512: 0300263fe5f86d6cdbc1b64bb258439b28c52136ce6257e2fcbffe4bf02e8730b6e29b67aa4d22127bd69fbdbbf69bc356aad5be28e8dc4e1af7046b3d6d339e Homepage: https://cran.r-project.org/package=graphql Description: CRAN Package 'graphql' (A GraphQL Query Parser) Bindings to the 'libgraphqlparser' C++ library. Parses GraphQL syntax and exports the AST in JSON format. Package: r-cran-grasps Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2649 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-grasps_0.1.1-1.ca2404.1_amd64.deb Size: 1612358 MD5sum: 21a14c83cc625d23280b9cde190f2df8 SHA1: f60c427a6400a63a2fec77517bb71761de8b21a9 SHA256: b450b01e1bbdb2326e93001073f7527529ceb76d96bc211ea9744a158c8a5d35 SHA512: 3b053f97d4e2e19cde1aa80a54a226ec232c9e6b783a961f329fd5c8c2730718c179c02d442514edfb615e633b086a208e5d95c7c19224ac21523628e3d3b527 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-grattaninflators Architecture: amd64 Version: 0.5.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-fy, r-cran-hutils Suggests: r-cran-distributional, r-cran-fable, r-cran-fabletools, r-cran-tinytest, r-cran-withr Filename: pool/dists/noble/main/r-cran-grattaninflators_0.5.7-1.ca2404.1_amd64.deb Size: 112756 MD5sum: 6daf4000bdc6c15dc9ed1435346c39c7 SHA1: 93410d52b7be1ecb281e11e712a4902437db2336 SHA256: 75e3a624f6939085f3d1047a11e29feb9b435bdd927d1655db4e7fd4df219cd3 SHA512: e7c66f40b1da14dbbcdc3c84bd187c1786f426c3a67de167f541947b83f6d9e2d640ad4d758c93f5e4e051f87231eb48d06ddcae134796ef5b95dd70714f1f8a Homepage: https://cran.r-project.org/package=grattanInflators Description: CRAN Package 'grattanInflators' (Inflators for Australian Policy Analysis) Using Australian Bureau of Statistics indices, provides functions that convert historical, nominal statistics to real, contemporary values without worrying about date input quality, performance, or the ABS catalogue. Package: r-cran-gravmagsubs Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 647 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-fields, r-cran-ggplot2, r-cran-gridextra, r-cran-scales, r-cran-scatterplot3d Filename: pool/dists/noble/main/r-cran-gravmagsubs_1.0.1-1.ca2404.1_amd64.deb Size: 312130 MD5sum: 3009cb70adcdd7b1b9bfa4b689db8890 SHA1: 6bd48b33cff92650199dd1d799b2df65d4791c17 SHA256: 03f4ccd11787c390165861265a1023d054883ff19f52fe3ee2736030b9fe26b6 SHA512: aa5d84b4c74373fa1f1a43bf06d356c059f3c8e74f163cdfcba1c71b38b21cc781f19cc366a6280624d1183ddc38645ee59500bb429fb80c0fdfec8e82047b6b Homepage: https://cran.r-project.org/package=gravmagsubs Description: CRAN Package 'gravmagsubs' (Gravitational and Magnetic Attraction of 3-D VerticalRectangular Prisms) Computes the gravitational and magnetic anomalies generated by 3-D vertical rectangular prisms at specific observation points using the method of Plouff (1976) . Package: r-cran-grbase Architecture: amd64 Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6186 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-microbenchmark, r-cran-markdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-grbase_2.0.3-1.ca2404.1_amd64.deb Size: 5135832 MD5sum: d8ec1f1da7abf9f72f6889754b563f88 SHA1: 15b9d96e7a363562eeedd39995119f07a82326db SHA256: ddf02ccdcaf336241ba366eeafa90039fc8d0a33086633e685426a03cd815e44 SHA512: 9654c76fe165b9c7b2cf9c8d63baa11da19c615168b4c66c948e00e4dc1bea001f70a1d12779c85a19611dbc5488a6281440e5f97fccfada2bc1c1dcb971f172 Homepage: https://cran.r-project.org/package=gRbase Description: CRAN Package 'gRbase' (A Package for Graphical Modelling in R) The 'gRbase' package provides graphical modelling features used by e.g. the packages 'gRain', 'gRim' and 'gRc'. 'gRbase' implements graph algorithms including (i) maximum cardinality search (for marked and unmarked graphs). (ii) moralization, (iii) triangulation, (iv) creation of junction tree. 'gRbase' facilitates array operations, 'gRbase' implements functions for testing for conditional independence. 'gRbase' illustrates how hierarchical log-linear models may be implemented and describes concept of graphical meta data. The facilities of the package are documented in the book by Højsgaard, Edwards and Lauritzen (2012, ) and in the paper by Dethlefsen and Højsgaard, (2005, ). Please see 'citation("gRbase")' for citation details. Package: r-cran-grc Architecture: amd64 Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-grbase, r-cran-mass, r-cran-igraph, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-microbenchmark, r-cran-knitr Filename: pool/dists/noble/main/r-cran-grc_0.5.1-1.ca2404.1_amd64.deb Size: 235138 MD5sum: 794651f239c8dab874d644f90a3479bd SHA1: 51767e601ffd9648274e3107ffb9b53556bedfdd SHA256: e707944b1fd434694204a606f1acd50e5cb1f44fdde2582a6b48454a042b1690 SHA512: 8989297657247a087ef81c0ae87e0445ff2d7dce5582eb100edc963cb434209eec1dad7eabda020934b8323e78055234e19ecc620d5c608c274e12ddd2a95276 Homepage: https://cran.r-project.org/package=gRc Description: CRAN Package 'gRc' (Inference in Graphical Gaussian Models with Edge and VertexSymmetries) Estimation, model selection and other aspects of statistical inference in Graphical Gaussian models with edge and vertex symmetries (Graphical Gaussian models with colours). Documentation about 'gRc' is provided in the paper by Hojsgaard and Lauritzen (2007, ) and the paper by Hojsgaard and Lauritzen (2008, ). Package: r-cran-greed Architecture: amd64 Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3617 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-future, r-cran-listenv, r-cran-ggplot2, r-cran-rspectra, r-cran-gtable, r-cran-gridextra, r-cran-cba, r-cran-cli, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-igraph, r-cran-tidygraph, r-cran-ggraph Filename: pool/dists/noble/main/r-cran-greed_0.6.2-1.ca2404.1_amd64.deb Size: 2519456 MD5sum: 52f68b493229bb807dd509e4be5a0df5 SHA1: 416b9a537bc260d2a5db52b9c8e83029fa41df91 SHA256: ca96999162bec65d09de402ab81ffbdcdbad2c140ecc3ac20f6cf71be0a9ecc7 SHA512: fd9ebfd81b477f77199c8f6d9b943946dd6152041c600f3902c4b3b5a6045e54a2ad7d193945e54f7c5f324a0c877abba850370946dce7f65d76041607e4ec89 Homepage: https://cran.r-project.org/package=greed Description: CRAN Package 'greed' (Clustering and Model Selection with the IntegratedClassification Likelihood) An ensemble of algorithms that enable the clustering of networks and data matrices (such as counts, categorical or continuous) with different type of generative models. Model selection and clustering is performed in combination by optimizing the Integrated Classification Likelihood (which is equivalent to minimizing the description length). Several models are available such as: Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of Multinomial, Latent Block Model. The optimization is performed thanks to a combination of greedy local search and a genetic algorithm (see for more details). Package: r-cran-greedyepl Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-greedyepl_1.3-1.ca2404.1_amd64.deb Size: 72034 MD5sum: 30873bb5369e174abb1dace2eeb3acaf SHA1: 55c5ab10f772d689d408da1bd2d5c41e5e37f29c SHA256: d7fcfcac1599141dcceff1dc1a6990e65f2104be1d085f4adba1661d0b9e509f SHA512: 5018e52b6918da0f059046dd1718cb65b0304ba10b4f262d3e4e97f532fa8bbe11809066bc8bc70af1924123bdd8d6abf5ac81f7f56368763b2ce6fda666cb55 Homepage: https://cran.r-project.org/package=GreedyEPL Description: CRAN Package 'GreedyEPL' (Greedy Expected Posterior Loss) Summarises a collection of partitions into a single optimal partition. The objective function is the expected posterior loss, and the minimisation is performed through a greedy algorithm described in Rastelli, R. and Friel, N. (2017) "Optimal Bayesian estimators for latent variable cluster models" . Package: r-cran-greedyexperimentaldesign Architecture: amd64 Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 747 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava, r-cran-rcpp, r-cran-checkmate, r-cran-nbpmatching, r-cran-rlist, r-cran-stringr, r-cran-stringi, r-cran-kernlab, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-pkgload, r-cran-r6 Filename: pool/dists/noble/main/r-cran-greedyexperimentaldesign_1.6.1-1.ca2404.1_amd64.deb Size: 490250 MD5sum: cc65ac9a4240174ee28f78c94f4e24cf SHA1: 302e6c468f716d72fcefa06b615e8c434eb682bc SHA256: 4841eb79aef58f0932887600af32381fee97f3e0d80a8898b63d64e781b52db5 SHA512: 8d103c8e729f21883eae699ab3307806f0b4f72fefbb126840a7343f0b255ba287cc8f9d16c7277908ad7999b8c46c247e5e7f5bf036ca7020c87e62a66951f2 Homepage: https://cran.r-project.org/package=GreedyExperimentalDesign Description: CRAN Package 'GreedyExperimentalDesign' (Greedy Experimental Design Construction) Computes experimental designs for two-arm experiments with covariates using multiple methods, including: (0) complete randomization and randomization with forced-balance; (1) greedy optimization of a balance objective function via pairwise switching; (2) numerical optimization via 'gurobi'; (3) rerandomization; (4) Karp's method for one covariate; (5) exhaustive enumeration for small sample sizes; (6) binary pair matching using 'nbpMatching'; (7) binary pair matching plus method (1) to further optimize balance; (8) binary pair matching plus method (3) to further optimize balance; (9) Hadamard designs; and (10) simultaneous multiple kernels. For the greedy, rerandomization, and related methods, three objective functions are supported: Mahalanobis distance, standardized sums of absolute differences, and kernel distances via the 'kernlab' library. This package is the result of a stream of research that can be found in Krieger, A. M., Azriel, D. A., and Kapelner, A. (2019). "Nearly Random Designs with Greatly Improved Balance." Biometrika 106(3), 695-701 . Krieger, A. M., Azriel, D. A., and Kapelner, A. (2023). "Better experimental design by hybridizing binary matching with imbalance optimization." Canadian Journal of Statistics, 51(1), 275-292 . Package: r-cran-greeks Architecture: amd64 Version: 1.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 536 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-magrittr, r-cran-dqrng, r-cran-rcpp, r-cran-tibble, r-cran-ggplot2, r-cran-plotly, r-cran-shiny, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-greeks_1.5.3-1.ca2404.1_amd64.deb Size: 316916 MD5sum: 7f1f031534c57e440d50ab336baccc03 SHA1: bbc6cdcdd46623a375895c41d45178bfb5cf04c6 SHA256: 512a4c1e36c71f6118a5208ade7472c2d3674e745505e676005634d11ab3a1ed SHA512: 943953a7107b901c0ee2405cd3d16c0578d6513e5272be92350450b8955f2054a7f2114c4950c2a62d60ad1a080f669b2b413d2d9bf51effe620430b9605e93f 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). 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Functions for estimating the parameter of interest and nuisance parameters, including baseline hazards, using maximum likelihood are also provided. A parallel set of functions allow for the incorporation of family structure of related individuals (e.g., trios). Note that the current implementation of the frailty model (Ripatti and Palmgren, 2000) is sensitive to departures from model assumptions, and should be considered experimental. For these data, the exact proportional-hazards-model-based likelihood is computed by evaluating multiple variable integration. The integration is accomplished using the 'Cuba' library (Hahn, 2005), and the source files are included in this package. The maximization process is carried out using Brent's algorithm, with the C++ code file from John Burkardt and John Denker (Brent, 2002). Package: r-cran-grouprar Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-stringr, r-cran-extradistr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-grouprar_0.1.0-1.ca2404.1_amd64.deb Size: 181578 MD5sum: 906c8c74e9a165f154a0abd2e6bdccaa SHA1: 8ebd5520804f09c698c3cb5d4a6292a54bbb6483 SHA256: 93f9b5bada0d10e8005c26b72f910219a72bb46ce388d6d10326a6fff0ee2edb SHA512: 283f424b15f16c7709362f74586a2048f074fe8e86d0fe2fa0d36dd7ce996c1f29a93d0086cb8bdc680690b4e8ba46d5c0e1ddd7a8d2c127b1d8cd47f5c82d29 Homepage: https://cran.r-project.org/package=grouprar Description: CRAN Package 'grouprar' (Group Response Adaptive Randomization for Clinical Trials) Implement group response-adaptive randomization procedures, which also integrates standard non-group response-adaptive randomization methods as specialized instances. It is also uniquely capable of managing complex scenarios, including those with delayed and missing responses, thereby expanding its utility in real-world applications. This package offers 16 functions for simulating a variety of response adaptive randomization procedures. These functions are essential for guiding the selection of statistical methods in clinical trials, providing a flexible and effective approach to trial design. Some of the detailed methodologies and algorithms used in this package, please refer to the following references: LJ Wei (1979) L. J. WEI and S. DURHAM (1978) Durham, S. D., FlournoY, N. AND LI, W. (1998) Ivanova, A., Rosenberger, W. F., Durham, S. D. and Flournoy, N. (2000) Bai Z D, Hu F, Shen L. (2002) Ivanova, A. (2003) Hu, F., & Zhang, L. X. (2004) Hu, F., & Rosenberger, W. F. (2006, ISBN:978-0-471-65396-7). Zhang, L. X., Chan, W. S., Cheung, S. H., & Hu, F. (2007) Zhang, L., & Rosenberger, W. F. (2006) Hu, F., Zhang, L. X., Cheung, S. H., & Chan, W. S. (2008) . Package: r-cran-grouptesting Architecture: amd64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: libc6 (>= 2.2.5), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bingroup2, r-cran-pracma Filename: pool/dists/noble/main/r-cran-grouptesting_1.3.0-1.ca2404.1_amd64.deb Size: 158908 MD5sum: 2bc0d7e066ccdf0b61e92eca25153349 SHA1: bc76e4cb67a8b188e20e54facc5db4a6427e47cf SHA256: b778a8ff47f9fccae1d491128f188b5ac8bc68b226d2606b1e447382d5f00fde SHA512: 0ee7b2120ada7ae3588a3944ff6d7c44c7a5c9e1684557f52e1b4460ebbbe62d84a2681e372318e89c9abc5cff8343d21671983fdded78ec63acd05a48c04af3 Homepage: https://cran.r-project.org/package=groupTesting Description: CRAN Package 'groupTesting' (Simulating and Modeling Group (Pooled) Testing Data) Provides an expectation-maximization (EM) algorithm using the approach introduced in Warasi (2023) . The EM algorithm can be used to estimate the prevalence (overall proportion) of a disease and to estimate a binary regression model from among the class of generalized linear models based on group testing data. The estimation framework we consider offers a flexible and general approach; i.e., its application is not limited to any specific group testing protocol. Consequently, the EM algorithm can model data arising from simple pooling as well as advanced pooling such as hierarchical testing, array testing, and quality control pooling. Also, provided are functions that can be used to conduct the Wald tests described in Buse (1982) and to simulate the group testing data described in Kim et al. (2007) . We offer a function to compute relative efficiency measures, which can be used to optimize the maximum likelihood estimator of disease prevalence. 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Fore more details see: Ma L. and Soriano J. (2018) Efficient functional ANOVA through wavelet-domain Markov groves. . 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Package: r-cran-grpcox Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-colorspace, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-grpcox_1.0.2-1.ca2404.1_amd64.deb Size: 145642 MD5sum: 56a29bfbfb58dcfc6d308fca016efd2a SHA1: 5a99d1bbf1cf33963784c12203de15c601f257b1 SHA256: c8baf20e39e6ca302e08233d3d6cd454c8aa05c19dfa1c9d537997419cb58155 SHA512: 5fb274552b6702b606f03d637cd75c4c03db83d74eca84a635394261d4c6556934f27c85875ff12abdfdcc1a02729da0e7514572ed25284a7f18345fdb7dcb10 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: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-grpnet_1.2-1.ca2404.1_amd64.deb Size: 603788 MD5sum: 715fae0d40622a7bf2c0cc05ff86fed2 SHA1: e989e3293c69eadf569a84446c9d8ec354500922 SHA256: 6c8008cc0e938ed63d6a178f86530b0ae2ec4666f23c5f351a17119ed42c9c56 SHA512: 029d006e6a37423e491a1d779cb11fbc49f099b19274815ce220b7cc8534aeb57cc1b7d6ae26ceb25f7c732e4716d86694576f3319ac34fb521374d6f0e35e39 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: amd64 Version: 3.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 542 Depends: libc6 (>= 2.4), 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/noble/main/r-cran-grpreg_3.6.0-1.ca2404.1_amd64.deb Size: 362470 MD5sum: b3772939d33014c9aa38f67b5a92ffc0 SHA1: 80ba150fdea2995b47b46acde785bc61707829c3 SHA256: ffc005e804306949a9eaa17977b3ba95073bc3494fc2440c08b2e612afe17b30 SHA512: fd1d2045a1b432ca311cf73ea5f5e57ee46e99dae9694a9a757720d90bd8e63378f0d2fc779f3b209c86f90fbb673f1807d5f182dc4483681a4970496e68db9d 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. 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Package: r-cran-gtfs2gps Architecture: amd64 Version: 2.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2439 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-furrr, r-cran-future, r-cran-gtfstools, r-cran-rcpp, r-cran-units, r-cran-sf, r-cran-terra, r-cran-sfheaders, r-cran-progressr, r-cran-lwgeom, r-cran-checkmate, r-cran-parallelly Suggests: r-cran-rmarkdown, r-cran-markdown, r-cran-knitr, r-cran-testthat, r-cran-dplyr, r-cran-bit64 Filename: pool/dists/noble/main/r-cran-gtfs2gps_2.1-4-1.ca2404.1_amd64.deb Size: 2130038 MD5sum: e3c4fd2a0f8a8616994641c76fa1ddea SHA1: 2372d0f79df67b1171abd8fb7454feb24b2475a0 SHA256: 745d1637c4943df9927311daed025cb8b2f8c75a7ffbc909a8a5e2d3301a709d SHA512: 8a3472cb5cf6db66ebf8e327b018d9f783f80d4596f1d61759253379191de553e26605949bb1ac15c186178d5fcd65609fdfa41ab435fe5625af1bf720454754 Homepage: https://cran.r-project.org/package=gtfs2gps Description: CRAN Package 'gtfs2gps' (Converting Transport Data from GTFS Format to GPS-Like Records) Convert general transit feed specification (GTFS) data to global positioning system (GPS) records in 'data.table' format. 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Package: r-cran-gtfsrouter Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5112 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-fs, r-cran-geodist, r-cran-rcpp Suggests: r-cran-digest, r-cran-dodgr, r-cran-dplyr, r-cran-ggplot2, r-cran-here, r-cran-hms, r-cran-knitr, r-cran-lubridate, r-cran-lwgeom, r-cran-markdown, r-cran-pbapply, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gtfsrouter_0.1.4-1.ca2404.1_amd64.deb Size: 1361320 MD5sum: 16a12f1d937ee6b3fce1c444134662a8 SHA1: fe4e629e27ba8139d87b7eb025b2d02cb9aa0eea SHA256: dcf78fea5049a11733b6627f313dd3ccf3f58a6e1923d73a2047ebec19e154bd SHA512: cc1ee5772968306021b7421b1f236fbc0ff7f22361bc4671f1f60d565be749e19092b399182b4452b437f463adfcb290625521b301a1feed77eabc8a97caf813 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: amd64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1614 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-curl, r-cran-data.table, r-cran-gtfsio, r-cran-parallelly, r-cran-processx, r-cran-sf, r-cran-sfheaders, r-cran-units, r-cran-zip, r-cran-cpp11 Suggests: r-cran-covr, r-cran-ggplot2, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gtfstools_1.4.0-1.ca2404.1_amd64.deb Size: 1289566 MD5sum: 0274986ee05fcb3279f9329d1a113753 SHA1: cfc1f89d36f84cc70874cda87a5874cf19588306 SHA256: a0667e40c889aa3628c1fb767e09bc6238ec9a99bbf18876c44ce57261e05411 SHA512: e71d6d7be55a198b107728c8955ea755c4d6f4efee7c82562e9992629f160323c7518002e4b4aa6fb046526fbd03e8e99ada0d258ea7b7ea8d5d7066d3d6bd3d Homepage: https://cran.r-project.org/package=gtfstools Description: CRAN Package 'gtfstools' (General Transit Feed Specification (GTFS) Editing and AnalysingTools) Utility functions to read, manipulate, analyse and write transit feeds in the General Transit Feed Specification (GTFS) data format. Package: r-cran-gtools Architecture: amd64 Version: 3.9.5-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-car, r-cran-gplots, r-cran-knitr, r-cran-rstudioapi, r-cran-sgp, r-cran-taxize Filename: pool/dists/noble/main/r-cran-gtools_3.9.5-1.ca2404.2_amd64.deb Size: 355434 MD5sum: 0d5443f334ef44cf3d3248c4a4a7a9d3 SHA1: 69557d78cf2124ce97a7e1ed98165096adbea44b SHA256: ffb6c67a4f343bf713df432c35b56ff23302ebf5c07a69cc0e4f62646b9ba280 SHA512: 1a19694c0fbf2111bf2ad48c242aff29e9da39727528cb38414f3d5c425f358b88d040777709360b6212503f4661f930c204419b947e129d671b79aaf3d89957 Homepage: https://cran.r-project.org/package=gtools Description: CRAN Package 'gtools' (Various R Programming Tools) Functions to assist in R programming, including: - assist in developing, updating, and maintaining R and R packages ('ask', 'checkRVersion', 'getDependencies', 'keywords', 'scat'), - calculate the logit and inverse logit transformations ('logit', 'inv.logit'), - test if a value is missing, empty or contains only NA and NULL values ('invalid'), - manipulate R's .Last function ('addLast'), - define macros ('defmacro'), - detect odd and even integers ('odd', 'even'), - convert strings containing non-ASCII characters (like single quotes) to plain ASCII ('ASCIIfy'), - perform a binary search ('binsearch'), - sort strings containing both numeric and character components ('mixedsort'), - create a factor variable from the quantiles of a continuous variable ('quantcut'), - enumerate permutations and combinations ('combinations', 'permutation'), - calculate and convert between fold-change and log-ratio ('foldchange', 'logratio2foldchange', 'foldchange2logratio'), - calculate probabilities and generate random numbers from Dirichlet distributions ('rdirichlet', 'ddirichlet'), - apply a function over adjacent subsets of a vector ('running'), - modify the TCP_NODELAY ('de-Nagle') flag for socket objects, - efficient 'rbind' of data frames, even if the column names don't match ('smartbind'), - generate significance stars from p-values ('stars.pval'), - convert characters to/from ASCII codes ('asc', 'chr'), - convert character vector to ASCII representation ('ASCIIfy'), - apply title capitalization rules to a character vector ('capwords'). Package: r-cran-gud Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3482 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-posterior, r-cran-rdpack, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-mass, r-cran-lattice, r-cran-bayesplot, r-cran-loo, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gud_1.0.2-1.ca2404.1_amd64.deb Size: 1248112 MD5sum: de1f73185183c0f1399799a830fb6239 SHA1: b74946e4ca13796d95b318e7f4864db7c1e98d90 SHA256: 17c7d18b8dc5b8fcfe00ff2949e34b351f64832cf405af8874581aa678d1d49f SHA512: 61f454cda02c1899102533c153d34a49987f32219d62077e148adc972b79a7773bfed86fe8aa811a663b1854ae933ee1cfe1460acbd5dd5a534834d1b3120e93 Homepage: https://cran.r-project.org/package=GUD Description: CRAN Package 'GUD' (Bayesian Modal Regression Based on the GUD Family) Provides probability density functions and sampling algorithms for three key distributions from the General Unimodal Distribution (GUD) family: the Flexible Gumbel (FG) distribution, the Double Two-Piece (DTP) Student-t distribution, and the Two-Piece Scale (TPSC) Student-t distribution. Additionally, this package includes a function for Bayesian linear modal regression, leveraging these three distributions for model fitting. The details of the Bayesian modal regression model based on the GUD family can be found at Liu, Huang, and Bai (2024) . Package: r-cran-guilds Architecture: amd64 Version: 1.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nloptr, r-cran-pracma, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-guilds_1.4.7-1.ca2404.1_amd64.deb Size: 189482 MD5sum: 9bb7f1bf67d7a81fee101f79e0555cf7 SHA1: 6a7bcf44cd54be547cbb51c668f2111380d19bcc SHA256: 623142fa44b5fd43a73efcd8aeae8dae31f0e1175ea7d453842138e39647dc09 SHA512: a5cb5cd0b3af3ccde71a3e765e13cc6398c7d82a4501d6468653caad0502b3f8d2183a0b081a92dd87049721f96e5e18567c798f2adc01a470a6943a373fa6cd Homepage: https://cran.r-project.org/package=GUILDS Description: CRAN Package 'GUILDS' (Implementation of Sampling Formulas for the Unified NeutralModel of Biodiversity and Biogeography, with or without GuildStructure) A collection of sampling formulas for the unified neutral model of biogeography and biodiversity. Alongside the sampling formulas, it includes methods to perform maximum likelihood optimization of the sampling formulas, methods to generate data given the neutral model, and methods to estimate the expected species abundance distribution. Sampling formulas included in the GUILDS package are the Etienne Sampling Formula (Etienne 2005), the guild sampling formula, where guilds are assumed to differ in dispersal ability (Janzen et al. 2015), and the guilds sampling formula conditioned on guild size (Janzen et al. 2015). Package: r-cran-gunifrac Architecture: amd64 Version: 1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1587 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-vegan, r-cran-ggplot2, r-cran-matrixstats, r-cran-matrix, r-cran-ape, r-cran-statmod, r-cran-rmutil, r-cran-dirmult, r-cran-mass, r-cran-ggrepel, r-cran-foreach, r-cran-modeest, r-cran-inline Suggests: r-cran-ade4, r-cran-knitr, r-cran-markdown, r-cran-ggpubr Filename: pool/dists/noble/main/r-cran-gunifrac_1.9-1.ca2404.1_amd64.deb Size: 1013918 MD5sum: b543de3499101a688e72e65988d2fcfa SHA1: bafde541d652b85c77e718889769c69df93402ab SHA256: d97da692270706c45e0eb0d9cbd9d819b61aece6813b14e51385afaa27b7073f SHA512: 6783ac52d76a04186fb365a6e718916a629bb7c9790652aad4f837c190377c1c7c1e9fd5a9af836cc1222b0459cd68dd0faca835dba1f214c956c723975431a6 Homepage: https://cran.r-project.org/package=GUniFrac Description: CRAN Package 'GUniFrac' (Generalized UniFrac Distances, Distance-Based MultivariateMethods and Feature-Based Univariate Methods for MicrobiomeData Analysis) A suite of methods for powerful and robust microbiome data analysis including data normalization, data simulation, community-level association testing and differential abundance analysis. It implements generalized UniFrac distances, Geometric Mean of Pairwise Ratios (GMPR) normalization, semiparametric data simulator, distance-based statistical methods, and feature-based statistical methods. The distance-based statistical methods include three extensions of PERMANOVA: (1) PERMANOVA using the Freedman-Lane permutation scheme, (2) PERMANOVA omnibus test using multiple matrices, and (3) analytical approach to approximating PERMANOVA p-value. Feature-based statistical methods include linear model-based methods for differential abundance analysis of zero-inflated high-dimensional compositional data. Package: r-cran-guts Architecture: amd64 Version: 1.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3341 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-adaptmcmc, r-cran-xlsx, r-cran-drc, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-guts_1.2.6-1.ca2404.1_amd64.deb Size: 2887350 MD5sum: 60fa4614e9d4f4f7b267db830e2e39a0 SHA1: ebf6b3d61fdef6f476dd1c9a57959a302341304c SHA256: 29c4494672268925fb661222d17990de882ce54ce269f9ac21c96f573ef0d5d9 SHA512: a38013c7bc404542e9bc29305d9526301b1b81f440dfc614542ed94270cef86ddf91fa33978db23546cc08b6bb190864cb235f33ba9fe9aa9e30b4d5f76364d2 Homepage: https://cran.r-project.org/package=GUTS Description: CRAN Package 'GUTS' (Fast Calculation of the Likelihood of a Stochastic SurvivalModel) Given exposure and survival time series as well as parameter values, GUTS allows for the fast calculation of the survival probabilities as well as the logarithm of the corresponding likelihood (see Albert, C., Vogel, S. and Ashauer, R. (2016) ). Package: r-cran-gwasexacthw Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gwasexacthw_1.2-1.ca2404.1_amd64.deb Size: 14764 MD5sum: f4f1ead949b9a5d99fc8764056d36ba9 SHA1: 48e7eaff3925f92c1f95c23b8c3a066ea473609a SHA256: b9c4a4aace19f164049fec9e5b3ef9f864bbcaccf96abf8b0bca70a1e0fa731d SHA512: cf1e752ad95e180126bf08aecc67b01ecc47fa308f875df4b67f68ec8403db878ea57b1c709a3db238cd13f67bea66ad867213272ceddedc7eafc0471c6cab30 Homepage: https://cran.r-project.org/package=GWASExactHW Description: CRAN Package 'GWASExactHW' (Exact Hardy-Weinburg Testing for Genome Wide Association Studies) Exact Hardy-Weinburg testing (using Fisher's test) for SNP genotypes as typically obtained in a Genome Wide Association Study (GWAS). Package: r-cran-gwasinlps Architecture: amd64 Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mombf, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-fastglm, r-cran-survival Suggests: r-cran-glmnet Filename: pool/dists/noble/main/r-cran-gwasinlps_2.4-1.ca2404.1_amd64.deb Size: 109082 MD5sum: 9e9528a8982e037d4078f92b5b517232 SHA1: 761de0579e8bd0589a7bf30596d247753da77f39 SHA256: f3cbd401c568b65478e49a3a0a758eec08757828684b8fd014f0810b2352fc31 SHA512: 942065a26488098c8fd73530f884da37363f812d16acc9024eac07477785ba7ab144a6b823a0d20b860f84bb2a4ea6a2401de78eae76a7322373efac086a1490 Homepage: https://cran.r-project.org/package=GWASinlps Description: CRAN Package 'GWASinlps' (Non-Local Prior Based Iterative Variable Selection Tool forGenome-Wide Association Studies) Performs variable selection with data from Genome-wide association studies (GWAS), or other high-dimensional data with continuous, binary or survival outcomes, combining in an iterative framework the computational efficiency of the structured screen-and-select variable selection strategy based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors (see Sanyal et al., 2019 ). Package: r-cran-gwex Architecture: amd64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 603 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-envstats, r-cran-mass, r-cran-mvtnorm, r-cran-nleqslv, r-cran-fgarch, r-cran-abind, r-cran-foreach, r-cran-doparallel, r-cran-renext, r-cran-lmomco, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-gwex_1.1.3-1.ca2404.1_amd64.deb Size: 430080 MD5sum: b953a14d76330b379559764edf048f63 SHA1: f7b0567d92ba88dc0960cda9ce2131ce84623676 SHA256: 51828a244f3bd366a781a050605a30d6c668838929f1aef8ffbb958a9004e40e SHA512: c16348b655115ff14730601c8ee5b7420ab4e3bdd8844afc49531bf399a0cbe18ce8f1d691db53f8fb22f5a1a0fafd8c85d82d8a2b5b4388046365f1f4bf9823 Homepage: https://cran.r-project.org/package=GWEX Description: CRAN Package 'GWEX' (Multi-Site Stochastic Models for Daily Precipitation andTemperature) Application of multi-site models for daily precipitation and temperature data. This package is designed for an application to 105 precipitation and 26 temperature gauges located in Switzerland. It applies fitting procedures and provides weather generators described in the following references: - Evin, G., A.-C. Favre, and B. Hingray. (2018) . - Evin, G., A.-C. Favre, and B. Hingray. (2018) . Package: r-cran-gwmodel Architecture: amd64 Version: 2.4-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2947 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-robustbase, r-cran-sp, r-cran-rcpp, r-cran-sf, r-cran-spacetime, r-cran-spdep, r-cran-spatialreg, r-cran-fnn, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-mvoutlier, r-cran-rcolorbrewer, r-cran-gstat, r-cran-spdata Filename: pool/dists/noble/main/r-cran-gwmodel_2.4-1-1.ca2404.1_amd64.deb Size: 2520488 MD5sum: bd88c11ceede6d4728206b1b1a6d1270 SHA1: 1314308d32a760c97fb0a2605f6e09abbd19bde9 SHA256: 0b98f4413aa09cf6ffdf87e60b07fb2dca1a4e712bcfea7f6083a9366a6f21f0 SHA512: 82067431f386afe54fe6a08c457214c07dadf13057fc5b6d57d89d58d933437db5af2bc0a736ca43a2ad3595eb36554a05b2af1266ff3350b43368476b1e64da Homepage: https://cran.r-project.org/package=GWmodel Description: CRAN Package 'GWmodel' (Geographically-Weighted Models) Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. 'GWmodel' includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002), GW principal components analysis (Harris et al., 2011), GW discriminant analysis (Brunsdon et al., 2007) and various forms of GW regression (Brunsdon et al., 1996); some of which are provided in basic and robust (outlier resistant) forms. Package: r-cran-gwnorm Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: libc6 (>= 2.14), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-bdgraph, r-cran-cholwishart, r-cran-mass, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-gwnorm_1.0-1.ca2404.1_amd64.deb Size: 112738 MD5sum: 51a1d9fc4e24c24e0e2431c61cddf184 SHA1: e1733bca62430f5c39dbc96af58371d1a5b26cc6 SHA256: d92653f145af53f13ee1dcf449156f75aa3cb5142ad8a2933eebec661fb326b7 SHA512: d4bb121fd96c56f7f1eef590546a4941b86e30276d358b37747eea7bdf512722f08dfc777c2f43e481f7db6eafe9054e5b0ae199fb249ac9f521a152eeaa35ee Homepage: https://cran.r-project.org/package=GWnorm Description: CRAN Package 'GWnorm' (G-Wishart Normalising Constants for Gaussian Graphical Models) Computes G-Wishart normalising constants through a Fourier approach. Either exact analytical results, numerical integration or Monte Carlo estimation are employed. Details at C. Wong, G. Moffa and J. Kuipers (2024), . Package: r-cran-gwpcormapper Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1857 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-gwpcormapper_0.1.3-1.ca2404.1_amd64.deb Size: 1737942 MD5sum: f94035410d1d170e0cefac1816b95913 SHA1: 7c697a9e10e8fb4983ba7434ffa53e958ab19bc7 SHA256: e0136594a78ee91f0575135324ca1dc6681c8d8fd738ab5f61509a53dade4310 SHA512: 14fdc3063fd5f26aa09759ef716e1e18409de09f65432d177ad55a45bd473659867e8387e88091339b01d65347a15fcde8fd498122cba3a0be7503f91d40b616 Homepage: https://cran.r-project.org/package=gwpcormapper Description: CRAN Package 'gwpcormapper' (Geographically Weighted Partial Correlation Mapper) An interactive mapping tool for geographically weighted correlation and partial correlation. Geographically weighted partial correlation coefficients are calculated following (Percival and Tsutsumida, 2017) and are described in greater detail in (Tsutsumida et al., 2019) and (Percival et al., 2021). Package: r-cran-gxescanr Architecture: amd64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 535 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-prodlim, r-cran-rcpparmadillo Suggests: r-cran-binarydosage, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gxescanr_2.0.2-1.ca2404.1_amd64.deb Size: 191394 MD5sum: e01d9948a89ed51edc029fb1af68ac5e SHA1: e47f1a52e4d2fb1f203063801e3d211bacc31e75 SHA256: e52c91123679ace5129027a1faa44d846224ed7549dbff212862cae04ec8b3a2 SHA512: 020c6901d68359db7f72c011d03df63afb9bf9df61074db7f4725da614aec12817f4fb90b917b5a404839fc0b53d30d14b6e988fbd18baf15f0175cb8d887e70 Homepage: https://cran.r-project.org/package=GxEScanR Description: CRAN Package 'GxEScanR' (Run GWAS/GWEIS Scans Using Binary Dosage Files) Tools to run genome-wide association study (GWAS) and genome-wide by environment interaction study (GWEIS) scans using the genetic data stored in a binary dosage file. 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The methods are based on the gyrovector space theory developed by A. A. Ungar that can be found in the book 'Analytic Hyperbolic Geometry: Mathematical Foundations And Applications' . The package provides functions to plot three-dimensional hyperbolic polyhedra and to plot hyperbolic tilings of the Poincaré disk. Package: r-cran-h3lib Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-h3lib_0.1.4-1.ca2404.1_amd64.deb Size: 58358 MD5sum: 01b1a1b3b327fa60bdffe103a518071f SHA1: 43ba6b1c8cccbe0c1d65b78eb9631f2bdad37883 SHA256: 250bc8f6e2cde099ecf3005d633e57a8239c4d3e0cef97ef9082b46c227fdd99 SHA512: eaa6a14265b5253899048d76af24708373241c17df1771e0bf77b7cb4205b727db08753a7968fad082a78c0017618238663ed25fd6c6ead875823461c0f30467 Homepage: https://cran.r-project.org/package=h3lib Description: CRAN Package 'h3lib' (Exposes the 'Uber' 'H3' Library to R Packages) 'H3' is a hexagonal hierarchical spatial index developed by 'Uber' . This package exposes the source code of 'H3' (written in 'C') to routines that are callable through 'R'. Package: r-cran-h3o Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1232 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-vctrs Suggests: r-cran-sf, r-cran-wk Filename: pool/dists/noble/main/r-cran-h3o_0.3.0-1.ca2404.1_amd64.deb Size: 555834 MD5sum: 8491bf59b7f75fbb40e9e68df413c782 SHA1: f641c8193b6a663731f3f737b2bf484b0346c4d0 SHA256: 9a06665c3539b766e2ad1eadee0078c1103deceac0fe665f9ec2fc9d25fc3502 SHA512: 38a2e8128340cc1457cd54280fa0b283cdee7533521991b62ec4835ef2dc9653739c97c5f9819b08582c4ad49b5433b50165dd08cee1d173717a06702ad3ab84 Homepage: https://cran.r-project.org/package=h3o Description: CRAN Package 'h3o' (H3 Geospatial Indexing System) A dependency free interface to the H3 geospatial indexing system utilizing the Rust library 'h3o' via the 'extendr' library . Package: r-cran-h3r Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-h3lib Suggests: r-cran-sfheaders, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-h3r_0.1.2-1.ca2404.1_amd64.deb Size: 127506 MD5sum: ec8810a89d6d7e6da4ab0ceda4748af7 SHA1: d5bcd59ff74d4c4c1c5a08a7bdf7f881cd40fcff SHA256: 61e1f50af56d5f3763c6fe48dfdb9e5b82e6693a615224134994c62c34115c06 SHA512: 2a4e2804db9e38cf962d7ca82f5888f15896dc9a529fb34f318d94c67efcc533ecfb9486c01365802afcdb25be558b84b5def9a90200af218357209bbae73110 Homepage: https://cran.r-project.org/package=h3r Description: CRAN Package 'h3r' (Hexagonal Hierarchical Geospatial Indexing System) Provides access to Uber's 'H3' geospatial indexing system via 'h3lib' . 'h3r' is designed to mimic the 'H3' Application Programming Interface (API) , so that any function in the API is also available in 'h3r'. Package: r-cran-h5lite Architecture: amd64 Version: 2.1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7014 Depends: libc6 (>= 2.38), r-base-core (>= 4.6.0), r-api-4.0, r-cran-hdf5lib Suggests: r-cran-bit64, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-h5lite_2.1.1.1-1.ca2404.1_amd64.deb Size: 2499664 MD5sum: 890e073958cc8d19104cae6597e42b8d SHA1: 7211aa96e2c1d07ba127b237f1f952f62edf0443 SHA256: 959b86dfefb02eb325c6ff1a5636ea22e744bac764177c3be7b4e76095816e64 SHA512: 691241cab07597992d36b573103449eba1be650161c0fd02e07da791481318bf131cd053b903d6655aa0ee2b142aea0f9f2c0e707868e5ba72f529c61d5384d8 Homepage: https://cran.r-project.org/package=h5lite Description: CRAN Package 'h5lite' (Simplified 'HDF5' Interface) A user-friendly interface for the Hierarchical Data Format 5 ('HDF5') library designed to "just work." It bundles the necessary system libraries to ensure easy installation on all platforms. Features smart defaults that automatically map R objects (vectors, matrices, data frames) to efficient 'HDF5' types, removing the need to manage low-level details like dataspaces or property lists. Uses the 'HDF5' library developed by The HDF Group . Package: r-cran-habcluster Architecture: amd64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1487 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-stars, r-cran-sf, r-cran-rcpp, r-cran-raster Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-habcluster_1.0.5-1.ca2404.1_amd64.deb Size: 1145130 MD5sum: f28a311ee46fe1024489271de4b6d9a2 SHA1: ebf603b6dbde30c913aea5031ce41c1229369daf SHA256: d4d0bef3e513761c27b793751222c7111c355199e2cfd9ee8c0e9e45cf768bf3 SHA512: a2a3c18a67081b994ef97861c60fd8f76a3ffefb8dcff475c419cac151456167e752140776b5d7861b3f5bf62ef2c6a4f783741cc4fd1306a671d850b17ebdba Homepage: https://cran.r-project.org/package=habCluster Description: CRAN Package 'habCluster' (Detecting Spatial Clustering Based on Connection Cost BetweenGrids) Based on landscape connectivity, spatial boundaries were identified using community detection algorithm at grid level. Methods using raster as input and the value of each cell of the raster is the "smoothness" to indicate how easy the cell connecting with neighbor cells. Details about the 'habCluster' package methods can be found in Zhang et al. . Package: r-cran-hacsim Architecture: amd64 Version: 1.0.7-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-data.table, r-cran-matrixstats, r-cran-pegas, r-cran-rcpp, r-cran-stringr, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-hacsim_1.0.7-1-1.ca2404.1_amd64.deb Size: 105900 MD5sum: 2c8aec58f8fbb9b7a1449444f0b79997 SHA1: 257b0956d4cac39f509c6332fd7e7749339d940f SHA256: 459bab4c56e5cdcbb978e51b168b8116f49259f7e59d9f0cfe9bfc76c6a7dc58 SHA512: 9f0c41efe4ce7007e1f164ad82dbc5d9387b6005d61f0ac1087dbd03c0a4b0d90579c0001bf09333ad325c87c7d0e7c8c8e46f6645603f0cb7ace75f97513ebd Homepage: https://cran.r-project.org/package=HACSim Description: CRAN Package 'HACSim' (Iterative Extrapolation of Species' Haplotype AccumulationCurves for Genetic Diversity Assessment) Performs iterative extrapolation of species' haplotype accumulation curves using a nonparametric stochastic (Monte Carlo) optimization method for assessment of specimen sampling completeness based on the approach of Phillips et al. (2015) , Phillips et al. (2019) and Phillips et al. (2020) . 'HACSim' outputs a number of useful summary statistics of sampling coverage ("Measures of Sampling Closeness"), including an estimate of the likely required sample size (along with desired level confidence intervals) necessary to recover a given number/proportion of observed unique species' haplotypes. Any genomic marker can be targeted to assess likely required specimen sample sizes for genetic diversity assessment. The method is particularly well-suited to assess sampling sufficiency for DNA barcoding initiatives. Users can also simulate their own DNA sequences according to various models of nucleotide substitution. A Shiny app is also available. Package: r-cran-hahmmr Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3488 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-bioc-genomicranges, r-cran-ggplot2, r-cran-glue, r-bioc-iranges, r-cran-patchwork, r-cran-rcpp, r-cran-stringr, r-cran-tibble, r-cran-zoo, r-cran-rcpparmadillo, r-cran-roptim Suggests: r-cran-ggrastr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hahmmr_1.0.0-1.ca2404.1_amd64.deb Size: 3343546 MD5sum: 1c4a9209e92bfe9050fee17738f7d577 SHA1: 20d51b8f7f5063d2736b69f7f404ac409a2c4fb2 SHA256: f23d5ba0a0c6ab2329c6fdb9435ae1ec68ad9d6035863a8e70ff122ba3ffea94 SHA512: 089e3f418ea9a8e8fdebc5784a90834189798bb2f792919434667eb3b75a4a36961f69115ab796a2f204d16e25aac15d7a8c8a54f4bbc48419aace01b16c93b9 Homepage: https://cran.r-project.org/package=hahmmr Description: CRAN Package 'hahmmr' (Haplotype-Aware Hidden Markov Model for RNA) Haplotype-aware Hidden Markov Model for RNA (HaHMMR) is a method for detecting copy number variations (CNVs) from bulk RNA-seq data. Additional examples, documentations, and details on the method are available at . 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For ease of use and increased flexibility, the Lasso fitting routines invoke code from the 'glmnet' package by default. The highly adaptive lasso was first formulated and described by MJ van der Laan (2017) , with practical demonstrations of its performance given by Benkeser and van der Laan (2016) . This implementation of the highly adaptive lasso algorithm was described by Hejazi, Coyle, and van der Laan (2020) . Package: r-cran-handwriter Architecture: amd64 Version: 3.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2766 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-igraph, r-cran-lpsolve, r-cran-magick, r-cran-mc2d, r-cran-png, r-cran-purrr, r-cran-rcpp, r-cran-reshape2, r-cran-rfast, r-cran-rjags, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-coda, r-cran-withr Filename: pool/dists/noble/main/r-cran-handwriter_3.2.4-1.ca2404.1_amd64.deb Size: 1860640 MD5sum: 6df99e8d014a6293cb57008d3f69f6b4 SHA1: 80fb4269943d774377659dfefd82bdbc25aee0d9 SHA256: e3264c8e5ea3ca8fc4310e237487d83f036a631b2ccb7b097cd7f6520c08a7d3 SHA512: 2716245f88bda0ee02bbd70c9ae0a8ab01229a367f02f7cfbb80e7b2306fd295220faa58bdf1fc8a487ff75339155187b664345227d7b981009c3ffb24d4b784 Homepage: https://cran.r-project.org/package=handwriter Description: CRAN Package 'handwriter' (Handwriting Analysis in R) Perform statistical writership analysis of scanned handwritten documents. Webpage provided at: . Package: r-cran-hann Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-hann_1.2-1.ca2404.1_amd64.deb Size: 154488 MD5sum: db9a12d42b130d60107a2ac8564b79a2 SHA1: 44648844081365ebe4bcf6ae81ef213844114797 SHA256: 5531d1543f1cf136c9d8472c78d44068b086e635f8c9ad51c1a870a8bc7f9d6b SHA512: 79fa9fc2739fe92f0cb3b131379d7fd720e168708cb88551c43067d49cd00de7a7bc26db28a17c5b260f2ec35e44064508b99648ef0af34600360b135388de96 Homepage: https://cran.r-project.org/package=hann Description: CRAN Package 'hann' (Hopfield Artificial Neural Networks) Builds and optimizes Hopfield artificial neural networks (Hopfield, 1982, ). One-layer and three-layer models are implemented. The energy of the Hopfield network is minimized with formula from Krotov and Hopfield (2016, ). Optimization (supervised learning) is done through a gradient-based method. Classification is done with S3 methods predict(). Parallelization with 'OpenMP' is used if available during compilation. Package: r-cran-hans Architecture: amd64 Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hans_0.1-1.ca2404.1_amd64.deb Size: 35006 MD5sum: cda6de78de608ea50f270e8c057414e4 SHA1: 75a0164249011ba538ad11ba26b2d8d24989e591 SHA256: d044263f13ccafc36c7be3d08cd64b852e3f5484ee80cf45b3ccb5780cae0275 SHA512: 76ef8b43b7af6bbdc2d579f8650a4cff58f6a554a2185e7eff3ae4a54da9371236dd8ce189fe2c3b60ea4af5be1b633afc51746f44135908ce7f9984632a6fda Homepage: https://cran.r-project.org/package=hans Description: CRAN Package 'hans' (Haversines are not Slow) The haversine is a function used to calculate the distance between a pair of latitude and longitude points while accounting for the assumption that the points are on a spherical globe. This package provides a fast, dataframe compatible, haversine function. For the first publication on the haversine calculation see Joseph de Mendoza y Ríos (1795) (In Spanish). Package: r-cran-hapassoc Architecture: amd64 Version: 1.2-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hapassoc_1.2-9-1.ca2404.1_amd64.deb Size: 278882 MD5sum: 167b330f670e5d3d808255b9dde60679 SHA1: 5a9cd18c33692a53689d129671cdde9f3165e718 SHA256: cd3129a315ac6b2f93295e4614aa2a83d80abfd4d2a71db3620acf425f506fc9 SHA512: e49ca2b714ece288535f85936a299998b7dd94de04c4a35b0675a85862221f391ae8ffc88f1b20bad7e1aece997553993a3faa6f7986c6c4726d37e63e9bd537 Homepage: https://cran.r-project.org/package=hapassoc Description: CRAN Package 'hapassoc' (Inference of Trait Associations with SNP Haplotypes and OtherAttributes using the EM Algorithm) The following R functions are used for inference of trait associations with haplotypes and other covariates in generalized linear models. The functions are developed primarily for data collected in cohort or cross-sectional studies. They can accommodate uncertain haplotype phase and handle missing genotypes at some SNPs. Package: r-cran-haplin Architecture: amd64 Version: 7.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4493 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv, r-cran-mass, r-cran-ff, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmpi, r-cran-ggplot2, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-haplin_7.3.2-1.ca2404.1_amd64.deb Size: 1423140 MD5sum: 673b3e86d62613c86ef5d33383fdac34 SHA1: 7c9700d2b520659745332be8679f3a9ef967a0ae SHA256: 4a0d985da4d228a28c90221224fdafc1bdbebd01bd0274f1fd40a24e72d1b081 SHA512: 801f271979e4f25059c63d4bfe698e16de3ddcb9b29b8383e07574f959635c8d1c010b802b9e2edc381ac2fe01de40f3abcb3a21158826c4f7a2a4e14328a1ae Homepage: https://cran.r-project.org/package=Haplin Description: CRAN Package 'Haplin' (Analyzing Case-Parent Triad and/or Case-Control Data with SNPHaplotypes) Performs genetic association analyses of case-parent triad (trio) data with multiple markers. It can also incorporate complete or incomplete control triads, for instance independent control children. Estimation is based on haplotypes, for instance SNP haplotypes, even though phase is not known from the genetic data. 'Haplin' estimates relative risk (RR + conf.int.) and p-value associated with each haplotype. It uses maximum likelihood estimation to make optimal use of data from triads with missing genotypic data, for instance if some SNPs has not been typed for some individuals. 'Haplin' also allows estimation of effects of maternal haplotypes and parent-of-origin effects, particularly appropriate in perinatal epidemiology. 'Haplin' allows special models, like X-inactivation, to be fitted on the X-chromosome. A GxE analysis allows testing interactions between environment and all estimated genetic effects. The models were originally described in "Gjessing HK and Lie RT. Case-parent triads: Estimating single- and double-dose effects of fetal and maternal disease gene haplotypes. Annals of Human Genetics (2006) 70, pp. 382-396". Package: r-cran-haplo.stats Architecture: amd64 Version: 1.9.8.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 819 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-arsenal, r-cran-mass Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-haplo.stats_1.9.8.7-1.ca2404.1_amd64.deb Size: 457772 MD5sum: 8f3b7a90d6485e6f6e6237011c594221 SHA1: 15232e8e6d4ce5b5d95ed396aef403d4c334e4c6 SHA256: 8e33f199e91e2ea86d0b7e0aa284141a4a32aeeef570678fb9adb7ba7f6afe1f SHA512: b954b115886bb52d20539b2d676490e2babb4c0f1d22a72fdd7d228fb5b79da39f2b1dfc6756d8bc256cb29f2bc3593cd517a7c9537bd9803f84242057922c72 Homepage: https://cran.r-project.org/package=haplo.stats Description: CRAN Package 'haplo.stats' (Statistical Analysis of Haplotypes with Traits and Covariateswhen Linkage Phase is Ambiguous) Routines for the analysis of indirectly measured haplotypes. The statistical methods assume that all subjects are unrelated and that haplotypes are ambiguous (due to unknown linkage phase of the genetic markers). The main functions are: haplo.em(), haplo.glm(), haplo.score(), and haplo.power(); all of which have detailed examples in the vignette. Package: r-cran-hapsim Architecture: amd64 Version: 0.31-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-hapsim_0.31-1.ca2404.1_amd64.deb Size: 54290 MD5sum: b99d804dd8701611aca5a0a54046df68 SHA1: 05cef211658fd6cd7036541c3bdb6761f5edd700 SHA256: e37b0fd2663595dc25ddbbad9136a7ad59ff71dc615b9d6dae7a2ed4329cb535 SHA512: 37bd533089bd0cbfcb7d5c732a173b7b7c28796184f473932e0ce1372347336c2b1a6ccae57781e414e77f1a2d1140193653a3690ec087b7bed5bb0dfcbc4fa7 Homepage: https://cran.r-project.org/package=hapsim Description: CRAN Package 'hapsim' (Haplotype Data Simulation) Package for haplotype-based genotype simulations. Haplotypes are generated such that their allele frequencies and linkage disequilibrium coefficients match those estimated from an input data set. Package: r-cran-hardyweinberg Architecture: amd64 Version: 1.7.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1677 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mice, r-cran-nnet, r-cran-rsolnp, r-cran-shape, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hardyweinberg_1.7.9-1.ca2404.1_amd64.deb Size: 1275084 MD5sum: 40cdc75294ee41a22f11ef1f828664aa SHA1: 03d7412c57704042b9e951aa277e79747e589467 SHA256: dd984df3e99d2b0adaad8f1eb9df9e43f53d812bcbedb37582ddf22fefecf378 SHA512: 7f6ee536f17500d5b3b67e007d434c22a90d76e109d762986f27cdc4514e65d04e75013afed7a9eec6223114aa95b64a5df2bc5c5605de8335ec28ff2147dd6f Homepage: https://cran.r-project.org/package=HardyWeinberg Description: CRAN Package 'HardyWeinberg' (Statistical Tests and Graphics for Hardy-Weinberg Equilibrium) Contains tools for exploring Hardy-Weinberg equilibrium (Hardy, 1908; Weinberg, 1908) for bi and multi-allelic genetic marker data. All classical tests (chi-square, exact, likelihood-ratio and permutation tests) with bi-allelic variants are included in the package, as well as functions for power computation and for the simulation of marker data under equilibrium and disequilibrium. Routines for dealing with markers on the X-chromosome are included (Graffelman & Weir, 2016) , including Bayesian procedures. Some exact and permutation procedures also work with multi-allelic variants. Special test procedures that jointly address Hardy-Weinberg equilibrium and equality of allele frequencies in both sexes are supplied, for the bi and multi-allelic case. Functions for testing equilibrium in the presence of missing data by using multiple imputation are also provided. Implements several graphics for exploring the equilibrium status of a large set of bi-allelic markers: ternary plots with acceptance regions, log-ratio plots and Q-Q plots. The functionality of the package is explained in detail in a related JSS paper . Package: r-cran-harmony Architecture: amd64 Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6271 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-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/noble/main/r-cran-harmony_2.0.3-1.ca2404.1_amd64.deb Size: 4785320 MD5sum: 541f51e7df7b402222c6d7be82f6a03a SHA1: 9d639e06a1d9ca697d8408d4ab67312e3bcc721f SHA256: 06265513168d014aa0f9734a1fb69deb3835efdeced54b5f65fa891cdd69f034 SHA512: 424c3cc5f108482e07198d78fe033de0067aca7f3550c7cc8a7ff5afde1906d0344610b4f2d97bc6c7bf08c116993800f9f2ad1b99ee7517b2a14d450317e11b Homepage: https://cran.r-project.org/package=harmony Description: CRAN Package 'harmony' (Fast, Sensitive, and Accurate Integration of Single Cell Data) Implementation of the Harmony algorithm for single cell integration, described in Patikas, Yao, et al. . Package includes a standalone Harmony function and interfaces to external frameworks. Package: r-cran-hashmapr Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hashmapr_1.0.1-1.ca2404.1_amd64.deb Size: 31568 MD5sum: 744d304a5878a0168458f89e9e665cf3 SHA1: 8d469d32d9eb4603054af36108d9f2f678210760 SHA256: ddad97b33eafd4f4587c8c29234afd41d322b137afc7ae0d4257aae9da98b678 SHA512: c1dc44934372587b4e7013c834e4f08d56266c5087e160b148415011f890758200ba911bb9ca6ee9e5cc6016a177b99f8ddda05c0ad523a3b82147a97d7d81cc Homepage: https://cran.r-project.org/package=hashmapR Description: CRAN Package 'hashmapR' (Fast, Vectorized Hashmap) A fast, vectorized hashmap that is built on top of 'C++' std::unordered_map . The map can hold any 'R' object as key / value as long as it is serializable and supports vectorized insertion, lookup, and deletion. Package: r-cran-hashr Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-hashr_0.1.4-1.ca2404.1_amd64.deb Size: 21948 MD5sum: dc09607bcb07bc91723a2ff17d89d996 SHA1: 544f0e09e95a77e54b01a8b80f0676e68eb28692 SHA256: 6377215147dd582f752f10946c12246b737c0b3f688942c557f9ab05a6493ecf SHA512: fe98aa04ac13d9e730da84ec0dc66163767a8dbae1cd152f4476c3610cb2f104f19e6c41980a39b7ce846a835f14fe6512746d26fd373bc741061a9dd179bc8e Homepage: https://cran.r-project.org/package=hashr Description: CRAN Package 'hashr' (Hash R Objects to Integers Fast) Apply an adaptation of the SuperFastHash algorithm to any R object. 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Package: r-cran-hausdorffgof Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-ksgeneral, r-cran-withr, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-hausdorffgof_0.3.0-1.ca2404.1_amd64.deb Size: 199890 MD5sum: 3d5954e572235fdaf37c90222ff7d4a6 SHA1: 7a9ae03d4a9efe3fdd8a03e25eb9e431e7049408 SHA256: 461a687829519616eaed7ecb700549c216d04e13f87ba60874a7a346fcf043a8 SHA512: d512836052bc2c8a008c14eaafa9b74b60d938f8a19be518cf8b71eae9a6eb0808fe27811cc421df86fb1db3f641981ed9b8859baa91bea329b9a949344eb402 Homepage: https://cran.r-project.org/package=HausdorffGoF Description: CRAN Package 'HausdorffGoF' (One- And Two-Sample Hausdorff Goodness-of-Fit Test) Computes the test statistic and p-values of the one-sample and two-sample Hausdorff (H) goodness-of-fit tests. The H statistic measures the Hausdorff distance under the Chebyshev (l-infinity) metric, between the two cumulative distribution functions (cdfs) underlying the corresponding one-sample and two-sample null hypothesis. It coincides to the side length of the largest axis-aligned square (hypercube) that can be inscribed between the two cdfs. The following cases are covered: (i) one-sample, univariate; (ii) two-sample univariate; and (iii) two-sample bivariate. Exact one-sample p-values are computed in O(n^2 log n) time via the 'Exact-KS-FFT' method of Dimitrova, Kaishev, and Tan (2020) ; two-sample p-values are obtained by permutation. A key advantage of the H test is that its sensitivity can be directed towards the left tail, body, or right tail of the distribution by tuning a scale parameter sigma, and therefore maximizing its power which as shown numerically is significantly higher than the power of the classical tests such as the Kolmogorov-Smirnov, Cramer-von Mises, and Anderson-Darling test, especially when the right tail of the distribution is targeted. The sensitivity of the test (left tail, body, or right tail) is governed by two parameters psi1 and psi2, whose values needs to be input. Then the optimal value of the scale parameter sigma is automatically computed. 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In a non-hierarchical setting the package produces a single derivative curve. Package: r-cran-hddesign Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hddesign_1.1-1.ca2404.1_amd64.deb Size: 230934 MD5sum: b4d9b2de3dd1c0a7a71f4253a27c09f4 SHA1: 523882e4667d16658ca9523cef1b8098f62dcd12 SHA256: 5a57fb545634b05308265689eaa11f4a62b2ca55dfac4507e22716973da9e212 SHA512: 4cd64a0b4b4809268120f54f9bc24286cad09347d8ad1412f0f3b95a9f27212bd4e1d2831de5993e1195e54d5fbc990995be73ba0fefdff63257bd0844df4185 Homepage: https://cran.r-project.org/package=HDDesign Description: CRAN Package 'HDDesign' (Sample Size Calculation for High Dimensional ClassificationStudy) Determine the sample size requirement to achieve the target probability of correct classification (PCC) for studies employing high-dimensional features. The package implements functions to 1) determine the asymptotic feasibility of the classification problem; 2) compute the upper bounds of the PCC for any linear classifier; 3) estimate the PCC of three design methods given design assumptions; 4) determine the sample size requirement to achieve the target PCC for three design methods. Package: r-cran-hdf5lib Architecture: amd64 Version: 2.1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13623 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hdf5lib_2.1.1.1-1.ca2404.1_amd64.deb Size: 2564506 MD5sum: c78c7f5f0ae5c187e9adb3eb42d51b2e SHA1: 128d3487834907067c5e3727476d79663cd18bcb SHA256: 90f08f57b78999a582b4ba32878bd70b24b28f03df4d7b1499023ae614c311b1 SHA512: e3c1dd02e1d772bb2a50df723ba574e800771af48965e11e8fe7b693c973fb9b8d9dc802f354a920964f2f3962d6346c34148fb700d8bb56b2223df7e67d717b 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 . 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This package provides a nearly feature complete, object oriented wrapper for the 'HDF5' API using R6 classes. Additionally, functionality is added so that 'HDF5' objects behave very similar to their corresponding R counterparts. Package: r-cran-hdflex Architecture: amd64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1332 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-ggplot2, r-cran-rcpp, r-cran-reshape2, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-testthat, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-hdflex_0.3.2-1.ca2404.1_amd64.deb Size: 979282 MD5sum: aa48b286a5a17cffa206644d183a8ad9 SHA1: 1286238d2cb549feaec80e3f8e982fe01d9edd60 SHA256: 4d70015b65da1b40a1b95343a5e9252414b7af28f1f66b6c716c44312910b4b3 SHA512: b44f7b49c9a8b3c61b7087c1986489b768caf6766b4e562cdfb0f4a1e86a2f7cdd9b44afc67340b62af2d9ffa4e0fa870800a26604eee9eafd0c3038e7fab6d2 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: amd64 Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hdglm_0.1-1.ca2404.1_amd64.deb Size: 23600 MD5sum: b8da0bf18e7f39819e328516b1605482 SHA1: 34edd7431cc0f2901e038526a66b51364bc2b614 SHA256: 23163815be5d2bd7c07755cbf35064b761805b8f9ef2d6265b8c1848237eb6f6 SHA512: 8252c6717e2c568dc2443571d9a431d214e26e9afe40bff708f88f3380e514c5f8ddb0fb7d82898ca4d386473cb9a54f9c14ef8c1ee6aecbc83fb142e096e65c Homepage: https://cran.r-project.org/package=HDGLM Description: CRAN Package 'HDGLM' (Tests for High Dimensional Generalized Linear Models) Test the significance of coefficients in high dimensional generalized linear models. Package: r-cran-hdjm Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 965 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-statmod, r-cran-rcpparmadillo, r-cran-rcppensmallen Filename: pool/dists/noble/main/r-cran-hdjm_0.1.0-1.ca2404.1_amd64.deb Size: 601076 MD5sum: ecc9896ab2e234e72ad0af7251ae141f SHA1: 59d9a520b3c261fed74644c027104ba44f3965ad SHA256: 07daff7fd928eb6e9f54d6b44290c2fe6377c021fb21606e62a79f80079f2b7b SHA512: 99c61ad27be007911f656a0bdf766bac24e6db5faea2f91f426234cd454f5943f50ec81416c506c03deb8018d96e9521e3a93b2081f22fc30db69702cee70a8a Homepage: https://cran.r-project.org/package=HDJM Description: CRAN Package 'HDJM' (Penalized High-Dimensional Joint Model) Joint models have been widely used to study the associations between longitudinal biomarkers and a survival outcome. However, existing joint models only consider one or a few longitudinal biomarkers and cannot deal with high-dimensional longitudinal biomarkers. This package can be used to fit our recently developed penalized joint model that can handle high-dimensional longitudinal biomarkers. Specifically, an adaptive lasso penalty is imposed on the parameters for the effects of the longitudinal biomarkers on the survival outcome, which allows for variable selection. Also, our algorithm is computationally efficient, which is based on the Gaussian variational approximation method. Package: r-cran-hdlsskst Architecture: amd64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-hdlsskst_2.1.0-1.ca2404.1_amd64.deb Size: 124960 MD5sum: e426f791ffbc934e968e923479eb6053 SHA1: 76c270dfe310ebe3cb9e0a518b5c0d007757b2f8 SHA256: 61ea360b1ecf3ea8274876069c5b10f89ab62ac6c4b7f69f031e2b1d76ce3c05 SHA512: bc0e339de95adf74b72b65a7c1878a0ed1ad07f7e388fa2d3ab95f4493cf80742ee36b1d7e569693011d66ccf8f2be54501c8c558bee8f0a18731ae6478adb97 Homepage: https://cran.r-project.org/package=HDLSSkST Description: CRAN Package 'HDLSSkST' (Distribution-Free Exact High Dimensional Low Sample Sizek-Sample Tests) Testing homogeneity of k multivariate distributions is a classical and challenging problem in statistics, and this becomes even more challenging when the dimension of the data exceeds the sample size. We construct some tests for this purpose which are exact level (size) alpha tests based on clustering. These tests are easy to implement and distribution-free in finite sample situations. Under appropriate regularity conditions, these tests have the consistency property in HDLSS asymptotic regime, where the dimension of data grows to infinity while the sample size remains fixed. We also consider a multiscale approach, where the results for different number of partitions are aggregated judiciously. Details are in Biplab Paul, Shyamal K De and Anil K Ghosh (2021) ; Soham Sarkar and Anil K Ghosh (2019) ; William M Rand (1971) ; Cyrus R Mehta and Nitin R Patel (1983) ; Joseph C Dunn (1973) ; Sture Holm (1979) ; Yoav Benjamini and Yosef Hochberg (1995) . Package: r-cran-hdmaadmm Architecture: amd64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dqrng, r-cran-rcppeigen Suggests: r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-hdmaadmm_0.0.1-1.ca2404.1_amd64.deb Size: 144890 MD5sum: 03e46167f9ca599eac80236d4e7829df SHA1: bd5699a6da5a9734d2ec19b81098ced22c3ae87d SHA256: bc354c7c0a455d4ceb818fb32ada9625e7bb8d91a718ccc96bedd53a81bc1f48 SHA512: a844eea52223a501f47eefe2826d1229165419e1c0bca52150b6a50062b885f1a1c93301ebb2f4930f0b70f607a67ec73cf0a09714e680b16b7cafea2af36ce3 Homepage: https://cran.r-project.org/package=HDMAADMM Description: CRAN Package 'HDMAADMM' (ADMM for High-Dimensional Mediation Models) We use the Alternating Direction Method of Multipliers (ADMM) for parameter estimation in high-dimensional, single-modality mediation models. To improve the sensitivity and specificity of estimated mediation effects, we offer the sure independence screening (SIS) function for dimension reduction. The available penalty options include Lasso, Elastic Net, Pathway Lasso, and Network-constrained Penalty. The methods employed in the package are based on Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). , Fan, J., & Lv, J. (2008) , Li, C., & Li, H. (2008) , Tibshirani, R. (1996) , Zhao, Y., & Luo, X. (2022) , and Zou, H., & Hastie, T. (2005) . Package: r-cran-hdme Architecture: amd64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 753 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-ggplot2, r-cran-rdpack, r-cran-rcpp, r-cran-rglpk, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr, r-cran-tidyr, r-cran-covr Filename: pool/dists/noble/main/r-cran-hdme_0.6.0-1.ca2404.1_amd64.deb Size: 454328 MD5sum: 0256cfa967afda1af47ebb41c14523bc SHA1: 49410d2b22c287152a5153b9498145d1414294ef SHA256: 9bc1da4762b518bcb21cae9c3b6cd1b1c32964d5fc2c48e0288d53a16cfda26e SHA512: f292a76fa2ee1aa70d8f962dab6be27bbf68cd79f3b25f22a059dc648eeaedb6e460a5a4cea4c3e4f4d8cb7871693111dfb549068cc899ab6ca6307f55bfd952 Homepage: https://cran.r-project.org/package=hdme Description: CRAN Package 'hdme' (High-Dimensional Regression with Measurement Error) Penalized regression for generalized linear models for measurement error problems (aka. errors-in-variables). The package contains a version of the lasso (L1-penalization) which corrects for measurement error (Sorensen et al. (2015) ). It also contains an implementation of the Generalized Matrix Uncertainty Selector, which is a version the (Generalized) Dantzig Selector for the case of measurement error (Sorensen et al. (2018) ). Package: r-cran-hdnom Architecture: amd64 Version: 6.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2023 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.6.0), r-api-4.0, r-cran-foreach, r-cran-ggplot2, r-cran-glmnet, r-cran-gridextra, r-cran-ncvreg, r-cran-penalized, r-cran-survival Suggests: r-cran-doparallel, r-cran-knitr, r-cran-ragg, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hdnom_6.2.0-1.ca2404.1_amd64.deb Size: 1172142 MD5sum: 019d9ef715d4fe8401ad79961f515044 SHA1: 1d7d31095009a3225708e4dfc2783e9e341f2e8b SHA256: 6e01878d8a125432f28f711a048a869ad320018aab6fb143c4d3669872d65efb SHA512: 28be351bda6dae63171197695785e7b1200aea95a2ca170a54fe6fa05f38c6c4c5e29f2bc0366ea0486f7c740b3b537180243e5b7502daa5e7b4aca5cbfd9554 Homepage: https://cran.r-project.org/package=hdnom Description: CRAN Package 'hdnom' (Benchmarking and Visualization Toolkit for Penalized Cox Models) Creates nomogram visualizations for penalized Cox regression models, with the support of reproducible survival model building, validation, calibration, and comparison for high-dimensional data. Package: r-cran-hdnra Architecture: amd64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5446 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-expm, r-cran-rcpp, r-cran-rdpack, r-cran-readr, r-cran-rcpparmadillo Suggests: r-cran-devtools, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-hdnra_2.1.0-1.ca2404.1_amd64.deb Size: 5247754 MD5sum: 12a8620033f97d2aa9f450f1f9e3e066 SHA1: b27074ebe652fb270d3ec0db9fd4ba05dcec2c71 SHA256: 964189636ebb4ba6862da09a97a2aa9e5aa2712beb04296db03f32364ce46b70 SHA512: 33ebb86ab6e67e626ae576d4bc418c25e680712965f1deac235402cd5200d4259dfbe3725d3b38405b5b922ba2e5c13005c87caba5372c7c79c8dccfabf01920 Homepage: https://cran.r-project.org/package=HDNRA Description: CRAN Package 'HDNRA' (High-Dimensional Location Testing with Normal-ReferenceApproaches) Provides inverse-free high-dimensional location tests for two-sample and general linear hypothesis testing (GLHT) problems under equal or unequal covariance structures. The package implements classical normal-approximation procedures, scale-invariant procedures, normal-reference procedures based on covariance-matched Gaussian companions, and F-type normal-reference calibrations for heteroscedastic Behrens-Fisher and GLHT settings. Implemented two-sample normal-approximation and scale-invariant procedures include Bai and Saranadasa (1996) , Chen and Qin (2010) , Srivastava and Du (2008) , and Srivastava et al. (2013) . Implemented two-sample normal-reference procedures include Zhang, Guo, Zhou and Cheng (2020) , Zhang, Zhou, Guo and Zhu (2021) , Zhang, Zhu and Zhang (2020) , Zhang, Zhu and Zhang (2023) , Zhang and Zhu (2022) , Zhang and Zhu (2022) , and Zhu, Wang and Zhang (2023) . Implemented GLHT normal-approximation procedures include Fujikoshi et al. (2004) , Srivastava and Fujikoshi (2006) , Yamada and Srivastava (2012) , Schott (2007) , and Zhou, Guo and Zhang (2017) . Implemented GLHT normal-reference procedures include Zhang, Guo and Zhou (2017) , Zhang, Zhou and Guo (2022) , Zhu, Zhang and Zhang (2022) , Zhu and Zhang (2022) , Zhang and Zhu (2022) , and Cao et al. (2024) . The package also includes the random-integration normal-approximation GLHT procedure of Li et al. (2025) . A package-level overview is given in Wang, Zhu and Zhang (2026) . 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The framework also supports SCAD and MCP penalties. It is designed for high-dimensional datasets and emphasizes numerical accuracy and computational efficiency. This package implements the algorithms proposed in Tang, Q., Zhang, Y., & Wang, B. (2022) . 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(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: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hdsvm_1.0.2-1.ca2404.1_amd64.deb Size: 111124 MD5sum: aaecd523a07efbe7f5c6e0326bdda89d SHA1: 49566db501656b0db6e79afeb0401c24ccb30889 SHA256: 6ada8dcf8bcd3d89558a62affdd0b8f526b7a49cee94a705e20ad76a803968d7 SHA512: b07c58d7d0a50e7f903396c5dc23c75d407aa91eee15e92250ac916cce13e84b558edc96dce5067ce2dd5d62c32104bbc72d09987d42e2561be4bd935c0d2a86 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-hdtweedie Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hdtweedie_1.2-1.ca2404.1_amd64.deb Size: 332260 MD5sum: 97c865dd75418d9d310087750f94469b SHA1: ec6a41a8ecced91bc55fb88ff7493f58c6f462ce SHA256: 36d5094e65f1eaf0358403fb5d903f310d68a8e06d160c8e60a4dcdd256249d0 SHA512: 90347f381a9f044332e75d2b47a384a507b7b219d77a554136d54f91990890744ebb1a656ca099e5ce6fd4bdb1916334e4429070ba9b54a683a5937403640137 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. 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Data is downloaded from the Brazilian Ministry of Health and 'IBGE' repositories. Data is returned in tidy format following tidyverse conventions. 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Package: r-cran-heatindex Architecture: amd64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-heatindex_0.0.2-1.ca2404.1_amd64.deb Size: 67402 MD5sum: 8028d54a9500c5b82602bceea0b3b7ed SHA1: ad5574c90c354e94cdf5b90ec6f46382908ca117 SHA256: a56edf4b9834b6c3dcc8556311e58ae626f82a40b6532f8d0daa3d8d8453d712 SHA512: 868f0018b938489cc0aeaf03690616cb87234f7f9d2781fb1602d922ad5770a8edfc8d6230af85f0d5460b6c20ea65bb59c362516166283286eb9cebe5cf2088 Homepage: https://cran.r-project.org/package=heatindex Description: CRAN Package 'heatindex' (Calculating Heat Stress) Implements the simpler and faster heat index, which matches the values of the original 1979 heat index and its 2022 extension for air temperatures above 300 K (27 C, 80 F) and with only minor differences at lower temperatures. 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Package: r-cran-heck Architecture: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1633 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-heck_0.1.5-1.ca2404.1_amd64.deb Size: 538710 MD5sum: a5178678a4b38b73d7f2cb4a2ea3cd5a SHA1: 0f4ac75a793271c975056e16cd011574557ccbff SHA256: 4b2a8ac8f2b02186dff4a13e2fbb3e24be00d532f347ade326419553639541cb SHA512: 2c01c49440dbe9a347f11bae07bb6917cce86799464f08405d13bce7df30debc136572bee95ea4d48b98acb6d21c0d638a82e50dc28b4a6477f0c93504742525 Homepage: https://cran.r-project.org/package=heck Description: CRAN Package 'heck' (Highly Performant String Case Converter) Provides a case conversion between common cases like CamelCase and snake_case. Using the 'rust crate heck' as the backend for a highly performant case conversion for 'R'. Package: r-cran-hellcor Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: libc6 (>= 2.14), libstdc++6 (>= 4.3), r-base-core (>= 4.4.0), r-api-4.0, r-cran-energy, r-cran-fnn, r-cran-orthopolynom Filename: pool/dists/noble/main/r-cran-hellcor_1.3-1.ca2404.1_amd64.deb Size: 76788 MD5sum: 2251f38e745c37e42745ef63b2839bfd SHA1: 3272b84b0efc386a756eb22b0f09e908a6cd400b SHA256: 9bf639d028f6d966458089cc2d074022f6eecc79134e93d920f7f4f486b448a5 SHA512: b50b8468ae6215d28b70b83821c9d26056357247698e1b5cce501c47fd21a45e5f8a500fec1a354ffe7a11772cbc37cf3b8564e4cc9d9db926a8d7d82a84c1f7 Homepage: https://cran.r-project.org/package=HellCor Description: CRAN Package 'HellCor' (The Hellinger Correlation) Empirical value of the Hellinger correlation, a measure of dependence between two continuous random variables. More details can be found in Geenens and Lafaye De Micheaux (2019) . Package: r-cran-hellorust Architecture: amd64 Version: 1.2.3-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1209 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hellorust_1.2.3-1.ca2404.2_amd64.deb Size: 431354 MD5sum: f0a4dbce734cec799a36e9934d9cc8ac SHA1: 5e3fafc66fb953f48df13fa66a27144e58a1b813 SHA256: ef131fc20873680bca6beedc31674cdf8c53b66f84be43f5ab640e5f07fd8af8 SHA512: f1ff149f5fb83219685225a2e3c83030877f15abf035adeace40de793d566ea72c167d10d9682207a6aff6f20b651ec4a60dcfccc1812e4136f066088855bc0b Homepage: https://cran.r-project.org/package=hellorust Description: CRAN Package 'hellorust' (Minimal Examples of Using Rust Code in R) Template R package with minimal setup to use Rust code in R without hacks or frameworks. Includes basic examples of importing cargo dependencies, spawning threads and passing numbers or strings from Rust to R. 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'HEMDAG' package: 1) reconciles flat predictions with the topology of the ontology; 2) can enhance the predictions of virtually any flat learning methods by taking into account the hierarchical relationships between ontology classes; 3) provides biologically meaningful predictions that always obey the true-path-rule, the biological and logical rule that governs the internal coherence of biomedical ontologies; 4) is specifically designed for exploiting the hierarchical relationships of DAG-structured taxonomies, such as the Human Phenotype Ontology (HPO) or the Gene Ontology (GO), but can be safely applied to tree-structured taxonomies as well (as FunCat), since trees are DAGs; 5) scales nicely both in terms of the complexity of the taxonomy and in the cardinality of the examples; 6) provides several utility functions to process and analyze graphs; 7) provides several performance metrics to evaluate HEMs algorithms. (Marco Notaro, Max Schubach, Peter N. Robinson and Giorgio Valentini (2017) ). Package: r-cran-hermiter Architecture: amd64 Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3988 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-bh Suggests: r-cran-testthat, r-cran-magrittr, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-data.table, r-cran-ggplot2, r-cran-dt, r-cran-mvtnorm, r-cran-patchwork, r-cran-colorspace Filename: pool/dists/noble/main/r-cran-hermiter_2.3.1-1.ca2404.1_amd64.deb Size: 2964038 MD5sum: cdb2a9467952ff03d5cc8eb3c2c2d261 SHA1: 902032b73b2bc7177630e555d4658183f3a42dc9 SHA256: 5d508b484e0d82f2d1fbb521414a8b6059f953a22b81a03bccf41fd64c871410 SHA512: 82694ddce51d01699c89d247e1ba8b802bd768e9de6cb9c4e4208d298c7a9797f002f959ec10624f7eeeb1d1850977c2b598bd5f4a97d4b08d81f8878abc837a Homepage: https://cran.r-project.org/package=hermiter Description: CRAN Package 'hermiter' (Efficient Sequential and Batch Estimation of Univariate andBivariate Probability Density Functions and CumulativeDistribution Functions along with Quantiles (Univariate) andNonparametric Correlation (Bivariate)) Facilitates estimation of full univariate and bivariate probability density functions and cumulative distribution functions along with full quantile functions (univariate) and nonparametric correlation (bivariate) using Hermite series based estimators. These estimators are particularly useful in the sequential setting (both stationary and non-stationary) and one-pass batch estimation setting for large data sets. Based on: Stephanou, Michael, Varughese, Melvin and Macdonald, Iain. "Sequential quantiles via Hermite series density estimation." Electronic Journal of Statistics 11.1 (2017): 570-607 , Stephanou, Michael and Varughese, Melvin. "On the properties of Hermite series based distribution function estimators." Metrika (2020) and Stephanou, Michael and Varughese, Melvin. "Sequential estimation of Spearman rank correlation using Hermite series estimators." Journal of Multivariate Analysis (2021) . Package: r-cran-hesim Architecture: amd64 Version: 0.5.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5779 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-flexsurv, r-cran-ggplot2, r-cran-mass, r-cran-msm, r-cran-rcpp, r-cran-r6, r-cran-survival, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-mstate, r-cran-nnet, r-cran-numderiv, r-cran-pracma, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-truncnorm Filename: pool/dists/noble/main/r-cran-hesim_0.5.8-1.ca2404.1_amd64.deb Size: 3057306 MD5sum: b85bfae085254dfbebc0bd3b67afe5c5 SHA1: 4ccc00a06a60b01d544a97757129863513b3e3c8 SHA256: 1ea047e86db61055f7978200c24905a8bb21b0e1d3aec67cf2b933fa54aa9604 SHA512: 0e4d238cc2e4a276b84fabd042dfc882861015fb5e57a2f61c2b3d18a2fd09d0695ee60452e417f07fba3fd6a09725dfb2e551e9e473ea3170d08630d2593223 Homepage: https://cran.r-project.org/package=hesim Description: CRAN Package 'hesim' (Health Economic Simulation Modeling and Decision Analysis) A modular and computationally efficient R package for parameterizing, simulating, and analyzing health economic simulation models. The package supports cohort discrete time state transition models (Briggs et al. 1998) , N-state partitioned survival models (Glasziou et al. 1990) , and individual-level continuous time state transition models (Siebert et al. 2012) , encompassing both Markov (time-homogeneous and time-inhomogeneous) and semi-Markov processes. Decision uncertainty from a cost-effectiveness analysis is quantified with standard graphical and tabular summaries of a probabilistic sensitivity analysis (Claxton et al. 2005, Barton et al. 2008) , . Use of C++ and data.table make individual-patient simulation, probabilistic sensitivity analysis, and incorporation of patient heterogeneity fast. 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Package: r-cran-hhsmm Architecture: amd64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 395 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cmapss, r-cran-mvtnorm, r-cran-rcpp, r-cran-rdpack, r-cran-mass, r-cran-mice, r-cran-progress, r-cran-magic, r-cran-splines2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hhsmm_0.4.2-1.ca2404.1_amd64.deb Size: 346730 MD5sum: 334f1e5f82e4729bcb3a103ef8c86d4a SHA1: 8273d4989ea54bd20473e6fe88de6a143f3a5cce SHA256: 775668ccbec8b67ed7d5f27f1484afbe0ee296fa2a99735374ead4549186819c SHA512: 85390635123c2a76557bf0af2c902c936b121ee8ee0c1caebc990b6b118a6215847b578691df5a8792fddd1fa8991ea21cdd3e46a17ef5e9846ab1fc38e277af Homepage: https://cran.r-project.org/package=hhsmm Description: CRAN Package 'hhsmm' (Hidden Hybrid Markov/Semi-Markov Model Fitting) Develops algorithms for fitting, prediction, simulation and initialization of the following models (1)- hidden hybrid Markov/semi-Markov model, introduced by Guedon (2005) , (2)- nonparametric mixture of B-splines emissions (Langrock et al., 2015 ), (3)- regime switching regression model (Kim et al., 2008 ) and auto-regressive hidden hybrid Markov/semi-Markov model, (4)- spline-based nonparametric estimation of additive state-switching models (Langrock et al., 2018 ) (5)- robust emission model proposed by Qin et al, 2024 (6)- several emission distributions, including mixture of multivariate normal (which can also handle missing data using EM algorithm) and multi-nomial emission (for modeling polymer or DNA sequences) (7)- tools for prediction of future state sequence, computing the score of a new sequence, splitting the samples and sequences to train and test sets, computing the information measures of the models, computing the residual useful lifetime (reliability) and many other useful tools ... (read for more description: Amini et al., 2022 and its arxiv version: ). Package: r-cran-hibayes Architecture: amd64 Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1421 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigmemory, r-cran-matrix, r-cran-stringr, r-cran-cmplot, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-bh Filename: pool/dists/noble/main/r-cran-hibayes_3.1.0-1.ca2404.1_amd64.deb Size: 542762 MD5sum: cbb5ee114578f88ba7e09c1fc03dc112 SHA1: 884f87d75abcf6dbe58b2d6fab9711aa6404690a SHA256: c828ccaff31cbd8eb786efc59fb73a4aa4e253e8b2ec44d5030b15af34c119dc SHA512: d333bbf5f40c90a9e0dc88ac31b166f8f5b71b35a50aee2fcdeeec7de1a6daf9479c365e6a1a1a04d93bd48ddb0f1c53e17c96199381bfa204d2d06bcd068a98 Homepage: https://cran.r-project.org/package=hibayes Description: CRAN Package 'hibayes' (Individual-Level, Summary-Level and Single-Step BayesianRegression Model) A user-friendly tool to fit Bayesian regression models. It can fit 3 types of Bayesian models using individual-level, summary-level, and individual plus pedigree-level (single-step) data for both Genomic prediction/selection (GS) and Genome-Wide Association Study (GWAS), it was designed to estimate joint effects and genetic parameters for a complex trait, including: (1) fixed effects and coefficients of covariates, (2) environmental random effects, and its corresponding variance, (3) genetic variance, (4) residual variance, (5) heritability, (6) genomic estimated breeding values (GEBV) for both genotyped and non-genotyped individuals, (7) SNP effect size, (8) phenotype/genetic variance explained (PVE) for single or multiple SNPs, (9) posterior probability of association of the genomic window (WPPA), (10) posterior inclusive probability (PIP). The functions are not limited, we will keep on going in enriching it with more features. References: Lilin Yin et al. (2025) ; Meuwissen et al. (2001) ; Gustavo et al. (2013) ; Habier et al. (2011) ; Yi et al. (2008) ; Zhou et al. (2013) ; Moser et al. (2015) ; Lloyd-Jones et al. (2019) ; Henderson (1976) ; Fernando et al. (2014) . Package: r-cran-hiclimr Architecture: amd64 Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 661 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ncdf4 Suggests: r-cran-covr, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hiclimr_2.2.1-1.ca2404.1_amd64.deb Size: 572822 MD5sum: 5818cb017eaeede900e4df2a7590a10c SHA1: 4f9ffff6f0f3babb3baad6f74264090ee76ee986 SHA256: 8c903ed1fba521819af918285d97bddf78271d6881f6d5944c846b8221d84658 SHA512: 844643decea15e620c6fbe64cd1613a2a002972217d9a605fcd253de0d949328b3a9cd289671f0519be4154dbec89574990adf918cb516dc0c7a2e2a600f4e56 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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The method enables comparison of module connectivity across two Hi-C datasets and is capable of detecting cell-type-specific regulatory modules. By integrating network analysis with chromatin conformation data, this approach provides insights into the spatial organization of the genome and its functional implications in gene regulation. Author: Sora Yoon (2025) . 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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. Package: r-cran-hidimda Architecture: amd64 Version: 0.2-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 893 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-hidimda_0.2-7-1.ca2404.1_amd64.deb Size: 824118 MD5sum: a83dc1ec3862585b316af5f2cce692d2 SHA1: 972f4f7edd480f56ef5f1d148f7243c8562c91d5 SHA256: 603ac45eea3a3166b802683d9e4184962978553575a50fe464c0a7be523e5555 SHA512: 7b9ec8a2be8abf3037324771940070bf5686eec80dc77d1f0987d92f1c4fb7bf0d41f5a6be0f645111def5c797fa4272c7ec4d6709b60f1d25ab3ee1ac4a71bf Homepage: https://cran.r-project.org/package=HiDimDA Description: CRAN Package 'HiDimDA' (High Dimensional Discriminant Analysis) Performs linear discriminant analysis in high dimensional problems based on reliable covariance estimators for problems with (many) more variables than observations. Includes routines for classifier training, prediction, cross-validation and variable selection. Package: r-cran-hierarchicalsets Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 871 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggdendro, r-cran-ggplot2, r-cran-rcpp, r-cran-scales, r-cran-matrix, r-cran-mass, r-cran-rcolorbrewer, r-cran-gtable, r-cran-viridis, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-hierarchicalsets_1.0.4-1.ca2404.1_amd64.deb Size: 667510 MD5sum: 813d0f9a396f15a084a0a5077ef568f7 SHA1: bed82a650523b117e9d81e372b7fc6ef3bef77c8 SHA256: d6f0cf43b7c80287dd21a122438864e239a1789e6bc4e156664a310a7776fbdb SHA512: cb8f6d622886e6d5b296bd460d3d0e694b015107d3fe380e3a3be91b58a634e2157b72ccf3a1f51cf1977397ad26db038e154064a293cdb3ae41d6ce7a37e78c Homepage: https://cran.r-project.org/package=hierarchicalSets Description: CRAN Package 'hierarchicalSets' (Set Data Visualization Using Hierarchies) Pure set data visualization approaches are often limited in scalability due to the combinatorial explosion of distinct set families as the number of sets under investigation increases. hierarchicalSets applies a set centric hierarchical clustering of the sets under investigation and uses this hierarchy as a basis for a range of scalable visual representations. hierarchicalSets is especially well suited for collections of sets that describe comparable comparable entities as it relies on the sets to have a meaningful relational structure. 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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. 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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: amd64 Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1516 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-highlight_0.5.2-1.ca2404.1_amd64.deb Size: 510226 MD5sum: b322e3ea48d923bbabd155ca493a581f SHA1: 88a53ae19c076471af7c5984a34516e78c181fcd SHA256: 0e1fe0a0542acfb4cca4f9651f26a043bb39ad6f6e28ef273bc9c673713770d4 SHA512: fa1df99c57589118b61cdb69b4d602800eab2203f2eaac3b9a89a9d4e899ae929420ddffaf846a27788908c94a0d13b601b8227e4075fcf6e86f89e3779d9663 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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Beaulieu and O'Meara (2016) . 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Package: r-cran-hlmdiag Architecture: amd64 Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1065 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-plyr, r-cran-reshape2, r-cran-mass, r-cran-matrix, r-cran-mgcv, r-cran-dplyr, r-cran-magrittr, r-cran-stringr, r-cran-purrr, r-cran-tibble, r-cran-tidyselect, r-cran-janitor, r-cran-rcpp, r-cran-rlang, r-cran-ggrepel, r-cran-diagonals, r-cran-rcpparmadillo Suggests: r-cran-mlmrev, r-cran-wwgbook, r-cran-lme4, r-cran-nlme, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-car, r-cran-gridextra, r-cran-qqplotr Filename: pool/dists/noble/main/r-cran-hlmdiag_0.5.1-1.ca2404.1_amd64.deb Size: 675176 MD5sum: 23f39cdae2a4cbb51124a74178f57934 SHA1: 08495cd002524082b029c7144a87d1c363ad4459 SHA256: 846fcbc6714c5652115f4ee22a8d2823dcfbf29332b5c0e9fa0aa95243a68064 SHA512: 51e764c134113e70e88111098a40fa5668579ffacb0ef22cf83b425ee113061d8e65d449e1753931c04b2756a3b9d6a4d4c2826c41d7d7ec44495c5ab5c549c1 Homepage: https://cran.r-project.org/package=HLMdiag Description: CRAN Package 'HLMdiag' (Diagnostic Tools for Hierarchical (Multilevel) Linear Models) A suite of diagnostic tools for hierarchical (multilevel) linear models. The tools include not only leverage and traditional deletion diagnostics (Cook's distance, covratio, covtrace, and MDFFITS) but also convenience functions and graphics for residual analysis. Models can be fit using either lmer in the 'lme4' package or lme in the 'nlme' package. Package: r-cran-hlsm Architecture: amd64 Version: 0.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-coda, r-cran-igraph, r-cran-abind Filename: pool/dists/noble/main/r-cran-hlsm_0.9.2-1.ca2404.1_amd64.deb Size: 213496 MD5sum: ebb40168e09193083a9060b381742570 SHA1: 77548db90289286249a303eee48094e784f77d57 SHA256: 2e2d2333aad4213c93b81bb508f2fe4f20a48759bca27971d85002e56da7915c SHA512: 5ee23d05d567f27a67e8740c71d02ced98f914ab51672b6f26432851199d5754f76d5e0091496044c427f1e32ed6064335da128f3fffc94d2ff6231fc8f7fb99 Homepage: https://cran.r-project.org/package=HLSM Description: CRAN Package 'HLSM' (Hierarchical Latent Space Network Model) Fits latent space models for single networks and hierarchical latent space models for ensembles of networks as described in Sweet, Thomas & Junker (2013). Package: r-cran-hlt Architecture: amd64 Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3712 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppdist, r-cran-rcppprogress, r-cran-tidyr, r-cran-ggplot2, r-cran-truncnorm, r-cran-foreach, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-hlt_1.3.1-1.ca2404.1_amd64.deb Size: 3216116 MD5sum: 67fba5d658645c443d3015fcb07c1e57 SHA1: 9e0752f9a9985520fcf6d6e1cf008d13523a3532 SHA256: 4afa8b029082ffe3da63167633e47f7ab191305b9cf21b8304ee03ae30bebedb SHA512: c86d07323775b6b29cbfa6e7ec80a64b1babbefb441217a69a6da0317b115ead17d3f56f62a9c3c5be377d9c9dfa17b8f709e4434622e8e17fa8d4dbdfba9f15 Homepage: https://cran.r-project.org/package=hlt Description: CRAN Package 'hlt' (Higher-Order Item Response Theory) Higher-order latent trait theory (item response theory). We implement the generalized partial credit model with a second-order latent trait structure. Latent regression can be done on the second-order latent trait. For a pre-print of the methods, see, "Latent Regression in Higher-Order Item Response Theory with the R Package hlt" . Package: r-cran-hmb Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4103 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-hmb_1.1-1.ca2404.1_amd64.deb Size: 3930366 MD5sum: a43a6e649249bbb6b9e89257e0778553 SHA1: 30718e03d1ba15dda2020a3d484c6a741ac0b5af SHA256: 26af06b3cf251f294222d69dd39ef457c86aad08cb817f4a0419c5ae7a048a75 SHA512: 7942f14711ae74563c36bfc89d03215e8cccabd93233d79ae3f157c7d9967f1bb8c45cfd6ad8e2e6bece16ef1e184f7f5a58624860b97ca3cc692fd26a96cb01 Homepage: https://cran.r-project.org/package=HMB Description: CRAN Package 'HMB' (Hierarchical Model-Based Estimation Approach) For estimation of a variable of interest using two sources of auxiliary information available in a nested structure. For reference see Saarela et al. (2016) and Saarela et al. (2018) . Package: r-cran-hmcdm Architecture: amd64 Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1710 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bayesplot, r-cran-rstantools, r-cran-rcpparmadillo, r-cran-progress Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hmcdm_2.1.3-1.ca2404.1_amd64.deb Size: 767852 MD5sum: 98b16684ee2ccfdc1099972ce76d223d SHA1: 81334efa043864e952c29a519eb1b49e4ad45efd SHA256: 51408a02370707b08d07528cc75839f05b136af7978152504cd5350165d1e282 SHA512: fdd6b05b1ca5c1c615d5310f8c814d8b638ed547c4411c7ffeddcbd75f25af8915a0a921b4a7f05c33e34f66e2eaf4f9f41d5e5057a1a8c85ad7bc83fdf0930b Homepage: https://cran.r-project.org/package=hmcdm Description: CRAN Package 'hmcdm' (Hidden Markov Cognitive Diagnosis Models for Learning) Fitting hidden Markov models of learning under the cognitive diagnosis framework. The estimation of the hidden Markov diagnostic classification model, the first order hidden Markov model, the reduced-reparameterized unified learning model, and the joint learning model for responses and response times. Package: r-cran-hmde Architecture: amd64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10517 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-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/noble/main/r-cran-hmde_1.4.0-1.ca2404.1_amd64.deb Size: 4801710 MD5sum: 2692d0b0ecb37f5281a118783cb2faba SHA1: 5d75f8c234e6551839091cdf31de203fc01b1197 SHA256: c89cb254339e922dd72ee66f204c4653e10bd99dfd58c4270a966e1fa40a0225 SHA512: f41c0c3a2132be7adaec6d082db8e15abfcc512d64ebcbae6d62c807b027cb5518e373d8578e2acc4bc961f65f78e1584010a6b06e7b915f72bc03a24a7a1d4c Homepage: https://cran.r-project.org/package=hmde Description: CRAN Package 'hmde' (Hierarchical Methods for Differential Equations) Wrapper for 'Stan' that offers a number of in-built models to implement a hierarchical Bayesian longitudinal model for repeat observation data. Model choice selects the differential equation that is fit to the observations. Single and multi-individual models are available. O'Brien et al. (2024) . Package: r-cran-hmisc Architecture: amd64 Version: 5.2-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3874 Depends: libc6 (>= 2.38), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-cluster, r-cran-rpart, r-cran-nnet, r-cran-foreign, r-cran-gtable, r-cran-gridextra, r-cran-data.table, r-cran-htmltable, r-cran-viridislite, r-cran-htmltools, r-cran-base64enc, r-cran-colorspace, r-cran-rmarkdown, r-cran-knitr, r-cran-formula Suggests: r-cran-survival, r-cran-qreport, r-cran-acepack, r-cran-chron, r-cran-rms, r-cran-mice, r-cran-rstudioapi, r-cran-tables, r-cran-plotly, r-cran-rlang, r-cran-vgam, r-cran-leaps, r-cran-pcapp, r-cran-digest, r-cran-polspline, r-cran-abind, r-cran-kableextra, r-cran-rio, r-cran-lattice, r-cran-latticeextra, r-cran-gt, r-cran-sparkline, r-cran-jsonlite, r-cran-htmlwidgets, r-cran-qs, r-cran-getpass, r-cran-keyring, r-cran-safer, r-cran-htm2txt, r-cran-boot Filename: pool/dists/noble/main/r-cran-hmisc_5.2-5-1.ca2404.1_amd64.deb Size: 3606946 MD5sum: b07445ddbe38426f7052be36a7d33bf9 SHA1: c725489474266dc77efabbeb34962f4993139874 SHA256: 108977dcdaa669d29a6d491dc9153f24f25cc5581c6c5a9b653c83554fc2efc1 SHA512: e465ab017fed56509c7c8c6e3a40e15e6385fb7b35532f7bcbc13c6334050832066c323ce3ab0c569c11cec1c85b42fa671be1669439f2fca51d481eb6f1b71c Homepage: https://cran.r-project.org/package=Hmisc Description: CRAN Package 'Hmisc' (Harrell Miscellaneous) Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, simulation, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, recoding variables, caching, simplified parallel computing, encrypting and decrypting data using a safe workflow, general moving window statistical estimation, and assistance in interpreting principal component analysis. Package: r-cran-hmm.discnp Architecture: amd64 Version: 3.0-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 814 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnet Filename: pool/dists/noble/main/r-cran-hmm.discnp_3.0-9-1.ca2404.1_amd64.deb Size: 686658 MD5sum: 2fe8c2eeb5f2b6332ae3d69db4d34e01 SHA1: 6468f7d393d5311e5f5548afb22462dedc5b13b1 SHA256: c94c8b16680ccae8603acaa13f2d95fe6c54c08caebb599f11ab0bbe8bd52518 SHA512: bc0497752b3d0f64ce10c346c9a5437f3482cb347463e7b05b4d6cdc903673e148daa01be68dca31e13b125af7681e782c651ffa508b2cf0ae8be96a2e52b2b0 Homepage: https://cran.r-project.org/package=hmm.discnp Description: CRAN Package 'hmm.discnp' (Hidden Markov Models with Discrete Non-Parametric ObservationDistributions) Fits hidden Markov models with discrete non-parametric observation distributions to data sets. The observations may be univariate or bivariate. Simulates data from such models. Finds most probable underlying hidden states, the most probable sequences of such states, and the log likelihood of a collection of observations given the parameters of the model. Auxiliary predictors are accommodated in the univariate setting. Package: r-cran-hmmesolver Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-hmmesolver_0.1.2-1.ca2404.1_amd64.deb Size: 51238 MD5sum: a74a576e14cefe9698e10d84ad7e23b5 SHA1: 6ba10b312645e58fa56ab9ceb275f1d2809e9f7a SHA256: 434b39bdf32a4565a5c32829179191e6ff3e687ab0e99d8b4e035fcbf408c844 SHA512: d9399ffbeab9192a5629798a75ed722791ec783f480be4a83bcf9a7dd84816a87bd783e3834c2ec9fd0b1a09e494fd8ea7a00b3b6459b91199178a9853a317b0 Homepage: https://cran.r-project.org/package=HMMEsolver Description: CRAN Package 'HMMEsolver' (A Fast Solver for Henderson Mixed Model Equation via RowOperations) Consider the linear mixed model with normal random effects. A typical method to solve Henderson's Mixed Model Equations (HMME) is recursive estimation of the fixed effects and random effects. We provide a fast, stable, and scalable solver to the HMME without computing matrix inverse. See Kim (2017) for more details. Package: r-cran-hmmextra0s Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-ellipse Suggests: r-cran-hiddenmarkov Filename: pool/dists/noble/main/r-cran-hmmextra0s_1.1.0-1.ca2404.1_amd64.deb Size: 117172 MD5sum: 72e43c4d0048cb91f0b718c1cf8577bf SHA1: 972353e5ec0d532b31580539ffc9b69d864dfb1c SHA256: 85419a3ebfc5c8e4fe16f30950335b96e61bb28a521e7b435ecd883e49cd126b SHA512: 550c6a69b7d82ffd7c2c2300483fc21cd07b6c2a63f476c4b65e496ac42e2417cf3cf154f142e060b9f53d67b02de2ab9a727f02cb19987ee3bebf16b63054ad Homepage: https://cran.r-project.org/package=HMMextra0s Description: CRAN Package 'HMMextra0s' (Hidden Markov Models with Extra Zeros) Contains functions for hidden Markov models with observations having extra zeros as defined in the following two publications, Wang, T., Zhuang, J., Obara, K. and Tsuruoka, H. (2016) ; Wang, T., Zhuang, J., Buckby, J., Obara, K. and Tsuruoka, H. (2018) . The observed response variable is either univariate or bivariate Gaussian conditioning on presence of events, and extra zeros mean that the response variable takes on the value zero if nothing is happening. Hence the response is modelled as a mixture distribution of a Bernoulli variable and a continuous variable. That is, if the Bernoulli variable takes on the value 1, then the response variable is Gaussian, and if the Bernoulli variable takes on the value 0, then the response is zero too. This package includes functions for simulation, parameter estimation, goodness-of-fit, the Viterbi algorithm, and plotting the classified 2-D data. Some of the functions in the package are based on those of the R package 'HiddenMarkov' by David Harte. This updated version has included an example dataset and R code examples to show how to transform the data into the objects needed in the main functions. We have also made changes to increase the speed of some of the functions. Package: r-cran-hmmhsmm Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 598 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-evd, r-cran-extremes, r-cran-mass, r-cran-mnormt Filename: pool/dists/noble/main/r-cran-hmmhsmm_0.1.0-1.ca2404.1_amd64.deb Size: 497092 MD5sum: 75a119d150b8a343c6cbdf4f7acac128 SHA1: aad189aae5ec6a71b8c94774b619edc4651d941d SHA256: 29ca0741a6cd0ec551b6869a7bf946b0005905d9822c8bcad554de31e40707f0 SHA512: 50de96357bae8dac74467c90776857b7b2cf254930ffe4a3fc761d19480f29a5cb1a02210e061c006ef72251dcccae5edbc133fb5c62e615d69b007ea449aa99 Homepage: https://cran.r-project.org/package=HMMHSMM Description: CRAN Package 'HMMHSMM' (Inference and Estimation of Hidden Markov Models and HiddenSemi-Markov Models) Provides flexible maximum likelihood estimation and inference for Hidden Markov Models (HMMs) and Hidden Semi-Markov Models (HSMMs), as well as the underlying systems in which they operate. The package supports a wide range of observation and dwell-time distributions, offering a flexible modelling framework suitable for diverse practical data. Efficient implementations of the forward-backward and Viterbi algorithms are provided via 'Rcpp' for enhanced computational performance. Additional functionality includes model simulation, residual analysis, non-initialised estimation, local and global decoding, calculation of diverse information criteria, computation of confidence intervals using parametric bootstrap methods, numerical covariance matrix estimation, and comprehensive visualisation functions for interpreting the data-generating processes inferred from the models. Methods follow standard approaches described by Guédon (2003) , Zucchini and MacDonald (2009, ISBN:9781584885733), and O'Connell and Højsgaard (2011) . Package: r-cran-hmmmlselect Architecture: amd64 Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 530 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-hiddenmarkov, r-cran-mclust, r-cran-mvtnorm, r-cran-mcmcpack, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-hmmmlselect_0.1.6-1.ca2404.1_amd64.deb Size: 394046 MD5sum: 11e18a80b75e36a16fcde73f14ca9b7e SHA1: 47d55dbc2552ff3ef328f3586a93b2c3b88832f2 SHA256: d40167859bd27623e1e8e366d38da412168ce1320091a08ede8ff56839a92416 SHA512: 5b4e05b1ad6e9686b08acbdedad2b288bd24ead3844b9e8f92f5e23f86612ce9b45fe7c8a012ab66bdf7d3db2a6220ddd7d8419401aa54ef4844f0a2895851c8 Homepage: https://cran.r-project.org/package=HMMmlselect Description: CRAN Package 'HMMmlselect' (Determine the Number of States in Hidden Markov Models viaMarginal Likelihood) Provide functions to make estimate the number of states for a hidden Markov model (HMM) using marginal likelihood method proposed by the authors. See the Manual.pdf file a detail description of all functions, and a detail tutorial. Package: r-cran-hmmtmb Architecture: amd64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3694 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-mgcv, r-cran-tmb, r-cran-ggplot2, r-cran-matrix, r-cran-stringr, r-cran-mass, r-cran-tmbstan, r-cran-rcppeigen Suggests: r-cran-rstan, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-movehmm, r-cran-scico, r-cran-mswm, r-cran-unmarked Filename: pool/dists/noble/main/r-cran-hmmtmb_1.1.2-1.ca2404.1_amd64.deb Size: 1593880 MD5sum: d78c74fdb1075113410d73aad2a455d4 SHA1: 429fa0f0c36ee36ceea5dea64d6ec15e25c88627 SHA256: f4d261563cc47f1fe85d3e7f7da4bf72debb4b6f374730fc4b1b9afae3b3288b SHA512: adbfef6fdfa7aaad6c987b42c882b1b916efaed98604c97125f1c442e21a963e24bd4e6dce1653c82ba0831448c7e281fcbda53bc1b267659fd56818c4385ede Homepage: https://cran.r-project.org/package=hmmTMB Description: CRAN Package 'hmmTMB' (Fit Hidden Markov Models using Template Model Builder) Fitting hidden Markov models using automatic differentiation and Laplace approximation, allowing for fast inference and flexible covariate effects (including random effects and smoothing splines) on model parameters. The package is described by Michelot (2025) . Package: r-cran-homals Architecture: amd64 Version: 1.0-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 778 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-scatterplot3d Filename: pool/dists/noble/main/r-cran-homals_1.0-10-1.ca2404.1_amd64.deb Size: 553740 MD5sum: c711779fd15c0032f1d6365368d0f146 SHA1: d3b28e7fd1f16059951da84b7389f53c34ab792e SHA256: 51548c764f679bc6479a28e6f8831fd9e576175119dce183f9164355f197b964 SHA512: d4f3f990fe423f5936358dcd761091aa64951ed52a3f8bf2a5a399f5d0b370a33361a2fc5e3689af86ef4bc5437a576d9efa48c6cfba2096f067c92f77546d99 Homepage: https://cran.r-project.org/package=homals Description: CRAN Package 'homals' (Gifi Methods for Optimal Scaling) Performs a homogeneity analysis (multiple correspondence analysis) and various extensions. Rank restrictions on the category quantifications can be imposed (nonlinear PCA). The categories are transformed by means of optimal scaling with options for nominal, ordinal, and numerical scale levels (for rank-1 restrictions). Variables can be grouped into sets, in order to emulate regression analysis and canonical correlation analysis. Package: r-cran-hommel Architecture: amd64 Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-hommel_1.8-1.ca2404.1_amd64.deb Size: 206916 MD5sum: 789f7722815cec2175a205ebf061d9f5 SHA1: 56243cb202981e1246b6569e4682c7e57f49671d SHA256: 0800fcc3cbbeb6e5cd607cf277343468bd4c5a39d81f58d83e6cc435577002db SHA512: 524256a8607d66a7dc4e4c736ba0cb1155f842494bf36e53da418255f41c412bb1b3b8eba9c441bccdcb108f88f56cb2ee6d290a15c48a912fd24fc5eaee6d9b Homepage: https://cran.r-project.org/package=hommel Description: CRAN Package 'hommel' (Methods for Closed Testing with Simes Inequality, in ParticularHommel's Method) Provides methods for closed testing using Simes local tests. In particular, calculates adjusted p-values for Hommel's multiple testing method, and provides lower confidence bounds for true discovery proportions. A robust but more conservative variant of the closed testing procedure that does not require the assumption of Simes inequality is also implemented. The methods have been described in detail in Goeman et al (Biometrika 106, 841-856, 2019). Package: r-cran-hopit Architecture: amd64 Version: 0.11.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 845 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survey, r-cran-mass, r-cran-rcpp, r-cran-questionr, r-cran-rdpack, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-r.rsp, r-cran-usethis, r-cran-knitr, r-cran-rmarkdown, r-cran-qpdf, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-hopit_0.11.6-1.ca2404.1_amd64.deb Size: 645250 MD5sum: ad09bb8b4298cd4100faa8e86a1998dd SHA1: 90a95b0ab1bea9fc101bc93e259da21ddaeb3c84 SHA256: acf6207c236a61ade4daeccaa313b4bb835f1d0bbfcfcb4b558efe43b675d60f SHA512: d3005fc2843c5d3793d80baef605eaf5982bdf1e78983eadc49ffc458d2d0a343af2727b63f2ddf6f846e83e067abadcf0781a9b39c77f2ae5610a15d9921903 Homepage: https://cran.r-project.org/package=hopit Description: CRAN Package 'hopit' (Hierarchical Ordered Probit Models with Application to ReportingHeterogeneity) Self-reported health, happiness, attitudes, and other statuses or perceptions are often the subject of biases that may come from different sources. For example, the evaluation of an individual’s own health may depend on previous medical diagnoses, functional status, and symptoms and signs of illness; as on well as life-style behaviors, including contextual social, gender, age-specific, linguistic and other cultural factors (Jylha 2009 ; Oksuzyan et al. 2019 ). The hopit package offers versatile functions for analyzing different self-reported ordinal variables, and for helping to estimate their biases. Specifically, the package provides the function to fit a generalized ordered probit model that regresses original self-reported status measures on two sets of independent variables (King et al. 2004 ; Jurges 2007 ; Oksuzyan et al. 2019 ). The first set of variables (e.g., health variables) included in the regression are individual statuses and characteristics that are directly related to the self-reported variable. In the case of self-reported health, these could be chronic conditions, mobility level, difficulties with daily activities, performance on grip strength tests, anthropometric measures, and lifestyle behaviors. The second set of independent variables (threshold variables) is used to model cut-points between adjacent self-reported response categories as functions of individual characteristics, such as gender, age group, education, and country (Oksuzyan et al. 2019 ). The model helps to adjust for specific socio-demographic and cultural differences in how the continuous latent health is projected onto the ordinal self-rated measure. The fitted model can be used to calculate an individual predicted latent status variable, a latent index, and standardized latent coefficients; and makes it possible to reclassify a categorical status measure that has been adjusted for inter-individual differences in reporting behavior. Package: r-cran-houba Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1271 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-bigmemory Filename: pool/dists/noble/main/r-cran-houba_0.1.1-1.ca2404.1_amd64.deb Size: 550642 MD5sum: 7ab3c826a0e47afad03790061dcba0d6 SHA1: 1fce33f3d3f48c432b353baaa6d92f716c27fd82 SHA256: 4991012d339dd39555519cad308444afc82020a873c41476bda89b0353ec5087 SHA512: c89a30cc9ad1b79f17f15a610463da0246b26d5a030673ce4146fc0f33e8fd8e9e72d779e6585c3a9a5246551eeb7d2c2a3f31eeab72afad33d7c0f72cadc9c9 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: amd64 Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1686 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-mvtnorm, r-cran-titanic, r-cran-sampleselection, r-cran-ga Filename: pool/dists/noble/main/r-cran-hpa_1.3.4-1.ca2404.1_amd64.deb Size: 701552 MD5sum: eca123651a89079018f9a3b3c6d6f077 SHA1: 15ce2382750d9d4bffcf89b614840a2963ba987e SHA256: 141cc5253d90aace2e65c86fe427f0c2982f70b18ef35c244bc0f46091d66afc SHA512: a07f7cf9bec99e3755f2d6618117a202d34dd5684b2c2c11e406294a80006fa87b14199cfa88499dac3da8cf17f4135c66f968b90b59c8be5fb407eca665616c 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: amd64 Version: 1.4-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hqreg_1.4-1-1.ca2404.1_amd64.deb Size: 99910 MD5sum: ea3530e303a321c6e35af5b0757c3916 SHA1: 411d81fcfaef96b1ddb00d75be94db88283121f8 SHA256: 69a54eab978430cf52f9846e1fdd1f712261a6d78661ea9fa9b762b4bde00979 SHA512: 08d15474bf2dd4c411e5ba950ab76d2a1ca1f54c24bb7ab5ea8a063e4bd334458353dc2af8deff85f585cf1ab8ccbdb7e81c53b89b0aa411a6f62396a0ce5953 Homepage: https://cran.r-project.org/package=hqreg Description: CRAN Package 'hqreg' (Regularization Paths for Lasso or Elastic-Net Penalized HuberLoss Regression and Quantile Regression) Offers efficient algorithms for fitting regularization paths for lasso or elastic-net penalized regression models with Huber loss, quantile loss or squared loss. Reference: Congrui Yi and Jian Huang (2017) . Package: r-cran-hrqglas Architecture: amd64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix Filename: pool/dists/noble/main/r-cran-hrqglas_1.1.2-1.ca2404.1_amd64.deb Size: 65664 MD5sum: a0b5ce919582251ab7f3d0129b63290a SHA1: 165b8be8c18a59507d6193a92c7d969c0d5794bb SHA256: 18916d79336be7b4c097444428021fe007f162e756121760e8966f83757ee9e3 SHA512: 94478f73385e6c8b8ba398dc29c006248fa58f07d270ff6de2c482988e5ad7d46f8bd2a1a945ff552adb0447f07781fb482aa48a07508fb77a08c9cc343a7af8 Homepage: https://cran.r-project.org/package=hrqglas Description: CRAN Package 'hrqglas' (Group Variable Selection for Quantile and Robust Mean Regression) A program that conducts group variable selection for quantile and robust mean regression (Sherwood and Li, 2022). The group lasso penalty (Yuan and Lin, 2006) is used for group-wise variable selection. Both of the quantile and mean regression models are based on the Huber loss. Specifically, with the tuning parameter in the Huber loss approaching to 0, the quantile check function can be approximated by the Huber loss for the median and the tilted version of Huber loss at other quantiles. Such approximation provides computational efficiency and stability, and has also been shown to be statistical consistent. Package: r-cran-hrt Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-compquadform, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-hrt_1.0.2-1.ca2404.1_amd64.deb Size: 162594 MD5sum: 4422c6a0425d6ed9ce0bc770d1c0f0a7 SHA1: cffb570e43d47a8baf27a6907d748eede4c5ffc1 SHA256: 382eafc92cc5f0bc1361cd871965a066d872f631affb695f7e74168f3be0f58c SHA512: a318f4af43ab4c31e96d8dc9b3460e05a50939fe6d6b815cdf510e930f49486cc098a949855b8e7d4b3988714096a44e7d222ac211830e1a92595f0fa85364f8 Homepage: https://cran.r-project.org/package=hrt Description: CRAN Package 'hrt' (Heteroskedasticity Robust Testing) Functions for testing affine hypotheses on the regression coefficient vector in regression models with heteroskedastic errors: (i) a function for computing various test statistics (in particular using HC0-HC4 covariance estimators based on unrestricted or restricted residuals); (ii) a function for numerically approximating the size of a test based on such test statistics and a user-supplied critical value; and, most importantly, (iii) a function for determining size-controlling critical values for such test statistics and a user-supplied significance level (also incorporating a check of conditions under which such a size-controlling critical value exists). The three functions are based on results in Poetscher and Preinerstorfer (2021) "Valid Heteroskedasticity Robust Testing" , which will appear as . Package: r-cran-hrtnomaly Architecture: amd64 Version: 25.11.22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 735 Depends: libc6 (>= 2.14), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-cellwise Filename: pool/dists/noble/main/r-cran-hrtnomaly_25.11.22-1.ca2404.1_amd64.deb Size: 581564 MD5sum: 869b0fcd010bbaa253dee0b23c546b09 SHA1: acd030edc48ac0380d33811dd19d80b8db7d86b8 SHA256: e1ca34109ee88ed1add06c38afce35f122416d76037a3714be417a705c61d15d SHA512: b133abdd7f8601e8557062d54bc88e30e20257a8aaef5fe4ed97b421f4390002f56b2b0658173d0f8dc8387c3931545040e687934017d075c91d1ba482efaaa2 Homepage: https://cran.r-project.org/package=HRTnomaly Description: CRAN Package 'HRTnomaly' (Historical, Relational, and Tail Anomaly-Detection Algorithms) The presence of outliers in a dataset can substantially bias the results of statistical analyses. To correct for outliers, micro edits are manually performed on all records. A set of constraints and decision rules is typically used to aid the editing process. However, straightforward decision rules might overlook anomalies arising from disruption of linear relationships. Computationally efficient methods are provided to identify historical, tail, and relational anomalies at the data-entry level (Sartore et al., 2024; ). A score statistic is developed for each anomaly type, using a distribution-free approach motivated by the Bienaymé-Chebyshev's inequality, and fuzzy logic is used to detect cellwise outliers resulting from different types of anomalies. Each data entry is individually scored and individual scores are combined into a final score to determine anomalous entries. In contrast to fuzzy logic, Bayesian bootstrap and a Bayesian test based on empirical likelihoods are also provided as studied by Sartore et al. (2024; ). These algorithms allow for a more nuanced approach to outlier detection, as it can identify outliers at data-entry level which are not obviously distinct from the rest of the data. --- This research was supported in part by the U.S. Department of Agriculture, National Agriculture Statistics Service. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA, or US Government determination or policy. Package: r-cran-hsar Architecture: amd64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 989 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-spdep, r-cran-spatialreg, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-matrix, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-sdsfun, r-cran-sf, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-hsar_0.6.0-1.ca2404.1_amd64.deb Size: 544462 MD5sum: 07a76da1d3603dc6e142daec1400100e SHA1: cd81d1534b9108b568416bf068fbe0bf53eb82f6 SHA256: d45994cde09242a12a29708728f312f86863b967a611345093c9a6a7aac1faee SHA512: e121223267afb6d85b2f9310366e822817f382a8d8f926a1802a2e5cf4b190ebe973397de4db117d149a802c7c2aec55803e20fdd25b0dcd7f282770facc2e11 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: amd64 Version: 1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2061 Depends: libc6 (>= 2.29), libgsl27 (>= 2.7.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda Suggests: r-cran-knitr, r-cran-raster, r-cran-sp, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-hsdm_1.4.4-1.ca2404.1_amd64.deb Size: 1310812 MD5sum: fde98b43c25301dc81ee76dc3796ecff SHA1: 9abc98c7aa6ec0f17e2f3fb43428c782b9c6bbb7 SHA256: 311a065aa12d46eaa650e1393862616aab14e5d62782f5dfdb033cc3453f92ed SHA512: 1dce3a7bcd1a934a0e40a37731926837f53909974726e162cb9eb0242b102ac8716f711a9bb0ef69af533078b262a0321509e6200983e8cf7880a314ace2a106 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: amd64 Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1836 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-snowfall, r-cran-rcpp, r-cran-gdata, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-hsphase_3.0.0-1.ca2404.1_amd64.deb Size: 1101796 MD5sum: a683352a77cb97e4d119503107a1c210 SHA1: 3f109b6329c86011340f648b68cd6f0adfbe979d SHA256: ce30e6ea7a7c43cd10c1bb1432d04f78fdc0abee73c357e2d935ccfdfd5cb7d5 SHA512: 40caa8ad2a036a76bb348e1be61adcf76247a48daeacaa6fc88d079ead417d4e03c57c6c724f507da0da4f3d987aa578ab7f9dab2f78d5b71d447fb90aa6d752 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: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-hsphase, r-cran-dplyr, r-cran-data.table, r-cran-rlist, r-cran-quadprog, r-cran-curl, r-cran-matrix, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-hsrecombi_1.1.1-1.ca2404.1_amd64.deb Size: 155074 MD5sum: d88187c9ddb83f963e3c3bf4f403b9be SHA1: 29da2070808e676568fc2c5304acfcc7fb7ef5c2 SHA256: 65b57b6923d258b362c5312b6ccadbeee01ef10f0e1028dfda5c121c9dca5a10 SHA512: 8fa26237146dc33bf69dbbe7e6260951a778b4bf601e82c902765ca8fbb227c3c321df73bae3694e690bcf765138e4c57b69f0aa625a40a5a9f09ca715238074 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: amd64 Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3878 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-loo, r-cran-proc, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hsstan_0.8.2-1.ca2404.1_amd64.deb Size: 1136140 MD5sum: 682f38e7b19da424ebd52dff20edda66 SHA1: 7ef99fa58c5b1cfaef4ffdc28024986a24f6f384 SHA256: c315390a1784ee2974af2c0ab5e3654af412613009c653247148501e7f717090 SHA512: 387c3e13343112d81ae237dfb92594fbcb83f6fcd1c86cac0ce827f0b391a8e2b9ad0950b5bd4cc71b845a2762f2c6643bb903bdcadcca2fff4676f89ecc4cf4 Homepage: https://cran.r-project.org/package=hsstan Description: CRAN Package 'hsstan' (Hierarchical Shrinkage Stan Models for Biomarker Selection) Linear and logistic regression models penalized with hierarchical shrinkage priors for selection of biomarkers (or more general variable selection), which can be fitted using Stan (Carpenter et al. (2017) ). It implements the horseshoe and regularized horseshoe priors (Piironen and Vehtari (2017) ), as well as the projection predictive selection approach to recover a sparse set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2020) ). Package: r-cran-htetree Architecture: amd64 Version: 0.1.23-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 521 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-grf, r-cran-partykit, r-cran-data.tree, r-cran-matching, r-cran-dplyr, r-cran-jsonlite, r-cran-rpart, r-cran-rpart.plot, r-cran-shiny, r-cran-stringr Suggests: r-cran-optmatch, r-cran-haven, r-cran-foreign, r-cran-data.table, r-cran-remotes, r-cran-party Filename: pool/dists/noble/main/r-cran-htetree_0.1.23-1.ca2404.1_amd64.deb Size: 380464 MD5sum: ffca4d3441291ba8194ff533ad5e540f SHA1: 2a0717a207ce201b1b5cc2db1fa470fbd552551a SHA256: 76b0fc3331ec9ab9e64fd3d3177d7891e68aab3a98d87c76bffb88f93225dff6 SHA512: 0c0782d68bcc3b1fca33b5f6673e92590ab987154712bbc77af5ee0ecfd9e4350a55b78d4591fa2a516d855232ddc612927d9f94b3318877b01e52abbb528fe1 Homepage: https://cran.r-project.org/package=htetree Description: CRAN Package 'htetree' (Causal Inference with Tree-Based Machine Learning Algorithms) Estimating heterogeneous treatment effects with tree-based machine learning algorithms and visualizing estimated results in flexible and presentation-ready ways. For more information, see Brand, Xu, Koch, and Geraldo (2021) . Our current package first started as a fork of the 'causalTree' package on 'GitHub' and we greatly appreciate the authors for their extremely useful and free package. 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The posterior of coefficients and hyper-parameters is sampled with restricted Gibbs sampling for leveraging the high-dimensionality and Hamiltonian Monte Carlo for handling the high-correlation among coefficients. A detailed description of the method: Li and Yao (2018), Journal of Statistical Computation and Simulation, 88:14, 2827-2851, . 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Package: r-cran-htt Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 802 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggraph, r-cran-igraph, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mass Filename: pool/dists/noble/main/r-cran-htt_0.1.2-1.ca2404.1_amd64.deb Size: 418068 MD5sum: 95bfa4370da896acba57a8c5df84b52c SHA1: 6f4afee883de6cdff14633776d2576b0ae421631 SHA256: 381887666f588cec64ed35a64f4f7f98440df997cdd9dc350a7b200f3f0db69e SHA512: a34957c4f6aed36220593303b0c86a79030fba261cf43e9089b083e6158b93cff6cb1e74ae004ecb029a6ca3417b9b86c4aee82e60525e0d50ee73ec2fc66f0c Homepage: https://cran.r-project.org/package=HTT Description: CRAN Package 'HTT' (Hypothesis Testing Tree) A novel decision tree algorithm in the hypothesis testing framework. The algorithm examines the distribution difference between two child nodes over all possible binary partitions. The test statistic of the hypothesis testing is equivalent to the generalized energy distance, which enables the algorithm to be more powerful in detecting the complex structure, not only the mean difference. It is applicable for numeric, nominal, ordinal explanatory variables and the response in general metric space of strong negative type. The algorithm has superior performance compared to other tree models in type I error, power, prediction accuracy, and complexity. Package: r-cran-httk Architecture: amd64 Version: 2.7.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4941 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-msm, r-cran-data.table, r-cran-survey, r-cran-mvtnorm, r-cran-truncnorm, r-cran-magrittr, r-cran-purrr, r-cran-rdpack, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gplots, r-cran-scales, r-cran-envstats, r-cran-mass, r-cran-rcolorbrewer, r-cran-stringr, r-cran-reshape, r-cran-viridis, r-cran-gmodels, r-cran-colorspace, r-cran-cowplot, r-cran-ggrepel, r-cran-forcats, r-cran-smatr, r-cran-gridextra, r-cran-readxl, r-cran-ks, r-cran-testthat, r-cran-ggpubr, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-httk_2.7.4-1.ca2404.1_amd64.deb Size: 4619876 MD5sum: 80fe5995f80fd39a3be95bc26cb2ea42 SHA1: 3d297e0444ff7270fdbaadaf859dff03f2ba31b0 SHA256: 3cc29485b2b18f70034f5500788f6efa8fba878c4ed968636ff2ce579ce2c72f SHA512: e1fca0e0b1af2fc32b528ceb1db1f8e99df27fd8e1290c2f790327bc39dd88d425e5a6fb359e4ac7035b3646c89ba01e7028c093d3fb55cb7a68325ae359abce Homepage: https://cran.r-project.org/package=httk Description: CRAN Package 'httk' (High-Throughput Toxicokinetics) Pre-made models that can be rapidly tailored to various chemicals and species using chemical-specific in vitro data and physiological information. These tools allow incorporation of chemical toxicokinetics ("TK") and in vitro-in vivo extrapolation ("IVIVE") into bioinformatics, as described by Pearce et al. (2017) (). Chemical-specific in vitro data characterizing toxicokinetics have been obtained from relatively high-throughput experiments. The chemical-independent ("generic") physiologically-based ("PBTK") and empirical (for example, one compartment) "TK" models included here can be parameterized with in vitro data or in silico predictions which are provided for thousands of chemicals, multiple exposure routes, and various species. High throughput toxicokinetics ("HTTK") is the combination of in vitro data and generic models. We establish the expected accuracy of HTTK for chemicals without in vivo data through statistical evaluation of HTTK predictions for chemicals where in vivo data do exist. The models are systems of ordinary differential equations that are developed in MCSim and solved using compiled (C-based) code for speed. A Monte Carlo sampler is included for simulating human biological variability (Ring et al., 2017 ) and propagating parameter uncertainty (Wambaugh et al., 2019 ). Empirically calibrated methods are included for predicting tissue:plasma partition coefficients and volume of distribution (Pearce et al., 2017 ). These functions and data provide a set of tools for using IVIVE to convert concentrations from high-throughput screening experiments (for example, Tox21, ToxCast) to real-world exposures via reverse dosimetry (also known as "RTK") (Wetmore et al., 2015 ). Package: r-cran-httpgd Architecture: amd64 Version: 2.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1567 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-unigd, r-cran-cpp11, r-cran-asioheaders Suggests: r-cran-testthat, r-cran-xml2, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-future, r-cran-httr, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-httpgd_2.1.4-1.ca2404.1_amd64.deb Size: 552232 MD5sum: 42ae4111938f1283d94ac24ce479bf6b SHA1: 28941fd933e1434f79f87840daaf736914cac559 SHA256: 0e42638ff3810c8794e8c4e2161c75d4a0c66a41eca45aa05eac750dbcaf7aae SHA512: 9715edf58d920e082dbdaf68d7ef2f3e7f7b21d737034f2c1ab56e9d370b6b31f307169fbc871be3b155f280d40b77c95d7205e2ba85d02cef4bbd5f242965aa 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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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 can analyze or check individual words as well as parse text, latex, html or xml documents. For a more user-friendly interface use the 'spelling' package which builds on this package to automate checking of files, documentation and vignettes in all common formats. 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Incidentally provides support for 'reverse geocoding', such as matching a point with its nearest neighbour in another array. Used as a complement to package 'hutils' by sacrificing compilation or installation time for higher running speeds. The name is a portmanteau of the author and 'Rcpp'. 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Methods are available to test for equilibrium and random mating at any even ploidy level (>2) in the presence of double reduction at biallelic loci. For autopolyploid populations in equilibrium, methods are available to estimate the degree of double reduction. We also provide functions to calculate genotype frequencies at equilibrium, or after one or several rounds of random mating, given rates of double reduction. The main function is hwefit(). This material is based upon work supported by the National Science Foundation under Grant No. 2132247. The opinions, findings, and conclusions or recommendations expressed are those of the author and do not necessarily reflect the views of the National Science Foundation. For details of these methods, see Gerard (2023a) and Gerard (2023b) . 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Package: r-cran-hystar Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hystar_1.0.0-1.ca2404.1_amd64.deb Size: 138514 MD5sum: be97e913dc392e9932c9cc886b4a1e36 SHA1: 707bef790c14f046da53a9bc7ac8a87d7bd03a49 SHA256: fc6e373568cf3c2002bec16bfdcf4cad53de92d3d74c324b2d4c2b5cd781cba2 SHA512: ed87ffcfddbf43e785d12799781556b0d2b00bdbaae17397e864ee9105557c7bdf6d0937d3bd4a39de47da6407da334b4712878d4759ccb282052ea819a679ca Homepage: https://cran.r-project.org/package=hystar Description: CRAN Package 'hystar' (Fit the Hysteretic Threshold Autoregressive Model) Estimate parameters of the hysteretic threshold autoregressive (HysTAR) model, using conditional least squares. In addition, you can generate time series data from the HysTAR model. For details, see Li, Guan, Li and Yu (2015) . 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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). 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The models available in this package are the irregular autoregressive model (Eyheramendy et al.(2018) ), the complex irregular autoregressive model (Elorrieta et al.(2019) ) and the bivariate irregular autoregressive model (Elorrieta et al.(2021) ). 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A Hidden Markov Model is used to compute identity coefficients, simulate IBD segments and to derive the distribution of total IBD sharing and segment count across chromosomes. The methods are applied in Kruijver (2025) . The probability that the total IBD sharing is zero can be computed using the method of Donnelly (1983) . Package: r-cran-ibdsim2 Architecture: amd64 Version: 2.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1735 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pedtools, r-cran-ggplot2, r-cran-glue, r-cran-rcpp, r-cran-ribd Suggests: r-cran-lubridate, r-cran-mass, r-cran-patchwork, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-testthat, r-cran-zip Filename: pool/dists/noble/main/r-cran-ibdsim2_2.3.2-1.ca2404.1_amd64.deb Size: 1586104 MD5sum: 6131951561f8a685595cd4c6f92f64e4 SHA1: 1fd8cd054452805cf1faeb165fb4aeb240aa0026 SHA256: 6544c95a481f80fa016d90e9f08e8697988cc29c7bd58a4be731bd3f1947244d SHA512: cb48a44bc292528356aac2dec5a880a75f3362234adc4d1bc7958ac85c2c57ea51aedcce9657ff578bd5761eed004302284f17d0e3f6205d64421f558597e9ee Homepage: https://cran.r-project.org/package=ibdsim2 Description: CRAN Package 'ibdsim2' (Simulation of Chromosomal Regions Shared by Family Members) Simulation of segments shared identical-by-descent (IBD) by pedigree members. Using sex specific recombination rates along the human genome (Halldorsson et al. (2019) ), phased chromosomes are simulated for all pedigree members. Applications include calculation of realised relatedness coefficients and IBD segment distributions. 'ibdsim2' is part of the 'pedsuite' collection of packages for pedigree analysis. A detailed presentation of the 'pedsuite', including a separate chapter on 'ibdsim2', is available in the book 'Pedigree analysis in R' (Vigeland, 2021, ISBN:9780128244302). A 'Shiny' app for visualising and comparing IBD distributions is available at . Package: r-cran-ibdsim Architecture: amd64 Version: 0.9-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-paramlink Filename: pool/dists/noble/main/r-cran-ibdsim_0.9-8-1.ca2404.1_amd64.deb Size: 3126756 MD5sum: 557ff80940a7c629d381eed0a7b0b51a SHA1: 1cf7853187cf2268b80db180b30d80809143d069 SHA256: 33bfc4edb592f598b910ae393b243e5dbe7003f07de26f84fd5d9712e3572699 SHA512: c624cc9624f77a28a5b1d8d94850fe9be8abcc6722972686f7d3d481857402f8945d3aaf279d1739538601354ab3649d034a1885474873c3a3dd22b94afb1d52 Homepage: https://cran.r-project.org/package=IBDsim Description: CRAN Package 'IBDsim' (Simulation of Chromosomal Regions Shared by Family Members) Simulation of segments shared identical-by-descent (IBD) by pedigree members. Using sex specific recombination rates along the human genome (Kong et. al (2010) ), phased chromosomes are simulated for all pedigree members, either by unconditional gene dropping or conditional on a specified IBD pattern. Additional functions provide summaries and further analysis of the simulated genomes. Package: r-cran-ibm Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-ibm_0.3.0-1.ca2404.1_amd64.deb Size: 66320 MD5sum: ede945979fb1468a1a7629c3a03caec6 SHA1: cd395ee24f8fda999787026577834879905c6626 SHA256: 50ebc639c017ab5dfbc1a513371a479997cc877faa8c16f3d266643684734619 SHA512: f9146c66034841c45696d7c729311f2092595ebacb5130632c2fb6a917cf93b6becc275918415aba4a92ad85ee28b016b6dc163e94b49b9b555128488eb673fa Homepage: https://cran.r-project.org/package=ibm Description: CRAN Package 'ibm' (Individual Based Models in R) Implementation of some Individual Based Models (IBMs, sensu Grimm and Railsback 2005) and methods to create new ones, particularly for population dynamics models (reproduction, mortality and movement). The basic operations for the simulations are implemented in Rcpp for speed. Package: r-cran-ibmcraftr Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ibmcraftr_1.0.0-1.ca2404.1_amd64.deb Size: 55582 MD5sum: 07b0dcffe65fbc4a6ab062910d9b5d20 SHA1: 1063d75cb66ad11a2c70d5b954add461d00ec729 SHA256: d4e46e63f198dfcaa58fb0ed7d811b9066fee62c224efff726cfa6a83c59db81 SHA512: 717883761e442bc0cdf8a9f37ea3d3e4a085ecb7a1b311b47483544fc5141bf7817aedaf1f3b0910b26f07062278ee4c496b654107bd2db4128a1b1a4bbc69e6 Homepage: https://cran.r-project.org/package=ibmcraftr Description: CRAN Package 'ibmcraftr' (Toolkits to Develop Individual-Based Models in InfectiousDisease) It provides a generic set of tools for initializing a synthetic population with each individual in specific disease states, and making transitions between those disease states according to the rates calculated on each timestep. The new version 1.0.0 has C++ code integration to make the functions run faster. It has also a higher level function to actually run the transitions for the number of timesteps that users specify. Additional functions will follow for changing attributes on demographic, health belief and movement. Package: r-cran-ibmpopsim Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4142 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-checkmate, r-cran-readr, r-cran-rlang, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-rcpparmadillo, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-ggfortify, r-cran-magick, r-cran-colorspace, r-cran-gganimate, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-ibmpopsim_1.1.0-1.ca2404.1_amd64.deb Size: 3624772 MD5sum: 79d48d17ee4f60ed6d83a2f4f306b4b6 SHA1: 7bb5c42f198cc4c91352b50645700f9a7d64c11c SHA256: 2399fd8512720568c5bd8dfb05970c1bd58ba7b474aa38c87d00088c433435bc SHA512: 64ebc07e7f95a3ed847e60df67deec12ec9eb16fcec52efc17883cf798e2580b459dbbf8f6f7f6c4dd29cf512af8fa7658f0331962aaa33e34188c96657c899e Homepage: https://cran.r-project.org/package=IBMPopSim Description: CRAN Package 'IBMPopSim' (Individual Based Model Population Simulation) Simulation of the random evolution of heterogeneous populations using stochastic Individual-Based Models (IBMs) . The package enables users to simulate population evolution, in which individuals are characterized by their age and some characteristics, and the population is modified by different types of events, including births/arrivals, death/exit events, or changes of characteristics. The frequency at which an event can occur to an individual can depend on their age and characteristics, but also on the characteristics of other individuals (interactions). Such models have a wide range of applications. For instance, IBMs can be used for simulating the evolution of a heterogeneous insurance portfolio with selection or for validating mortality forecasts. This package overcomes the limitations of time-consuming IBMs simulations by implementing new efficient algorithms based on thinning methods, which are compiled using the 'Rcpp' package while providing a user-friendly interface. Package: r-cran-ibr Architecture: amd64 Version: 2.4-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-ibr_2.4-1-1.ca2404.1_amd64.deb Size: 400276 MD5sum: e70d7c72ac37ed4ab04dd70494fec93d SHA1: 0c181b74011be88d187fabbe262f27da72d4b248 SHA256: e98b7dab455e777a6726ba87332ab4e3b78377c5f8bc0675b6f8f1f251000897 SHA512: 5df0c2a72bd2825c4b286c24e79d6f077a46fde867e0245bd55a8da0021b96036ff4307e996919031562114712f463d5998d34ea942eeeee0eaf1b28479020e0 Homepage: https://cran.r-project.org/package=ibr Description: CRAN Package 'ibr' (Iterative Bias Reduction) Multivariate smoothing using iterative bias reduction with kernel, thin plate splines, Duchon splines or low rank splines. Package: r-cran-ibs Architecture: amd64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-ibs_1.4-1.ca2404.1_amd64.deb Size: 49072 MD5sum: 07480827f48765b639fbc849de8353b3 SHA1: e69f49b1103c486c1576b0274937e416fb14d941 SHA256: 5e227cff49dd834ce534593090d7281a95551e6a415c19aaf60c307c1bf7c8e2 SHA512: 1f516181bc7e20a843c98ddcf3e5e623bdbac5f4b25adb38984dda02afd2255bf2f06421243e8ae0bb05b766ddcb29600349fa3916b9b35b4ec64601aeee5b8a Homepage: https://cran.r-project.org/package=ibs Description: CRAN Package 'ibs' (Integral of B-Spline Functions) Calculate B-spline basis functions with a given set of knots and order, or a B-spline function with a given set of knots and order and set of de Boor points (coefficients), or the integral of a B-spline function. Package: r-cran-ibst Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-ibst_1.2-1.ca2404.1_amd64.deb Size: 120482 MD5sum: 6ce8c84663217961db4ddf238a0e797e SHA1: 0b8b06a9c443b76751e1d0d4052fc4759cf454e0 SHA256: 57647340f7757c7bed5d6b774d3427cc203f8376cc52971f857da48c92392b23 SHA512: f72833b8140227a33446308b8044d40e5ca97f2ed4488981f660be016c2062401a52fec767a7b313838311346d9ca01a9b8523132bd56917b1e9cb25e40a16bb Homepage: https://cran.r-project.org/package=iBST Description: CRAN Package 'iBST' (Improper Bagging Survival Tree) Fit a full or subsampling bagging survival tree on a mixture of population (susceptible and nonsusceptible) using either a pseudo R2 criterion or an adjusted Logrank criterion. The predictor is evaluated using the Out Of Bag Integrated Brier Score (IBS) and several scores of importance are computed for variable selection. The thresholds values for variable selection are computed using a nonparametric permutation test. See 'Cyprien Mbogning' and 'Philippe Broet' (2016) for an overview about the methods implemented in this package. 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See, for details, Masoudi et al. (2022) , Masoudi et al. (2017) and Masoudi et al. (2019) . 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Methods include functions to fit calibration models from interval-censored data and modified partial likelihood for the proportional hazard model, Nevo et al. (2018+) . Package: r-cran-iccbeta Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-lme4, r-cran-rcpparmadillo Suggests: r-cran-rlrsim, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-iccbeta_1.2.1-1.ca2404.1_amd64.deb Size: 98552 MD5sum: b3ee950de62d1fe91272ee724fc09ab5 SHA1: 6b30a54cf8cf8e69fd8e07511b0fd02525a8bf53 SHA256: ff95cd531e150d490f437b5868c31f7f1337bc61925354cf523798b130bb816f SHA512: 4fb54924dc9ed44699ace24f36188a8d34fcf37451328784df0280e772750d33f21117e8842ab070fe2c3face232d44756de8459a0749deeba5b877c5779a232 Homepage: https://cran.r-project.org/package=iccbeta Description: CRAN Package 'iccbeta' (Multilevel Model Intraclass Correlation for Slope Heterogeneity) A function and vignettes for computing an intraclass correlation described in Aguinis & Culpepper (2015) . This package quantifies the share of variance in a dependent variable that is attributed to group heterogeneity in slopes. 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ICE plots refine Friedman's partial dependence plot by graphing the functional relationship between the predicted response and a covariate of interest for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate of interest, suggesting where and to what extent they may exist. Package: r-cran-icellr Architecture: amd64 Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1069 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-plotly, r-cran-matrix, r-cran-rtsne, r-cran-gridextra, r-cran-ggrepel, r-cran-ggpubr, r-cran-scatterplot3d, r-cran-rcolorbrewer, r-cran-knitr, r-cran-nbclust, r-cran-shiny, r-cran-pheatmap, r-cran-ape, r-cran-ggdendro, r-cran-plyr, r-cran-reshape, r-cran-hmisc, r-cran-htmlwidgets, r-cran-uwot, r-cran-progress, r-cran-igraph, r-cran-data.table, r-cran-rcpp, r-cran-hdf5r, r-cran-rann, r-cran-jsonlite, r-cran-png Filename: pool/dists/noble/main/r-cran-icellr_1.7.0-1.ca2404.1_amd64.deb Size: 727478 MD5sum: 7f14f4ffd4bf097535d695deee15c9ff SHA1: 8b0de98f863e8bf180b3a171abd194cce68a2b08 SHA256: b206cdbdd16cdc2287f90149a268c3895acc91281e8e6fbe1b28b0a6b50d8ba9 SHA512: c8e7dcf340aad2cb24e3b7f5c8ef9d43a84fad41c5f2f853634d87e3a8cb5da145f709d965a8be5bfed5a92cd0a3014bf9f04e907a2d50255488ec15ba3488ab Homepage: https://cran.r-project.org/package=iCellR Description: CRAN Package 'iCellR' (Analyzing High-Throughput Single Cell Sequencing Data) A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) and Khodadadi-Jamayran, et al (2020) for more details. Package: r-cran-icenreg Architecture: amd64 Version: 2.0.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1964 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-coda, r-cran-foreach, r-cran-mlecens, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-icenreg_2.0.16-1.ca2404.1_amd64.deb Size: 1353020 MD5sum: 8de35c24d6376d7c924cda9484aa6324 SHA1: 39318512a3728c3019dfcc373ab810a3d5614523 SHA256: d84bd789beb79bbc85562bad3e7c10ea4a764111844c30c6e6435ef064c8b672 SHA512: d5a4cb7b9ce3c3a4a656beccf54ecc8545e81f6d04858613546822f79de97fed568f7488922254dc0670be1a17a6363ea8b5f59a593a10ffdd8db5b86d6da65e Homepage: https://cran.r-project.org/package=icenReg Description: CRAN Package 'icenReg' (Regression Models for Interval Censored Data) Regression models for interval censored data. Currently supports Cox-PH, proportional odds, and accelerated failure time models. Allows for semi and fully parametric models (parametric only for accelerated failure time models) and Bayesian parametric models. Includes functions for easy visual diagnostics of model fits and imputation of censored data. Package: r-cran-icensmis Architecture: amd64 Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-icensmis_1.5.0-1.ca2404.1_amd64.deb Size: 230950 MD5sum: 682a56f8552e2953116712372c619289 SHA1: da7c06d324ed84be5841c844356e77180caf7d84 SHA256: 629fdc9bf90fd199533570f4f56ed61d17f4175b96c12ad4145bb2ada4fcfab0 SHA512: 92b0efa8bbf46849e3ac74e90499f48aa22d3d9dbf8ba42dd4f4f6996b5cff06c5bbfaf3f107181fa578927258ff65ed8a4d13dbcf3b4e62f5805e2798b3e7bd Homepage: https://cran.r-project.org/package=icensmis Description: CRAN Package 'icensmis' (Study Design and Data Analysis in the Presence of Error-ProneDiagnostic Tests and Self-Reported Outcomes) We consider studies in which information from error-prone diagnostic tests or self-reports are gathered sequentially to determine the occurrence of a silent event. Using a likelihood-based approach incorporating the proportional hazards assumption, we provide functions to estimate the survival distribution and covariate effects. We also provide functions for power and sample size calculations for this setting. Please refer to Xiangdong Gu, Yunsheng Ma, and Raji Balasubramanian (2015) , Xiangdong Gu and Raji Balasubramanian (2016) , Xiangdong Gu, Mahlet G Tadesse, Andrea S Foulkes, Yunsheng Ma, and Raji Balasubramanian (2020) . Package: r-cran-ichimoku Architecture: amd64 Version: 1.5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1399 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mirai, r-cran-nanonext, r-cran-rcppsimdjson, r-cran-secretbase, r-cran-shiny, r-cran-xts, r-cran-zoo Suggests: r-cran-keyring, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ichimoku_1.5.6-1.ca2404.1_amd64.deb Size: 901810 MD5sum: b189a595e50ea19ef3882327af0207ab SHA1: fb826c4541f451b3a8c15e8bb3f3bee0f8283b7e SHA256: 42103c27733ffd1d6c6f2888e8f5cf0affd0a7c5458ca84c045008849fe0559a SHA512: 2b0ead58bf694a788a4e7ac22742b0b708fcaab040fb22aa622b4ed8c32fd992e4cbcf28f92f030df805fa57fd785b56d10e7df138b1bf443a643612ea3970d8 Homepage: https://cran.r-project.org/package=ichimoku Description: CRAN Package 'ichimoku' (Visualization and Tools for Ichimoku Kinko Hyo Strategies) An implementation of 'Ichimoku Kinko Hyo', also commonly known as 'cloud charts'. Static and interactive visualizations with tools for creating, backtesting and development of quantitative 'ichimoku' strategies. As described in Sasaki (1996, ISBN:4925152009), the technique is a refinement on candlestick charting, originating from Japan and now in widespread use in technical analysis worldwide. Translating as 'one-glance equilibrium chart', it allows the price action and market structure of financial securities to be determined 'at-a-glance'. Incorporates an interface with the OANDA fxTrade API for retrieving historical and live streaming price data for major currencies, metals, commodities, government bonds and stock indices. Package: r-cran-iclogcondist Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-flexsurv, r-cran-ggplot2, r-cran-icenreg, r-cran-monotone, r-cran-fdrtool, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-iclogcondist_1.0.1-1.ca2404.1_amd64.deb Size: 163636 MD5sum: 74f3829fcbd38ee9454f2a2d30aa60fe SHA1: 518534f577fbfed53d2ccd3f6156af154686e802 SHA256: 33fa1061da276d6f61498da304f9f36d365d3f543331b366fa39e9c2e2c58288 SHA512: 061413bd23e3357f6cc394a673ca74d6babde7198820b437530ac42e4ab7f9b0797eae512d644711ef3f69f76945130dd7984e4bc223d722daf69220911bf217 Homepage: https://cran.r-project.org/package=iclogcondist Description: CRAN Package 'iclogcondist' (Log-Concave Distribution Estimation with Interval-Censored Data) We consider the non-parametric maximum likelihood estimation of the underlying distribution function, assuming log-concavity, based on mixed-case interval-censored data. The algorithm implemented is base on Chi Wing Chu, Hok Kan Ling and Chaoyu Yuan (2024, ). Package: r-cran-iclustervb Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2978 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-clustmixtype, r-cran-cowplot, r-cran-ggplot2, r-cran-mclust, r-cran-mcmcpack, r-cran-mvtnorm, r-cran-pheatmap, r-cran-polca, r-cran-rcpp, r-cran-varsellcm, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-survminer Filename: pool/dists/noble/main/r-cran-iclustervb_0.1.4-1.ca2404.1_amd64.deb Size: 2695962 MD5sum: 93099cfeb01a44edeaed33a90b178513 SHA1: 48efaec6907aaebd18bfb1ac3f7ad3a0443a8143 SHA256: 34958e7cd761465ef9169e0a91839d2de51bc9894fad6ed6d2609fe812b6ea68 SHA512: f434673c1c52cfa6921c1a9dca89b98d778d91740f42c9208d1226fd7134f4891d8050d88cffe54cd347bc0be246c7a7fc00b18a634095f54c74a95aea5b9a6e Homepage: https://cran.r-project.org/package=iClusterVB Description: CRAN Package 'iClusterVB' (Fast Integrative Clustering and Feature Selection for HighDimensional Data) A variational Bayesian approach for fast integrative clustering and feature selection, facilitating the analysis of multi-view, mixed type, high-dimensional datasets with applications in fields like cancer research, genomics, and more. Package: r-cran-icmstate Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1290 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mstate, r-cran-prodlim, r-cran-igraph, r-cran-checkmate, r-cran-ggplot2, r-cran-desolve, r-cran-msm, r-cran-survival, r-cran-jops Suggests: r-cran-testthat, r-cran-icenreg, r-cran-profvis, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-latex2exp Filename: pool/dists/noble/main/r-cran-icmstate_0.2.0-1.ca2404.1_amd64.deb Size: 1110086 MD5sum: 0886601fbace36472bce364bb2533bf0 SHA1: efb5dab27f0ad8bfaf1c4e5a85fe9f3ac993cc51 SHA256: 47439a58067426620d7f76d29d8ee3ff68fa313811d6484321e25b6d135f8a29 SHA512: 5c2d651ecfbb266f2dc3d5d51cd07591853e977f16b1e487c9ffb865e3858f8a1df05b636f5a6be7c1bc7f2ead2c69dc30d00976edcd09c364b6d355a45c8a26 Homepage: https://cran.r-project.org/package=icmstate Description: CRAN Package 'icmstate' (Interval Censored Multi-State Models) Allows for the non-parametric estimation of transition intensities in interval-censored multi-state models using the approach of Gomon and Putter (2024) or Gu et al. (2023) . Package: r-cran-icosa Architecture: amd64 Version: 0.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1289 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-sp, r-cran-igraph, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-terra, r-cran-rgl Filename: pool/dists/noble/main/r-cran-icosa_0.12.0-1.ca2404.1_amd64.deb Size: 771184 MD5sum: 2a5456ac2123239822d1a1ef8fab28f7 SHA1: 75cd07450070c0f683a0554aead52da488576c77 SHA256: a576abd277348337131920fb00135330ddfdf449dcd645dcf3210e55f6ea1766 SHA512: 5c08a21421ea98391d59c05dfe7cde7d84dbb60cdf72a35d1b0e3d1aad0d97bc3bc7e1bfddd310e2a776e74add72b0c377b5538500b6bcc2921d89257895a84e Homepage: https://cran.r-project.org/package=icosa Description: CRAN Package 'icosa' (Global Triangular and Penta-Hexagonal Grids Based on TessellatedIcosahedra) Implementation of icosahedral grids in three dimensions. The spherical-triangular tessellation can be set to create grids with custom resolutions. Both the primary triangular and their inverted penta-hexagonal grids can be calculated. Additional functions are provided that allow plotting of the grids and associated data, the interaction of the grids with other raster and vector objects, and treating the grids as a graphs. Package: r-cran-icr Architecture: amd64 Version: 0.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-ggplot2, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-icr_0.6.6-1.ca2404.1_amd64.deb Size: 219612 MD5sum: 35b48d2318b1ed5e6cf0b1c689e8914a SHA1: 24c63decf2c23ffe5c9a72123b27a0b750ff5bc7 SHA256: e053106da39d2fc6c15bb7eaadbceec8c322fc825c954d24ed8b495c674253d6 SHA512: 21c1a73503ea985aeb753225ade3ce826036435084d29cb88743098250c17f4dd6f0d32876d062f9ce1e4f7460792599db8ed7e4ba2303a22b5c4e4a50d2784c Homepage: https://cran.r-project.org/package=icr Description: CRAN Package 'icr' (Compute Krippendorff's Alpha) Provides functions to compute and plot Krippendorff's inter-coder reliability coefficient alpha and bootstrapped uncertainty estimates (Krippendorff 2004, ISBN:0761915443). The bootstrap routines are set up to make use of parallel threads where supported. Package: r-cran-icranks Architecture: amd64 Version: 3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-multcomp, r-cran-gmp Filename: pool/dists/noble/main/r-cran-icranks_3.2-1.ca2404.1_amd64.deb Size: 132838 MD5sum: bb9415467c54e92f39f2550f7227f291 SHA1: e279237c7ab13edc7623db95e066d27f2d77e355 SHA256: 589a3f0013dcfda29e7f508f5382df873b85d3217f76ee07e130d359f601f7ce SHA512: 796cc88b8585762d3b7151f69c11ea4f3ca46dc6b0d200120d63d513c2dfe3a81c37904e737f7be9ca9f260bd5e880322d8280137acf45f595a4837eb0ff3fd1 Homepage: https://cran.r-project.org/package=ICRanks Description: CRAN Package 'ICRanks' (Simultaneous Confidence Intervals for Ranks) Algorithms to construct simultaneous confidence intervals for the ranks of means mu_1,...,mu_n based on an independent Gaussian sample using multiple testing techniques. Package: r-cran-icrf Architecture: amd64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-survival, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-icrf_2.0.2-1.ca2404.1_amd64.deb Size: 176022 MD5sum: 476faddf407bce4e4cc7b6843087517d SHA1: f2b9b4240eaec2f67e9f6476926b5f0572f4fccf SHA256: a019fc4227ae847d7d06c3691f301d283b394b7970d1bba18f1bdaabbd7517aa SHA512: 7dfbbcd8b79dcbe10ece247e324ee850e5818828162f68d78dcd9479b682583f2839102de7f1835abcdf216fd21e8ab9e132abe47066542988713a34c429caa9 Homepage: https://cran.r-project.org/package=icrf Description: CRAN Package 'icrf' (Interval Censored Recursive Forests) Implements interval censored recursive forests (ICRF) based on Cho, Jewell, and Kosorok (2021+). ICRF is a variant of random forests where the outcome variable is interval censored survival data. It can be used for usual right censored data and current status data as well. A recursion technique is used to improve accuracy and smoothed survival curves are provided. Package: r-cran-icrsf Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-icensmis Filename: pool/dists/noble/main/r-cran-icrsf_1.2-1.ca2404.1_amd64.deb Size: 209704 MD5sum: 9b2eed6150e80a150f1fd997372a7d73 SHA1: 7186d6ff954ce583cd8e43e6a16544837f8556ba SHA256: a6974c871104ef4e964cd9330916678c20fbe7570346769ed6c881aa409493e4 SHA512: 9c9ec7ef2ca9b3098d8e86bd19fa67ebfc5cbbb74250a671f2be043aedbd86fbb6e6fb6ce5379dafe75dff35205c1481dc541ac4ffc2ad732662c8f1b92e017b Homepage: https://cran.r-project.org/package=icRSF Description: CRAN Package 'icRSF' (A Modified Random Survival Forest Algorithm) Implements a modification to the Random Survival Forests algorithm for obtaining variable importance in high dimensional datasets. The proposed algorithm is appropriate for settings in which a silent event is observed through sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The modified algorithm incorporates a formal likelihood framework that accommodates sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The original Random Survival Forests algorithm is modified by the introduction of a new splitting criterion based on a likelihood ratio test statistic. Package: r-cran-icsclust Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ics, r-cran-ggplot2, r-cran-cluster, r-cran-fpc, r-cran-ggally, r-cran-heplots, r-cran-mclust, r-cran-moments, r-cran-mvtnorm, r-cran-otrimle, r-cran-rcpproll, r-cran-rrcov, r-cran-scales, r-cran-tclust, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-icsclust_0.1.1-1.ca2404.1_amd64.deb Size: 197142 MD5sum: 213658acc48259dc74c4631f5aa027b2 SHA1: b9e41ac03ea84504e0bf6eeb247e60e56d7f4120 SHA256: 69d972bd864f78954ed1a9e7dfd4c9d4b5236d4edfbb932059a00af01fea51d9 SHA512: 6a237340edd86b585b9ca3089fe5a5cc986d498f77af6b8094a49a890394de6ff409f4af0703951a33aba07f73b8a9ed2d4e0a01baf6d052e9725b28130bbf1d Homepage: https://cran.r-project.org/package=ICSClust Description: CRAN Package 'ICSClust' (Tandem Clustering with Invariant Coordinate Selection) Implementation of tandem clustering with invariant coordinate selection with different scatter matrices and several choices for the selection of components as described in Alfons, A., Archimbaud, A., Nordhausen, K.and Ruiz-Gazen, A. (2024) . Package: r-cran-icskat Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-compquadform, r-cran-dplyr, r-cran-magrittr, r-cran-rcpp, r-cran-rje, r-cran-survival, r-cran-zoo, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-icskat_0.3.0-1.ca2404.1_amd64.deb Size: 180630 MD5sum: a1ed45c1f8922cae47acaf588f0bbb63 SHA1: 5f5520f59cebb6371dfe91a5760a96c679215121 SHA256: 6744179980f0c01cedae07767554fd139e626a26c33c27d77f5c07e7d2cb0f61 SHA512: bca57499c84e727997749ccd008513fd99597133bbb0daf3de60b184ecdf3ac7e53f723bbc254bf0498c4f4a200b9c174be94597ecf560659c565eb9bf136605 Homepage: https://cran.r-project.org/package=ICSKAT Description: CRAN Package 'ICSKAT' (Interval-Censored Sequence Kernel Association Test) Implements the Interval-Censored Sequence Kernel Association (ICSKAT) test for testing the association between interval-censored time-to-event outcomes and groups of single nucleotide polymorphisms (SNPs). Interval-censored time-to-event data occur when the event time is not known exactly but can be deduced to fall within a given interval. For example, some medical conditions like bone mineral density deficiency are generally only diagnosed at clinical visits. If a patient goes for clinical checkups yearly and is diagnosed at, say, age 30, then the onset of the deficiency is only known to fall between the date of their age 29 checkup and the date of the age 30 checkup. Interval-censored data include right- and left-censored data as special cases. This package also implements the interval-censored Burden test and the ICSKATO test, which is the optimal combination of the ICSKAT and Burden tests. Please see the vignette for a quickstart guide. The paper describing these methods is " Inference for Set-Based Effects in Genetic Association Studies with Interval-Censored Outcomes" by Sun R, Zhu L, Li Y, Yasui Y, & Robison L (Biometrics 2023, ). Package: r-cran-icsnp Architecture: amd64 Version: 1.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: libc6 (>= 2.2.5), libstdc++6 (>= 4.3), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-ics Filename: pool/dists/noble/main/r-cran-icsnp_1.1-2-1.ca2404.1_amd64.deb Size: 204082 MD5sum: f5c877d5cd5d837811912f99f7cb34ca SHA1: 042cfa919561138778f55da58345e6f25b578439 SHA256: 2f9201ebbaad680d0c18b31c129e5f7217afaa0c315a554b136131c0851a5882 SHA512: fb69cf48fd94c92e6edcbb56d971f0eb86bfec25414566926bac0bb319e95532c7cb1b9380dab999b97d1bf5dee7ebff585db6847cdafea896b268666110d3ef Homepage: https://cran.r-project.org/package=ICSNP Description: CRAN Package 'ICSNP' (Tools for Multivariate Nonparametrics) Tools for multivariate nonparametrics, as location tests based on marginal ranks, spatial median and spatial signs computation, Hotelling's T-test, estimates of shape are implemented. 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Package: r-cran-icvectorfields Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2104 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fftwtools, r-cran-rcpp, r-cran-terra Suggests: r-cran-ggnewscale, r-cran-ggplot2, r-cran-knitr, r-cran-metr, r-cran-ncf, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-icvectorfields_0.1.2-1.ca2404.1_amd64.deb Size: 1924256 MD5sum: 850797ec10f39aa30154feb109b24de8 SHA1: 98a56aeaecc250e08ceb5922b311327ff7c54b92 SHA256: 525f8d92e84a3741e11b465a2328b18ef111cdb990d15d111078867f94e35da5 SHA512: 05608cf9dedcdc6733c03555de85302cca2780ead11216733722bb5c978db4c9e4a707ab8ee43495bb7c5bc7354a499a9840d7febb892b7956f6cb173ad43a31 Homepage: https://cran.r-project.org/package=ICvectorfields Description: CRAN Package 'ICvectorfields' (Vector Fields from Spatial Time Series of Population Abundance) Functions for converting time series of spatial abundance or density data in raster format to vector fields of population movement using the digital image correlation technique. More specifically, the functions in the package compute cross-covariance using discrete fast Fourier transforms for computational efficiency. Vectors in vector fields point in the direction of highest two dimensional cross-covariance. The package has a novel implementation of the digital image correlation algorithm that is designed to detect persistent directional movement when image time series extend beyond a sequence of two raster images. Package: r-cran-idar Architecture: amd64 Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-picante, r-cran-spatstat, r-cran-ade4, r-cran-ape, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-spatstat.random Suggests: r-cran-ecespa, r-cran-vegan Filename: pool/dists/noble/main/r-cran-idar_1.7-1.ca2404.1_amd64.deb Size: 239838 MD5sum: 6061f5e26a8bb702b2985e32c431b7e2 SHA1: 1ef032b2563db8587a2728376c46756789930869 SHA256: 228b842fcbfcfde72df54fc355c001e360ef73750badee2bd2162bfc04c05957 SHA512: 2aa39a8e909f8ab0282a8d34f7c8e305d2a3be1ed0ccc140149a5f7866f2bdd50e6c8a2527089e951d5bf0f7a14ec52c984ba5657b3290b6f885883e6f777ff2 Homepage: https://cran.r-project.org/package=idar Description: CRAN Package 'idar' (Individual Diversity-Area Relationships) Computes and tests individual (species, phylogenetic and functional) diversity-area relationships, i.e., how species-, phylogenetic- and functional-diversity varies with spatial scale around the individuals of some species in a community. See applications of these methods in Wiegand et al. (2007) or Chacon-Labella et al. (2016) . 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The generated designs can be presented on screen and choice data can be gathered using a shiny application. Traets F, Sanchez G, and Vandebroek M (2020) . 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Package: r-cran-idove Architecture: amd64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 805 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-idove_1.5-1.ca2404.1_amd64.deb Size: 560488 MD5sum: 86a0da1cf50245aad64f0c8e0af7e2b4 SHA1: 0958ebb54f7327043f41f187106e6f98370ea949 SHA256: 4a5d5a15f588ae9dd37eeb5f6beefc697aab5204fe7e2387a9ab7d9fbc4fa1c4 SHA512: c7e3a113014bc57cdaab00290494acb42850c5621765050700c1955ef28a0434fdbb59ba12d0024947c51ea7dd2b6186cec48035dc561c315f97227e0dba66c9 Homepage: https://cran.r-project.org/package=iDOVE Description: CRAN Package 'iDOVE' (Durability of Vaccine Efficacy Against SARS-CoV-2 Infection) Implements a nonparametric maximum likelihood method for assessing potentially time-varying vaccine efficacy (VE) against SARS-CoV-2 infection under staggered enrollment and time-varying community transmission, allowing crossover of placebo volunteers to the vaccine arm. Lin, D. Y., Gu, Y., Zeng, D., Janes, H. E., and Gilbert, P. B. (2021) . 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Package: r-cran-image.libfacedetection Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2648 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-magick Filename: pool/dists/noble/main/r-cran-image.libfacedetection_0.1.1-1.ca2404.1_amd64.deb Size: 1949030 MD5sum: 8fa3f3d215f997c851b3f8f13f8821b1 SHA1: d8ab2bcc7b8d7991cd0daa374ae243cd7b1b82f5 SHA256: f81c46163a6341dab8978b9cd4e6e72fea4f6caff87445b1f87a128ec2a3ddc2 SHA512: 6625df3222dbd8bfa7592975dfcac3cdef54161d31ac2db85aa6a783ce0aec68de09de38550c271db1f8a739f99c530e5d9db169c4b8cd9a2b520de01177e3aa Homepage: https://cran.r-project.org/package=image.libfacedetection Description: CRAN Package 'image.libfacedetection' (Convolutional Neural Network for Face Detection) An open source library for face detection in images. Provides a pretrained convolutional neural network based on which can be used to detect faces which have size greater than 10x10 pixels. Package: r-cran-image.linesegmentdetector Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2004 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-sp Suggests: r-cran-pixmap, r-cran-magick Filename: pool/dists/noble/main/r-cran-image.linesegmentdetector_0.1.1-1.ca2404.1_amd64.deb Size: 961816 MD5sum: 9bde8ca4b22c463c01b9f1deb4fb08ba SHA1: f2671842726a2f6770d75f2de2e4d09b00476542 SHA256: ed8132b8930bfb125242997475012b097b7f551b07bb31dc81276a8c8786a084 SHA512: 288fffb1e467af22e671a1a20013099741bf5a24cdb41917c20fa4c35234e8bf7e54550dae9e447aefefd3b5e79701b6059888d155221651323dec0249a2c895 Homepage: https://cran.r-project.org/package=image.LineSegmentDetector Description: CRAN Package 'image.LineSegmentDetector' (Detect Line Segments in Images) An implementation of the Line Segment Detector on digital images described in the paper: "LSD: A Fast Line Segment Detector with a False Detection Control" by Rafael Grompone von Gioi et al (2012). The algorithm is explained at . Package: r-cran-image.otsu Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-magick Filename: pool/dists/noble/main/r-cran-image.otsu_0.1.1-1.ca2404.1_amd64.deb Size: 114840 MD5sum: 2675f9627fb7d2649c6d54a3f116b9ee SHA1: 8c7dcaa61bbf32fe64ffa2d067fc8b1da622796f SHA256: d71c0a2780999a20352c88e9a17a8e3d14668e4a11fceef4f5ff784e10a30b62 SHA512: e859bf0d29787bc5ddea89a21330416a92ae07ce2e15b2bbddca8c5a3ec7946f00f247d5d121c909ce7ad8e96c2a3ce0ebb84600c4817afffd11d0ed63e82902 Homepage: https://cran.r-project.org/package=image.Otsu Description: CRAN Package 'image.Otsu' (Otsu's Image Segmentation Method) An implementation of the Otsu's Image Segmentation Method described in the paper: "A C++ Implementation of Otsu's Image Segmentation Method". The algorithm is explained at . Package: r-cran-image.textlinedetector Architecture: amd64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1268 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libopencv-core406t64 (>= 4.6.0+dfsg), libopencv-imgproc406t64 (>= 4.6.0+dfsg), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-magick Suggests: r-cran-opencv Filename: pool/dists/noble/main/r-cran-image.textlinedetector_0.2.3-1.ca2404.1_amd64.deb Size: 1007558 MD5sum: 75ee2401775f0dcda4352942ee8751b4 SHA1: 29c7e598f0c094d6547f4e0afeeb6637fc288d5b SHA256: 6016d6e7d1aa9214f74797c232e245cc827cbfa0313c614a794cfcffb3fd4f8a SHA512: 336a52c551df1b4dc30d32325bfec93ce4a4295cede89b14d6b343cc55ae85bd5519341a5a744a6c08fd6f73f5b28010c5271a5fb0434286fdbbf979af7faa73 Homepage: https://cran.r-project.org/package=image.textlinedetector Description: CRAN Package 'image.textlinedetector' (Segment Images in Text Lines and Words) Find text lines in scanned images and segment the lines into words. Includes implementations of the paper 'Novel A* Path Planning Algorithm for Line Segmentation of Handwritten Documents' by Surinta O. et al (2014) available at , an implementation of 'A Statistical approach to line segmentation in handwritten documents' by Arivazhagan M. et al (2007) , and a wrapper for an image segmentation technique to detect words in text lines as described in the paper 'Scale Space Technique for Word Segmentation in Handwritten Documents' by Manmatha R. and Srimal N. (1999) paper at , wrapper for code available at . Provides as well functionality to put cursive text in images upright using the approach defined in the paper 'A new normalization technique for cursive handwritten words' by Vinciarelli A. and Luettin J. (2001) . Package: r-cran-imager Architecture: amd64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13570 Depends: libc6 (>= 2.38), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), libtiff6 (>= 4.0.3), libx11-6, r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-rcpp, r-cran-stringr, r-cran-png, r-cran-jpeg, r-cran-readbitmap, r-cran-purrr, r-cran-downloader, r-cran-igraph Suggests: r-cran-cairo, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-magick, r-cran-raster, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-spatstat.geom, r-cran-webp Filename: pool/dists/noble/main/r-cran-imager_1.0.8-1.ca2404.1_amd64.deb Size: 5823258 MD5sum: 6c5bb68afae600ff0aeea741590eee50 SHA1: b1e94ca5e4bab99bb2fc098e91802995acf86ac3 SHA256: a0ea7cc1fe4f56fe245097e2be05684162fae6cd800e738cd39936d91bc49c72 SHA512: 6a0e077f92288e5c051e1c78e6a02882df4490381245d2d061f3e490cb2daa31a3121ecfceb23cd24a452ca2214a2c099f5ada99672bbdd93c2a3da769e3d260 Homepage: https://cran.r-project.org/package=imager Description: CRAN Package 'imager' (Image Processing Library Based on 'CImg') Fast image processing for images in up to 4 dimensions (two spatial dimensions, one time/depth dimension, one colour dimension). Provides most traditional image processing tools (filtering, morphology, transformations, etc.) as well as various functions for easily analysing image data using R. The package wraps 'CImg', , a simple, modern C++ library for image processing. Package: r-cran-imagerextra Architecture: amd64 Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1138 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-imager, r-cran-fftwtools, r-cran-magrittr, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tesseract Filename: pool/dists/noble/main/r-cran-imagerextra_1.3.2-1.ca2404.1_amd64.deb Size: 662628 MD5sum: 31e1480cbffb0c046549d431484fe089 SHA1: 949296810c98fd0bfb08b28221c7cde3e5240588 SHA256: 17c32716f5514ebc5fb781cf87767476b5db2944270aa3d90abe1ad628d8c860 SHA512: aac29889cbcdeec5c0e296685407fad1ee6cb8ed49810ae916c66b3356947b1765c31c899d9298714062bbf146ff617fae08096c42dbfd70b5fb35cece3856fd Homepage: https://cran.r-project.org/package=imagerExtra Description: CRAN Package 'imagerExtra' (Extra Image Processing Library Based on 'imager') Provides advanced functions for image processing based on the package 'imager'. Package: r-cran-imaginarycss Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 985 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-barry Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-quarto Filename: pool/dists/noble/main/r-cran-imaginarycss_0.1.0-1.ca2404.1_amd64.deb Size: 495316 MD5sum: 61ec633ade6db08ed0f89010a3a56441 SHA1: 20bd4a02be2f4312686f423dbb375f533545d987 SHA256: 6875439be3aefc3db30edd53a5d1fa7b5917e23b40558099f3851c8d9fdb107d SHA512: e30e9730ad30edc8556ce18e4c1ae8efd1e6c6e4aeeb303dbafaddfef7896ef02de2c7f6b2da8eaacde134279ae55df1ea9494d76ad3f757ed32748d159edf67 Homepage: https://cran.r-project.org/package=imaginarycss Description: CRAN Package 'imaginarycss' (Tools for Studying Imaginary Cognitive Social Structure) Provides functions to measure and test imaginary cognitive social structure (CSS) motifs, which are patterns of perceived relationships among individuals in a social network. Includes tools for calculating motif frequencies, comparing observed motifs to expected distributions, and visualizing motif structures. Implements methods described in Tanaka and Vega Yon (2023) . Package: r-cran-imagine Architecture: amd64 Version: 2.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6158 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-quarto Filename: pool/dists/noble/main/r-cran-imagine_2.1.4-1.ca2404.1_amd64.deb Size: 4898784 MD5sum: 166b6b6e24f301b7d92807dd5ffdc70c SHA1: 2eac0edd107318f6d74e652d0b63e89fce3706ca SHA256: 73a0c7d55856ef12159d2b89ef2b13e0b3b55e89a78c3553e17a539f0f04e5c5 SHA512: 8cc634531dc1d0e53f42f1f3f307781426890aa67bfd884a26f2dcbbb20316a7c4e7bf2032d1596e46e92459cda82b32e9609aa9411c9ab83872e4fdd2c7d512 Homepage: https://cran.r-project.org/package=imagine Description: CRAN Package 'imagine' (IMAGing engINEs, Tools for Application of Image Filters to DataMatrices) Provides fast application of image filters to data matrices, using R and C++ algorithms. 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Package: r-cran-imbibe Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 496 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rnifti, r-cran-magrittr Suggests: r-cran-mmand, r-cran-tinytest, r-cran-covr Filename: pool/dists/noble/main/r-cran-imbibe_0.1.1-1.ca2404.1_amd64.deb Size: 193870 MD5sum: f843a3f07d3bc052b4e9b145c6969700 SHA1: a7df201472c94e56f9c3c932469c29ce00b607de SHA256: bf610f4c1442582e5e4af03d1d5f1293534a10f0915df948c127491365229d4c SHA512: 759ebd16368ea64fbaec0461d696999bfecf874b3978c588a2b131662beb27acaaca098d296b7d778c3f94b34c35a9d49f2c07f0a1b5cb666ceab85cdd8908ba Homepage: https://cran.r-project.org/package=imbibe Description: CRAN Package 'imbibe' (A Pipe-Friendly Image Calculator) Provides a set of fast, chainable image-processing operations which are applicable to images of two, three or four dimensions, particularly medical images. 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See Robitzsch and Steinfeld (2018) for a description of the functionality of the package. See Wang, Su and Qiu (2014; ) for an overview of modeling alternatives. Package: r-cran-immigrate Architecture: amd64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-proc Filename: pool/dists/noble/main/r-cran-immigrate_0.2.1-1.ca2404.1_amd64.deb Size: 165768 MD5sum: ac9bf8fb7e5f1355b83f98cdc56f17da SHA1: c0f28428164587b967c5e33f87da7d41af109f8b SHA256: af7400480443a9d4d04886c3357c637d971796634f9c071b8c19e966fa72a45a SHA512: a87d95c6675cbc5074cb2df5c953b2ffb53f6ae9046289ad95282c5e005dd302db2feb6d456e61eb586c68cca748dabab5b1c1c9c5621bc74cd85bb7c5e091a6 Homepage: https://cran.r-project.org/package=Immigrate Description: CRAN Package 'Immigrate' (Iterative Max-Min Entropy Margin-Maximization with InteractionTerms for Feature Selection) Based on large margin principle, this package performs feature selection methods: "IM4E"(Iterative Margin-Maximization under Max-Min Entropy Algorithm); "Immigrate"(Iterative Max-Min Entropy Margin-Maximization with Interaction Terms Algorithm); "BIM"(Boosted version of IMMIGRATE algorithm); "Simba"(Iterative Search Margin Based Algorithm); "LFE"(Local Feature Extraction Algorithm). This package also performs prediction for the above feature selection methods. Package: r-cran-immunarch Architecture: amd64 Version: 0.10.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3600 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-immundata, r-cran-patchwork, r-cran-dplyr, r-cran-dtplyr, r-cran-data.table, r-cran-cli, r-cran-pheatmap, r-cran-reshape2, r-cran-circlize, r-cran-airr, r-cran-rcpp, r-cran-magrittr, r-cran-scales, r-cran-rlang, r-cran-plyr, r-cran-stringdist, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-tidyselect, r-cran-tidyr, r-cran-ape, r-cran-doparallel, r-cran-rlist, r-cran-glue, r-cran-checkmate, r-cran-duckplyr, r-cran-dbplyr, r-cran-lifecycle, r-cran-purrr, r-cran-vctrs, r-cran-ggthemes, r-cran-ggsci Suggests: r-cran-knitr, r-cran-roxygen2, r-cran-testthat, r-cran-pkgdown, r-cran-assertthat, r-cran-rmarkdown, r-cran-factoextra, r-cran-fpc, r-cran-ggpubr, r-cran-ggraph, r-cran-ggseqlogo, r-cran-igraph, r-cran-phangorn, r-cran-ggalluvial, r-cran-upsetr, r-cran-ggrepel, r-cran-shiny, r-cran-shinythemes, r-cran-quarto, r-cran-mass, r-cran-rtsne Filename: pool/dists/noble/main/r-cran-immunarch_0.10.3-1.ca2404.1_amd64.deb Size: 3456506 MD5sum: df42e9044bda07e02c517da5311a247b SHA1: ee2b35bd856830c46448ca91f4c3b6a0d617f298 SHA256: cf3d90024bf7b9ab82626a7f3e9148318e7b7a82fbcbd4a1e8f04841c1b24358 SHA512: b92eefefa509971ac839cb38778e51d5a415c7601e0bd13c6277b6b7429715c1e0fdcc8a112bff3a15a7dbd0a5e235d0efe7177ae697c0ea0fe585dde0360439 Homepage: https://cran.r-project.org/package=immunarch Description: CRAN Package 'immunarch' (Multi-Modal Immune Repertoire Analytics for Immunotherapy andVaccine Design in R) A comprehensive analytics framework for building reproducible pipelines on T-cell and B-cell immune receptor repertoire data. Delivers multi-modal immune profiling (bulk, single-cell, CITE-seq/AbSeq, spatial, immunogenicity data), feature engineering (ML-ready feature tables and matrices), and biomarker discovery workflows (cohort comparisons, longitudinal tracking, repertoire similarity, enrichment). Provides a user-friendly interface to widely used AIRR methods — clonality/diversity, V(D)J usage, similarity, annotation, tracking, and many more. Think Scanpy or Seurat, but for AIRR data, a.k.a. Adaptive Immune Receptor Repertoire, VDJ-seq, RepSeq, or VDJ sequencing data. A successor to our previously published "tcR" R package (Nazarov 2015). 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They can be used with stochastic volatility models and Hidden Markov Models (HMM). This improves the results in Duchesne, Ghoudi & Remillard (2012) . 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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. 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Package: r-cran-infercsn Architecture: amd64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1446 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggnetwork, r-cran-ggplot2, r-cran-ggraph, r-cran-l0learn, r-cran-matrix, r-cran-purrr, r-cran-rcpp, r-cran-thisutils Suggests: r-bioc-complexheatmap, r-cran-circlize, r-cran-gtools, r-cran-gganimate, r-cran-ggextra, r-cran-ggpointdensity, r-cran-ggpubr, r-cran-igraph, r-cran-irlba, r-cran-network, r-cran-patchwork, r-cran-plotly, r-cran-precrec, r-cran-proc, r-cran-proxy, r-cran-tidygraph, r-cran-rann, r-cran-rcolorbrewer, r-cran-rtsne, r-cran-rtransferentropy, r-cran-uwot, r-cran-viridis Filename: pool/dists/noble/main/r-cran-infercsn_1.2.0-1.ca2404.1_amd64.deb Size: 1072314 MD5sum: 2e53da22a3f4e252d96b24c0d5b2fc9f SHA1: 72599ffa1e621544f611506719a4a7c34ecfe1d4 SHA256: 01557270cbece470057b71409ae31055668321198d610ef3e48c5d5b66b5bb12 SHA512: c72af7068f411546625d18ca253583af72da8fb70f12cdca4e36c07274aa893e242f2f2725f50e4d4427ba9b375eb79abc39c9ed48a856308b8778126bb842d6 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. 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'inferr' builds upon the solid set of statistical tests provided in 'stats' package by including additional data types as inputs, expanding and restructuring the test results. The tests included are t tests, variance tests, proportion tests, chi square tests, Levene's test, McNemar Test, Cochran's Q test and Runs test. Package: r-cran-infinitefactor Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 418 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-infinitefactor_1.0-1.ca2404.1_amd64.deb Size: 200264 MD5sum: bde65ae9fa558d67300b7ee187309c7a SHA1: 11d0635afd0b7e31ebda4892beed4581395a83e8 SHA256: 5d41a14b4f8c6c77b275f70cb212a34bbc41011c11ec5b764068a83f9a2e5bb3 SHA512: 961846a8dd64278e1b16027131ae1a1139ca23ef6a7794e5ebbaca6c816a7fd93255b914e4394ac60d55f68a41778de55f05722b925fa4185304e47d1986029f Homepage: https://cran.r-project.org/package=infinitefactor Description: CRAN Package 'infinitefactor' (Bayesian Infinite Factor Models) Sampler and post-processing functions for semi-parametric Bayesian infinite factor models, motivated by the Multiplicative Gamma Shrinkage Prior of Bhattacharya and Dunson (2011) . Contains component C++ functions for building samplers for linear and 2-way interaction factor models using the multiplicative gamma and Dirichlet-Laplace shrinkage priors. The package also contains post processing functions to return matrices that display rotational ambiguity to identifiability through successive application of orthogonalization procedures and resolution of column label and sign switching. This package was developed with the support of the National Institute of Environmental Health Sciences grant 1R01ES028804-01. Package: r-cran-influencer Architecture: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: libc6 (>= 2.14), libgomp1 (>= 6), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-influencer_0.1.5-1.ca2404.1_amd64.deb Size: 45960 MD5sum: 8aec0665030bcc895446be042716ac61 SHA1: fae09125eb3a6f7fa51057d4a2782c1bec5e56e2 SHA256: c4d4c01d6eaf77ea7112462335da5fe1483d47bee7e5f88b7d45c3e4e3dd8887 SHA512: 70da72cd70f4a053db6a193932baa1e74d87e057f1196e5dee18529c31c9e1856cf0db78f352bb7f39086385ce5ca611d67e39044c68ebd520b0fadcb589d29f Homepage: https://cran.r-project.org/package=influenceR Description: CRAN Package 'influenceR' (Software Tools to Quantify Structural Importance of Nodes in aNetwork) Provides functionality to compute various node centrality measures on networks. 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(2024) . Package: r-cran-infotheo Architecture: amd64 Version: 1.2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-infotheo_1.2.0.1-1.ca2404.1_amd64.deb Size: 50308 MD5sum: 5dfd49c55e044522cf23d4e91b28298a SHA1: eae58b950d73f1354bc356b9ad6b477b4d8124cb SHA256: 5dcec9d29c4ef6a6d738db83dfb39307eb1ce265ae6a8a924ac40d7c2aff656a SHA512: 21c4c8e1da8182c5e7880d80b451522f7578109aa8331e2c590c8dc3723426e8c39775f8073858498fd278717fcb7de142236308b2bb7ca2a7536e7e8b7d3e29 Homepage: https://cran.r-project.org/package=infotheo Description: CRAN Package 'infotheo' (Information-Theoretic Measures) Implements various measures of information theory based on several entropy estimators. Package: r-cran-infoxtr Architecture: amd64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1037 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sdsfun, r-cran-sf, r-cran-terra, r-cran-rcpp, r-cran-rcppthread Suggests: r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-spedm, r-cran-tedm Filename: pool/dists/noble/main/r-cran-infoxtr_0.2-1.ca2404.1_amd64.deb Size: 408560 MD5sum: 201db6ae5e1c94ebb65a18f11232247c SHA1: d42c5674c646ffa99c6bfaf4d398eb813367fc38 SHA256: a4892a1a6921e4e83139c8cc163063d129658892a52f9fb1393136fea5e11df6 SHA512: a950b6036592fac75d04e954cefea017594ee0cd76dec91c10fc60a954cc685dd05c710f8d60eedc463779ecebdbefc5756f1d09e0554db7fa77118aaea0966b Homepage: https://cran.r-project.org/package=infoxtr Description: CRAN Package 'infoxtr' (Information-Theoretic Measures for Revealing VariableInteractions) Implements information-theoretic measures to explore variable interactions, including KSG mutual information estimation for continuous variables from Kraskov et al. (2004) , knockoff conditional mutual information described in Zhang & Chen (2025) , synergistic-unique-redundant decomposition introduced by Martinez-Sanchez et al. (2024) , allowing detection of complex and diverse relationships among variables. Package: r-cran-inlabru Architecture: amd64 Version: 2.14.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3987 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fmesher, r-cran-glue, r-cran-lifecycle, r-cran-matrixmodels, r-cran-matrix, r-cran-plyr, r-cran-rlang, r-cran-sf, r-cran-tibble, r-cran-withr, r-cran-rcpp Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-maps, r-cran-mgcv, r-cran-patchwork, r-cran-raster, r-cran-rcolorbrewer, r-cran-rgl, r-cran-rmarkdown, r-cran-scales, r-cran-scoringrules, r-cran-shiny, r-cran-sn, r-cran-sp, r-cran-spatstat.geom, r-cran-spatstat.data, r-cran-sphereplot, r-cran-splancs, r-cran-terra, r-cran-tidyterra, r-cran-testthat, r-cran-tidyr, r-cran-diagrammer Filename: pool/dists/noble/main/r-cran-inlabru_2.14.1-1.ca2404.1_amd64.deb Size: 3258904 MD5sum: 8ec850a57073fd3133f0787fcc7936c5 SHA1: af83e279e3d076fb0b968de418077a4c7c58ba2f SHA256: c335afe72887a000ab7d49cb24711840289905a9ec41874b12e16325e6a8f9d5 SHA512: 489d0db6eb6d4db5a351124b3d52f8e0212b64a7a47b3107b01f39425f06771f0b88d5aacd01a2c66d5c3b0c28e978b99a4e42f72f160c9709d3375e30f81a0a 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) . 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These objects contain data to for the 'cgeneric' interface in 'INLA', enabling fast parallel computations. We implemented the spatial barrier model, see Bakka et. al. (2019) , and some of the spatio-temporal models proposed in Lindgren et. al. (2024) . Details are provided in the available vignettes and from the URL bellow. 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The cgeneric contain code to specify GMRF elements such as the graph and the precision matrix, and also the initial and prior for its parameters, useful for model inference. It can be accessed from a C program and is the recommended way to implement new GMRF models in the 'INLA' package (). The 'INLAtools' implement functions to evaluate each one of the model specifications from R. The implemented functionalities leverage the use of 'cgeneric' models and provide a way to debug the code as well to work with the prior for the model parameters and to sample from it. The `generic0` can be used to implement intrinsic models with the scaling as proposed in Sørbye & Rue (2014) , and the required constraints. A very useful functionality is the Kronecker product method that creates a new model from multiple cgeneric models. It also works with the rgeneric, the R version of the cgeneric intended to easy try implementation of new GMRF models. The Kronecker between two cgeneric models where each one needs a constraint, such as spatio-temporal intrinsic interaction models, the needed constraints are automatically set. 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You have also access to sweep operations (in-place). 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Functions report missingness, categorical levels, numeric distribution, correlation, column types and memory usage. Package: r-cran-inspire Architecture: amd64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3279 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-missmda Filename: pool/dists/noble/main/r-cran-inspire_1.5-1.ca2404.1_amd64.deb Size: 3307982 MD5sum: c301b946e3a7ac8361fbba518b947e43 SHA1: 913462c78228515d55a1cc32d8f8b4e0d51a4263 SHA256: 80714df1c93365f445d7e94fca324904dc385e5e259a3d96491da6f3c0fa1577 SHA512: 25adbecee6fc4e95c9e5e920546a22ac2334a99a80f5b8c90f97af8d92c000d748e2af83575908d819ba570332a565713ce45b2865cc99b0166ec29a45171165 Homepage: https://cran.r-project.org/package=INSPIRE Description: CRAN Package 'INSPIRE' (Inferring Shared Modules from Multiple Gene Expression Datasetswith Partially Overlapping Gene Sets) A method to infer modules of co-expressed genes and the dependencies among the modules from multiple expression datasets that may contain different sets of genes. Please refer to: Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer, Safiye Celik, Benjamin A. Logsdon, Stephanie Battle, Charles W. Drescher, Mara Rendi, R. David Hawkins and Su-In Lee (2016) . 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The models compile once during installation, the executables live inside the file systems of their respective packages, and users have the full power and convenience of 'cmdstanr' without any additional compilation after package installation. This approach saves time and helps R package developers migrate from 'rstan' to the more modern 'cmdstanr'. Packages 'rstantools', 'cmdstanr', 'stannis', and 'stanapi' are similar Stan clients with different objectives. 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Partially monotone tensor spline estimation of the joint distribution function with bivariate current status data. Ann. Statist. 40, 2012, 1609-1636 ). The key function computes the density function of the joint distribution of event time and the marker and returns the receiver operating characteristic (ROC) curve for the interval censored survival data as well as area under the curve (AUC). 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Package: r-cran-interep Architecture: amd64 Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 913 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-interep_0.4.1-1.ca2404.1_amd64.deb Size: 820142 MD5sum: ff7518f79a8b007d97e8bd39caf178ba SHA1: a8c53d86363b24d9038a8ee1d4c48fa5049e03cc SHA256: 9dba1e16d7803454afc999559303cee0c885d94094ab82e21105dec9c37b09a9 SHA512: 21fc5841a539aa9999de2bcf8e7624761bb05c018700c5022cd892db6fb5cdbff482992c97e0fb9fb10d79684910ca359256f51658b39a4bebeb66f4d7f29b16 Homepage: https://cran.r-project.org/package=interep Description: CRAN Package 'interep' (Interaction Analysis of Repeated Measure Data) Extensive penalized variable selection methods have been developed in the past two decades for analyzing high dimensional omics data, such as gene expressions, single nucleotide polymorphisms (SNPs), copy number variations (CNVs) and others. However, lipidomics data have been rarely investigated by using high dimensional variable selection methods. This package incorporates our recently developed penalization procedures to conduct interaction analysis for high dimensional lipidomics data with repeated measurements. The core module of this package is developed in C++. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University. Package: r-cran-interflex Architecture: amd64 Version: 1.2.8-1.ca2404.1 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), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-sandwich, r-cran-lmoments, r-cran-doparallel, r-cran-foreach, r-cran-mgcv, r-cran-lfe, r-cran-gridextra, r-cran-ggplotify, r-cran-rcolorbrewer, r-cran-pcse, r-cran-gtable, r-cran-mass, r-cran-mvtnorm, r-cran-proc, r-cran-modelmetrics, r-cran-rcpp, r-cran-lmtest, r-cran-aer, r-cran-future, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-interflex_1.2.8-1.ca2404.1_amd64.deb Size: 762644 MD5sum: b29c318029b58f31e7a2eee3d9792326 SHA1: 022f42a428a5c0aae6e5b6bb891a0e54743d9aa1 SHA256: 5c5cd20632b99900eb2973b113b93a9cc314672b313f828dcad74c6cf34f41c6 SHA512: fff4dd5e1db95d76bbbda31ee00ac95610b0628f5fb8687ab17ac0322124ae76c81b8b9c06b0db2af1c40ab20501afb40d25bdb9eda09f1974360a3884a1b3d1 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. 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It is intended to provide FOSS replacement functions for the ACM licensed akima::interp and tripack::tri.mesh functions. Linear interpolation is implemented in interp::interp(..., method="linear"), this corresponds to the call akima::interp(..., linear=TRUE) which is the default setting and covers most of akima::interp use cases in depending packages. A re-implementation of Akimas irregular grid spline interpolation (akima::interp(..., linear=FALSE)) is now also available via interp::interp(..., method="akima"). Estimators for partial derivatives are now also available in interp::locpoly(), these are a prerequisite for the spline interpolation. The basic part is a GPLed triangulation algorithm (sweep hull algorithm by David Sinclair) providing the starting point for the irregular grid interpolator. As side effect this algorithm is also used to provide replacements for almost all functions of the tripack package which also suffers from the same ACM license restrictions. All functions are designed to be backward compatible with their akima / tripack counterparts. 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Package: r-cran-intervalpsych Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3222 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggdist, r-cran-ggokabeito, r-cran-ggplot2, r-cran-posterior, r-cran-purrr, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-intervalpsych_0.1.0-1.ca2404.1_amd64.deb Size: 974676 MD5sum: 8b81ac0e0929d5088e5f1f4b12d218d1 SHA1: c328f86e1cf6b8bfb494d7252ef4b77751857323 SHA256: 6f1c65c684e7223a4f16f9a18ca3f8c82aece3a66bb39725491ede368f87cc48 SHA512: 41365b212b34affea8ac18953178cd0f7baa91cc053b907b0a01158f4f362fb62a93c9362cad9d5ab55e7f45983ccb119c740ca13cdb83a9dca1e6a788102192 Homepage: https://cran.r-project.org/package=intervalpsych Description: CRAN Package 'intervalpsych' (Analyzing Interval Data in Psychometrics) Implements the Interval Consensus Model (ICM) for analyzing continuous bounded interval-valued responses in psychometrics using 'Stan' for 'Bayesian' estimation. 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Package: r-cran-intkrige Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1157 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp, r-cran-gstat, r-cran-raster, r-cran-rcpp, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-doparallel, r-cran-foreach, r-cran-lattice, r-cran-latticeextra, r-cran-gridextra, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-intkrige_1.0.2-1.ca2404.1_amd64.deb Size: 658074 MD5sum: 702739d0fa5b99fda9e5dd5f0fa9700c SHA1: d276eb96e2e319c7db5e4e55e834b104b685f762 SHA256: 52e02db782351e14890099902738506c01349a93a6d77629e7c5697d8812e696 SHA512: 97228ea7d46fb0af47459a170fda7517efcfefd05939ac96efb8d00d7d4fd0afdc7e81aebe387dcd81b27116161c6d23a42029ba006199b6b99edd8e2f770f3d Homepage: https://cran.r-project.org/package=intkrige Description: CRAN Package 'intkrige' (A Numerical Implementation of Interval-Valued Kriging) An interval-valued extension of ordinary and simple kriging. Optimization of the function is based on a generalized interval distance. This creates a non-differentiable cost function that requires a differentiable approximation to the absolute value function. This differentiable approximation is optimized using a Newton-Raphson algorithm with a penalty function to impose the constraints. Analyses in the package are driven by the 'intsp' and 'intgrd' classes, which are interval-valued extensions of 'SpatialPointsDataFrame' and 'SpatialPixelsDataFrame' respectively. The package includes several wrappers to functions in the 'gstat' and 'sp' packages. Package: r-cran-intmap Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-maybe, r-cran-r6, r-cran-rcpp, r-cran-bh Filename: pool/dists/noble/main/r-cran-intmap_1.0.0-1.ca2404.1_amd64.deb Size: 158800 MD5sum: 3f21a86379020f76645560a8b2f8b3ff SHA1: b46bddced72501e16244b34ad3d09b6b6e376327 SHA256: 85e7891ad7af8a6a1f2fdd2a045a5301819f563513fcf2b2c153cd79c264391a SHA512: 434d0c7a3af41bb96931911d56701b5eb6a4cd95cb777bd59db011d7a0fe70f4971c0c8d8304a0a3739e2751f13ac486ad064d9868fb9ddf4f88d9170970c77b Homepage: https://cran.r-project.org/package=intmap Description: CRAN Package 'intmap' (Ordered Containers with Integer Keys) Provides a key-value store data structure. The keys are integers and the values can be any R object. 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Package: r-cran-intreggof Architecture: amd64 Version: 0.85-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-intreggof_0.85-5-1.ca2404.1_amd64.deb Size: 53606 MD5sum: fe012f537cc3698f0e36f0a88fe851e4 SHA1: 9d599a0a5749fd151795143e382d39651f93b669 SHA256: 9952201994c429f9edebc821e84f684c69b9f94c5b5475983485dfd402285b8c SHA512: ff3717316d7e4c5784b652b27ce5eed2534c85f3bce8e5664391b902094099475762effd5c7cf7ef333bbcc8ac67c45ccbfeb6afdfca92a8452f91b37f871b2c Homepage: https://cran.r-project.org/package=intRegGOF Description: CRAN Package 'intRegGOF' (Integrated Regression Goodness of Fit) Performs Goodness of Fit for regression models using Integrated Regression method. Works for several different fitting techniques. 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Package: r-cran-intrinsicfrp Architecture: amd64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1071 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-intrinsicfrp_2.1.0-1.ca2404.1_amd64.deb Size: 509762 MD5sum: 68120589f8fa44b1d16f0754cefe511a SHA1: 78a4237e13fea21a6100371d326bc2fa1f50a40e SHA256: 35e9e0daf77b22f60c090bbb8183280320a984bdcc2ed6180a4a580b78a39acf SHA512: ef0c62d7c9662f22509ef806bcdb2690044cf255057b5581ae8c7e2ddb4c1e231c11763ca4bc0d409be30ae1390efe1135ad011048f813cf213996566d4076f2 Homepage: https://cran.r-project.org/package=intrinsicFRP Description: CRAN Package 'intrinsicFRP' (An R Package for Factor Model Asset Pricing) Functions for evaluating and testing asset pricing models, including estimation and testing of factor risk premia, selection of "strong" risk factors (factors having nonzero population correlation with test asset returns), heteroskedasticity and autocorrelation robust covariance matrix estimation and testing for model misspecification and identification. The functions for estimating and testing factor risk premia implement the Fama-MachBeth (1973) two-pass approach, the misspecification-robust approaches of Kan-Robotti-Shanken (2013) , and the approaches based on tradable factor risk premia of Quaini-Trojani-Yuan (2023) . The functions for selecting the "strong" risk factors are based on the Oracle estimator of Quaini-Trojani-Yuan (2023) and the factor screening procedure of Gospodinov-Kan-Robotti (2014) . The functions for evaluating model misspecification implement the HJ model misspecification distance of Kan-Robotti (2008) , which is a modification of the prominent Hansen-Jagannathan (1997) distance. The functions for testing model identification specialize the Kleibergen-Paap (2006) and the Chen-Fang (2019) rank test to the regression coefficient matrix of test asset returns on risk factors. Finally, the function for heteroskedasticity and autocorrelation robust covariance estimation implements the Newey-West (1994) covariance estimator. 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(2020) , the regularized Cox cure rate model with uncertain event status proposed by Wang, et al. (2023) , and other survival analysis routines including the Cox cure rate model proposed by Kuk and Chen (1992) via an EM algorithm proposed by Sy and Taylor (2000) , the regularized Cox cure rate model with elastic net penalty following Masud et al. (2018) . Package: r-cran-invgamstochvol Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 845 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-invgamstochvol_1.0.0-1.ca2404.1_amd64.deb Size: 289396 MD5sum: 1dd4ab94a23f00403e6671176c0774e4 SHA1: db7743af3fc76f756d23b823bb739f4595ff71a0 SHA256: 1dce0b7d5d63b5f3d58700e72fe181f21d9f0ed984defca88085b45ab5446ad0 SHA512: 8b3f5b776dcb467a8281065a4daa66c77180d5c0b9c3c6876d7660b435f81d9cd25480dd22b42b1e267df2ec11a9430109c3beed1196ae844973ead990bd2122 Homepage: https://cran.r-project.org/package=invgamstochvol Description: CRAN Package 'invgamstochvol' (Obtains the Log Likelihood for an Inverse Gamma StochasticVolatility Model) Computes the log likelihood for an inverse gamma stochastic volatility model using a closed form expression of the likelihood. 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Package: r-cran-iq Architecture: amd64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 801 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-iq_2.0.1-1.ca2404.1_amd64.deb Size: 478532 MD5sum: e0b66747c3cb77aaa2272735953a5e2b SHA1: fc8446348de683acf7e58a17b315f35237825732 SHA256: 7d80255c31250fe54a0570ed974307a3c69d8d351630d3dd0c6879ab770b3f11 SHA512: 95d10e07af48bf083b93211e616704b4d0e2a3400dfeaccfabb03248028cfb7eae4ca7e3abeb3fea78a6702d4faa8670230d53b7238744020cd612e78635bca9 Homepage: https://cran.r-project.org/package=iq Description: CRAN Package 'iq' (Protein Quantification in Mass Spectrometry-Based Proteomics) An implementation of the MaxLFQ algorithm by Cox et al. 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Package: r-cran-irace Architecture: amd64 Version: 4.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2415 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-codetools, r-cran-data.table, r-cran-fs, r-cran-matrixstats, r-cran-spacefillr, r-cran-withr Suggests: r-cran-rmpi, r-cran-highr, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-irace_4.4.3-1.ca2404.1_amd64.deb Size: 1993250 MD5sum: e992e3e3420abdc188f371c9ff4acb44 SHA1: 32e3aa7cb455ffd7e2810dd9aab7c959995886e6 SHA256: 2c7f76396b0bb2c0559e3797a3911f2a179787639283d3569d0b2a9559cc2318 SHA512: e1dda878ef668069cbd228274b398aca6ebbfb7b26d38dff27d95b9a6cf84b2982d04b788e38e9cc15ae63cc98c126850639c4cc1a0e101ba718b4cd514ba84e Homepage: https://cran.r-project.org/package=irace Description: CRAN Package 'irace' (Iterated Racing for Automatic Algorithm Configuration) Iterated race is an extension of the Iterated F-race method for the automatic configuration of optimization algorithms, that is, (offline) tuning their parameters by finding the most appropriate settings given a set of instances of an optimization problem. M. López-Ibáñez, J. Dubois-Lacoste, L. Pérez Cáceres, T. Stützle, and M. Birattari (2016) . 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See Henzi, Ziegel, Gneiting (2020) . 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Package: r-cran-isopurer Architecture: amd64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1276 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-futile.logger, r-cran-rcppeigen Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-isopurer_1.1.3-1.ca2404.1_amd64.deb Size: 1092482 MD5sum: 1d3dd85c21b01f08bf4e11092146d1a7 SHA1: 85d640bb8e17296e00aefbd1411db5ac8b020b19 SHA256: 1fc4f26ddb980e3b4be67f3701bb258fb49d3bcb6cad16acec5e26c13ab18960 SHA512: f377ae75be3d255a7ccd2559e63ece5ec14a9d622af7b735ebff5cd0859b0a09c01c95d8d715a72bd18a2f590b6606aab784f7247709c2a0e3e51d53cc84f306 Homepage: https://cran.r-project.org/package=ISOpureR Description: CRAN Package 'ISOpureR' (Deconvolution of Tumour Profiles) Deconvolution of mixed tumour profiles into normal and cancer for each patient, using the ISOpure algorithm in Quon et al. Genome Medicine, 2013 5:29. 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Package: r-cran-isospecr Architecture: amd64 Version: 2.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 402 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-isospecr_2.3.3-1.ca2404.1_amd64.deb Size: 149576 MD5sum: 2c412f550d192c81cb227c834bbca665 SHA1: 56f0bb989ea57ff0cd61ceea6af9db6c5076ae0d SHA256: 9351dec2723095c0cd892f839d75a4f20aebfcfc1ad28939c4b999c0b1a9c123 SHA512: aeedc574923862054cb9bae11b1bcf5cb3b565b033a81cefcd6ac2e93ae0643fb4878d392ea3af16632600bf1236fe8502bd4179d96d0f2bb86d0e1f8b474460 Homepage: https://cran.r-project.org/package=IsoSpecR Description: CRAN Package 'IsoSpecR' (The IsoSpec Algorithm) IsoSpec is a fine structure calculator used for obtaining the most probable masses of a chemical compound given the frequencies of the composing isotopes and their masses. It finds the smallest set of isotopologues with a given probability. The probability is assumed to be that of the product of multinomial distributions, each corresponding to one particular element and parametrized by the frequencies of finding these elements in nature. These numbers are supplied by IUPAC - the International Union of Pure and Applied Chemistry. See: Lacki, Valkenborg, Startek (2020) and Lacki, Startek, Valkenborg, Gambin (2017) for the description of the algorithms used. Package: r-cran-isotone Architecture: amd64 Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 486 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnls Filename: pool/dists/noble/main/r-cran-isotone_1.1-1-1.ca2404.1_amd64.deb Size: 366310 MD5sum: df80a09bb60290e60d585dea8fe06576 SHA1: dee67eaa4b32d6c9897d3ea474ba579a7d3ea618 SHA256: f2a951dcd5ee36e9d14a00ee671c3eb7ad2e8ba4e8098a86a2108c00de8c056f SHA512: e41739dc246bd05cc42af855f36da8643807c91d6961223fe41f0d8a96e0da6fc1efe249a8940b42b629a3e1cbcb4fa7e580c86a279295008ecff85d9319027c Homepage: https://cran.r-project.org/package=isotone Description: CRAN Package 'isotone' (Active Set and Generalized PAVA for Isotone Optimization) Contains two main functions: one for solving general isotone regression problems using the pool-adjacent-violators algorithm (PAVA); another one provides a framework for active set methods for isotone optimization problems with arbitrary order restrictions. Various types of loss functions are prespecified. Package: r-cran-isotracer Architecture: amd64 Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7780 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-data.table, r-cran-dplyr, r-cran-latex2exp, r-cran-magrittr, r-cran-pillar, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-bayesplot, r-cran-covr, r-cran-cowplot, r-cran-ggdist, r-cran-ggplot2, r-cran-ggraph, r-cran-gridbase, r-cran-gridextra, r-cran-here, r-cran-igraph, r-cran-knitr, r-cran-lattice, r-cran-readxl, r-cran-rmarkdown, r-cran-testthat, r-cran-tidygraph, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-isotracer_1.1.8-1.ca2404.1_amd64.deb Size: 4365890 MD5sum: 987b6cd9dea01947ff001f3179d61731 SHA1: 541fbfbd794cdf277f692abcc8243a545037fb44 SHA256: dd99e38f524f0d8e1b6820e33738f1cdde9dfb5213ad6372c9a88f59882c0582 SHA512: dbf38789a61b75d442a171042c566553311971bca7b8320ca37941b463838496d2862bfeec8a341b2c17acdd114abe17cac0d3fe59e727f725bbcbffd950c1e5 Homepage: https://cran.r-project.org/package=isotracer Description: CRAN Package 'isotracer' (Isotopic Tracer Analysis Using MCMC) Implements Bayesian models to analyze data from tracer addition experiments. The implemented method was originally described in the article "A New Method to Reconstruct Quantitative Food Webs and Nutrient Flows from Isotope Tracer Addition Experiments" by López-Sepulcre et al. (2020) . 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Provides simple heuristics for fitting the model to categorical columns and handling missing data, and offers options for varying between random and guided splits, and for using different splitting criteria. Package: r-cran-isr Architecture: amd64 Version: 2025.01.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-isr_2025.01.14-1.ca2404.1_amd64.deb Size: 346794 MD5sum: 02c9e483719887d0ac817997462ecaa0 SHA1: 88d58d4e8b804e95d6dc40b5c4496490a693e60b SHA256: 699799fec5e3d131dd0e6b5c6e25614041b47678c9b4b2d864f76a39c1d40454 SHA512: e175f5bc4dd8c62f59c97d29badf4a779d8d495910929bf084f252b53d22d50c1bc60ea757c6184b1fbee9fb0bd4cd65ba8b2ecdc192fca214a048712673884a Homepage: https://cran.r-project.org/package=ISR Description: CRAN Package 'ISR' (The Iterated Score Regression-Based Estimation) We use the ISR to handle with PCA-based missing data with high correlation, and the DISR to handle with distributed PCA-based missing data. The philosophy of the package is described in Guo G. (2024) . 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Package: r-cran-itdr Architecture: amd64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1728 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-geigen, r-cran-magic, r-cran-energy, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-itdr_2.0.1-1.ca2404.1_amd64.deb Size: 1514118 MD5sum: e16585512f437333aafb82f4ad136685 SHA1: f943b9c13a5ce88fca45d10f051afe57ffa35961 SHA256: c42500bbf3a5e3c9883aa48729d6905025638668ab105343fc3e3008e395055a SHA512: bee1d620570895ade092f4d8f98ac487d3a97edca6754bfdb327f95ac2a8802fb159f347a496c5061d0b5a7b70d6bed1d9a3209ea1c49236dac5c859165205a3 Homepage: https://cran.r-project.org/package=itdr Description: CRAN Package 'itdr' (Integral Transformation Methods for SDR in Regression) The itdr() routine allows for the estimation of sufficient dimension reduction subspaces in univariate regression such as the central mean subspace or central subspace in regression. This is achieved using Fourier transformation methods proposed by Zhu and Zeng (2006) , convolution transformation methods proposed by Zeng and Zhu (2010) , and iterative Hessian transformation methods proposed by Cook and Li (2002) . Additionally, mitdr() function provides optimal estimators for sufficient dimension reduction subspaces in multivariate regression by optimizing a discrepancy function using a Fourier transform approach proposed by Weng and Yin (2022) , and selects the sufficient variables using Fourier transform sparse inverse regression estimators proposed by Weng (2022) . Package: r-cran-iterlap Architecture: amd64 Version: 1.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog, r-cran-randtoolbox Filename: pool/dists/noble/main/r-cran-iterlap_1.1-4-1.ca2404.1_amd64.deb Size: 70968 MD5sum: b3e132de20e24f551ae6b21f081843dc SHA1: 07b4d8902379071d492dd2712c3c1ce6b0bbbb2a SHA256: 54c7017540412bb3abff6d63389d3cc95005e8866c25f1ccc7638c78cf8f4392 SHA512: 50aa87cc35a6530c99a682113d169d703dded1dbdbf5e2341b5e9d737c1c272182dc752f4a1ba77808e2fb66ef6d8c91587fc6cef6158a2e34aae6bc45313b19 Homepage: https://cran.r-project.org/package=iterLap Description: CRAN Package 'iterLap' (Approximate Probability Densities by Iterated LaplaceApproximations) The iterLap (iterated Laplace approximation) algorithm approximates a general (possibly non-normalized) probability density on R^p, by repeated Laplace approximations to the difference between current approximation and true density (on log scale). The final approximation is a mixture of multivariate normal distributions and might be used for example as a proposal distribution for importance sampling (eg in Bayesian applications). The algorithm can be seen as a computational generalization of the Laplace approximation suitable for skew or multimodal densities. Package: r-cran-itmsa Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-sdsfun, r-cran-sf, r-cran-rcpp, r-cran-rcppthread Suggests: r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/noble/main/r-cran-itmsa_0.1.0-1.ca2404.1_amd64.deb Size: 146028 MD5sum: 22dfe6e526817583c94f09a32f56897d SHA1: 4b932c506a958480df733da6d59a02c4ede6a043 SHA256: 298fded283b13b9f735882f63ab877c0ed973c8e09adbded1575d69aecb017cf SHA512: f19d89f1709160bde12096c680d518af41b6ef29582497c0f783b08d7490302243b75b3c5e2df31757e3164bfe0c426222a5a566e48e240799f15dca6c86fe89 Homepage: https://cran.r-project.org/package=itmsa Description: CRAN Package 'itmsa' (Information-Theoretic Measures for Spatial Association) Leveraging information-theoretic measures like mutual information and v-measure to quantify spatial associations between patterns (Nowosad and Stepinski (2018) ; Bai, H. et al. (2023) ). Package: r-cran-itp Architecture: amd64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-itp_1.2.2-1.ca2404.1_amd64.deb Size: 138036 MD5sum: 983de70b1ba23a69086a1568b648689d SHA1: 0740e024fca28bfceb06d5931a6750bf9f3eaefd SHA256: 4184fd0cb167071bb7f8e6d151b3910f59b65e0f19eafe9a525202f11dec5484 SHA512: da3f9f4ed3d3f3e65b90ec1eeda0008e7cfc753d9569b349f5cd22eb1c1fea1bcab485cb5a2ed4006afaaa7ea07e1e5475d256cb90b587face67b2d17f617dc2 Homepage: https://cran.r-project.org/package=itp Description: CRAN Package 'itp' (The Interpolate, Truncate, Project (ITP) Root-Finding Algorithm) Implements the Interpolate, Truncate, Project (ITP) root-finding algorithm developed by Oliveira and Takahashi (2021) . The user provides the function, from the real numbers to the real numbers, and an interval with the property that the values of the function at its endpoints have different signs. If the function is continuous over this interval then the ITP method estimates the value at which the function is equal to zero. If the function is discontinuous then a point of discontinuity at which the function changes sign may be found. The function can be supplied using either an R function or an external pointer to a C++ function. Tuning parameters of the ITP algorithm can be set by the user. Default values are set based on arguments in Oliveira and Takahashi (2021). Package: r-cran-ivdoctr Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 469 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-aer, r-cran-coda, r-cran-data.table, r-cran-mass, r-cran-rcpp, r-cran-rgl, r-cran-sandwich, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-haven, r-cran-mcmcpack, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ivdoctr_1.0.1-1.ca2404.1_amd64.deb Size: 322118 MD5sum: f2c00ee43b0596a5cbf9279fb5ee5b79 SHA1: 48ab4c57409f2a59105c63db47eeda9ae1d9572c SHA256: 1138c2e614f6adb15c65c3d9634b1e3fd44feb394dd03c0168d6e357e3ca60a8 SHA512: ea9fef54ed300ec0a07d0842acc46b95d05d84a3ae9b8c07a3f1174f14764a7813de0cd386435c8c910b60fae76b81042c4c6734f545434aa50e2507a300ad25 Homepage: https://cran.r-project.org/package=ivdoctr Description: CRAN Package 'ivdoctr' (Ensures Mutually Consistent Beliefs When Using IVs) Uses data and researcher's beliefs on measurement error and instrumental variable (IV) endogeneity to generate the space of consistent beliefs across measurement error, instrument endogeneity, and instrumental relevance for IV regressions. Package based on DiTraglia and Garcia-Jimeno (2020) . Package: r-cran-ivsacim Architecture: amd64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-ivsacim_2.1.0-1.ca2404.1_amd64.deb Size: 102368 MD5sum: ed8c7978c41b62006d001043ce30d522 SHA1: 23337fb7ce6d8a5055c7e242c2764e1d88eeca45 SHA256: 8d9d9b41459bfe926a3ee7e9cef654b00f0065e62f13eb2cc16b0b982df112d2 SHA512: 5426ab860f39c41b73b4b0380ad841c4363f1007c15dac232979c41e808a7a41b51050cc1300f83a0665d5e49ab7d612e58751537ea846a9b2943d4044176f15 Homepage: https://cran.r-project.org/package=ivsacim Description: CRAN Package 'ivsacim' (Structural Additive Cumulative Intensity Models with IV) An instrumental variable estimator under structural cumulative additive intensity model is fitted, that leverages initial randomization as the IV. The estimator can be used to fit an additive hazards model under time to event data which handles treatment switching (treatment crossover) correctly. We also provide a consistent variance estimate. Package: r-cran-ivtools Architecture: amd64 Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-numderiv, r-cran-nleqslv, r-cran-survival, r-cran-ahaz, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-ivtools_2.3.0-1.ca2404.1_amd64.deb Size: 212320 MD5sum: c5593f457d4aadebcc52f0a146b8b820 SHA1: 4e918ee8185936d4e97e7361eb1b8aecb871426a SHA256: cbab2e82370d920a672ae7f855dd166639e16ef69feb08ba04fa091e15f32029 SHA512: 725d14797c6862619625c5ccb32c76e23f74fbc9a9054999f865f90758b06cccd26b9c24167310467c389ab14cc655255c6be44a0101f9be81b8781c7c08dded Homepage: https://cran.r-project.org/package=ivtools Description: CRAN Package 'ivtools' (Instrumental Variables) Contains tools for instrumental variables estimation. Currently, non-parametric bounds, two-stage estimation and G-estimation are implemented. Balke, A. and Pearl, J. (1997) , Vansteelandt S., Bowden J., Babanezhad M., Goetghebeur E. (2011) . Package: r-cran-ivx Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 696 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-forecast, r-cran-spelling, r-cran-testthat, r-cran-lmtest Filename: pool/dists/noble/main/r-cran-ivx_1.1.1-1.ca2404.1_amd64.deb Size: 403006 MD5sum: 185285709ecd5727d163dbd3a97b1a87 SHA1: 66348ca64b9aa385f263a8e9e936c7eaa7e3e1c0 SHA256: 359ebba11728ff4425b7f9b0372a3dfd3e164e6ddf4c1406ab04fa2aa17ef059 SHA512: d7d823212a28464c981b993b86cc6c054dda6105a008cd851a85fde38fb1c8b7907619b110f667df6b2fbcd5a87cb094788a3fca29ca69aad60eaa93a43482b1 Homepage: https://cran.r-project.org/package=ivx Description: CRAN Package 'ivx' (Robust Econometric Inference) Drawing statistical inference on the coefficients of a short- or long-horizon predictive regression with persistent regressors by using the IVX method of Magdalinos and Phillips (2009) and Kostakis, Magdalinos and Stamatogiannis (2015) . Package: r-cran-jaccard Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-bioc-qvalue, r-cran-shiny Filename: pool/dists/noble/main/r-cran-jaccard_0.1.2-1.ca2404.1_amd64.deb Size: 83726 MD5sum: 54b5b0df145e50fee8b56be846c1b716 SHA1: 78f326f737afb53ccc819fb50428c33bfb27162f SHA256: 22e06c5bcf351a790a2ee4c345c4b5b1323fb49a229b8e039c9dc7c0019f8fed SHA512: 2cabee35c50d1d9a302614cca9c5db8c23a1a030b79766382aee202393be8fedaac70af11653c5ab8d2ddcc9b76d3cb2e392aecf252283443a58b92eb140d4f7 Homepage: https://cran.r-project.org/package=jaccard Description: CRAN Package 'jaccard' (Testing Similarity Between Binary Datasets usingJaccard/Tanimoto Coefficients) Calculate statistical significance of Jaccard/Tanimoto similarity coefficients. Package: r-cran-jack Architecture: amd64 Version: 6.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1991 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-qspray, r-cran-ratioofqsprays, r-cran-symbolicqspray, r-cran-desctools, r-cran-gmp, r-cran-multicool, r-cran-mvp, r-cran-partitions, r-cran-rationalmatrix, r-cran-rcpp, r-cran-spray, r-cran-syt, r-cran-bh, r-cran-rcppcgal Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-jack_6.1.0-1.ca2404.1_amd64.deb Size: 784916 MD5sum: 936a04cd7379b43c1ad90bbd752015e9 SHA1: dd21142687e7d4b0207a87f57c76167fc1f752b5 SHA256: c9e3fef86dd4b2a11bd47b69bbf3d6001221140eaf9ddc5ab207392ad5bd94fb SHA512: e77d1d52b8278c583d2e426abd7c88fae6d98183baf80ebf8d13f88090d6e0fc2faf594415a07016287e367e00b2081dc2ddb6d6e51ee69e7f10f3d2d5470b7a Homepage: https://cran.r-project.org/package=jack Description: CRAN Package 'jack' (Jack, Zonal, Schur, and Other Symmetric Polynomials) Schur polynomials appear in combinatorics and zonal polynomials appear in random matrix theory. They are particular cases of Jack polynomials. This package allows to compute these polynomials and other symmetric multivariate polynomials: flagged Schur polynomials, factorial Schur polynomials, t-Schur polynomials, Hall-Littlewood polynomials, Macdonald polynomials, and modified Macdonald polynomials. In addition, it can compute the Kostka-Jack numbers, the Kostka-Foulkes polynomials, the Kostka-Macdonald polynomials, and the Hall polynomials. Mainly based on Demmel & Koev's paper (2006) and Macdonald's book (1995) . Package: r-cran-jackalope Architecture: amd64 Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4134 Depends: libblas3 | libblas.so.3, libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 13.1), zlib1g (>= 1:1.2.3.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-r6, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppprogress, r-bioc-rhtslib Suggests: r-cran-coala, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-scrm, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jackalope_1.1.6-1.ca2404.1_amd64.deb Size: 2774612 MD5sum: c2f5b8940e9c3b33e8d724d7451628f3 SHA1: 18d37b87a94b665c796a0be67e20768508fd1d11 SHA256: bfa42445984ce8d5f7d6a46e50aeb6a65149b4d4563e5340c4e1b1c56e94a9ae SHA512: 1adf6ea336b23c5e1a22f52ffee7c34ef9532aec6fed056e1564ec26d7d7965503731027db07a1b88f30db170b091d891f6a1c1df4223643eb63aa902a3a95cc Homepage: https://cran.r-project.org/package=jackalope Description: CRAN Package 'jackalope' (A Swift, Versatile Phylogenomic and High-Throughput SequencingSimulator) Simply and efficiently simulates (i) variants from reference genomes and (ii) reads from both Illumina and Pacific Biosciences (PacBio) platforms. It can either read reference genomes from FASTA files or simulate new ones. Genomic variants can be simulated using summary statistics, phylogenies, Variant Call Format (VCF) files, and coalescent simulations—the latter of which can include selection, recombination, and demographic fluctuations. 'jackalope' can simulate single, paired-end, or mate-pair Illumina reads, as well as PacBio reads. These simulations include sequencing errors, mapping qualities, multiplexing, and optical/polymerase chain reaction (PCR) duplicates. Simulating Illumina sequencing is based on ART by Huang et al. (2012) . PacBio sequencing simulation is based on SimLoRD by Stöcker et al. (2016) . All outputs can be written to standard file formats. Package: r-cran-jacobi Architecture: amd64 Version: 3.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-carlson, r-cran-rcpp, r-cran-rgl, r-cran-rvcg Suggests: r-cran-testthat, r-cran-elliptic, r-cran-rcppcolors Filename: pool/dists/noble/main/r-cran-jacobi_3.1.1-1.ca2404.1_amd64.deb Size: 172478 MD5sum: 401103353d55aaf790f0d6d82c8a5d55 SHA1: 850204af6b1986c4202aba00a527c42a99aab445 SHA256: 6819cd7fc7c1cded78538d119d2880617c66b4bb0e3c2343d4d235f4a6442b6d SHA512: 81adb510585ad4e9cd1122323bc0949b6a1cff4f35c26484edd44a1e32530b9beaad505b196e34cdd708f7d805563c49f313088ac5524430114889fc1394f878 Homepage: https://cran.r-project.org/package=jacobi Description: CRAN Package 'jacobi' (Jacobi Theta Functions and Related Functions) Evaluation of the Jacobi theta functions and related functions: Weierstrass elliptic function, Weierstrass sigma function, Weierstrass zeta function, Klein j-function, Dedekind eta function, lambda modular function, Jacobi elliptic functions, Neville theta functions, Eisenstein series, lemniscate elliptic functions, elliptic alpha function, Rogers-Ramanujan continued fractions, and Dixon elliptic functions. Complex values of the variable are supported. Package: r-cran-jacobieigen Architecture: amd64 Version: 0.3-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-rbenchmark, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-jacobieigen_0.3-4-1.ca2404.1_amd64.deb Size: 223210 MD5sum: 3a0917c93805ee5ce086e30b26efd05a SHA1: 250a60d8476d7e1b92fe33817af4c12d63946d5e SHA256: c37d554a1eb7105cfa3ba8ec83c1dd8c6562e4e90baa4099d92b6e68cc9c17b8 SHA512: df48ec8653748049b734b025a30ec4266aea01657880fcebb2f8b0736464df18d8db16ceefb23338096e469a6afbd9e504b20877a1fea888b0b687cb7c77f8dd Homepage: https://cran.r-project.org/package=JacobiEigen Description: CRAN Package 'JacobiEigen' (Classical Jacobi Eigenvalue Algorithm) Implements the classical Jacobi algorithm for the eigenvalues and eigenvectors of a real symmetric matrix, both in pure 'R' and in 'C++' using 'Rcpp'. Mainly as a programming example for teaching purposes. Package: r-cran-jade Architecture: amd64 Version: 2.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2470 Depends: libc6 (>= 2.2.5), libstdc++6 (>= 4.3), r-base-core (>= 4.4.0), r-api-4.0, r-cran-clue Suggests: r-cran-ics, r-cran-icsnp Filename: pool/dists/noble/main/r-cran-jade_2.0-4-1.ca2404.1_amd64.deb Size: 2282032 MD5sum: 6a0d8faf8732d2449efb024724a33c1c SHA1: 99446c8383c20aaa8442da2abc614e0e02b79a73 SHA256: 6e45fe36e78242ed4b3690f3fe8c01660b97434023ea47b9df637ba82d6a261f SHA512: 68c722e5575cb93dec047342a0b65c33b685c3f181782124b8f34a6be041e6d2ca68fe099e2caba81c2e382fc15ab268744b1149e72783a411f8c0ec88ddc183 Homepage: https://cran.r-project.org/package=JADE Description: CRAN Package 'JADE' (Blind Source Separation Methods Based on Joint Diagonalizationand Some BSS Performance Criteria) Cardoso's JADE algorithm as well as his functions for joint diagonalization are ported to R. Also several other blind source separation (BSS) methods, like AMUSE and SOBI, and some criteria for performance evaluation of BSS algorithms, are given. The package is described in Miettinen, Nordhausen and Taskinen (2017) . Package: r-cran-jane Architecture: amd64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1117 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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/noble/main/r-cran-jane_2.1.0-1.ca2404.1_amd64.deb Size: 519104 MD5sum: d8c5318e4376234fed561e70c77311ad SHA1: e5ba730d23dba1c9d6faa5ce30c5a3e06938298d SHA256: dc1fe1780c6a378f19e42dba840e373ead46d6879022035e6ef1c381f7c47eaf SHA512: 9ae715fee5c015dc8b05b77a5799dd90a1462a82cc0b9bd93ed84380374a048a7309986dd42c9530b52d3bcb96c701cd90fb6a1c0f1381c87b797461b2f3e541 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) . 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Package: r-cran-jmvconnect Architecture: amd64 Version: 2.5.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-jmvcore, r-cran-evaluate, r-cran-rappdirs, r-cran-httr, r-cran-rcpp, r-cran-bh Filename: pool/dists/noble/main/r-cran-jmvconnect_2.5.7-1.ca2404.1_amd64.deb Size: 75640 MD5sum: acb5ff3f4266960308fc292b87e10fbf SHA1: 20072bc936b7bfafe66c793b716b2e45d2b8ae67 SHA256: 89d959e5f632a2dd16bf48bdedc6a6ec02d931ad3ba6537b9e43e2b7dbbd9faf SHA512: 301c6f0435fb3275841a5cbc853d710ffcb5485863549a29f097b65d02e803cbace8f4854c01ed51b570f0682b50b4348bb7ba3c583271be7219d6f4b4cc7ea1 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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These algorithms are particularly useful in the context of blind source separation. Original publications of the algorithms can be found in Ziehe et al. (2004), Pham and Cardoso (2001) , Souloumiac (2009) , Vollgraff and Obermayer . An example of application in the context of Brain-Computer Interfaces EEG denoising can be found in Gouy-Pailler et al (2010) . 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A typical application is the joint segmentation of total DNA copy numbers and allelic ratios obtained from Single Nucleotide Polymorphism (SNP) microarrays in cancer studies. The methods are described in Pierre-Jean, Rigaill and Neuvial (2015) . 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'jrSiCKLSNMF' specifically deals with dual-assay scRNA-seq and scATAC-seq data. This package contains functions to extract meaningful latent factors that are shared across omics modalities. These factors enable accurate cell-type clustering and facilitate visualizations. Methods for pre-processing, clustering, and mini-batch updates and other adaptations for larger datasets are also included. For further details on the methods used in this package please see Ellis, Roy, and Datta (2023) . 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Offers simple, flexible tools for working with JSON in R, and is particularly powerful for building pipelines and interacting with a web API. The implementation is based on the mapping described in the vignette (Ooms, 2014). In addition to converting JSON data from/to R objects, 'jsonlite' contains functions to stream, validate, and prettify JSON data. The unit tests included with the package verify that all edge cases are encoded and decoded consistently for use with dynamic data in systems and applications. 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Package: r-cran-jti Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 990 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-igraph, r-cran-sparta, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-tinytest, r-cran-ess Filename: pool/dists/noble/main/r-cran-jti_1.0.0-1.ca2404.1_amd64.deb Size: 697134 MD5sum: efc5bde7694dd6454a4cd361043b70e5 SHA1: 417a0a7d1c374eda73bab514f6cc211ffe3de5a5 SHA256: 775653de33e69a9d6ff1972abccf2fed280aa13b73740a1213def7298d3695d3 SHA512: cf33c502d62704995f8cbe0796995e92b31255db3376808d68932fd9e2d7fde18ba54c199bb128a8bade6566a1b155abf8953532047f464df86f620676585454 Homepage: https://cran.r-project.org/package=jti Description: CRAN Package 'jti' (Junction Tree Inference) Minimal and memory efficient implementation of the junction tree algorithm using the Lauritzen-Spiegelhalter scheme; S. L. Lauritzen and D. J. Spiegelhalter (1988) . The jti package is part of the paper . Package: r-cran-juliacall Architecture: amd64 Version: 0.17.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2664 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-knitr, r-cran-rjson Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-rappdirs, r-cran-sass Filename: pool/dists/noble/main/r-cran-juliacall_0.17.6-1.ca2404.1_amd64.deb Size: 809512 MD5sum: c326ae49acac207c11afd9bcd20d9d84 SHA1: bbf622cbd16bcffc6a01632e33b6057a29df63ec SHA256: 76b577c2e9ef2094db41dc1e85a5627a63b8b92ac3d5e12fe86fb94d8460446c SHA512: a38583704e234453702aa4e2d7bac45e8f34dd6435a95efcc8efdff13a519a41f406b0fdb55e2df298121ed78888fea84efccae64dea4e5f4e7070cca6c3112c Homepage: https://cran.r-project.org/package=JuliaCall Description: CRAN Package 'JuliaCall' (Seamless Integration Between R and 'Julia') Provides an R interface to 'Julia', which is a high-level, high-performance dynamic programming language for numerical computing, see for more information. It provides a high-level interface as well as a low-level interface. Using the high level interface, you could call any 'Julia' function just like any R function with automatic type conversion. Using the low level interface, you could deal with C-level SEXP directly while enjoying the convenience of using a high-level programming language like 'Julia'. Package: r-cran-jump Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-jump_1.0.2-1.ca2404.1_amd64.deb Size: 45524 MD5sum: af00f95691f94cec33e47a597e1a856f SHA1: acdb53a9f8d1f355595b818560e0ee0a14d70273 SHA256: 52917207a5c6525e86d831e06b064f5c5f7b6d2eeb3b7da713f0061e45fef1d0 SHA512: 5c9c258072f61f7f095af53dffcf1b90f811f0ec89de4768de6f8edb6e88cdd35b054029336ed6f4e7c222480b265d90bcbe14a237e5de2f2d685bb4392101c8 Homepage: https://cran.r-project.org/package=JUMP Description: CRAN Package 'JUMP' (Replicability Analysis of High-Throughput Experiments) Implementing a computationally scalable false discovery rate control procedure for replicability analysis based on maximum of p-values. Please cite the manuscript corresponding to this package [Lyu, P. et al., (2023), ]. Package: r-cran-jumps Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-nloptr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-xts Filename: pool/dists/noble/main/r-cran-jumps_1.0-1.ca2404.1_amd64.deb Size: 170104 MD5sum: 287184468d4d9551fb7dfd3ac452f5de SHA1: 39150978651c95cc0f31c4f2bd9a527b76daecd2 SHA256: 2e58781dd31ea28b4d102d6ed7a93f9ae5ec9e080f0a068ab3d8ac22cfcb070f SHA512: 56f65588548ee99ddb58f5028a1123eaf77c233a4a7d975fa8ae4c7ccd19de5b7b36e083492e88bf2ed7fadef97f5b69280bd6d2631dba768f406ae2a1bc7f69 Homepage: https://cran.r-project.org/package=jumps Description: CRAN Package 'jumps' (Hodrick-Prescott Filter with Jumps) A set of functions to compute the Hodrick-Prescott (HP) filter with automatically selected jumps. The original HP filter extracts a smooth trend from a time series, and our version allows for a small number of automatically identified jumps. See Maranzano and Pelagatti (2024) for details. Package: r-cran-junctions Architecture: amd64 Version: 2.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2142 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcppparallel, r-cran-nloptr, r-cran-rcpp, r-cran-tibble Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-junctions_2.1.4-1.ca2404.1_amd64.deb Size: 1025950 MD5sum: 95b907ad3dbc9eca53452dc14a5b62f6 SHA1: 53ea891301ba8c0f4f3322ac70de4df2b56d63b4 SHA256: 1ea837bca2d968474e520cdd550890d571e87133218fbc7030297c9472991096 SHA512: 14a798b85ecceb634d7121b5f98eeb0fc39e2fadc68453b8d4ac65c4bca8d9ca20a3e449becf455cbda0fc68cd2a28ea97f596c7eda4a009ca48f9c030771d58 Homepage: https://cran.r-project.org/package=junctions Description: CRAN Package 'junctions' (The Breakdown of Genomic Ancestry Blocks in Hybrid Lineages) Individual based simulations of hybridizing populations, where the accumulation of junctions is tracked. Furthermore, mathematical equations are provided to verify simulation outcomes. Both simulations and mathematical equations are based on Janzen (2018, ) and Janzen (2022, ). Package: r-cran-kalmanfilter Architecture: amd64 Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 438 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-data.table, r-cran-maxlik, r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kalmanfilter_2.2.0-1.ca2404.1_amd64.deb Size: 140092 MD5sum: ab777c83b165e921929ba8b281749597 SHA1: 6995b190672ffb558be03be07fd99169dda59d5b SHA256: db1c8716038540861e217f80c0fd231b20480049da47753c8f43e8a79c36ea18 SHA512: 64049ffdc1f6c059e07477433b5f87a0c6508b7e8285014a052b79946079c556925f5cf4cfc7178fef930661385488cc6781e606ed63797b27cdd46a584995bf Homepage: https://cran.r-project.org/package=kalmanfilter Description: CRAN Package 'kalmanfilter' (Kalman Filter) 'Rcpp' implementation of the multivariate Kalman filter for state space models that can handle missing values and exogenous data in the observation and state equations. There is also a function to handle time varying parameters. Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" . Package: r-cran-kamila Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-kernsmooth, r-cran-gtools, r-cran-rcpp, r-cran-plyr Suggests: r-cran-testthat, r-cran-clustmd, r-cran-ggplot2, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-kamila_0.1.2-1.ca2404.1_amd64.deb Size: 140718 MD5sum: 65a32cb380041f328a32ec9ec167e4c6 SHA1: 348e5a562044c7ca5429e380f877ee780b3eed35 SHA256: b4d8a9c4181be045ba73ad07ad5a01d4b41612fe8643a2f7dbae5744c22d1c3e SHA512: b31e01831d04c409b00bc68e85899c32338f4574a0d6531ff213276f3ef12c4416a2480170a1180bb76ea207263d093e0e1bc6b0019cb0f8dd90db52113f50b2 Homepage: https://cran.r-project.org/package=kamila Description: CRAN Package 'kamila' (Methods for Clustering Mixed-Type Data) Implements methods for clustering mixed-type data, specifically combinations of continuous and nominal data. Special attention is paid to the often-overlooked problem of equitably balancing the contribution of the continuous and categorical variables. This package implements KAMILA clustering, a novel method for clustering mixed-type data in the spirit of k-means clustering. It does not require dummy coding of variables, and is efficient enough to scale to rather large data sets. Also implemented is Modha-Spangler clustering, which uses a brute-force strategy to maximize the cluster separation simultaneously in the continuous and categorical variables. For more information, see Foss, Markatou, Ray, & Heching (2016) and Foss & Markatou (2018) . Package: r-cran-kanjistat Architecture: amd64 Version: 0.14.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2724 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-dendextend, r-cran-gsubfn, r-cran-matrix, r-cran-png, r-cran-purrr, r-cran-rann, r-cran-rlang, r-cran-roi, r-cran-sysfonts, r-cran-showtext, r-cran-stringi, r-cran-stringr, r-cran-transport, r-cran-xml2, r-cran-lifecycle, r-cran-rcpp Suggests: r-cran-dplyr, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-roi.plugin.glpk, r-cran-systemfonts, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-kanjistat_0.14.2-1.ca2404.1_amd64.deb Size: 1781318 MD5sum: 683816362b1e4dad3b4811e86d784b51 SHA1: a77a0f61727bae511f9c7f3d485a13fe13ea4805 SHA256: 9602155717dac3ef29337d2ad889a2ac4ddd9da815613b27edd66e166ecbe6e9 SHA512: d9909b0e9646e68ec305487efdfc703621761bc9764c5a424210a16179c0e3deb643546687054eba56de03fdf859afd333d09db4f2eba955497d23e5ba4d6192 Homepage: https://cran.r-project.org/package=kanjistat Description: CRAN Package 'kanjistat' (A Statistical Framework for the Analysis of Japanese KanjiCharacters) Various tools and data sets that support the study of kanji, including their morphology, decomposition and concepts of distance and similarity between them. Package: r-cran-kappalab Architecture: amd64 Version: 0.4-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 763 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-quadprog, r-cran-kernlab Filename: pool/dists/noble/main/r-cran-kappalab_0.4-12-1.ca2404.1_amd64.deb Size: 564702 MD5sum: 16b68571ca4ccf3f1e8ac12187193a32 SHA1: 7d2263d36d17749e7d4bf4d9a5265a3ee7d78edd SHA256: a04d38ffdb34763ae099715566d59d11498b0615994ffd982972af16ffbea4f5 SHA512: 1aa692674fe9bdc6402417a0d78ab587df0543dedf5231b5e55f84cb2569281dd5bc3826ff31849c4a5633b0d32b606dbd3acd30ac70023e992c72ff9f5e58c1 Homepage: https://cran.r-project.org/package=kappalab Description: CRAN Package 'kappalab' (Non-Additive Measure and Integral Manipulation Functions) S4 tool box for capacity (or non-additive measure, fuzzy measure) and integral manipulation in a finite setting. It contains routines for handling various types of set functions such as games or capacities. It can be used to compute several non-additive integrals: the Choquet integral, the Sugeno integral, and the symmetric and asymmetric Choquet integrals. An analysis of capacities in terms of decision behavior can be performed through the computation of various indices such as the Shapley value, the interaction index, the orness degree, etc. The well-known Möbius transform, as well as other equivalent representations of set functions can also be computed. Kappalab further contains seven capacity identification routines: three least squares based approaches, a method based on linear programming, a maximum entropy like method based on variance minimization, a minimum distance approach and an unsupervised approach based on parametric entropies. The functions contained in Kappalab can for instance be used in the framework of multicriteria decision making or cooperative game theory. Package: r-cran-kazaam Architecture: amd64 Version: 0.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 774 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-pbdmpi Filename: pool/dists/noble/main/r-cran-kazaam_0.1-0-1.ca2404.1_amd64.deb Size: 624256 MD5sum: dfabfa5842afa637b47727d41c4955ab SHA1: 3f1de30e17a2ee16577e1db65c6c04c42398e657 SHA256: eb3019f2d63b646a516a00d28ce7671dfdf2da78d2229454df4567c740842e0d SHA512: 779e9b59830aa513e268806739f176e36627aa776804c926435b703752aa5bf519ffa68a2107455c94f96919d19b95896aa1d09990b7ce5cd1758b2830bb737c Homepage: https://cran.r-project.org/package=kazaam Description: CRAN Package 'kazaam' (Tools for Tall Distributed Matrices) Many data science problems reduce to operations on very tall, skinny matrices. However, sometimes these matrices can be so tall that they are difficult to work with, or do not even fit into main memory. One strategy to deal with such objects is to distribute their rows across several processors. To this end, we offer an 'S4' class for tall, skinny, distributed matrices, called the 'shaq'. We also provide many useful numerical methods and statistics operations for operating on these distributed objects. The naming is a bit "tongue-in-cheek", with the class a play on the fact that 'Shaquille' 'ONeal' ('Shaq') is very tall, and he starred in the film 'Kazaam'. Package: r-cran-kbal Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-dplyr, r-cran-rspectra Filename: pool/dists/noble/main/r-cran-kbal_0.1.4-1.ca2404.1_amd64.deb Size: 206162 MD5sum: ef9bfafa54f7876aa68034eb719e6457 SHA1: 6f0c09fe7fc1aefcae9d1b9d4c4edd27c44022f3 SHA256: 5da21ff8fcff705b18ebd6c5513b9841430819be74f1d2039bb0eb236a6ce96b SHA512: c0b885364869844e4d65d3a7bc66a8117571727e67ebf63ec1a57d29de7bd403e399c6372956e22b60d3b35d6d3acaf398254ba979ae156ad08053e60016562d Homepage: https://cran.r-project.org/package=kbal Description: CRAN Package 'kbal' (Kernel Balancing) Provides a weighting approach that employs kernels to make one group have a similar distribution to another group on covariates. This method matches not only means or marginal distributions but also higher-order transformations implied by the choice of kernel. 'kbal' is applicable to both treatment effect estimation and survey reweighting problems. Based on Hazlett, C. (2020) "Kernel Balancing: A flexible non-parametric weighting procedure for estimating causal effects." Statistica Sinica. . Package: r-cran-kcprs Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-roll, r-cran-foreach, r-cran-doparallel, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-kcprs_1.1.1-1.ca2404.1_amd64.deb Size: 134650 MD5sum: ad8a1fc202b845edd9520d1775260d63 SHA1: dc22ec12b94c45f304ce04aae55a6523d7738449 SHA256: 25d36877dd2db75df8cc4c1d90935aaeadef7c8751b761170a54a1d19f036b74 SHA512: 140f15ad4d43f43bdb2e00a8692ed89ae8e3891589606bbea6b9ff19b56d64cd38fb4eec411e3a586d873203c8b5d495af03ef348c8f6275b015b917af2f2252 Homepage: https://cran.r-project.org/package=kcpRS Description: CRAN Package 'kcpRS' (Kernel Change Point Detection on the Running Statistics) The running statistics of interest is first extracted using a time window which is slid across the time series, and in each window, the running statistics value is computed. KCP (Kernel Change Point) detection proposed by Arlot et al. (2012) is then implemented to flag the change points on the running statistics (Cabrieto et al., 2018, ). Change points are located by minimizing a variance criterion based on the pairwise similarities between running statistics which are computed via the Gaussian kernel. KCP can locate change points for a given k number of change points. To determine the optimal k, the KCP permutation test is first carried out by comparing the variance of the running statistics extracted from the original data to that of permuted data. If this test is significant, then there is sufficient evidence for at least one change point in the data. Model selection is then used to determine the optimal k>0. Package: r-cran-kde1d Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-randtoolbox, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-kde1d_1.1.1-1.ca2404.1_amd64.deb Size: 147266 MD5sum: de3928d3d3517f182cee84743726d96d SHA1: 0995303061d826485c2d2ffe13d8e8f3f0d91f6e SHA256: a11659dd3cf8a4a69d6d0bdfbaf233d998feee3429a409899fd1600aac730be1 SHA512: e65c511ad3daa555b265206905e3b31205128e1d6d6c3381ba39b5591e842a28a79a7692291b4fdbe994be2ce823aca4d3c3dd548763526a3340fd364cc0529e Homepage: https://cran.r-project.org/package=kde1d Description: CRAN Package 'kde1d' (Univariate Kernel Density Estimation) Provides an efficient implementation of univariate local polynomial kernel density estimators that can handle bounded and discrete data. See Geenens (2014) , Geenens and Wang (2018) , Nagler (2018a) , Nagler (2018b) . Package: r-cran-kdecopula Architecture: amd64 Version: 0.9.3-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1131 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-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/noble/main/r-cran-kdecopula_0.9.3-1.ca2404.2_amd64.deb Size: 889934 MD5sum: 232b3c2b8e5358f81e38b0f0cc6280da SHA1: 757e5566de53d8cab24f5a0dcf44d8c9b3b325c7 SHA256: 084678a0ac32c55057e7ca92573ca304f5b74601115ddc24b7d74e69e0808489 SHA512: 5b5297d8f036229a1cf616e90bd82349f01fa4dac8f1789b9c0fd47371b4af718211e60a619e21ffde13f536321e97835cf584a004cba3dbbe4791968a64c94b Homepage: https://cran.r-project.org/package=kdecopula Description: CRAN Package 'kdecopula' (Kernel Smoothing for Bivariate Copula Densities) Provides fast implementations of kernel smoothing techniques for bivariate copula densities, in particular density estimation and resampling, see Nagler (2018) . 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For more details about the methods applied, see Chester (2025). . 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Package: r-cran-keyperm Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-slam, r-cran-tm, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-keyperm_0.1.1-1.ca2404.1_amd64.deb Size: 90348 MD5sum: 8ac05b55397b08979a90cb66b7376636 SHA1: fc0fc37516f4610462e3ea01a0b103f08622955d SHA256: 27442646fec433187db397deea0acf2f7b9c58eaeb1a04c99ad162be22e62aa7 SHA512: 18a9d1554242f2d5b3efc410f974a8efae9c62f3b4372d1f85af5a1a4e362f7547c58dedf9bad28a09e68accd87231279e5570da3d6379c494d66905e8f10029 Homepage: https://cran.r-project.org/package=keyperm Description: CRAN Package 'keyperm' (Keyword Analysis Using Permutation Tests) Efficient implementation of permutation tests for keyword analysis in corpus linguistics as described in Mildenberger (2023) . 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Package: r-cran-keyring Architecture: amd64 Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 542 Depends: libc6 (>= 2.33), libglib2.0-0t64 (>= 2.16.0), libsecret-1-0 (>= 0.7), r-base-core (>= 4.5.0), r-api-4.0, r-cran-askpass, r-cran-filelock, r-cran-r6, r-cran-yaml Suggests: r-cran-callr, r-cran-covr, r-cran-openssl, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-keyring_1.4.1-1.ca2404.1_amd64.deb Size: 423862 MD5sum: c059e79f874ee1d6c1c3ff96a3ee0db7 SHA1: e41aedbe3f59bf9f668e75bea54f3672121f4df7 SHA256: 1318195a27e5b5bfccf175d6d4e8de77da3f344f58d0460a34efd5291e2d9073 SHA512: d24743e5d22478eec5714f29fb69dd14f71f917f4eacc7e462d76753ea6a68639b04a4a6e17afed2f1feab5bb005eb7a753e852215aa99a38f2452b0561930ea Homepage: https://cran.r-project.org/package=keyring Description: CRAN Package 'keyring' (Access the System Credential Store from R) Platform independent 'API' to access the operating system's credential store. 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The filter is designed for state space models and can handle missing values and exogenous data in the observation and state equations. Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" . Package: r-cran-kira Architecture: amd64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-kira_1.0.8-1.ca2404.1_amd64.deb Size: 131142 MD5sum: 58502519ed0d6b1aa4f29eec3871789f SHA1: e13d553abbc5f4e7ebf167e9c825be8e276d9292 SHA256: a67f322a3a4cf2d97bfd85594e9bed578fd691d3a7a58e2b3fb90456061bc481 SHA512: ffad737900acc3ffd5f8f1f2153afa5cb3619c3a02c12b0101bae2bd247870630d2a663f1428e90bda44582390b340a013c5c425aac64a70db80bc5aa7dd1b20 Homepage: https://cran.r-project.org/package=Kira Description: CRAN Package 'Kira' (Machine Learning) Machine learning, containing several algorithms for supervised and unsupervised classification, in addition to a function that plots the Receiver Operating Characteristic (ROC) and Precision-Recall (PRC) curve graphs, and also a function that returns several metrics used for model evaluation, the latter can be used in ranking results from other packs. Package: r-cran-kissmig Architecture: amd64 Version: 2.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-terra Filename: pool/dists/noble/main/r-cran-kissmig_2.0-1-1.ca2404.1_amd64.deb Size: 769400 MD5sum: 918c2fdb27a4ee19c0a4a9b6c7de480b SHA1: 8c4fe63424096634ae3ebafb35914cda203c986e SHA256: 0b6f35bc9a2ded344f5d8782011900f97c695b1018ee6eeaecd8e0f25db8568a SHA512: b7ee3de99949dbfeee099aabd6d5bf8ca50b584d17c306c1b64c3af25724fe8e565dbea2549e2380a914bc8c399c5e7313421fb48fa36aa9cd5b0a6edf0840fa 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. 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Most of these functions are callable at C level. Package: r-cran-kkmeans Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-kkmeans_0.1.3-1.ca2404.1_amd64.deb Size: 46180 MD5sum: 53e1fd6e065d3e45c34cac887c408664 SHA1: 86dc723f2bad2558785cfc2a3315360e837a6b5f SHA256: 62b8480e81ad7a5911d473378662ae031b221edc9569204ce83bccdb34838213 SHA512: 73ee07a476479f0f0ef017a74d199b24bff256ecec1bf19ac253f07e59ef36c5efa416cd1906c38cd7a17498c5c205effe9d9123f7a02b2d8ea94d6639afb53f Homepage: https://cran.r-project.org/package=kkmeans Description: CRAN Package 'kkmeans' (Fast Implementations of Kernel K-Means) Implementations several algorithms for kernel k-means. The default 'OTQT' algorithm is a fast alternative to standard implementations of kernel k-means, particularly in cases with many clusters. 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Package: r-cran-kmblock Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-blockmodeling, r-cran-doparallel, r-cran-dorng, r-cran-foreach, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-kmblock_0.1.4-1.ca2404.1_amd64.deb Size: 110824 MD5sum: 0c0593cf6cf671ab900483a141b1c358 SHA1: 5db7060833cbd5150ee9b3d6b31a6a344f8bae6d SHA256: 507fc881f578fe15515cf30680a3e87fa8b4cd6a4b879767e39788492812294f SHA512: 2e945d5e3806717d217cddbd3f515273ed27d57019f9446aa8be7822ac514a69837a9652ac481d0870b76bd7c5fa7850444ab71857a98e5b9fa4aa9fde89410d Homepage: https://cran.r-project.org/package=kmBlock Description: CRAN Package 'kmBlock' (k-Means Like Blockmodeling of One-Mode and Linked Networks) Implements k-means like blockmodeling of one-mode and linked networks as presented in Žiberna (2020) . The development of this package is financially supported by the Slovenian Research Agency () within the research programs P5-0168 and the research projects J7-8279 (Blockmodeling multilevel and temporal networks) and J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks). 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The constraint is assumed given in linear estimating equations or mean functions. We also illustrate how this leads to the empirical likelihood ratio test with right censored data and accelerated failure time model with given coefficients. EM algorithm from emplik package is used to get the initial value. The properties and performance of the EM algorithm is discussed in Mai Zhou and Yifan Yang (2015) and Mai Zhou and Yifan Yang (2017) . More applications could be found in Mai Zhou (2015) . Package: r-cran-kmer Architecture: amd64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 685 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-openssl, r-cran-phylogram, r-cran-rcpp Suggests: r-cran-ape, r-cran-dendextend, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kmer_1.1.3-1.ca2404.1_amd64.deb Size: 418752 MD5sum: d64974d93a990a9f12a09a56e6e36968 SHA1: 6f7b794aa94d1dbcd9b34ef520693b9953bda410 SHA256: fd7257809b2bda6fb4cf9dad15aba412ebdc593c8aa5f1e2279149aaede56f71 SHA512: 94e05ce28aa6c4b4c5238361c8a2385d41fbfde7dbf536a62d3490a823d6682c1f1999af19b69f2ba37ec25453edcde95b7799e71bd1310067c2da2b1dc2344e Homepage: https://cran.r-project.org/package=kmer Description: CRAN Package 'kmer' (Fast K-Mer Counting and Clustering for Biological SequenceAnalysis) Contains tools for rapidly computing distance matrices and clustering large sequence datasets using fast alignment-free k-mer counting and recursive k-means partitioning. See Vinga and Almeida (2003) for a review of k-mer counting methods and applications for biological sequence analysis. Package: r-cran-kmertone Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1921 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-r6, r-cran-rcpp, r-cran-r.utils, r-cran-openxlsx, r-cran-png, r-cran-rcppsimdjson, r-cran-venneuler, r-cran-stringi, r-cran-curl, r-cran-future, r-cran-future.apply, r-cran-jsonlite, r-cran-progressr, r-bioc-biostrings, r-bioc-seqlogo Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kmertone_1.0-1.ca2404.1_amd64.deb Size: 1435302 MD5sum: 1de3914cd9f6580ce79ebc19c7f538e4 SHA1: 9f1260235f1182614398752b4ef4fd6cb63cbce2 SHA256: 2d082dfba4afc757d390053d2fdb51ef23292501f7a979495fc0698cc43cbb81 SHA512: adc01c21bf4546a3c55c9d24dfd70de0efb3d0688d6f018c4c1ace275710b06fd9426f638f6115f31e957681f33ba5239a4fe093f05a7d084af502ba13e184ab Homepage: https://cran.r-project.org/package=kmeRtone Description: CRAN Package 'kmeRtone' (Multi-Purpose and Flexible k-Meric Enrichment Analysis Software) A multi-purpose and flexible k-meric enrichment analysis software. 'kmeRtone' measures the enrichment of k-mers by comparing the population of k-mers in the case loci with a carefully devised internal negative control group, consisting of k-mers from regions close to, yet sufficiently distant from, the case loci to mitigate any potential sequencing bias. This method effectively captures both the local sequencing variations and broader sequence influences, while also correcting for potential biases, thereby ensuring more accurate analysis. The core functionality of 'kmeRtone' is the SCORE() function, which calculates the susceptibility scores for k-mers in case and control regions. Case regions are defined by the genomic coordinates provided in a file by the user and the control regions can be constructed relative to the case regions or provided directly. The k-meric susceptibility scores are calculated by using a one-proportion z-statistic. 'kmeRtone' is highly flexible by allowing users to also specify their target k-mer patterns and quantify the corresponding k-mer enrichment scores in the context of these patterns, allowing for a more comprehensive approach to understanding the functional implications of specific DNA sequences on a genomic scale (e.g., CT motifs upon UV radiation damage). Adib A. Abdullah, Patrick Pflughaupt, Claudia Feng, Aleksandr B. Sahakyan (2024) Bioinformatics (submitted). Package: r-cran-kml Architecture: amd64 Version: 2.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 403 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-clv, r-cran-longitudinaldata Filename: pool/dists/noble/main/r-cran-kml_2.5.0-1.ca2404.1_amd64.deb Size: 310198 MD5sum: 6f32172815829bde00ea3d34ba9274f3 SHA1: 69d01fa9de1908905053c15d01eb3fd361111dd1 SHA256: 701eacdd0e16ec29859a749651169e6ec20e629ab9e063d4430706a287848ff0 SHA512: ad7ddbb017a876a7f2c6a49d9fefb229a42c13bcbe23a9bad418bf9cd67ca0d3bbd245505937368323fc85db2716c652bcccae9c91c9ee131e16adbff4511215 Homepage: https://cran.r-project.org/package=kml Description: CRAN Package 'kml' (K-Means for Longitudinal Data) An implementation of k-means specifically design to cluster longitudinal data. It provides facilities to deal with missing value, compute several quality criterion (Calinski and Harabatz, Ray and Turie, Davies and Bouldin, BIC, ...) and propose a graphical interface for choosing the 'best' number of clusters. Package: r-cran-kmt Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6301 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-rsolnp, r-cran-gumbel, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-kmt_1.0.0-1.ca2404.1_amd64.deb Size: 6288688 MD5sum: 9e21cbd3abb61d892b5b7aca5ad58b34 SHA1: 3f6a214ddee2ac23c5e4a7b407f4ae3370a29318 SHA256: e50c832516a1feaea5ed038f9b051bc34aa35660b195bcfe06f64478344f4989 SHA512: dce87d840957defe57d2cf2f73c1a4e7d5663b7084b78bc3e29eadc31eef2014a9247c53fb926bc64a1d24fcfa6c4ddaac3a062f2f54af8007fbc54036e56986 Homepage: https://cran.r-project.org/package=KMT Description: CRAN Package 'KMT' (Khmaladze Martingale Transformation Goodness-of-Fit Test) Consider a goodness-of-fit problem of testing whether a random sample comes from one sample location-scale model where location and scale parameters are unknown. It is well known that Khmaladze-martingale-transformation method proposed by Khmaladze (1981) provides asymptotic distribution free test. This package provides test statistic and critical value of the test for normal, Cauchy, and logistic distributions. This package used the main algorithm proposed by Kim (2020) and tests for other distributions will be available at the later version. Package: r-cran-knn.covertree Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-matrix Suggests: r-cran-testthat, r-cran-fnn Filename: pool/dists/noble/main/r-cran-knn.covertree_1.1-1.ca2404.1_amd64.deb Size: 81456 MD5sum: ab0a17aac435fb61f9cc095fa1c437a5 SHA1: 4803f5a5abb5795635d69124f88220883117deed SHA256: d96a66c3a93af39385a0646e6d98c29a18363738943010590d52fd0c24f1c71b SHA512: 6328ab74dea01a3a89bf58b44c0f8d0d64c094f48b3d52a04fc63b9fb5eda7194af58771a2768a576fbc05f767d5c54bf7aa8f39436c474cdeb8f869b53fa70f Homepage: https://cran.r-project.org/package=knn.covertree Description: CRAN Package 'knn.covertree' (An Accurate kNN Implementation with Multiple Distance Measures) Similarly to the 'FNN' package, this package allows calculation of the k nearest neighbors (kNN) of a data matrix. The implementation is based on cover trees introduced by Alina Beygelzimer, Sham Kakade, and John Langford (2006) . Package: r-cran-knnmi Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-knnmi_1.0-1.ca2404.1_amd64.deb Size: 58678 MD5sum: 99fba42a3a284aceb5694da9e6942920 SHA1: ef200b080e1a8a7853a1eb9cd3102eaf33b2203d SHA256: 0feb458efa358bc8bee51c014f435119361ea42718229aa5d0359fc512e2a35c SHA512: 9fcfb4242c9e7e538fe5253e41cbafd99ab994184a6025cfc064b0cca2f290919c3252f377b8e6ab23d317edfcceedce75339c74e4945ffa645bd06640142870 Homepage: https://cran.r-project.org/package=knnmi Description: CRAN Package 'knnmi' (k-Nearest Neighbor Mutual Information Estimator) This is a 'C++' mutual information (MI) library based on the k-nearest neighbor (KNN) algorithm. There are three functions provided for computing MI for continuous values, mixed continuous and discrete values, and conditional MI for continuous values. They are based on algorithms by A. Kraskov, et. al. (2004) , BC Ross (2014), and A. Tsimpiris (2012) , respectively. Package: r-cran-kodama Architecture: amd64 Version: 3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2992 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rtsne, r-cran-umap, r-cran-rcpp, r-cran-rnanoflann, r-cran-matrix, r-cran-rcpparmadillo Suggests: r-cran-rgl, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kodama_3.3-1.ca2404.1_amd64.deb Size: 2789608 MD5sum: 781aef36496ac0c995671afe79534674 SHA1: be223e04b3f785ca00172de9d78919e87c26675f SHA256: 873e025aa6c6f5031558a6c73c954acb2855a267cbcbd2effc5d33c5c7ab5a89 SHA512: cd14cc2a25c29e8dcd90fe104ac68f17194d96e73a7faf4f62c7188607dc97b0e33d8b9b257e6e315dff6359ce388861eb7cbc4c741e734da999d7f47136a6d3 Homepage: https://cran.r-project.org/package=KODAMA Description: CRAN Package 'KODAMA' (Knowledge Discovery by Accuracy Maximization) A self-guided, weakly supervised learning algorithm for feature extraction from noisy and high-dimensional data. It facilitates the identification of patterns that reflect underlying group structures across all samples in a dataset. The method incorporates a novel strategy to integrate spatial information, improving the clarity of results in spatially resolved data. Package: r-cran-kohonen Architecture: amd64 Version: 3.0.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1877 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-rcolorbrewer, r-cran-lattice, r-cran-vegan Filename: pool/dists/noble/main/r-cran-kohonen_3.0.13-1.ca2404.1_amd64.deb Size: 1707980 MD5sum: 42ffcad0a0650c2040d0b14fbe7d9dda SHA1: 3dde7cbf327732f83c8e50d79e8ccac1a6fc24f6 SHA256: af7ff6211b0e1c65be87aa7fc8be05be9c972b690d8f644451a5a19adaa56cd7 SHA512: 46717820f9c061ea55c451432da57720882f2952bedb75bc98bcff38e33c0a673ad27de4bbfa52bde63bddfe4e95b00a56106c846c4c42974fbd07f618f7c096 Homepage: https://cran.r-project.org/package=kohonen Description: CRAN Package 'kohonen' (Supervised and Unsupervised Self-Organising Maps) Functions to train self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM. Package: r-cran-konpsurv Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-konpsurv_1.0.4-1.ca2404.1_amd64.deb Size: 117844 MD5sum: 0c80102482954d92e42ca47ba8aa2b99 SHA1: 0fd362c3ef79c7e5d5354ac6687fc4aeb9426f23 SHA256: 853b687562197aa7f347df0bda10efa5b645583f1c9274d5fc4b19a2fa15bce9 SHA512: cde595dc4e374bd3658237824a0e7b1b9a1d20def49957a3c5f64a9ab0f9c569264becb83a8ace290ba187b6755a1ea528342c9d725ee88fcfcfb9e5123ae0e7 Homepage: https://cran.r-project.org/package=KONPsurv Description: CRAN Package 'KONPsurv' (KONP Tests: Powerful K-Sample Tests for Right-Censored Data) The K-sample omnibus non-proportional hazards (KONP) tests are powerful non-parametric tests for comparing K (>=2) hazard functions based on right-censored data (Gorfine, Schlesinger and Hsu, 2020, ). These tests are consistent against any differences between the hazard functions of the groups. The KONP tests are often more powerful than other existing tests, especially under non-proportional hazard functions. Package: r-cran-koulmde Architecture: amd64 Version: 3.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-expm, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-koulmde_3.2.1-1.ca2404.1_amd64.deb Size: 132536 MD5sum: 923eccd1a5f5155fbda731eb0f7ae0eb SHA1: a3efef42bff018c224acc0784b3a09b271acbd8b SHA256: fe719694f56a19851171623f77501e123e235790ef8cfb777e29df3b949fd18a SHA512: c30a4837bec94349506113c158567f9b601e09a01f6b3b93a21b6a8ffb661fcd72baa0ef53d0544105fb52c01e7217e8da669104bdf19c2c535c45ab3e2caae5 Homepage: https://cran.r-project.org/package=KoulMde Description: CRAN Package 'KoulMde' (Koul's Minimum Distance Estimation in Regression and ImageSegmentation Problems) Many methods are developed to deal with two major statistical problems: image segmentation and nonparametric estimation in various regression models. Image segmentation is nowadays gaining a lot of attention from various scientific subfields. Especially, image segmentation has been popular in medical research such as magnetic resonance imaging (MRI) analysis. When a patient suffers from some brain diseases such as dementia and Parkinson's disease, those diseases can be easily diagnosed in brain MRI: the area affected by those diseases is brightly expressed in MRI, which is called a white lesion. For the purpose of medical research, locating and segment those white lesions in MRI is a critical issue; it can be done manually. However, manual segmentation is very expensive in that it is error-prone and demands a huge amount of time. Therefore, supervised machine learning has emerged as an alternative solution. Despite its powerful performance in a classification problem such as hand-written digits, supervised machine learning has not shown the same satisfactory result in MRI analysis. Setting aside all issues of the supervised machine learning, it exposed a critical problem when employed for MRI analysis: it requires time-consuming data labeling. Thus, there is a strong demand for an unsupervised approach, and this package - based on Hira L. Koul (1986) - proposes an efficient method for simple image segmentation - here, "simple" means that an image is black-and-white - which can easily be applied to MRI analysis. This package includes a function GetSegImage(): when a black-and-white image is given as an input, GetSegImage() separates an area of white pixels - which corresponds to a white lesion in MRI - from the given image. For the second problem, consider linear regression model and autoregressive model of order q where errors in the linear regression model and innovations in the autoregression model are independent and symmetrically distributed. Hira L. Koul (1986) proposed a nonparametric minimum distance estimation method by minimizing L2-type distance between certain weighted residual empirical processes. He also proposed a simpler version of the loss function by using symmetry of the integrating measure in the distance. Kim (2018) proposed a fast computational method which enables practitioners to compute the minimum distance estimator of the vector of general multiple regression parameters for several integrating measures. This package contains three functions: KoulLrMde(), KoulArMde(), and Koul2StageMde(). The former two provide minimum distance estimators for linear regression model and autoregression model, respectively, where both are based on Koul's method. These two functions take much less time for the computation than those based on parametric minimum distance estimation methods. Koul2StageMde() provides estimators for regression and autoregressive coefficients of linear regression model with autoregressive errors through minimum distant method of two stages. The new version is written in Rcpp and dramatically reduces computational time. Package: r-cran-krige Architecture: amd64 Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 939 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-coda Filename: pool/dists/noble/main/r-cran-krige_0.6.2-1.ca2404.1_amd64.deb Size: 772738 MD5sum: 0578b58793be47fc6d41b33cecaaa20a SHA1: 6892118f34c985b89326ac12f081694d45b32c38 SHA256: e50a65f2ddd5a4cdb766f5a51e238db230bf1432267c98f76c4caa672a0b9429 SHA512: d20c483447826517afadaab2a9df99c5c51101c37ed8f850c2a08b016e7ef03720077afae612823555c7b6443b391d15c601596453bf84a5ad0ce4e1dfb0f4e4 Homepage: https://cran.r-project.org/package=krige Description: CRAN Package 'krige' (Geospatial Kriging with Metropolis Sampling) Estimates kriging models for geographical point-referenced data. Method is described in Gill (2020) . 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Package: r-cran-krm Architecture: amd64 Version: 2022.10-17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-kyotil Suggests: r-cran-runit, r-cran-mass Filename: pool/dists/noble/main/r-cran-krm_2022.10-17-1.ca2404.1_amd64.deb Size: 131188 MD5sum: de6c93fa55102b69792b9d59a6ebe9c0 SHA1: cc3ae94343433019ba21fb94a1fb29e221c51097 SHA256: d9f0acb1c0d78e851eb9995c4a6a6f874696959aaf14a221b542748ccdd242c2 SHA512: 79748c185afd1bcba40048b9bdab625889e31bc7b65c27b6ac2493d262c0770b8413b37c19e9af88ea18a80ebd7e4eb9abf49cf8cf8b7a4c2c753553a83e1132 Homepage: https://cran.r-project.org/package=krm Description: CRAN Package 'krm' (Kernel Based Regression Models) Implements several methods for testing the variance component parameter in regression models that contain kernel-based random effects, including a maximum of adjusted scores test. Several kernels are supported, including a profile hidden Markov model mutual information kernel for protein sequence. This package is described in Fong et al. (2015) . 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Chacon & Duong (2018) . Package: r-cran-ksamples Architecture: amd64 Version: 1.2-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-suppdists Filename: pool/dists/noble/main/r-cran-ksamples_1.2-12-1.ca2404.1_amd64.deb Size: 254120 MD5sum: 8fb3edf2cc17e82fab4a8f3926a1d8d5 SHA1: ed1ca93b03a5e41d2ecc7c4a20d0ddd3686ef170 SHA256: 8d79c42fdfe36fe14195e710c7978202818089b929b25e00a3cdb4c1dff9ee38 SHA512: d0502ec76e300f3ea50225b9a266846f56507879754dfd5763a556857b7a12f140b7144426034dbb908216d75dc37b598a4fb5d0c93856c0f76e16391a324236 Homepage: https://cran.r-project.org/package=kSamples Description: CRAN Package 'kSamples' (K-Sample Rank Tests and their Combinations) Compares k samples using the Anderson-Darling test, Kruskal-Wallis type tests with different rank score criteria, Steel's multiple comparison test, and the Jonckheere-Terpstra (JT) test. It computes asymptotic, simulated or (limited) exact P-values, all valid under randomization, with or without ties, or conditionally under random sampling from populations, given the observed tie pattern. Except for Steel's test and the JT test it also combines these tests across several blocks of samples. Also analyzed are 2 x t contingency tables and their blocked combinations using the Kruskal-Wallis criterion. Steel's test is inverted to provide simultaneous confidence bounds for shift parameters. A plotting function compares tail probabilities obtained under asymptotic approximation with those obtained via simulation or exact calculations. Package: r-cran-ksgeneral Architecture: amd64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: libc6 (>= 2.38), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-dgof Filename: pool/dists/noble/main/r-cran-ksgeneral_2.0.2-1.ca2404.1_amd64.deb Size: 224224 MD5sum: 5ef91dfc313a002cd9e9f8b55a21a82a SHA1: 4cf524de8b8b069e867a3a8dcb61560a506b7467 SHA256: 8f88a592f539b00781879146ad82d42dec25e31d25aa98f562fbdd186958b10e SHA512: e012617ee61c74a2aad4100f48619e092b12ac21ffc54a8892d176f183ec4fabc0e9ef8153c4901661fb370a483e1ba32aac393a63da202c8da001ae2d829382 Homepage: https://cran.r-project.org/package=KSgeneral Description: CRAN Package 'KSgeneral' (Computing P-Values of the One-Sample K-S Test and the Two-SampleK-S and Kuiper Tests for (Dis)Continuous Null Distribution) Contains functions to compute p-values for the one-sample and two-sample Kolmogorov-Smirnov (KS) tests and the two-sample Kuiper test for any fixed critical level and arbitrary (possibly very large) sample sizes. For the one-sample KS test, this package implements a novel, accurate and efficient method named Exact-KS-FFT, which allows the pre-specified cumulative distribution function under the null hypothesis to be continuous, purely discrete or mixed. In the two-sample case, it is assumed that both samples come from an unspecified (unknown) continuous, purely discrete or mixed distribution, i.e. ties (repeated observations) are allowed, and exact p-values of the KS and the Kuiper tests are computed. Note, the two-sample Kuiper test is often used when data samples are on the line or on the circle (circular data). To cite this package in publication: (for the use of the one-sample KS test) Dimitrina S. Dimitrova, Vladimir K. Kaishev, and Senren Tan. Computing the Kolmogorov-Smirnov Distribution When the Underlying CDF is Purely Discrete, Mixed, or Continuous. Journal of Statistical Software. 2020; 95(10): 1--42. . (for the use of the two-sample KS and Kuiper tests) Dimitrina S. Dimitrova, Yun Jia and Vladimir K. Kaishev (2024). The R functions KS2sample and Kuiper2sample: Efficient Exact Calculation of P-values of the Two-sample Kolmogorov-Smirnov and Kuiper Tests. submitted. Package: r-cran-ksm Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-cubature, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-ksm_1.0-1.ca2404.1_amd64.deb Size: 234672 MD5sum: b586bf12473cc0624bac5fc62629553a SHA1: a570f02897fc3304200df2b513ee5fa65c8a73d7 SHA256: 8e9037284368627bd471a1f394a538955db15a101e7ff22558357f42a095a508 SHA512: d30b1051e6835d0494d1194f14c2bc208b9cb69c43e5c5d2471ae67e00417a009cdce846e0dc9b4942364416e92f7516ce0b2f57e7025ae87031526588206ba7 Homepage: https://cran.r-project.org/package=ksm Description: CRAN Package 'ksm' (Kernel Density Estimation for Random Symmetric Positive DefiniteMatrices) Kernel smoothing for Wishart random matrices described in Daayeb, Khardani and Ouimet (2025) , Gaussian and log-Gaussian models using least square or likelihood cross validation criteria for optimal bandwidth selection. Package: r-cran-ksnn Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-ksnn_0.1.2-1.ca2404.1_amd64.deb Size: 44510 MD5sum: 625ac41cdb02d1a7ab66c169d8e908a5 SHA1: 6162c1fb7345cfacca5f184c5e70a917126c13a3 SHA256: ac043267fda696fce87546d4de0a331f57ceda630a7fa7e19b8d8004aa8e494e SHA512: a254b6220f3449ff6e7473ced393fdfcb4e36bce03e7fd0b6defc11d142991933b1113f7f2827c4eef58007d74d6f20009cf2e8f0be0bc9d8a3b3d5c4d9392c2 Homepage: https://cran.r-project.org/package=ksNN Description: CRAN Package 'ksNN' (K* Nearest Neighbors Algorithm) Prediction with k* nearest neighbor algorithm based on a publication by Anava and Levy (2016) . Package: r-cran-kstmatrix Architecture: amd64 Version: 2.3-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sets, r-cran-pks, r-cran-tidyr, r-cran-diagrammer, r-cran-rsvg Suggests: r-cran-diagrammersvg, r-cran-litedown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-kstmatrix_2.3-2-1.ca2404.1_amd64.deb Size: 297178 MD5sum: 1d533ca5d7c70da1f02fc96d6f108696 SHA1: e9018694e761f80abc806ce99d077a81475dcc4e SHA256: 3e88b006e0b92b305c870a0dcc53e941f4ccb79aaf859f19d46146a94c5d09ed SHA512: 67da864410907c15624e6092909b70c659aa08ceb6d42bab9b88134956f788c9b6e1872787b07ff97790221462b751daa7058b986d07d28ebaca4079fef50936 Homepage: https://cran.r-project.org/package=kstMatrix Description: CRAN Package 'kstMatrix' (Basic Functions in Knowledge Space Theory Using MatrixRepresentation) Knowledge space theory by Doignon and Falmagne (1999) is a set- and order-theoretical framework, which proposes mathematical formalisms to operationalize knowledge structures in a particular domain. The 'kstMatrix' package provides basic functionalities to generate, handle, and manipulate knowledge structures and knowledge spaces. Opposed to the 'kst' package, 'kstMatrix' uses matrix representations for knowledge structures. Furthermore, 'kstMatrix' contains several knowledge spaces developed by the research group around Cornelia Dowling through querying experts. Package: r-cran-ktaucenters Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1797 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-gse, r-cran-rcpp Suggests: r-cran-jpeg, r-cran-tclust, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ktaucenters_1.0.0-1.ca2404.1_amd64.deb Size: 1617494 MD5sum: 833bb14a5bfe9eeaf73af3b142e2331a SHA1: f67bff11676fc48aeed5c5236ac97d5ab5f190ee SHA256: 3b8b4b78fe3b91dcd74c60fca4c5cc96cfd3695da1688492b7ae3b0f290249bc SHA512: 0446660d5e9bcb68a9a23a9053b9b204b060aeb6fd5571b89de2571c679b0442dc145f3192f4072247adc0a27cd5c47a74943ea601002d769416fb1e7d927dd2 Homepage: https://cran.r-project.org/package=ktaucenters Description: CRAN Package 'ktaucenters' (Robust Clustering Procedures) A clustering algorithm similar to K-Means is implemented, it has two main advantages, namely (a) The estimator is resistant to outliers, that means that results of estimator are still correct when there are atypical values in the sample and (b) The estimator is efficient, roughly speaking, if there are no outliers in the sample, results will be similar to those obtained by a classic algorithm (K-Means). Clustering procedure is carried out by minimizing the overall robust scale so-called tau scale. (see Gonzalez, Yohai and Zamar (2019) ). Package: r-cran-ktweedie Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 723 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tweedie Filename: pool/dists/noble/main/r-cran-ktweedie_1.0.3-1.ca2404.1_amd64.deb Size: 465526 MD5sum: 05096f573b8d6a826d85dc8374553268 SHA1: cd71f80bf3707b3a4bea3bc1a7876a7b04d7c26d SHA256: 9a4961e080c2d1169ebfe176ce20f0051e6c6ccdf5b972b06960ed908d2ff8b1 SHA512: d1ba8a24ae209ed79dc1077a301e72328add82be0475f3b2503a7e730ec170baa3ad475d57eeec745355a579004727a73424ed87d915c448b94b3481a6cf826e Homepage: https://cran.r-project.org/package=ktweedie Description: CRAN Package 'ktweedie' ('Tweedie' Compound Poisson Model in the Reproducing KernelHilbert Space) Kernel-based 'Tweedie' compound Poisson gamma model using high-dimensional predictors for the analyses of zero-inflated response variables. The package features built-in estimation, prediction and cross-validation tools and supports choice of different kernel functions. For more details, please see Yi Lian, Archer Yi Yang, Boxiang Wang, Peng Shi & Robert William Platt (2023) . Package: r-cran-kvh Architecture: amd64 Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-kvh_1.4.2-1.ca2404.1_amd64.deb Size: 85620 MD5sum: e21ede4696420d5857abd05797d80826 SHA1: 7dded97008785059b5e80c0d7c04135941b6057f SHA256: 259629d61d45d4ebc6f91135aa7a997f6c2f40a664b390b0a1518b15af95799d SHA512: db51222b7d662c9a447dd1598154fa44766a9faf3f7369b8632f19737daf1f351ce4060fa6a32115b8f5dab5093192c9a9103bd2498b321469eda96da5726432 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. We provide an ability to parse KVH files in R pretty fast due to 'Rcpp' use. Package: r-cran-kwcchangepoint Architecture: amd64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ddalpha, r-cran-fda.usc, r-cran-tibble, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-kwcchangepoint_0.2.3-1.ca2404.1_amd64.deb Size: 199998 MD5sum: 736fdf3a281f3cddfbb36e178942595e SHA1: 6d7cc30795914b0d26a83e1f266cae55ce27077f SHA256: 3606a5db4c8ce5acb93bb96585517f8c224ed031506146a39a93819d5200d459 SHA512: 28e8f3d43be90e5b32392c47fc50b57902ca22371c49f5b577607c50529639e06dc05f55f74f0ef9f8428e44e265524ac38656bf9030ed205fdba6c7d5dfdcc9 Homepage: https://cran.r-project.org/package=KWCChangepoint Description: CRAN Package 'KWCChangepoint' (Robust Changepoint Detection for Functional and MultivariateData) Detect and test for changes in covariance structures of functional data, as well as changepoint detection for multivariate data more generally. Method for detecting non-stationarity in resting state functional Magnetic Resonance Imaging (fMRI) scans as seen in Ramsay, K., & Chenouri, S. (2025) is implemented in fmri_changepoints(). Also includes depth- and rank-based implementation of the wild binary segmentation algorithm for detecting multiple changepoints in multivariate data. Package: r-cran-kyotil Architecture: amd64 Version: 2024.11-01-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 744 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-runit, r-cran-r.rsp, r-cran-lme4, r-cran-nlme, r-cran-xtable, r-cran-mass, r-cran-survival, r-cran-abind, r-cran-pracma, r-cran-vgam, r-cran-copula, r-cran-mvtnorm, r-cran-hmisc, r-cran-rcolorbrewer, r-cran-zoo, r-cran-doparallel, r-cran-exact, r-cran-survey, r-cran-magick Filename: pool/dists/noble/main/r-cran-kyotil_2024.11-01-1.ca2404.1_amd64.deb Size: 633682 MD5sum: af2079ec8c0a8599f11d057ff6f3c1a0 SHA1: c723b0961ded686bf7515379bb83a0f729c95d72 SHA256: 28c6d526cbc9187de8730d60133100828e1858a3af545edb22a548404f50ea6b SHA512: c85e7c9dbc5907eedeaa773c2a40e52721f3bcf7f5d5d70137cc11d40ef514016fb52bce2d63131ba898e54c18ff3b4dca41b6c33432a282060ec741d320bf9d Homepage: https://cran.r-project.org/package=kyotil Description: CRAN Package 'kyotil' (Utility Functions for Statistical Analysis Report Generation andMonte Carlo Studies) Helper functions for creating formatted summary of regression models, writing publication-ready tables to latex files, and running Monte Carlo experiments. 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Package: r-cran-l0ara Architecture: amd64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-l0ara_0.1.7-1.ca2404.1_amd64.deb Size: 112048 MD5sum: e29572f7b3b0563a350c448a36372d5e SHA1: 0987e82cffa9725d47341dfff3712179d1718165 SHA256: bc5270c86408c3b3c079df012b35a197f49d147eda6e9a31449b71b946559a28 SHA512: 82d9a93a7dafefebe8fa50f29036a3fad02c23475a1218b6eb6be9601a965ece7ccb24d5f54053bebdc54bc69562c14d6ad6b665a841d821dcb1099d70573c40 Homepage: https://cran.r-project.org/package=l0ara Description: CRAN Package 'l0ara' (Sparse Generalized Linear Model with L0 Approximation forFeature Selection) Fits sparse generalized linear models using an adaptive ridge approximation to an L0 penalty. 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Network estimation is performed using the Local Linear Approximation (LLA) framework (Fan & Li, 2001 ; Zou & Li, 2008 ) with five penalty functions: arctangent (Wang & Zhu, 2016 ), EXP (Wang, Fan, & Zhu, 2018 ), Gumbel, Log (Candes, Wakin, & Boyd, 2008 ), and Weibull. Adaptive penalty parameters for EXP, Gumbel, and Weibull are estimated via maximum likelihood, and model selection uses information criteria including AIC, BIC, and EBIC (Extended BIC). Simulation functions generate multivariate normal data from GGMs with stochastic block model or small-world (Watts-Strogatz) network structures. 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The algorithms are based on coordinate descent and local combinatorial search. For more details, check the paper by Hazimeh and Mazumder (2020) . 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Functions for deriving global, local, and group L1 centrality/prestige are provided. Routines for visual inspection of a graph/network are also provided. Details are in Kang and Oh (2026a) , Kang and Oh (2026b) , and Kang (2025) . Package: r-cran-l1pack Architecture: amd64 Version: 0.62-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastmatrix Filename: pool/dists/noble/main/r-cran-l1pack_0.62-4-1.ca2404.1_amd64.deb Size: 162966 MD5sum: 21c810452993c78638bd197b0c2c360f SHA1: 37eafbb5531a97e5c5e00b4b3843eae6207618ae SHA256: 7dff52fde802224c561e24063c714f87f8f4d8a773606a15fdfa148a03d71683 SHA512: 1b68c955d9f1adc9272503514cb8ec6f3f0275e229d66b93006197ffa02e7b1e53aaf858cb60274bd278f0cecad4685a108fd575bf2eaa16e94ea0531c349790 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: amd64 Version: 0.99.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-igraph, r-cran-matrix, r-cran-aricode, r-cran-caret, r-cran-glmnet, r-cran-ggplot2, r-cran-cvtools, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-l1spectral_0.99.6-1.ca2404.1_amd64.deb Size: 101114 MD5sum: cc179ca18702d17b65fb7d922428d88a SHA1: f7640cc027f7a93bf2ab8b778990e9e9f78500e1 SHA256: f305e898665b1b25d5a27db8b9009e538c7bab9baa16a325250d755aa006d8c5 SHA512: 50fb57130066cf78e1613f08cc53726788ad939b097043440d32d71b22ff23ee3f6a87ac71b32a38bbdb5c860a36016b2cb28f04a8e00b4d3d93b84e250f993e Homepage: https://cran.r-project.org/package=l1spectral Description: CRAN Package 'l1spectral' (An L1-Version of the Spectral Clustering) Provides an l1-version of the spectral clustering algorithm devoted to robustly clustering highly perturbed graphs using l1-penalty. This algorithm is described with more details in the preprint C. Champion, M. Champion, M. Blazère, R. Burcelin and J.M. Loubes, "l1-spectral clustering algorithm: a spectral clustering method using l1-regularization" (2022). Package: r-cran-la Architecture: amd64 Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-la_2.3-1.ca2404.1_amd64.deb Size: 113638 MD5sum: 7e6b698e78d665fc5b57321fa14358c1 SHA1: 01d8cbe2ba6cc082f4a9e5283cee0a0a95e1e551 SHA256: a77116e01438c47859381e854d3646ec7edf382f56a05f35c0f21c42f1b56330 SHA512: 96c08eb58ea0cca97a9aba51d4e08e6498f250a2a680d0aea890b37373c349299e31e1e6f14254f2a50e07a50ca7c6aa314a698b81d247d8d3e4984eb2f8f34d Homepage: https://cran.r-project.org/package=LA Description: CRAN Package 'LA' (Lioness Algorithm (LA)) Contains Lioness Algorithm (LA) for finding optimal designs over continuous design space, optimal Latin hypercube designs, and optimal order-of-addition designs. LA is a brand new nature-inspired meta-heuristic optimization algorithm. Detailed methodologies of LA and its implementation on numerical simulations can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) . Package: r-cran-labdsv Architecture: amd64 Version: 2.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 410 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-cluster, r-cran-rtsne, r-cran-mass Suggests: r-cran-fso Filename: pool/dists/noble/main/r-cran-labdsv_2.3-1-1.ca2404.1_amd64.deb Size: 340734 MD5sum: 8c04c2fd6e58cabaa0ba34c0f89238df SHA1: fc2a174065208e856ea100b8434efc7c8749756a SHA256: 6d2456e3256044ec0de92315bd88d7bb39436a6d7598165ddaf3b93541bf4aeb SHA512: 3b4b8687536511e843b6da7c537e1b912e27f2b27cfed4074aa3172756dc162fbb9727424a9733aeda50606923898b23ead423350e7c4846382b40a57cd1b2e2 Homepage: https://cran.r-project.org/package=labdsv Description: CRAN Package 'labdsv' (Ordination and Multivariate Analysis for Ecology) A variety of ordination and community analyses useful in analysis of data sets in community ecology. Includes many of the common ordination methods, with graphical routines to facilitate their interpretation, as well as several novel analyses. Package: r-cran-lacm Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-statmod Filename: pool/dists/noble/main/r-cran-lacm_0.1.2-1.ca2404.1_amd64.deb Size: 57838 MD5sum: 4fa11a2e123ebe16606b28c6897973b1 SHA1: a79d551c731561ba4cf10d91f2e5af8042979cb4 SHA256: 32685d1c94282aab92cac9ad09eeab2b152d914ea2aa30942d9cb83cdcefea23 SHA512: 08e8b3f393ace30b72fef6fb5221d9c1744a884d300e93749f423f16af22b543a23e084f93ca313c5d79d0f8881444e3652573ae88246fb4994cff6fa1a33599 Homepage: https://cran.r-project.org/package=lacm Description: CRAN Package 'lacm' (Latent Autoregressive Count Models) Perform pairwise likelihood inference in latent autoregressive count models. See Pedeli and Varin (2020) for details. Package: r-cran-lacunr Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1419 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-abind, r-cran-ggplot2, r-cran-rlang, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-knitr, r-cran-lidr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lacunr_1.0.2-1.ca2404.1_amd64.deb Size: 1063552 MD5sum: ad3259abae7867d722ef3a381d2d81f4 SHA1: 86791a768257deec8b6a054493446c318c69312e SHA256: 908e7d467fe177bf478e3e76f285ea14e02fe0e8db160b76e9ee7741a8e71a7d SHA512: a51e82f7713281dbd0f3e6d78bd6fff96c8130e4c35b9e22c8fc7abc225a43cac0f7ad89db8538bc06d8b8019979d7bdcb8d96c7acded5cdba53bfa6992ad991 Homepage: https://cran.r-project.org/package=lacunr Description: CRAN Package 'lacunr' (Fast 3D Lacunarity for Voxel Data) Calculates 3D lacunarity from voxel data. It is designed for use with point clouds generated from Light Detection And Ranging (LiDAR) scans in order to measure the spatial heterogeneity of 3-dimensional structures such as forest stands. It provides fast 'C++' functions to efficiently bin point cloud data into voxels and calculate lacunarity using different variants of the gliding-box algorithm originated by Allain & Cloitre (1991) . Package: r-cran-laf Architecture: amd64 Version: 0.8.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 992 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-yaml Filename: pool/dists/noble/main/r-cran-laf_0.8.6-1.ca2404.1_amd64.deb Size: 693074 MD5sum: 1bd873d72ab3c6c67663d1e1f89e6ecb SHA1: 105aca1a613d77a2fd93dfa17deffdf7a5f20e22 SHA256: c389945c1c4cc677b386457b43995a53b412842c2ec371d47e4d06177185efbd SHA512: 233629034331282b009ac24397fe4500e8382b0909684f58692cfe364b4a11c5c9855010b8705c4c4789ab9cca3a85a19baa879f0b77c6e6ec7ff741f789aed2 Homepage: https://cran.r-project.org/package=LaF Description: CRAN Package 'LaF' (Fast Access to Large ASCII Files) Methods for fast access to large ASCII files. Currently the following file formats are supported: comma separated format (CSV) and fixed width format. 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Package: r-cran-lagp Architecture: amd64 Version: 1.5-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1600 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-tgp Suggests: r-cran-mvtnorm, r-cran-mass, r-cran-interp, r-cran-lhs, r-cran-crs, r-cran-diceoptim Filename: pool/dists/noble/main/r-cran-lagp_1.5-9-1.ca2404.1_amd64.deb Size: 1348428 MD5sum: 9edb19e0ba4fdf01a92c557ef1fc7414 SHA1: 7316b49214c7d1c9a1361a3435e552f94e28c2fc SHA256: 7eac39aa83f5ffadfb1473bed22403b3c92fcf19f699fa743e75d959de72ae65 SHA512: 574060bc502dda91eedda483c30aa939c8cca5eae8c92aaeb9641577edee2c862848c33bf086fc8631c8c136a8f7d93a73b84230c82ae3b98a5628c19d39b8f6 Homepage: https://cran.r-project.org/package=laGP Description: CRAN Package 'laGP' (Local Approximate Gaussian Process Regression) Performs approximate GP regression for large computer experiments and spatial datasets. The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine. OpenMP and GPU features may require special compilation. An interface to lower-level (full) GP inference and prediction is provided. Wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration, are also provided. For details and tutorial, see Gramacy (2016 . Package: r-cran-lakemetabolizer Architecture: amd64 Version: 1.5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3287 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlakeanalyzer, r-cran-plyr Suggests: r-cran-r2jags, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lakemetabolizer_1.5.6-1.ca2404.1_amd64.deb Size: 614114 MD5sum: 089f8ac77fe0689139451fe2a725fce8 SHA1: 2502491b69933d6d4c8c9e4e5be2ca56d0703813 SHA256: c6a974bfc25b966a51dcc5070d6c5a38b731d0f4e15267c0ea8910dd253a0943 SHA512: 1977c4df4939da63be808eee05fbbd63287991c8ea04dbf677a019e667df2920506748920fc371207a0b5d94e612ca9de549a7e99f942f7eeb5957c84d41d915 Homepage: https://cran.r-project.org/package=LakeMetabolizer Description: CRAN Package 'LakeMetabolizer' (Tools for the Analysis of Ecosystem Metabolism) A collection of tools for the calculation of freewater metabolism from in situ time series of dissolved oxygen, water temperature, and, optionally, additional environmental variables. LakeMetabolizer implements 5 different metabolism models with diverse statistical underpinnings: bookkeeping, ordinary least squares, maximum likelihood, Kalman filter, and Bayesian. Each of these 5 metabolism models can be combined with 1 of 7 models for computing the coefficient of gas exchange across the air–water interface (k). LakeMetabolizer also features a variety of supporting functions that compute conversions and implement calculations commonly applied to raw data prior to estimating metabolism (e.g., oxygen saturation and optical conversion models). Package: r-cran-lakhesis Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 590 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-readr, r-cran-ca, r-cran-ggplot2, r-cran-rdpack, r-cran-shiny, r-cran-shinydashboard, r-cran-bslib Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lakhesis_1.1-1.ca2404.1_amd64.deb Size: 283462 MD5sum: e6ffdf7ec254dceecc9c7e82293b94e4 SHA1: 24e2ad87fcb45e9cd0d62e5fef87fcdea8dcb452 SHA256: 42f3335c67efb2e5ef3f5d858f25a9cc999037ba31d3ecd2bb3223b414705aa5 SHA512: 3ea798c6e9a1ad2472706fc461d9a46ca3aa1742a8e72744d63aab0d48540b2fba0b60f8b253ec9943ebac1a15ff309c9d27961fb92efd9d0439eb5b935a30c0 Homepage: https://cran.r-project.org/package=lakhesis Description: CRAN Package 'lakhesis' (Consensus Seriation for Binary Data) Determining consensus seriations for binary incidence matrices, using a two-step process of Procrustes-fit correspondence analysis for heuristic selection of partial seriations and iterative regression to establish a single consensus. Contains the Lakhesis Calculator, a graphical platform for identifying seriated sequences. Collins-Elliott (2024) . Package: r-cran-lam Architecture: amd64 Version: 0.7-22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cdm, r-cran-rcpp, r-cran-sirt, r-cran-rcpparmadillo Suggests: r-cran-coda, r-cran-expm, r-cran-mass, r-cran-numderiv, r-cran-tam Filename: pool/dists/noble/main/r-cran-lam_0.7-22-1.ca2404.1_amd64.deb Size: 295388 MD5sum: 8d66db45aba87f86453c03cc82567aac SHA1: fc8077ef19166ebb5d88a9cd711a601061d4468a SHA256: 013b83736e78fda668d270c0afd89f8a2ae2da5fabb6a8ad612f4624ac7f7ac8 SHA512: e2b46acae68374b623a679ea5a2ae84171e149cf1932da4a9c938a4482c63fe995e879eb1ce4a95bb422bdde1ddcfdb913f44f2772a2473a0dfd561ddf3ffbc8 Homepage: https://cran.r-project.org/package=LAM Description: CRAN Package 'LAM' (Some Latent Variable Models) Includes some procedures for latent variable modeling with a particular focus on multilevel data. The 'LAM' package contains mean and covariance structure modelling for multivariate normally distributed data (mlnormal(); Longford, 1987; ), a general Metropolis-Hastings algorithm (amh(); Roberts & Rosenthal, 2001, ) and penalized maximum likelihood estimation (pmle(); Cole, Chu & Greenland, 2014; ). Package: r-cran-lama Architecture: amd64 Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4408 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rtmb, r-cran-rcpp, r-cran-matrix, r-cran-splines2, r-cran-mgcv, r-cran-mass, r-cran-numderiv, r-cran-rtmbdist, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-phsmm, r-cran-mswm, r-cran-scales Filename: pool/dists/noble/main/r-cran-lama_2.1.1-1.ca2404.1_amd64.deb Size: 2931660 MD5sum: 4b83b4d83902f2dbaecda44a1c73fe9b SHA1: 191edacefdc3da33c7a970015fdaae69cb30b3c2 SHA256: 8a6241f38c4b06a00eb8e17e75b30e65925f4854d3c7afd504ebdd8be939120b SHA512: 10a39d05fcede41b5ad9497057f348e8eff5b45f444eb35ba4583ec08057b8875aa3da5e41b9dc5faa56912592b7195221fe37f9b2701dca1758b85f6a389b11 Homepage: https://cran.r-project.org/package=LaMa Description: CRAN Package 'LaMa' (Fast Numerical Maximum Likelihood Estimation for Latent MarkovModels) A variety of latent Markov models, including hidden Markov models, hidden semi-Markov models, state-space models and continuous-time variants can be formulated and estimated within the same framework via directly maximising the likelihood function using the so-called forward algorithm. Applied researchers often need custom models that standard software does not easily support. Writing tailored 'R' code offers flexibility but suffers from slow estimation speeds. We address these issues by providing easy-to-use functions (written in 'C++' for speed) for common tasks like the forward algorithm. These functions can be combined into custom models in a Lego-type approach, offering up to 10-20 times faster estimation via standard numerical optimisers. To aid in building fully custom likelihood functions, several vignettes are included that show how to simulate data from and estimate all the above model classes. Package: r-cran-lambertw Architecture: amd64 Version: 0.6.9-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1174 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-ggplot2, r-cran-lamw, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rcpp Suggests: r-cran-boot, r-cran-rsolnp, r-cran-nortest, r-cran-numderiv, r-cran-testthat, r-cran-data.table, r-cran-moments, r-cran-knitr, r-cran-markdown, r-cran-vars Filename: pool/dists/noble/main/r-cran-lambertw_0.6.9-2-1.ca2404.1_amd64.deb Size: 857136 MD5sum: 5c4e6790a5ae5c9cfa9d34a387796c08 SHA1: fdd48f833f5109c905ee9237820a2b0a944cb570 SHA256: 2011860647d6a08ed73b33745e64ab22f3c929c866649e816d882c77ee8beb5e SHA512: 4ada915a91856a0e4a073a76ddde19fa20e7bb096c1b7cd8d70b7f4b29feb9156aba71aa7c6f548e8322184816a93b05741ad672fec6c971827a43233d40afec Homepage: https://cran.r-project.org/package=LambertW Description: CRAN Package 'LambertW' (Probabilistic Models to Analyze and Gaussianize Heavy-Tailed,Skewed Data) Lambert W x F distributions are a generalized framework to analyze skewed, heavy-tailed data. It is based on an input/output system, where the output random variable (RV) Y is a non-linearly transformed version of an input RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed). The transformed RV Y has a Lambert W x F distribution. This package contains functions to model and analyze skewed, heavy-tailed data the Lambert Way: simulate random samples, estimate parameters, compute quantiles, and plot/ print results nicely. The most useful function is 'Gaussianize', which works similarly to 'scale', but actually makes the data Gaussian. A do-it-yourself toolkit allows users to define their own Lambert W x 'MyFavoriteDistribution' and use it in their analysis right away. Package: r-cran-lamle Architecture: amd64 Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 950 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-numderiv, r-cran-fastghquad, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-lamle_0.3.1-1.ca2404.1_amd64.deb Size: 441150 MD5sum: ec8b597f6ec5524f9f1797f5b1d3a09d SHA1: a7831371ac0a8da43ae3fcc5f9b93c70967c1b90 SHA256: 1a384b9d2e15e7aa19d8a31f8261d487dcd1b73f8b21156aad0a221c61ac54ce SHA512: a3a2df83736b7d7970b41d4a97c301fefb00b0d29ab291b86f1228e490e4da01a9217f86cd3051756036ab526894d7dca3b8d37d88e26e98173fd0021c1ef152 Homepage: https://cran.r-project.org/package=lamle Description: CRAN Package 'lamle' (Maximum Likelihood Estimation of Latent Variable Models) Approximate marginal maximum likelihood estimation of multidimensional latent variable models via adaptive quadrature or Laplace approximations to the integrals in the likelihood function, as presented for confirmatory factor analysis models in Jin, S., Noh, M., and Lee, Y. (2018) , for item response theory models in Andersson, B., and Xin, T. (2021) , and for generalized linear latent variable models in Andersson, B., Jin, S., and Zhang, M. (2023) . Models implemented include the generalized partial credit model, the graded response model, and generalized linear latent variable models for Poisson, negative-binomial and normal distributions. Supports a combination of binary, ordinal, count and continuous observed variables and multiple group models. Package: r-cran-lamw Architecture: amd64 Version: 2.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Suggests: r-cran-covr, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-lamw_2.2.7-1.ca2404.1_amd64.deb Size: 47874 MD5sum: 458e51d2abf4e1d16e1555a279943f85 SHA1: 6968447bd214b3c05e226e58a0fdb360133f8e92 SHA256: dea52be80e278d16445cc69f8dcf3b46e651cede49f93d73e1d60bd5c6122cf9 SHA512: a530787f4d8f2c965b4b538af7210ce99793937b8f0899d1a368bce114ef7f511a957c888169947eb5b158af16dc81f56ee4daa66749773892931e3afc28cda0 Homepage: https://cran.r-project.org/package=lamW Description: CRAN Package 'lamW' (Lambert-W Function) Implements both real-valued branches of the Lambert-W function (Corless et al, 1996) without the need for installing the entire GSL. 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'landscapemetrics' reimplements the most common metrics from 'FRAGSTATS' () and new ones from the current literature on landscape metrics. This package supports 'terra' SpatRaster objects as input arguments. It further provides utility functions to visualize patches, select metrics and building blocks to develop new metrics. Package: r-cran-landscaper Architecture: amd64 Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra, r-cran-rcpp Suggests: r-cran-markdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-landscaper_1.3.1-1.ca2404.1_amd64.deb Size: 113074 MD5sum: b0e98dbfe397ea5ada6b657672cecc8e SHA1: a866cf51c0fac020dc99ca900b984458b4af5b47 SHA256: f9e013e88e7ce8e23e81ead343d4f4f42fd4a8877bf183a9c9ee28cd6e3de71e SHA512: 1d39fb7cbe943507916d07ed5cc761f6ee70da73c4d791611451cff61dcb1fcac334ea4524ddd1c4d8d9618c378ac7ef9f74e47c28da35a261108ff2ff5a8064 Homepage: https://cran.r-project.org/package=landscapeR Description: CRAN Package 'landscapeR' (Categorical Landscape Simulation Facility) Simulates categorical maps on actual geographical realms, starting from either empty landscapes or landscapes provided by the user (e.g. land use maps). 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Important reference papers include Berrington de Gonzalez et al. (2012) , National Research Council (2006, ISBN:978-0-309-09156-5). Package: r-cran-lars Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lars_1.3-1.ca2404.1_amd64.deb Size: 227602 MD5sum: f43c0f6620b68eccdd4a42fc885b77c5 SHA1: c2e7ab3d9cd072cbf4a270a1c178233c2379210e SHA256: 70af9f0634fb1454c2b7be7989e84da64aa0a90fa350082538da020a82fb018b SHA512: 5a7af49f098aa2e18eacd2f0fc09318321553df2bde8bdca15bbf11298f317f181e3a5b2333ba9459c89107070ab6f550713513f009b1c59b2deaaa1f04178db Homepage: https://cran.r-project.org/package=lars Description: CRAN Package 'lars' (Least Angle Regression, Lasso and Forward Stagewise) Efficient procedures for fitting an entire lasso sequence with the cost of a single least squares fit. 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Package: r-cran-lassoshooting Architecture: amd64 Version: 0.1.5-1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lassoshooting_0.1.5-1.1-1.ca2404.1_amd64.deb Size: 21790 MD5sum: 35bab84c3619b132b618119a7c45ec7d SHA1: da1e8abf8546c5509a736153f7a2d90dff7a40f6 SHA256: 6491d31c8601b824e83939aa1e6905de25ca296975ce6992ecfcebbe2d2d6cf7 SHA512: bc44916b3bce1533da9ec1a27d8dcab9a8646e2a8fa12568e4e6bc5caa067e0a246d930f21edc47e4c24a9002815152e6fce3c83e5ada99fed635b9ca737865e Homepage: https://cran.r-project.org/package=lassoshooting Description: CRAN Package 'lassoshooting' (L1 Regularized Regression (Lasso) Solver using the CyclicCoordinate Descent Algorithm aka Lasso Shooting) L1 regularized regression (Lasso) solver using the Cyclic Coordinate Descent algorithm aka Lasso Shooting is fast. This implementation can choose which coefficients to penalize. It support coefficient-specific penalties and it can take X'X and X'y instead of X and y. Package: r-cran-latentcor Architecture: amd64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3268 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pcapp, r-cran-fmultivar, r-cran-mnormt, r-cran-matrix, r-cran-mass, r-cran-heatmaply, r-cran-ggplot2, r-cran-plotly, r-cran-geometry, r-cran-dofuture, r-cran-foreach, r-cran-future, r-cran-dorng, r-cran-microbenchmark Suggests: r-cran-rmarkdown, r-cran-markdown, r-cran-knitr, r-cran-testthat, r-cran-lattice, r-cran-cubature, r-cran-plot3d, r-cran-covr Filename: pool/dists/noble/main/r-cran-latentcor_2.0.2-1.ca2404.1_amd64.deb Size: 3135078 MD5sum: f75f659140500d9bd93e9295e45e176c SHA1: 67c9bb51b55adc529095c8ee9471be1824cb3e69 SHA256: ac5be83d6643a9933882cdd6b6ae8403853aa4a63a881583e3a5f5fc4b488d23 SHA512: a88d83b2ee8fe24e5a8a8adbd908e007e44a3311be55c351dbfe7c193f0fdf7b4be7cf0bdb93637b191f14c77a87d61da4038bc6a349a6e57d77453fd4350d8c Homepage: https://cran.r-project.org/package=latentcor Description: CRAN Package 'latentcor' (Fast Computation of Latent Correlations for Mixed Data) The first stand-alone R package for computation of latent correlation that takes into account all variable types (continuous/binary/ordinal/zero-inflated), comes with an optimized memory footprint, and is computationally efficient, essentially making latent correlation estimation almost as fast as rank-based correlation estimation. The estimation is based on latent copula Gaussian models. For continuous/binary types, see Fan, J., Liu, H., Ning, Y., and Zou, H. (2017). For ternary type, see Quan X., Booth J.G. and Wells M.T. (2018) . For truncated type or zero-inflated type, see Yoon G., Carroll R.J. and Gaynanova I. (2020) . For approximation method of computation, see Yoon G., Müller C.L. and Gaynanova I. (2021) . The latter method uses multi-linear interpolation originally implemented in the R package . Package: r-cran-latentgraph Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-pracma, r-cran-glmnet, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-latentgraph_1.1-1.ca2404.1_amd64.deb Size: 94858 MD5sum: 08efc0b984e4e181b68d55da48816311 SHA1: 9036f6836eb9b5a14534b181ddb675ffd6ceb593 SHA256: 59e15057161b2f06714167d6720fac359c77b556cb48e1e0eed0f5bf2f14c0cd SHA512: 2944c123a8e80286566c5615c4ddc581a3b847989c199cb654d70feb32304967c105249823e0a18a4141f079c6559809e2a0c0f4397ccdbbf78dc1e68e64ce3f Homepage: https://cran.r-project.org/package=latentgraph Description: CRAN Package 'latentgraph' (Graphical Models with Latent Variables) Three methods are provided to estimate graphical models with latent variables: (1) Jin, Y., Ning, Y., and Tan, K. M. (2020) (preprint available); (2) Chandrasekaran, V., Parrilo, P. A. & Willsky, A. S. (2012) ; (3) Tan, K. M., Ning, Y., Witten, D. M. & Liu, H. (2016) . Package: r-cran-latentnet Architecture: amd64 Version: 2.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 630 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-network, r-cran-ergm, r-cran-sna, r-cran-mvtnorm, r-cran-abind, r-cran-coda, r-cran-statnet.common Suggests: r-cran-snowft, r-cran-rgl, r-cran-heplots, r-cran-rlecuyer, r-cran-covr, r-cran-kernsmooth Filename: pool/dists/noble/main/r-cran-latentnet_2.12.0-1.ca2404.1_amd64.deb Size: 509104 MD5sum: 7eac507af84a1ee630a72f2c17cb92b8 SHA1: ed4e54f2578fecd42ae77f27dfa13399395b5fe2 SHA256: c83add8bfd00dc0250ea8454429ca1b7a375a8c207e2b33e4ba836ee47bc937b SHA512: 24b6920bd1c862694bf68d083ad03d1fb0362504a6f896d81f8a0124b394b4f0a3f4d64fed8edb7253ff445ee1043647b7f4b87c5c209864e5b9c97ebebabf27 Homepage: https://cran.r-project.org/package=latentnet Description: CRAN Package 'latentnet' (Latent Position and Cluster Models for Statistical Networks) Fit and simulate latent position and cluster models for statistical networks. See Krivitsky and Handcock (2008) and Krivitsky, Handcock, Raftery, and Hoff (2009) . Package: r-cran-later Architecture: amd64 Version: 1.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rlang Suggests: r-cran-knitr, r-cran-nanonext, r-cran-promises, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-later_1.4.8-1.ca2404.1_amd64.deb Size: 137058 MD5sum: b8ceb93a2a142a3eeec95153a69c6a03 SHA1: 6b9638ddc7b95f3fa3e3d80a56d04a9bf98b40e7 SHA256: 0ede6e0c6260cc2ebe05439072f85e3bba19f955f89d3304f597cf6cdd80841a SHA512: f615eba2d12c2e6184a85c7c9a8efa0f621fcc4565cdca5dde08c602d8056381779dfcee353bbc991b89ad25c5f10353b88083bd29f82de8f71a0892e660c271 Homepage: https://cran.r-project.org/package=later Description: CRAN Package 'later' (Utilities for Scheduling Functions to Execute Later with EventLoops) Executes arbitrary R or C functions some time after the current time, after the R execution stack has emptied. The functions are scheduled in an event loop. Package: r-cran-lattice Architecture: amd64 Version: 0.22-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1688 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-kernsmooth, r-cran-mass, r-cran-latticeextra, r-cran-colorspace Filename: pool/dists/noble/main/r-cran-lattice_0.22-9-1.ca2404.1_amd64.deb Size: 1399452 MD5sum: 12426b0c3d48ead0044f7d90c1f3347d SHA1: f36a73096d86329761eead1fe873fbfe00db89b3 SHA256: 775a9bc73db4120744d19b54d6c12a94b057929965f464f053185fa85caa8ad9 SHA512: adbf69b921517eed9a7e93489da0c553c052820abb2a282979c2604a2759a287a39bdfbc7ec9b22177f589bcbb87663f9c2fa2d35deaa779d0523117678f5cdb Homepage: https://cran.r-project.org/package=lattice Description: CRAN Package 'lattice' (Trellis Graphics for R) A powerful and elegant high-level data visualization system inspired by Trellis graphics, with an emphasis on multivariate data. Lattice is sufficient for typical graphics needs, and is also flexible enough to handle most nonstandard requirements. See ?Lattice for an introduction. Package: r-cran-latticedesign Architecture: amd64 Version: 4.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nloptr Filename: pool/dists/noble/main/r-cran-latticedesign_4.0-1-1.ca2404.1_amd64.deb Size: 378884 MD5sum: 8c3194afec5985ceee55a5335f2f4914 SHA1: 62692ca7c8fb988d9c898e50a20042cafcc7bebc SHA256: 6e0741c0efbb5e1c6ff1fab3c3b216497a349c709909a237707f7fd44edf87e6 SHA512: a984ce65e38924632a2fed2c5df29801b193760f0fdc9f5a67861ca58f1f85b8d7b30548ebe2495affa62214e2bf165ad1633c2155e5afedca0d673f6b94a46f Homepage: https://cran.r-project.org/package=LatticeDesign Description: CRAN Package 'LatticeDesign' (Lattice-Based Space-Filling Designs) Lattice-based space-filling designs with fill or separation distance properties including interleaved lattice-based minimax distance designs proposed in Xu He (2017) , interleaved lattice-based maximin distance designs proposed in Xu He (2018) , interleaved lattice-based designs with low fill and high separation distance properties proposed in Xu He (2024) , (sliced) rotated sphere packing designs proposed in Xu He (2017) and Xu He (2019) , densest packing-based maximum projections designs proposed in Xu He (2020) and Xu He (2018) , maximin distance designs for mixed continuous, ordinal, and binary variables proposed in Hui Lan and Xu He (2025) , and optimized and regularly repeated lattice-based Latin hypercube designs for large-scale computer experiments proposed in Xu He, Junpeng Gong, and Zhaohui Li (2025) . Package: r-cran-latticekrig Architecture: amd64 Version: 9.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 677 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-spam, r-cran-spam64, r-cran-fftwtools, r-cran-fields Filename: pool/dists/noble/main/r-cran-latticekrig_9.3.0-1.ca2404.1_amd64.deb Size: 607462 MD5sum: c4c3c773d25d5bdda10137df52dbf7ba SHA1: d2d5c45f9fa386c33cfe5e44899aa23c945a0be6 SHA256: fb785898ce98dc89ca1323bb5820ba2e54636bbac52dadc75f6cf96aa6ebbe4a SHA512: 37fd9ff8fdb4a4813e7c0d77163dc1184b81f6daf485bd458436b6e633f0202521b58b7888f90d408ee86ff8805dd6642ef8bb5f234db4050f1c3872d358b393 Homepage: https://cran.r-project.org/package=LatticeKrig Description: CRAN Package 'LatticeKrig' (Multi-Resolution Kriging Based on Markov Random Fields) Methods for the interpolation of large spatial datasets. This package uses a basis function approach that provides a surface fitting method that can approximate standard spatial data models. Using a large number of basis functions allows for estimates that can come close to interpolating the observations (a spatial model with a small nugget variance.) Moreover, the covariance model for this method can approximate the Matern covariance family but also allows for a multi-resolution model and supports efficient computation of the profile likelihood for estimating covariance parameters. This is accomplished through compactly supported basis functions and a Markov random field model for the basis coefficients. These features lead to sparse matrices for the computations and this package makes of the R spam package for sparse linear algebra. An extension of this version over previous ones ( < 5.4 ) is the support for different geometries besides a rectangular domain. The Markov random field approach combined with a basis function representation makes the implementation of different geometries simple where only a few specific R functions need to be added with most of the computation and evaluation done by generic routines that have been tuned to be efficient. One benefit of this package's model/approach is the facility to do unconditional and conditional simulation of the field for large numbers of arbitrary points. There is also the flexibility for estimating non-stationary covariances and also the case when the observations are a linear combination (e.g. an integral) of the spatial process. Included are generic methods for prediction, standard errors for prediction, plotting of the estimated surface and conditional and unconditional simulation. See the 'LatticeKrigRPackage' GitHub repository for a vignette of this package. Development of this package was supported in part by the National Science Foundation Grant 1417857 and the National Center for Atmospheric Research. Package: r-cran-lavacreg Architecture: amd64 Version: 0.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-fastghquad, r-cran-pracma, r-cran-sparsegrid, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lavacreg_0.2-2-1.ca2404.1_amd64.deb Size: 167272 MD5sum: dbfdc1e94497d376e767f2561e130e96 SHA1: 381fa3bc3a593d7ae183594729357ceeafba30e6 SHA256: 5093e1ba6c917a170014c7c7acb01c733d3452f6fa8652f9320cce129b2dd5e9 SHA512: 46b10b13b8b4405158a746b012815935ac460030fff20cd4c994796f9bb59ad9f21942a6fae68b7730daa57fe96e31630a8fa52f1eed573a8ac787f3c92598ae Homepage: https://cran.r-project.org/package=lavacreg Description: CRAN Package 'lavacreg' (Latent Variable Count Regression Models) Estimation of a multi-group count regression models (i.e., Poisson, negative binomial) with latent covariates. This packages provides two extensions compared to ordinary count regression models based on a generalized linear model: First, measurement models for the predictors can be specified allowing to account for measurement error. Second, the count regression can be simultaneously estimated in multiple groups with stochastic group weights. The marginal maximum likelihood estimation is described in Kiefer & Mayer (2020) . 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The package contains three main functionalities: Wald tests/F-tests with improved control of the type 1 error in small samples, adjustment for multiple comparisons when searching for local dependencies, and adjustment for multiple comparisons when doing inference for multiple latent variable models. Package: r-cran-lazy Architecture: amd64 Version: 1.2-18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lazy_1.2-18-1.ca2404.1_amd64.deb Size: 56938 MD5sum: 29bd55f8d27b7c3bf823c86c6ae2155a SHA1: 4c0a3aba20ce1dba24a2b32e40d15fdc75c288fc SHA256: 030afb1e3c2cfb08593dedd2f0a98f27f2494d2a13ba4ce7606107ed7e1ec061 SHA512: b3204dae43e68fa15ab18248ed1049f6fdeab9728bdea646d4f913401a59771ecbd7f931da3301c41ab6f3a9a58f44f28ad7bbbdfcabf6edaf7497bc5c3c3626 Homepage: https://cran.r-project.org/package=lazy Description: CRAN Package 'lazy' (Lazy Learning for Local Regression) By combining constant, linear, and quadratic local models, lazy estimates the value of an unknown multivariate function on the basis of a set of possibly noisy samples of the function itself. 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It allows to store and load extremely large data on demand within seconds without occupying too much memories. With data stored on hard drive, a lazy-array data can be loaded, shared across multiple R sessions. For arrays with partition mode on, multiple R sessions can write to a same array simultaneously along the last dimension (partition). The internal storage format is provided by 'fstcore' package geared by 'LZ4' and 'ZSTD' compressors. Package: r-cran-lazyeval Architecture: amd64 Version: 0.2.3-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-lazyeval_0.2.3-1.ca2404.2_amd64.deb Size: 167674 MD5sum: d5a359eb9aeac7562e33702608c445a5 SHA1: b9ecb12cfde0da8c825e289ec35130a83226269b SHA256: fc5a97af3fa2685c80db92b0042d804cfdb8e268eaa818811a9ec5110eac6ca1 SHA512: 6c97aa742f1494fc1b60a4643e87f0fe365cddf1743b0a130a5fda3d86879cef53f146b0344c93adfaaa9920d42738c3c22d600a27560b8e9b48c29209bc6c04 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. 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The package supports model specifications, parameter estimation, and likelihood computation, facilitating simulation and statistical inference for LBA-based experiments. For details on the LBA model, see Brown and Heathcote (2008) . 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The lbfgs package implements both the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and the Orthant-Wise Quasi-Newton Limited-Memory (OWL-QN) optimization algorithms. The L-BFGS algorithm solves the problem of minimizing an objective, given its gradient, by iteratively computing approximations of the inverse Hessian matrix. The OWL-QN algorithm finds the optimum of an objective plus the L1-norm of the problem's parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems. 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L-BFGS-B.3.0 (See ) limited memory BFGS minimizer with bounds on parameters. This is a fork of 'lbfgsb3'. This registers a 'R' compatible 'C' interface to L-BFGS-B.3.0 that uses the same function types and optimization as the optim() function (see writing 'R' extensions and source for details). This package also adds more stopping criteria as well as allowing the adjustment of more tolerances. Package: r-cran-lbspr Architecture: amd64 Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1922 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-plotrix, r-cran-rcpp, r-cran-rcolorbrewer, r-cran-shiny, r-cran-tidyr Suggests: r-cran-colorspace, r-cran-knitr, r-cran-scales, r-cran-shinybs, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lbspr_0.1.6-1.ca2404.1_amd64.deb Size: 808098 MD5sum: 4190b8cd8460b338b0e9eca6124f8b8c SHA1: a50ee917b28545e1bc24a4f5e84c905646846a96 SHA256: a0577d60f8451a53d448f0781c5e4768daacaa28ef06c87a53adc66caa0683b4 SHA512: 1add93b8096c9227a1b8a235cc238c6162907fff28c1b0f9a4f4e3b0f55d650cb09ffd8a2b749bc846f43862617b5ddf83a6a9b71c1b5df23bdd5e98775a3194 Homepage: https://cran.r-project.org/package=LBSPR Description: CRAN Package 'LBSPR' (Length-Based Spawning Potential Ratio) Simulate expected equilibrium length composition, yield-per-recruit, and the spawning potential ratio (SPR) using the length-based SPR (LBSPR) model. Fit the LBSPR model to length data to estimate selectivity, relative apical fishing mortality, and the spawning potential ratio for data-limited fisheries. See Hordyk et al (2016) for more information about the LBSPR assessment method. Package: r-cran-lcc Architecture: amd64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 553 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-ggplot2, r-cran-hnp, r-cran-dosnow, r-cran-dorng, r-cran-foreach Suggests: r-cran-roxygen2, r-cran-covr, r-cran-testthat, r-cran-mass Filename: pool/dists/noble/main/r-cran-lcc_1.1.4-1.ca2404.1_amd64.deb Size: 508368 MD5sum: c636c8ee56390a96772832f4176b2a59 SHA1: f168016fe55b091e9e0b5480a8549e1fb422d77f SHA256: 69650e9396c95e52d746993f196dcd0d4a2a965dd81cf7fc87cdfb608b453f9f SHA512: 2478f67f44cc68df37c089dd5d9e4632f330f904f20af77980ceed12be20f258ab709b2dd9cfb6f004b2761188cbb902f25c0d9699e40f75fb84e81fdad7eabf Homepage: https://cran.r-project.org/package=lcc Description: CRAN Package 'lcc' (Longitudinal Concordance Correlation) Estimates the longitudinal concordance correlation to access the longitudinal agreement profile. The estimation approach implemented is variance components approach based on polynomial mixed effects regression model, as proposed by Oliveira, Hinde and Zocchi (2018) . In addition, non-parametric confidence intervals were implemented using percentile method or normal-approximation based on Fisher Z-transformation. Package: r-cran-lcmcr Architecture: amd64 Version: 0.4.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lcmcr_0.4.14-1.ca2404.1_amd64.deb Size: 217142 MD5sum: a178e5a2172bd76c5047d9f11d429b34 SHA1: e3752a0b77d9d396e3f89b99529a2af7a8df8062 SHA256: 7d4a3aaaaf41eebc26e2fa5a2eabe1022f1af0b74c3a320c395d317ab1f5eabf SHA512: 7812b6417c14256d8b5ed9dbbacb75b9800791ded17fbecd13b7c555fa93cf5d647dc6c5681b85cf6e97fb113d81a97319944e373cf7f81d97d4fbca81535924 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: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-igraph, r-cran-rcpp, r-cran-scales Filename: pool/dists/noble/main/r-cran-lconnect_0.1.2-1.ca2404.1_amd64.deb Size: 192588 MD5sum: cb0a9806b5e2994ee9ae55c147b3034a SHA1: 218975a697b6614825ac612787564a1c3c18a721 SHA256: c3c2923d91bd495e0e01d9c55643727daee9fa9a10adab7a8ce40e1905dc7762 SHA512: 50903b1b3f56168731d3e5c7f531dc3031addb2d45ccb61a1325dbd0d944b99e90b3555de3ecb823edd106997293af18fcd30673c963b323496ab8f2eed977e2 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: amd64 Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula, r-cran-rcpp Suggests: r-cran-wdm Filename: pool/dists/noble/main/r-cran-lcopula_1.0.7-1.ca2404.1_amd64.deb Size: 216520 MD5sum: 5b08d6ec3097316dacb4f2d394f78931 SHA1: 5730a43145344ed02abba47d539ff6f35cd2406d SHA256: 477d5ed4c2593524cc849d69a52c1473bea28cf011f6e6ad77bd227301c1f4f8 SHA512: d83f065357875c556826e5fb1d461f1360192fe3d0b044bd617c00195405119831abb47a5e6d879ae1dcc1fe7f480b982851bbdd08e2243999f67e587d1c1436 Homepage: https://cran.r-project.org/package=lcopula Description: CRAN Package 'lcopula' (Liouville Copulas) Collections of functions allowing random number generations and estimation of 'Liouville' copulas, as described in Belzile and Neslehova (2017) . 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Finally, it includes several user-friendly auxiliary functions to enhance interactive usability. Package: r-cran-lda Architecture: amd64 Version: 1.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3897 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-matrix, r-cran-reshape2, r-cran-ggplot2, r-cran-penalized, r-cran-nnet Filename: pool/dists/noble/main/r-cran-lda_1.5.2-1.ca2404.1_amd64.deb Size: 3899938 MD5sum: 1a4d762546ea556c8b4050822f06bd85 SHA1: c742f44d1d417f83dd5929eaf27550a04fa594ec SHA256: eb82de35b96b19a078ef0242b0c5c6b912b86be35a7b0e23f546a06ba7bbdce7 SHA512: 88c180757b4754e8bf5584f499e8fdd7a2995d880f5ebf088afcecff04e566398504fdad7f4144efbbbd569c8ee2478074cf39ea7627a68a27578afcf4fe36b9 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. This includes (but is not limited to) sLDA, corrLDA, and the mixed-membership stochastic blockmodel. Inference for all of these models is implemented via a fast collapsed Gibbs sampler written in C. Utility functions for reading/writing data typically used in topic models, as well as tools for examining posterior distributions are also included. 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You provide a reference marked point process, a set of raster images containing location specific covariates, and select the estimation algorithm and type of mark model. 'ldmppr' estimates the process and mark models and allows you to check the appropriateness of the model using a variety of diagnostic tools. Once a satisfactory model fit is obtained, you can simulate from the model and visualize the results. Documentation for the package 'ldmppr' is available in the form of a vignette. Package: r-cran-ldsep Architecture: amd64 Version: 2.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1212 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-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/noble/main/r-cran-ldsep_2.1.6-1.ca2404.1_amd64.deb Size: 705642 MD5sum: 10b3af63432d192cf2c47efb5d31fe62 SHA1: 0749a7913c66dd92084483f156e175ae00451aa0 SHA256: 16c9abd1fd6a10d0b5808904d1eca7475767f77d006ba2d7a034bfe94cf46fc4 SHA512: 98135e2ce3eabceaed5dcf9c796946a27f37db171abee7b49b1a6feca6201676b6b873c1908d754db5b8f77acb7b4bace718f0871beb68b3646aa95e1d41100b 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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Package: r-cran-ldsr Architecture: amd64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 673 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-foreach, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ga, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-patchwork, r-cran-dofuture, r-cran-future Filename: pool/dists/noble/main/r-cran-ldsr_0.0.2-1.ca2404.1_amd64.deb Size: 396508 MD5sum: 10903c6115bb85ddf5efc22750d515d0 SHA1: be4d13dec6a346819373cbd8bc2e9987f3b7bb2f SHA256: f5c9e6a4b4f830fc2091c78b2e523ba1509ce1c79723b1dd87bc08c3198fa98b SHA512: 694a7f4312b0af0f02fccc510dc7ac0f94115dde82a173b54e4f4e6cab127b00f50eeae92318078e64915e51c74e882a6153e961e0ef4e64fc2ecd3b5b068c72 Homepage: https://cran.r-project.org/package=ldsr Description: CRAN Package 'ldsr' (Linear Dynamical System Reconstruction) Streamflow (and climate) reconstruction using Linear Dynamical Systems. The advantage of this method is the additional state trajectory which can reveal more information about the catchment or climate system. For details of the method please refer to Nguyen and Galelli (2018) . Package: r-cran-ldt Architecture: amd64 Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3682 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-tdata, r-cran-rdpack, r-cran-mass, r-cran-bh Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-kableextra, r-cran-moments, r-cran-systemfit Filename: pool/dists/noble/main/r-cran-ldt_0.5.3-1.ca2404.1_amd64.deb Size: 1860198 MD5sum: 5fca43611185bee1ffeef75d6512d71f SHA1: 5f2adefbd682bbc993595fb0cc998c3c864c474c SHA256: e5b25a334b309d50901fca685c957da7576543d543f41715bbb507162367b4ba SHA512: b499ef266e88d3443b3e77686d07f36ed1c5b2aa01ad15ede43bad4d01f2579877deb4059360e3764ed3789187bcf3f0dfb1eddfb6fc30d86dea2cc57871f152 Homepage: https://cran.r-project.org/package=ldt Description: CRAN Package 'ldt' (Automated Uncertainty Analysis) Methods and tools for model selection and multi-model inference (Burnham and Anderson (2002) , among others). 'SUR' (for parameter estimation), 'logit'/'probit' (for binary classification), and 'VARMA' (for time-series forecasting) are implemented. Evaluations are both in-sample and out-of-sample. It is designed to be efficient in terms of CPU usage and memory consumption. Package: r-cran-leadercluster Architecture: amd64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-leadercluster_1.5-1.ca2404.1_amd64.deb Size: 18632 MD5sum: f25f83be1ef1bef40432709cd5d30541 SHA1: 0c34d1dac6e2e18ef482de56c86419ec3d305e8f SHA256: 3b589d19792c592f12dab30a94d9fdecf389b06c5a407b322cb7b7bdd1ccc38b SHA512: 85f20cb9b9b7adedc602a88e4a2db51a3a303134f10fcaaeca52e0053d0e8cf36e8c1bb7914359c7fba720cbe407f636beffe1175cf9e02284f1b7b088373a39 Homepage: https://cran.r-project.org/package=leaderCluster Description: CRAN Package 'leaderCluster' (Leader Clustering Algorithm) The leader clustering algorithm provides a means for clustering a set of data points. 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Package: r-cran-leafletzh Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3730 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-geojsonsf, r-cran-geosphere, r-cran-htmltools, r-cran-htmlwidgets, r-cran-leaflet, r-cran-leaflet.extras, r-cran-purrr, r-cran-rcpp, r-cran-scales, r-cran-sf, r-cran-stringr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-leafletzh_0.1.1-1.ca2404.1_amd64.deb Size: 2601860 MD5sum: fbeff412335296b72539a5677e70e852 SHA1: c32c49a160a27037cbf11a3e9263354520c8a4ad SHA256: 059ea1e2212891fc522e111a583694b22c80615d37ae7c3b76a280bb16a6b9dc SHA512: af5f67f75ba9fa3135cbdefdc5eeabcfa1261febf8f0bbe2c154b925d84b4dee44fb106e5283157406bd988800a2858ca8f42d88caf1fb1640bfea1aa50d6bab Homepage: https://cran.r-project.org/package=leafletZH Description: CRAN Package 'leafletZH' (Chinese Leaflet Map Relate Operation) Provides 'sf' data for Chinese provinces and cities, methods for plotting shape maps of Chinese provinces and cities, Convert Coordinates Between Different Systems, and a layer for 'leaflet' with Gaode tiles. It is designed to facilitate geographical data visualization in China. Package: r-cran-leaps Architecture: amd64 Version: 3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-biglm Filename: pool/dists/noble/main/r-cran-leaps_3.2-1.ca2404.1_amd64.deb Size: 82722 MD5sum: 8e46d11ecbaca9b5fdd705c42228520e SHA1: 3b931bdfaefd87c8177b3f3304e23257e14f2735 SHA256: 28d4d5c3e7cf92178eb50cea49c65c864e8ed96ee0dbe632d881a37f37a5b9eb SHA512: 9eca227241fa55fe9bc697019e1460a2452dfad81c2ab00755529566d0f2d88d7c447283bd2cbeabd9063ee3b5b244629598dc6ec677c86835efc3dcb86ba103 Homepage: https://cran.r-project.org/package=leaps Description: CRAN Package 'leaps' (Regression Subset Selection) Regression subset selection, including exhaustive search. Package: r-cran-learner Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 765 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-screenot, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-learner_1.0.0-1.ca2404.1_amd64.deb Size: 484834 MD5sum: 03bb763b9ab20264e7e477552c0750e7 SHA1: 5db6fe6537378ec68c8db73dc600c636ce6889f7 SHA256: 00af30e34533f8911d18b4ded16f15f59edc526009348b98b365ed55e2e01fc3 SHA512: 2473902c3e0f63b81665f4c315c2172f935a08a326b1a6b794d0b1004fc37d7aedf73c9d65e1d95c5adb6cc9f6a1b42c5ec1aa705218b78416bc315b38f17422 Homepage: https://cran.r-project.org/package=learner Description: CRAN Package 'learner' (Latent Space-Based Transfer Learning) Implements transfer learning methods for low-rank matrix estimation. These methods leverage similarity in the latent row and column spaces between the source and target populations to improve estimation in the target population. The methods include the LatEnt spAce-based tRaNsfer lEaRning (LEARNER) method and the direct projection LEARNER (D-LEARNER) method described by McGrath et al. (2024) . Package: r-cran-learningrlab Architecture: amd64 Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 706 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magick, r-cran-crayon Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-learningrlab_2.4-1.ca2404.1_amd64.deb Size: 481314 MD5sum: 9664f42b7702d18b60b836305888ca59 SHA1: 869234ab6f0a5adbb66520c3921d404dc044b191 SHA256: 23997535a6d969fa79f0063df675edf1283e94c43808fd157a49837ac2722f8c SHA512: 6962b1777c95b31884d14b9cde082c14ecff08efcd0687eb0c27532cba55f44263776b8c69c26a6f9b632cab820281df5e82a810b810513c24e53084e04c99dc Homepage: https://cran.r-project.org/package=LearningRlab Description: CRAN Package 'LearningRlab' (Statistical Learning Functions) Aids in learning statistical functions incorporating the result of calculus done with each function and how they are obtained, that is, which equation and variables are used. Also for all these equations and their related variables detailed explanations and interactive exercises are also included. All these characteristics allow to the package user to improve the learning of statistics basics by means of their use. Package: r-cran-learnnonparam Architecture: amd64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1133 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rcpp Suggests: r-cran-quickr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-learnnonparam_1.3.0-1.ca2404.1_amd64.deb Size: 709862 MD5sum: 4109099889ff8d5aa433d6484fc917c7 SHA1: 52d0c0c80e5a58498945c3eb1b3ad1ae454f0514 SHA256: d810420ffb3a122026ac4a5107f87ede2caeefdef039086f99dbbc4bf93565d0 SHA512: da2d5fb001aa2b776ef5e43b8622cc751e40ca1bb85298762e7f8749dcbc57744f5f68f758e355d8d3cf1c99a94b79c1c87483c9f742733d78b83096834f30a2 Homepage: https://cran.r-project.org/package=LearnNonparam Description: CRAN Package 'LearnNonparam' ('R6'-Based Flexible Framework for Permutation Tests) Implements non-parametric tests from Higgins (2004, ISBN:0534387756), including tests for one sample, two samples, k samples, paired comparisons, blocked designs, trends and association. Built with 'Rcpp' for efficiency and 'R6' for flexible, object-oriented design, the package provides a unified framework for performing or creating custom permutation tests. Package: r-cran-lefko3 Architecture: amd64 Version: 6.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10202 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-glmmtmb, r-cran-lme4, r-cran-mass, r-cran-matrix, r-cran-mumin, r-cran-pscl, r-cran-rlang, r-cran-vgam, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-popbio, r-cran-rmarkdown, r-cran-rcompadre Filename: pool/dists/noble/main/r-cran-lefko3_6.7.3-1.ca2404.1_amd64.deb Size: 4266788 MD5sum: 232cc5e373ce10f4311beed3c0849ba4 SHA1: b5997d866d2d680ee7d13c4ac89fbce7ff231b10 SHA256: 094d89fddd5912183511373f9ea287e4c53ba454186c66f77bfbd8158b81a730 SHA512: 14465d3fcf09e81ae0ddc2dc8bc77275f945990048eac3608f9df570d17f37ed76c430e478d034aa1c3134f209ac8777af4403d659cdac09e85b5f047dfc411f Homepage: https://cran.r-project.org/package=lefko3 Description: CRAN Package 'lefko3' (Historical and Ahistorical Population Projection Matrix Analysis) Complete analytical environment for the construction and analysis of matrix population models and integral projection models. Includes the ability to construct historical matrices, which are 2d matrices comprising 3 consecutive times of demographic information. Estimates both raw and function-based forms of historical and standard ahistorical matrices. It also estimates function-based age-by-stage matrices and raw and function-based Leslie matrices. Package: r-cran-legion Architecture: amd64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1406 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-greybox, r-cran-smooth, r-cran-rcpp, r-cran-generics, r-cran-matrix, r-cran-nloptr, r-cran-zoo, r-cran-rcpparmadillo Suggests: r-cran-numderiv, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-domc, r-cran-doparallel, r-cran-foreach Filename: pool/dists/noble/main/r-cran-legion_0.2.1-1.ca2404.1_amd64.deb Size: 893656 MD5sum: 17df7dfd086194ede0d88ab0e0616826 SHA1: e595e26bf76d884f733185503ae820da06eeeef7 SHA256: e4568ea6802236dcf35552b27009149d0ff9cf3f5fb475e93dc3b2e832fc3117 SHA512: fd565b4cba9b3272c7f3efd48ddc63276f1ab7c03f87517ce523b44dde684298cdfc0ffabb929b1c553ec63cbbb80f0cdcc101bb4d6519b09e081305210b3433 Homepage: https://cran.r-project.org/package=legion Description: CRAN Package 'legion' (Forecasting Using Multivariate Models) Functions implementing multivariate state space models for purposes of time series analysis and forecasting. The focus of the package is on multivariate models, such as Vector Exponential Smoothing, Vector ETS (Error-Trend-Seasonal model) etc. It currently includes Vector Exponential Smoothing (VES, de Silva et al., 2010, ), Vector ETS (Svetunkov et al., 2023, ) and simulation function for VES. Package: r-cran-leidenalg Architecture: amd64 Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 568 Depends: libc6 (>= 2.35), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-igraph, r-cran-rcpp, r-cran-sccore, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-pbapply, r-cran-testthat Filename: pool/dists/noble/main/r-cran-leidenalg_1.1.7-1.ca2404.1_amd64.deb Size: 220466 MD5sum: b8a74f0478b55ebae03ae40cdda6ce8e SHA1: fc2313f6c8780f10bb6278004114d65a1faf5dbf SHA256: 426b166cd259b46dc0a4f501f21862a1db3a9163b0e8d61b88d020f9b5548b48 SHA512: 8d21c6b5efd84911ebe153308da554c0c49c31ddbef3190acc5e7ed4acffad9709786b0e80539dfbb4daff7b76086a0ac08e3c379ff3357b82138f5ba59ec5a3 Homepage: https://cran.r-project.org/package=leidenAlg Description: CRAN Package 'leidenAlg' (Implements the Leiden Algorithm via an R Interface) An R interface to the Leiden algorithm, an iterative community detection algorithm on networks. The algorithm is designed to converge to a partition in which all subsets of all communities are locally optimally assigned, yielding communities guaranteed to be connected. The implementation proves to be fast, scales well, and can be run on graphs of millions of nodes (as long as they can fit in memory). The original implementation was constructed as a python interface "leidenalg" found here: . The algorithm was originally described in Traag, V.A., Waltman, L. & van Eck, N.J. "From Louvain to Leiden: guaranteeing well-connected communities". Sci Rep 9, 5233 (2019) . Package: r-cran-leidenbase Architecture: amd64 Version: 0.1.37-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3036 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libglpk40 (>= 4.59), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-igraph Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-pandoc Filename: pool/dists/noble/main/r-cran-leidenbase_0.1.37-1.ca2404.1_amd64.deb Size: 1187636 MD5sum: 803d7dbd417937f91467af4b7ca56956 SHA1: 4df10cab29677e0c0d09343cae0c9d261f36f023 SHA256: 5953c2f24cf37d9548c4c53502a320e36b65197a298654751e1f9c4ba16c7911 SHA512: 0108eee6e1441d4186a00a4bf6574683d2e1633a8b0accadee5318b47ac39277389d56608a83551e68fe10f8c75d4a196f04486c62d69a136d0fe926d46ef79e Homepage: https://cran.r-project.org/package=leidenbase Description: CRAN Package 'leidenbase' (R and C/C++ Wrappers to Run the Leiden find_partition() Function) An R to C/C++ interface that runs the Leiden community detection algorithm to find a basic partition (). It runs the equivalent of the 'leidenalg' find_partition() function, which is given in the 'leidenalg' distribution file 'leiden/src/functions.py'. This package includes the required source code files from the official 'leidenalg' distribution and functions from the R 'igraph' package. The 'leidenalg' distribution is available from and the R 'igraph' package is available from . The Leiden algorithm is described in the article by Traag et al. (2019) . Leidenbase includes code from the packages: igraph version 0.9.8 with license GPL (>= 2), leidenalg version 0.8.10 with license GPL 3. Package: r-cran-lemarns Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1023 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-abind, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lemarns_0.1.2-1.ca2404.1_amd64.deb Size: 521974 MD5sum: 9e42f1901ee13f9b1929b75730945aba SHA1: cc52ee394be584a8dc12dc622210d8191dcdf0f0 SHA256: 895e835328b2e7f24059c28c529711574dfe9cbe54f67dc09eec8c02bfee05ce SHA512: e8e9597173634ff859ffb998fcf22faf08f285ad560dbec0e35e94e0fcb76d231dd829b7a2d4ea99a7bd49120e998a2d3e32ddbbfeb1ff5760e06fa1cca2f646 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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Package: r-cran-libgeos Architecture: amd64 Version: 3.11.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3046 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-libgeos_3.11.1-3-1.ca2404.1_amd64.deb Size: 851004 MD5sum: b90fa99caa462b4a388ffabc230aa1f3 SHA1: 5006d7088c7343c62aeb8d3fbaf622219905ae3f SHA256: 506b07bd9017fdb0db2c31c622753cfbd5e818f0b24b6b072e8eb5c4276c25c2 SHA512: 0ae0a03da7538583863b36e40bee22b86ccb8bdc36a6d336e81e3b97d75e91ebe96fc0915e93e8b8144bd7fc04e8a22715a31f98f65ba15d3107a876be82b3bc 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: amd64 Version: 2.10-24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-sparsem, r-cran-matrix Filename: pool/dists/noble/main/r-cran-liblinear_2.10-24-1.ca2404.1_amd64.deb Size: 78528 MD5sum: 466373839013ef3278911dd3498484ec SHA1: f96aaca3b95a84507c1ea70328b30de2d58ee8d8 SHA256: 6588f01ffe475ca454cb301de33e52b06f09f50f9ba1452bdaede012a087ce21 SHA512: cd387773e307e25cd3f967579381c427f1bcffaf0ea2ab8a3fddfa17354d06c3d51f682a56cd323f37b284d0b872533a04330dce1db5a8982c6d8ba88bca583c Homepage: https://cran.r-project.org/package=LiblineaR Description: CRAN Package 'LiblineaR' (Linear Predictive Models Based on the LIBLINEAR C/C++ Library) A wrapper around the LIBLINEAR C/C++ library for machine learning (available at ). 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Package: r-cran-libopf Architecture: amd64 Version: 2.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 999 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-libopf_2.6.2-1.ca2404.1_amd64.deb Size: 749736 MD5sum: 6b34b5fab169d6907efb8070b86c4b27 SHA1: 42583c3b87202353ecbff38fe133b8dc95d13c08 SHA256: ae5ed5fbd440fddf12121105765a6fd9b25b8f738be589c94add459db78e288b SHA512: 71d3a32cd52421e669c72cff1323873ccbad7eb954e0ed87ce537620be6ac93adc371c6cbcb6c1cc29a95d398bc00c6fe8d1b81b728c80e95c0daf50f6fa1aeb Homepage: https://cran.r-project.org/package=LibOPF Description: CRAN Package 'LibOPF' (Design of Optimum-Path Forest Classifiers) The 'LibOPF' is a framework to develop pattern recognition techniques based on optimum-path forests (OPF), João P. Papa and Alexandre X. Falcão (2008) , with methods for supervised learning and data clustering. 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Package: r-cran-lightgbm Architecture: amd64 Version: 4.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7579 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-data.table, r-cran-jsonlite, r-cran-matrix Suggests: r-cran-knitr, r-cran-markdown, r-cran-rhpcblasctl, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lightgbm_4.6.0-1.ca2404.1_amd64.deb Size: 2025836 MD5sum: eb0dae8492401972a3432313a2d3e77c SHA1: 14a9d86d50ded2f8c6bf6c49bacdfdb50f37d3cc SHA256: 75790afe40ede08fb7d1a29267472d7dd0ab8aa7a595d7b5c715fb16fda3e207 SHA512: 2c7dbf93c1d1627135718d5e2a0f67a3f98ffc480cd289c84fe22a190be7b2c17c137587ccb0b7c43196d299ccc828fe24be388d6914e5bd37c098a18025e07f Homepage: https://cran.r-project.org/package=lightgbm Description: CRAN Package 'lightgbm' (Light Gradient Boosting Machine) Tree based algorithms can be improved by introducing boosting frameworks. 'LightGBM' is one such framework, based on Ke, Guolin et al. (2017) . This package offers an R interface to work with it. It is designed to be distributed and efficient with the following advantages: 1. Faster training speed and higher efficiency. 2. Lower memory usage. 3. Better accuracy. 4. Parallel learning supported. 5. Capable of handling large-scale data. In recognition of these advantages, 'LightGBM' has been widely-used in many winning solutions of machine learning competitions. Comparison experiments on public datasets suggest that 'LightGBM' can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. In addition, parallel experiments suggest that in certain circumstances, 'LightGBM' can achieve a linear speed-up in training time by using multiple machines. Package: r-cran-likertmaker Architecture: amd64 Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1335 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-gtools, r-cran-matrix, r-cran-matrixstats, r-cran-rcpp, r-cran-tibble, r-cran-rcpparmadillo Suggests: r-cran-effectsize, r-cran-kableextra, r-cran-knitr, r-cran-ggplot2, r-cran-ggrepel, r-cran-psych, r-cran-polycor, r-cran-psychtools, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-likertmaker_2.3.0-1.ca2404.1_amd64.deb Size: 579614 MD5sum: 84272624b3026c772b6d51bdf2651279 SHA1: f1efedf15c7eaee41fef5eb055edaef91fbae437 SHA256: 56e80b7ea1a136d929c79589ef028b785b49f055b338c357f7e78f106c39488c SHA512: 9f1c8dc3a2e0e4a41e13bd71a461819a07b1705b4a4eecf6b90f333dedb194dd4559a8a87c3e123c0e8c210653c2709b14390841759590fba4128e37087836d2 Homepage: https://cran.r-project.org/package=LikertMakeR Description: CRAN Package 'LikertMakeR' (Synthesise and Correlate Likert Scale and Rating-Scale DataBased on Summary Statistics) Generate and correlate synthetic Likert and rating-scale questionnaire responses with predefined means, standard deviations, Cronbach's Alpha, Factor Loading table, coefficients, and other summary statistics. It can be used to simulate Likert data, construct multi-item scales, generate correlation matrices, and create example survey datasets for teaching statistics, psychometrics, and methodological research. Worked examples and documentation are available in the package articles, accessible via the package website, . Package: r-cran-lime Architecture: amd64 Version: 0.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1916 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-ggplot2, r-cran-glmnet, r-cran-glue, r-cran-gower, r-cran-lifecycle, r-cran-matrix, r-cran-rcpp, r-cran-rlang, r-cran-stringi, r-cran-rcppeigen Suggests: r-cran-covr, r-cran-h2o, r-cran-htmlwidgets, r-cran-keras, r-cran-knitr, r-cran-magick, r-cran-mass, r-cran-mlr, r-cran-ranger, r-cran-rmarkdown, r-cran-sessioninfo, r-cran-shiny, r-cran-shinythemes, r-cran-testthat, r-cran-text2vec, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-lime_0.5.4-1.ca2404.1_amd64.deb Size: 1440444 MD5sum: 52ba53971635ed3e6e5c0b8d7f45b62f SHA1: 7cba0bc0036be7360a087d1069a184af64301781 SHA256: 1bfcf6eeb3443d616ac5961ac6bcb5d1f4478735699af217069c9f192f1a570a SHA512: 5a955f42f15cbe13bec76d13d6b70eeb32ad451ee9af3de1c4b93d9a2b12f6971cf0c51447f25a283571f019f01e67a71ef6d93b1bbc16e06d6430562b07f271 Homepage: https://cran.r-project.org/package=lime Description: CRAN Package 'lime' (Local Interpretable Model-Agnostic Explanations) When building complex models, it is often difficult to explain why the model should be trusted. While global measures such as accuracy are useful, they cannot be used for explaining why a model made a specific prediction. 'lime' (a port of the 'lime' 'Python' package) is a method for explaining the outcome of black box models by fitting a local model around the point in question an perturbations of this point. The approach is described in more detail in the article by Ribeiro et al. (2016) . Package: r-cran-limsolve Architecture: amd64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1770 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgfortran5 (>= 8), r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-lpsolve, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-limsolve_2.0.1-1.ca2404.1_amd64.deb Size: 696264 MD5sum: fef7bd734c4f00107d4ab40eb8daa2e8 SHA1: 62285ce656b12becf7c601131f5dbc1629c4f663 SHA256: 0bf5aff39dddd263182c2dac9650260c599ed2e4270b8eef31bdfcd56f36bde3 SHA512: da1cd436b3d879443f330d35d5daffb78c9ce3ea3b510025087fd2d31a3c571cc94325966691131092d05536bf760a7c8076d753b9aca0aab71a7d7efeb5ebbc Homepage: https://cran.r-project.org/package=limSolve Description: CRAN Package 'limSolve' (Solving Linear Inverse Models) Functions that (1) find the minimum/maximum of a linear or quadratic function: min or max (f(x)), where f(x) = ||Ax-b||^2 or f(x) = sum(a_i*x_i) subject to equality constraints Ex=f and/or inequality constraints Gx>=h, (2) sample an underdetermined- or overdetermined system Ex=f subject to Gx>=h, and if applicable Ax~=b, (3) solve a linear system Ax=B for the unknown x. It includes banded and tridiagonal linear systems. Package: r-cran-lincom Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sparsem, r-cran-rmosek Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lincom_1.2-1.ca2404.1_amd64.deb Size: 43248 MD5sum: e51bd22557e6444d2602f91fbde31373 SHA1: 45bd12964c181c7a4a124c4f6f4e9b577a7276a6 SHA256: 08d0ee41ef6028899aa929bf9296fb5497954e9d391df410ea232c3ac9ab4ec2 SHA512: e40cfd63831be606bcde5e64a5352af51663aec86972ea1fd5e5806f409d9baa090fbc04e45c0ac5f79930fb3e105275cf8a3e45198e11d983457db4a1e7c09e Homepage: https://cran.r-project.org/package=lincom Description: CRAN Package 'lincom' (Linear Biomarker Combination: Empirical Performance Optimization) Perform two linear combination methods for biomarkers: (1) Empirical performance optimization for specificity (or sensitivity) at a controlled sensitivity (or specificity) level of Huang and Sanda (2022) , and (2) weighted maximum score estimator with empirical minimization of averaged false positive rate and false negative rate. Both adopt the algorithms of Huang and Sanda (2022) . 'MOSEK' solver is used and needs to be installed; an academic license for 'MOSEK' is free. 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Package: r-cran-linelistbayes Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 553 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-magrittr, r-cran-lubridate, r-cran-coda, r-cran-dplyr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-linelistbayes_1.0-1.ca2404.1_amd64.deb Size: 284544 MD5sum: c4f219bdb3d9233db531fcb4f318900a SHA1: ca5ca78f639a64388ee808770c8140d2c596a635 SHA256: 15e4f26b7bd9c94724aa13d83e2d5583f399668fd2a84c785f8ab16eec03920b SHA512: b7ec1d4b082824e667cb894cd6a32a1d3fffed6e2b9718dd4445b66716c108a66ffc3a35f57f22c84aebde82d22b4743439349d53a6b02b561bb3c0c28ac8cb5 Homepage: https://cran.r-project.org/package=linelistBayes Description: CRAN Package 'linelistBayes' (Bayesian Analysis of Epidemic Data Using Line List and CaseCount Approaches) Provides tools for performing Bayesian inference on epidemiological data to estimate the time-varying reproductive number and other related metrics. 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The linkcomm package also includes tools for generating, visualizing, and analysing Overlapping Cluster Generator (OCG) communities. Kalinka and Tomancak (2011) . 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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: amd64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 644 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lmm_1.4-1.ca2404.1_amd64.deb Size: 439624 MD5sum: 3818c39c18379a137a3681d50ed7e8d9 SHA1: 9279d90abb51a1515c8d9219b1ecfef0d579d887 SHA256: 86bdec323b0112bc0a566bc57366b98179ae39811d7b32f386179768f7c79c1d SHA512: 70a80fb4b75742cdb7f4cc8f550f11e2b368e0db005247b016d57b8f7ab9e836c6a8b844e92247bbde72a1e94c0c9d4eed596442f5e78ea6ea8aa8c66ab4d4bc 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) . Package: r-cran-lmmprobe Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1595 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-lme4, r-cran-future.apply, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass Filename: pool/dists/noble/main/r-cran-lmmprobe_0.1.0-1.ca2404.1_amd64.deb Size: 1364632 MD5sum: 40532a526bada8b936c6ad70e0ca644a SHA1: ad13bb34a5d088d0670c56098cb2e4bc2f8b9285 SHA256: ed3feb0eed487a0e48ade0a7aa602466d8a9cd08ede740c11559a13084ee9265 SHA512: 9755c881a59dea04df8e4c24172c0572119a44f87e2bac65abbb62f62a66bbd54e2fa7a84378f8e5e8999a3a51016cfd3125e589d7d98ed9141fe84e019eb391 Homepage: https://cran.r-project.org/package=lmmprobe Description: CRAN Package 'lmmprobe' (Sparse High-Dimensional Linear Mixed Modeling with a PartitionedEmpirical Bayes ECM Algorithm) Implements a partitioned Empirical Bayes Expectation Conditional Maximization (ECM) algorithm for sparse high-dimensional linear mixed modeling as described in Zgodic, Bai, Zhang, and McLain (2025) . 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While designed and appropriate for use in studies involving medicine and the life sciences, the package can be used in other situations involving outcomes with multiple confounders. The package implements a family of methods for non-parametric bias correction when comparing treatments in observational studies, including survival analysis settings, where competing risks and/or censoring may be present. The approach extends to bias-corrected personalized predictions of treatment outcome differences, and analysis of heterogeneity of treatment effect-sizes across patient subgroups. For further details, please see: Lauve NR, Nelson SJ, Young SS, Obenchain RL, Lambert CG. LocalControl: An R Package for Comparative Safety and Effectiveness Research. Journal of Statistical Software. 2020. p. 1–32. Available from . Package: r-cran-localcop Architecture: amd64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2085 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tmb, r-cran-vinecopula, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-kableextra, r-cran-dplyr, r-cran-readr, r-cran-tidyr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-localcop_0.0.2-1.ca2404.1_amd64.deb Size: 776206 MD5sum: 45eee8eb0513e177dc4a559afff722fd SHA1: f03026940ed85e30ab52492f9748a2e9223f225c SHA256: a65cbf8c35d0e97b9eeaa6fd710b6fde6aed99c733ff9fe8bf29899af1815564 SHA512: 15a27407e8ee535f888a55072d4f68e5f6afec40769c4268a5fdca921c2e810fd73e927ac4d594471184731debb2fc0d72ce67bdf3526a1d7a8a2dc7a2e5cfe0 Homepage: https://cran.r-project.org/package=LocalCop Description: CRAN Package 'LocalCop' (Local Likelihood Inference for Conditional Copula Models) Implements a local likelihood estimator for the dependence parameter in bivariate conditional copula models. 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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 . 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Package: r-cran-localscore Architecture: amd64 Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1313 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-localscore_2.0.5-1.ca2404.1_amd64.deb Size: 780526 MD5sum: 4fb374c6e7cfea1e44c129b8b57698df SHA1: e73e00f7a443f7e167a493076e90c22d76a24627 SHA256: 5e5ec07ae4acbff9ff7cd8739bc38aec4cd4277bd7af724b577413ee7993b136 SHA512: 74e782ddbfc5e485edf9e140417e85fed3e6dae5b4a273ca4cb7be3703ae7e8148edc8e907e2cfb8e49235dd8edbdd43d334e8b26b8c4150946e73f38c3886b3 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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Implements an approach to model the dependency structure in time series that generalizes the concept of autoregression to local auto-patterns. Generates a pattern-based representation of time series along with a similarity measure called Learned Pattern Similarity (LPS). Introduces a generalized autoregressive kernel. This package adapts C code from the 'randomForest' package by Andy Liaw and Matthew Wiener, itself based on original Fortran code by Leo Breiman and Adele Cutler. Package: r-cran-lpwc Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nleqslv Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-pkgdown, r-cran-ggplot2, r-cran-knitr, r-cran-devtools Filename: pool/dists/noble/main/r-cran-lpwc_1.0.0-1.ca2404.1_amd64.deb Size: 143476 MD5sum: 8db6d2abb7011c09d75a415dae1a3d50 SHA1: d34db765a254e835fe32e8ce35d652d6ff78a230 SHA256: d965ad07f35d4f6cc1d1ec4d4c0a36e12095150b41a64144175d88a7a827b5a5 SHA512: 67f43e730a321208ea1a017cd80624f69bb938ed03ce71d4ce840e445d55ed638b50e9e9359c94fde9957bcd6e0d3f345c9e4bb257a409de6033018c688a0450 Homepage: https://cran.r-project.org/package=LPWC Description: CRAN Package 'LPWC' (Lag Penalized Weighted Correlation for Time Series Clustering) Computes a time series distance measure for clustering based on weighted correlation and introduction of lags. The lags capture delayed responses in a time series dataset. The timepoints must be specified. T. Chandereng, A. Gitter (2020) . Package: r-cran-lqmm Architecture: amd64 Version: 1.5.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-sparsegrid Filename: pool/dists/noble/main/r-cran-lqmm_1.5.8-1.ca2404.1_amd64.deb Size: 283396 MD5sum: c39ab477fb14f3dd833723e82951ad5f SHA1: 6ef5a6de6aea747a17e9b65485cb708e73481454 SHA256: 67f05f8ed2e2c69d0bf1c6ea6d819b485bf208efd9e18eab20e5c41c330a4c65 SHA512: c9b7b4669c4985a9d45d35afb9ee417b9da01d620f8968c63596cf288b7bcf37f2dae5c3c7ae56ede65e0adbd704d4d2bbe7609d7b9e517f231e54f88d60672b Homepage: https://cran.r-project.org/package=lqmm Description: CRAN Package 'lqmm' (Linear Quantile Mixed Models) Functions to fit quantile regression models for hierarchical data (2-level nested designs) as described in Geraci and Bottai (2014, Statistics and Computing) . A vignette is given in Geraci (2014, Journal of Statistical Software) and included in the package documents. The packages also provides functions to fit quantile models for independent data and for count responses. Package: r-cran-lrcontrast Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dosefinding Filename: pool/dists/noble/main/r-cran-lrcontrast_1.0-1.ca2404.1_amd64.deb Size: 57854 MD5sum: 5c8e6c5bebf8274110344d6e993f3521 SHA1: 88ca6fd3b73618aa6db1f379bc2bef267cd6c08b SHA256: aeacec61bef4f45cb51595889f855027757738dc04071fece1bd06a05a134af9 SHA512: 03ac36e496d12b7b9aa4c16b12c970d75e2351d661a31db1783930fb7a846245a591f431efb96f6c17d789ed467988cc3c27d5ef49c8effb6a64811409668bb8 Homepage: https://cran.r-project.org/package=LRcontrast Description: CRAN Package 'LRcontrast' (Dose Response Signal Detection under Model Uncertainty) Provides functions for calculating test statistics, simulating quantiles and simulating p-values of likelihood ratio contrast tests in regression models with a lack of identifiability. Package: r-cran-lrqvb Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-lava, r-cran-mass, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-lrqvb_1.0.0-1.ca2404.1_amd64.deb Size: 94472 MD5sum: f5930d381e3236c71c0ba91b71cf6f76 SHA1: f27409e5c46264d749ff2ec53b0f3aa627c1191b SHA256: d57acf10573b3baf9b737dac5ce58368c7747a5231a8eba10498a3d74c53a382 SHA512: a7e57c960807b39c797837a017a4802fe6f713ba7ddd85ef5dd7c76a3f67f2f757147ba5865285b257f1557c1aeda731fe9a62d488ca8f895be91c6d2f77c9a2 Homepage: https://cran.r-project.org/package=LRQVB Description: CRAN Package 'LRQVB' (Low Rank Correction Quantile Variational Bayesian Algorithm forMulti-Source Heterogeneous Models) A Low Rank Correction Variational Bayesian algorithm for high-dimensional multi-source heterogeneous quantile linear models. More details have been written up in a paper submitted to the journal Statistics in Medicine, and the details of variational Bayesian methods can be found in Ray and Szabo (2021) . It simultaneously performs parameter estimation and variable selection. The algorithm supports two model settings: (1) local models, where variable selection is only applied to homogeneous coefficients, and (2) global models, where variable selection is also performed on heterogeneous coefficients. Two forms of parameter estimation are output: one is the standard variational Bayesian estimation, and the other is the variational Bayesian estimation corrected with low-rank adjustment. Package: r-cran-lrstat Architecture: amd64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7612 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rlang, r-cran-lpsolve, r-cran-ggplot2, r-cran-shiny, r-cran-rcppthread, r-cran-bh Suggests: r-cran-testthat, r-cran-dplyr, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown, r-cran-mvtnorm, r-cran-survival, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-lrstat_0.3.2-1.ca2404.1_amd64.deb Size: 3758622 MD5sum: 50519ea754eb7ec1ed643b73ac031023 SHA1: 4f42d8f1518a6ec56c8bb2de28c56ec1c0b619f4 SHA256: 65c651452dd82c54af1312b9b5ddbc8a4bfc793f0123a9d2ece648f57069a18d SHA512: efa655ef472768aaa02f0611052235950547d0f4d2ed8936e15bc85dbdc76f3e9fc40282b9bd153f176ba4fc4f35eb1e336d6408d2178ff34724aebaf2d196cb Homepage: https://cran.r-project.org/package=lrstat Description: CRAN Package 'lrstat' (Power and Sample Size Calculation for Non-Proportional Hazardsand Beyond) Performs power and sample size calculation for non-proportional hazards model using the Fleming-Harrington family of weighted log-rank tests. The sequentially calculated log-rank test score statistics are assumed to have independent increments as characterized in Anastasios A. Tsiatis (1982) . The mean and variance of log-rank test score statistics are calculated based on Kaifeng Lu (2021) . The boundary crossing probabilities are calculated using the recursive integration algorithm described in Christopher Jennison and Bruce W. Turnbull (2000, ISBN:0849303168). The package can also be used for continuous, binary, and count data. For continuous data, it can handle missing data through mixed-model for repeated measures (MMRM). In crossover designs, it can estimate direct treatment effects while accounting for carryover effects. For binary data, it can design Simon's 2-stage, modified toxicity probability-2 (mTPI-2), and Bayesian optimal interval (BOIN) trials. For count data, it can design group sequential trials for negative binomial endpoints with censoring. Additionally, it facilitates group sequential equivalence trials for all supported data types. Moreover, it can design adaptive group sequential trials for changes in sample size, error spending function, number and spacing or future looks. Finally, it offers various options for adjusted p-values, including graphical and gatekeeping procedures. Package: r-cran-ls2w Architecture: amd64 Version: 1.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1390 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-wavethresh, r-cran-mass Filename: pool/dists/noble/main/r-cran-ls2w_1.3.7-1.ca2404.1_amd64.deb Size: 1303718 MD5sum: 984f9206199f4e6acd8d6537c0c1dbd9 SHA1: 9215a7a0f75c52a2a1dd361bab16f083706019a4 SHA256: 90fbc0b625b2bb0de2ab3367005c5255f9b80282b44a0613d326438dea65a41e SHA512: 81312c92b727d27fd1403f9563ba53d8435daed1cd7bcc0f595fbde9c02da32a0dbd0050148d3fd1c659ce2413fb16fa542a8b39c2af4c66881b9b8a7c6dd73a Homepage: https://cran.r-project.org/package=LS2W Description: CRAN Package 'LS2W' (Locally Stationary Two-Dimensional Wavelet Process EstimationScheme) Estimates two-dimensional local wavelet spectra. Package: r-cran-lsbclust Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-plyr, r-cran-clue, r-cran-gridextra, r-cran-reshape2, r-cran-rcpp, r-cran-mvtnorm, r-cran-doparallel, r-cran-foreach Filename: pool/dists/noble/main/r-cran-lsbclust_1.1-1.ca2404.1_amd64.deb Size: 377004 MD5sum: a542c6626b4f53417df9816312467bd6 SHA1: d9c1f0b8939f9e39d368a39e547278961c7150f4 SHA256: 847f58d8fb3c7ccc2bef06b7cb9a7f6c514b9f9ba24d5d3a1e50143bd8b9fd24 SHA512: a6666d34926adc74b292cdeecb151cabc1ba958f4cb3ea36acdcc70d877a41ed351660caac9ac48fed8ae2bb2cb786bd4eddbfd47b26e203ee6c24bc7e8459b1 Homepage: https://cran.r-project.org/package=lsbclust Description: CRAN Package 'lsbclust' (Least-Squares Bilinear Clustering for Three-Way Data) Functions for performing least-squares bilinear clustering of three-way data. The method uses the bilinear decomposition (or bi-additive model) to model two-way matrix slices while clustering over the third way. Up to four different types of clusters are included, one for each term of the bilinear decomposition. In this way, matrices are clustered simultaneously on (a subset of) their overall means, row margins, column margins and row-column interactions. The orthogonality of the bilinear model results in separability of the joint clustering problem into four separate ones. Three of these sub-problems are specific k-means problems, while a special algorithm is implemented for the interactions. Plotting methods are provided, including biplots for the low-rank approximations of the interactions. Package: r-cran-lsei Architecture: amd64 Version: 1.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lsei_1.3-1-1.ca2404.1_amd64.deb Size: 65356 MD5sum: ceaef707036a0e4d4c24626aa11fbe99 SHA1: 9fde9e4e7dbfab9075fcd9a7e6f7776ec459eab6 SHA256: 6d258ef69ffc906737027dd42ca5e089ffc3a0e9621d5fd4f35d3b5d5599a16b SHA512: ee721f2326b8aa79525c33efa91db1995ddf6ad1fb84d712c034e5720903236f221d461c3c8a53bd7f05cbdc1443fc2a7adcd84ad51865a0fd88fe7d040193d6 Homepage: https://cran.r-project.org/package=lsei Description: CRAN Package 'lsei' (Solving Least Squares or Quadratic Programming Problems underEquality/Inequality Constraints) It contains functions that solve least squares linear regression problems under linear equality/inequality constraints. Functions for solving quadratic programming problems are also available, which transform such problems into least squares ones first. It is developed based on the 'Fortran' program of Lawson and Hanson (1974, 1995), which is public domain and available at . Package: r-cran-lsirm12pl Architecture: amd64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1620 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-mcmcpack, r-cran-ggplot2, r-cran-gparotation, r-cran-dplyr, r-cran-rlang, r-cran-proc, r-cran-coda, r-cran-spatstat, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-plotly, r-cran-gridextra, r-cran-tidyr, r-cran-fpc, r-cran-kernlab, r-cran-plyr, r-cran-purrr, r-cran-rcpparmadillo Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-lsirm12pl_2.0.1-1.ca2404.1_amd64.deb Size: 1182012 MD5sum: 09b5efb92f016d40a038faaceedc3859 SHA1: 35a309f91372782a7ee216ce1e06760315723832 SHA256: bd3f2043e364b6da0db1e4125e0b617750c8049a3626a1a2fa52e65b2f711eb7 SHA512: fd6c7106191532b37d3317373b81ad8aa178c1d463b33274be12ae50c2ef6479f24ea5525380f48ccb11a5671172b9d63a79cc929eb26609fc375d566f77ab69 Homepage: https://cran.r-project.org/package=lsirm12pl Description: CRAN Package 'lsirm12pl' (Latent Space Item Response Model) Analysis of dichotomous, ordinal, and continuous response data using latent space item response model ('LSIRM'). Provides 1PL and 2PL 'LSIRM' for binary response data as described in Jeon et al. (2021) , graded response models ('GRM') for ordinal data (De Carolis et al., 2025, ), and extensions for continuous response data. Supports Bayesian model selection with spike-and-slab priors, adaptive MCMC algorithms, and methods for handling missing data under missing at random ('MAR') and missing completely at random ('MCAR') assumptions. Provides various diagnostic plots to inspect the latent space and summaries of estimated parameters. Package: r-cran-lslx Architecture: amd64 Version: 0.6.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3284 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-ggplot2, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lavaan Filename: pool/dists/noble/main/r-cran-lslx_0.6.11-1.ca2404.1_amd64.deb Size: 1891154 MD5sum: 376d2af87bf28566d08b1883f09f5535 SHA1: fe549eb633d6131c2444abc4f2182bb26dbe33d4 SHA256: f6a8bdb81c815a30bcdd6fbb5c0f44b86b4562582d360b4daf3043b925288f85 SHA512: 18e065ca7c35ff4d2f66b760c0654e8d9b5aa346f04b0a3cddb30a41a876e39a5ef923893afce97302e4341bb95ebed74913dad88b6e50d60ee909182471d0f9 Homepage: https://cran.r-project.org/package=lslx Description: CRAN Package 'lslx' (Semi-Confirmatory Structural Equation Modeling via PenalizedLikelihood or Least Squares) Fits semi-confirmatory structural equation modeling (SEM) via penalized likelihood (PL) or penalized least squares (PLS). For details, please see Huang (2020) . Package: r-cran-lsm Architecture: amd64 Version: 0.2.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-lsm_0.2.1.4-1.ca2404.1_amd64.deb Size: 142698 MD5sum: 4a1ead5f5681a7fb38dcd03a76954c46 SHA1: edf35ba46e5d213b8f0fbc25510c2346f8ddd58d SHA256: f81c68afc6519c3013e0e0dcefecb131dfc088edbb0f0a8468b53f203be97522 SHA512: bd8bba6507dbe25f776e94c1c7b792378a6761215fdfb85f9e34903f2ee817759d6b2cfb98a543143ac9f2ce6138bf7dd3c128d97bd8d341b9e144d49e6f8b44 Homepage: https://cran.r-project.org/package=lsm Description: CRAN Package 'lsm' (Estimation of the log Likelihood of the Saturated Model) When the values of the outcome variable Y are either 0 or 1, the function lsm() calculates the estimation of the log likelihood in the saturated model. This model is characterized by Llinas (2006, ISSN:2389-8976) in section 2.3 through the assumptions 1 and 2. The function LogLik() works (almost perfectly) when the number of independent variables K is high, but for small K it calculates wrong values in some cases. For this reason, when Y is dichotomous and the data are grouped in J populations, it is recommended to use the function lsm() because it works very well for all K. Package: r-cran-lsmjml Architecture: amd64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 523 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-lavaan, r-cran-proc, r-cran-psych, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-lsmjml_0.6.0-1.ca2404.1_amd64.deb Size: 227544 MD5sum: d5e6732948a4d31e70d5d4ec294fc08c SHA1: 5c7efe0e440da12e49ea306e135bec0dad7c6d5e SHA256: 63804423b42be20eb904e2508b501b5adf57d3156b8ec4252fdd8eb05796846f SHA512: d25d3f6287b297e0279bfda2b8b5f8b3d8d070ee515ff2114e18fe4f2bf1dc489f9f01959c6fcf15a9af449a319d7e6babf9d7d75a09356a2abf14cb96feddd2 Homepage: https://cran.r-project.org/package=LSMjml Description: CRAN Package 'LSMjml' (Fitting Latent Space Item Response Models using Joint MaximumLikelihood Estimation) In Latent Space Item Response Models, subjects and items are embedded in a multidimensional Euclidean latent space. As such, interactions among persons, items, and person-item combinations can be revealed that are unmodelled in more conventional item response theory models. This package implements the methods from Molenaar & Jeon (in press) and can be used to fit Latent Space Item Response Models to data using joint maximum likelihood estimation. The package can handle binary data, ordinal data, and data with mixed scales. The package incorporates facilities for data simulation, rotation of the latent space, and K-fold cross-validation to select the number of dimensions of the latent space. Package: r-cran-lsoda Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-desolve, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-lsoda_1.2-1.ca2404.1_amd64.deb Size: 85360 MD5sum: cf93337a1a6743d6743acde76c2bd409 SHA1: 6b3b3cae8062dd5bb224b4444a4f2611e4b1a6e8 SHA256: b44183875aafdb7e568b6cde2424840eba337f9e2f505cf7485d68beb803da05 SHA512: 8a979cfc95bc37755aead7360ba17b37867ae9c3574d298f43e78c1ea507d37e50bb2468e600929a0b2b82fce7e811aa1fb8a1079f58c813ca3ba1fc685f247e Homepage: https://cran.r-project.org/package=lsoda Description: CRAN Package 'lsoda' ('C++' Header Library for Ordinary Differential Equations) A 'C++' header library for using the 'libsoda-cxx' library with R. The 'C++' header reimplements the 'lsoda' function from the 'ODEPACK' library for solving initial value problems for first order ordinary differential equations (Hindmarsh, 1982; ). The 'C++' header can be used by other R packages by linking against this package. The 'C++' functions can be called inline using 'Rcpp'. Finally, the package provides an 'ode' function to call from R. Package: r-cran-lsreg Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 695 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-statmod Filename: pool/dists/noble/main/r-cran-lsreg_1.0.0-1.ca2404.1_amd64.deb Size: 285818 MD5sum: eb3872b2300f7612e298fe1f3067bf5a SHA1: 86392cba0fe99088a798ada43a0a78348573764b SHA256: b8b99726f5da0eb0f4a7e98f455b55c63fcf48a127d84970e2c9a2d1103820ef SHA512: 3b5fb2c1312deef9d7ae8a28ccbbe33846c595f2120958f77ebed683c6202893326c96d46bb71c1ba31687bab60c0dd8adcd97a8d3299906b309ef5c30bec169 Homepage: https://cran.r-project.org/package=lsReg Description: CRAN Package 'lsReg' (Performs Large Scale Regressions) Routines to perform large scale regression. Linear, logistic, and Poisson regressions are supported. Large scale regression efficiently fits models where a small number of covariates are changing and the subjects have complete data. A genome wide association study would be an example. 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Two methods are implemented. One is very fast and uses 'Rcpp' in conjunction with data from the 'Javascript' library (). This method also works outside of countries' borders and in international waters, however speed comes at the cost of accuracy - near time zone borders away from populated centres there is a chance that it will return the incorrect time zone. The other method is slower but more accurate - it uses the 'sf' package to intersect points with a detailed map of time zones from here: . The package also contains several utility functions for helping to understand and visualize time zones, such as listing of world time zones, including information about daylight savings times and their offsets from UTC. You can also plot a time zone to visualize the UTC offset over a year and when daylight savings times are in effect. Package: r-cran-lvmcomp Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-coda, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-lvmcomp_1.2-1.ca2404.1_amd64.deb Size: 174922 MD5sum: 36cc81cb48f272fa365936cc40abce99 SHA1: 28e30c85043dab536fef4a4139c0d6437b6d7f53 SHA256: e8e17c58c5819c8d43769321d6bebb14172037b64de72ce825f082c0c0d33169 SHA512: c26cc1cb9d7565e43452fdb8c2b776fdcce214459c0db80faa0a83990dcfc09d300b0f90d89762fdc87c2292c5efc6db31d47323fb7ea40fd660bd60625e569a Homepage: https://cran.r-project.org/package=lvmcomp Description: CRAN Package 'lvmcomp' (Stochastic EM Algorithms for Latent Variable Models with aHigh-Dimensional Latent Space) Provides stochastic EM algorithms for latent variable models with a high-dimensional latent space. So far, we provide functions for confirmatory item factor analysis based on the multidimensional two parameter logistic (M2PL) model and the generalized multidimensional partial credit model. These functions scale well for problems with many latent traits (e.g., thirty or even more) and are virtually tuning-free. The computation is facilitated by multiprocessing 'OpenMP' API. For more information, please refer to: Zhang, S., Chen, Y., & Liu, Y. (2018). An Improved Stochastic EM Algorithm for Large-scale Full-information Item Factor Analysis. British Journal of Mathematical and Statistical Psychology. . Package: r-cran-lwfbrook90r Architecture: amd64 Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2270 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-vegperiod, r-cran-foreach, r-cran-iterators, r-cran-dofuture, r-cran-future, r-cran-parallelly, r-cran-progressr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lwfbrook90r_0.6.3-1.ca2404.1_amd64.deb Size: 1922402 MD5sum: 1b38a059578f6794aa1842714454e932 SHA1: 8cb4fe9cf441e2ed3aaaa71e0fc35546550e264e SHA256: 126ef5a324f8569045ab61ec80d53c11607e5dc9eccd81c03e68d7054b12e656 SHA512: 1ba49facd2461031459405dbd81f6d5ed8b674822c3b0fce87b9166b69fd5858d1014e0cc76c75ab1d034cc108c348e5dfd1f000c79a79d62be0f0f153e9ea1f Homepage: https://cran.r-project.org/package=LWFBrook90R Description: CRAN Package 'LWFBrook90R' (Simulate Evapotranspiration and Soil Moisture with the SVATModel LWF-Brook90) Provides a flexible and easy-to use interface for the soil vegetation atmosphere transport (SVAT) model LWF-BROOK90, written in Fortran. The model simulates daily transpiration, interception, soil and snow evaporation, streamflow and soil water fluxes through a soil profile covered with vegetation, as described in Hammel & Kennel (2001, ISBN:978-3-933506-16-0) and Federer et al. (2003) . A set of high-level functions for model set up, execution and parallelization provides easy access to plot-level SVAT simulations, as well as multi-run and large-scale applications. Package: r-cran-lwgeom Architecture: amd64 Version: 0.2-16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1034 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgeos-c1t64 (>= 3.5.0), libproj25 (>= 6.0.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-units, r-cran-sf Suggests: r-cran-covr, r-cran-sp, r-cran-geosphere, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lwgeom_0.2-16-1.ca2404.1_amd64.deb Size: 427902 MD5sum: e13d25200b49231d407d6fb94498d4f0 SHA1: 5d91061555aa10d13fe461d553660d10a9233f7e SHA256: 38ae5b035c95a6fdc625070a82ef3f157d5b958400af81eabcbfa0b401067f9d SHA512: 29ea5ab0b22888432e15977caddddfa50b96296a08ef570c7678708a60b19db33c53c627672536d3d98b1d8791692f52fc7343e50be93455a62a5a6acb5313ea 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: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1766 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-bench, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-lzstring_0.2.0-1.ca2404.1_amd64.deb Size: 1683462 MD5sum: 53620b3a5f30420799af441eff1c2b8b SHA1: 35991d4c3b4a642c4a4531d68b1b5293052dac2b SHA256: fbdfbc7cfa2c47d0b429f62bc9c377c3ae69f4500eb13f2e8d6fc9e87389523c SHA512: 2061c653dfb56b0208e60657689a6276537f6526bdddc70525aa3d04fdc6563a6d803a73fe0cfc9f4bab15a1fa84863fd050c1d0e4026fde2d5951c8bf6e8e48 Homepage: https://cran.r-project.org/package=lzstring Description: CRAN Package 'lzstring' (Wrapper for 'lz-string' 'C++' Library) Provide access to the 'lz-string' 'C++' library for Lempel-Ziv (LZ) based compression and decompression of strings. Package: r-cran-m2r Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 793 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mpoly, r-cran-stringr, r-cran-memoise, r-cran-gmp, r-cran-usethis, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-m2r_1.0.3-1.ca2404.1_amd64.deb Size: 646324 MD5sum: 04d8fa8ccbcbc2a5c3f4f96e595ea680 SHA1: 1d8f7d3ce9468c7e9ece120ebf728588c0ae3877 SHA256: e3bf5260babc660fd7e55500a6252cd58752f4bdae7e903c1b24a97c23d61fe3 SHA512: 27210a00753693187c7e85e15f6eae421916bfbb69f2e4d4d3a5c96f0fb7eaf0c2c9f031d10fd4754631271af250e1b1ff2e51951b36576a73c1284fe57aa5a9 Homepage: https://cran.r-project.org/package=m2r Description: CRAN Package 'm2r' (Interface to 'Macaulay2') Persistent interface to 'Macaulay2' and front-end tools facilitating its use in the 'R' ecosystem. For details see Kahle et. al. (2020) . Package: r-cran-mable Architecture: amd64 Version: 4.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1468 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-icenreg, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-quadprog, r-cran-lowrankqp, r-cran-mnormt, r-cran-rlang Suggests: r-cran-mixtools, r-cran-epi, r-cran-icsurv, r-cran-interval, r-cran-knitr, r-cran-rmarkdown, r-cran-pbapply, r-cran-markdown, r-cran-ks, r-cran-multimode Filename: pool/dists/noble/main/r-cran-mable_4.1.1-1.ca2404.1_amd64.deb Size: 1068968 MD5sum: 09306a6eca71921b035a9e8770c7aa36 SHA1: 47f8e9a6c8dbf6a28eafc887ac6c5ceebbe96076 SHA256: 60497d40e458fddb7745044bb2d2999ff91d8f1abcdf43804ed6454aff9d8cdb SHA512: 920f1dfc9cb94c2b0e7dceb7d7d120db7c469f642d128ecf3b20b4fb64f2d09a86ead5c7f06454d0a40f3036738da1fc8eb2d4a4b413737f6744d607b5ddf6b2 Homepage: https://cran.r-project.org/package=mable Description: CRAN Package 'mable' (Maximum Approximate Bernstein/Beta Likelihood Estimation) Fit data from a continuous population with a smooth density on finite interval by an approximate Bernstein polynomial model which is a mixture of certain beta distributions and find maximum approximate Bernstein likelihood estimator of the unknown coefficients. Consequently, maximum likelihood estimates of the unknown density, distribution functions, and more can be obtained. If the support of the density is not the unit interval then transformation can be applied. This is an implementation of the methods proposed by the author of this package published in the Journal of Nonparametric Statistics: Guan (2016) and Guan (2017) . For data with covariates, under some semiparametric regression models such as Cox proportional hazards model and the accelerated failure time model, the baseline survival function can be estimated smoothly based on general interval censored data. Package: r-cran-maboust Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-maboust_1.0.1-1.ca2404.1_amd64.deb Size: 106436 MD5sum: 66d9d24d57b5b5792e15a652d98bc654 SHA1: f075143b2cf0d6286ee2d9fed9e3bddc2613c721 SHA256: d41f4788ae9e39e10e37cc5b8f1004c72dff49fcd085ef06def12c9b8fc16b02 SHA512: 0b25da750ed2249284d9b468eafc6966d544e7e806db8c5055ba75cec3ae626276bd83d05b20b5c85c86ec66d6f3fb5b26c063f235073f656b697d69e9fa908f Homepage: https://cran.r-project.org/package=MABOUST Description: CRAN Package 'MABOUST' (Multi-Armed Bayesian Ordinal Utility-Based Sequential Trial) Conducts and simulates the MABOUST design, including making interim decisions to stop a treatment for inferiority or stop the trial early for superiority or equivalency. Package: r-cran-machineshop Architecture: amd64 Version: 3.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3282 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-cli, r-cran-dials, r-cran-foreach, r-cran-ggplot2, r-cran-kernlab, r-cran-magrittr, r-cran-matrix, r-cran-nnet, r-cran-party, r-cran-polspline, r-cran-progress, r-cran-recipes, r-cran-rlang, r-cran-rsample, r-cran-rsolnp, r-cran-survival, r-cran-tibble Suggests: r-cran-adabag, r-cran-bart, r-cran-bartmachine, r-cran-c50, r-cran-censored, r-cran-cluster, r-cran-doparallel, r-cran-e1071, r-cran-earth, r-cran-elasticnet, r-cran-generics, r-cran-gbm, r-cran-glmnet, r-cran-gridextra, r-cran-hmisc, r-cran-kableextra, r-cran-kknn, r-cran-knitr, r-cran-lars, r-cran-mass, r-cran-mboost, r-cran-mda, r-cran-parsnip, r-cran-partykit, r-cran-pls, r-cran-pso, r-cran-randomforest, r-cran-randomforestsrc, r-cran-ranger, r-cran-rbayesianoptimization, r-cran-rmarkdown, r-cran-rms, r-cran-rpart, r-cran-testthat, r-cran-tree, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-machineshop_3.9.2-1.ca2404.1_amd64.deb Size: 2184942 MD5sum: 9a820d13c0ce215281e468dc919d3acb SHA1: ab27f0f704a5737868bf583c4c287d22284c835e SHA256: 06830619fe56e89f2855bccbe694e87a35a4a8f24da9950fa8c2c8e5b8438adf SHA512: 721d1cb5f26c3ba2b027413bfe763059c04ad2c9628ffedad29acd50008a06a151a6b1007005389dfbb68db7b1c48512309ef83cc3718aa7b1da0cd55428be83 Homepage: https://cran.r-project.org/package=MachineShop Description: CRAN Package 'MachineShop' (Machine Learning Models and Tools) Meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Approaches for model fitting and prediction of numerical, categorical, or censored time-to-event outcomes include traditional regression models, regularization methods, tree-based methods, support vector machines, neural networks, ensembles, data preprocessing, filtering, and model tuning and selection. Performance metrics are provided for model assessment and can be estimated with independent test sets, split sampling, cross-validation, or bootstrap resampling. Resample estimation can be executed in parallel for faster processing and nested in cases of model tuning and selection. Modeling results can be summarized with descriptive statistics; calibration curves; variable importance; partial dependence plots; confusion matrices; and ROC, lift, and other performance curves. Package: r-cran-mactivate Architecture: amd64 Version: 0.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 572 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mactivate_0.6.6-1.ca2404.1_amd64.deb Size: 441670 MD5sum: 11b6b230348c599de9688c7017e17089 SHA1: 867215ef4d3c940fd65fc126f4056fcccbe823ce SHA256: 3a3a3f667462d6779f041b59007ee2a7b26edfcd0ae556f75138c854eeb73d33 SHA512: bf37c244c199dbd9b545c06e667a4a82ca05576cf4c391f2b3301cf08c23fdbae275833159e8651fe3cbfc0bf4a4724c3d959822c05da9f554999d002dd873b3 Homepage: https://cran.r-project.org/package=mactivate Description: CRAN Package 'mactivate' (Multiplicative Activation) Provides methods and classes for adding m-activation ("multiplicative activation") layers to MLR or multivariate logistic regression models. M-activation layers created in this library detect and add input interaction (polynomial) effects into a predictive model. M-activation can detect high-order interactions -- a traditionally non-trivial challenge. Details concerning application, methodology, and relevant survey literature can be found in this library's vignette, "About." Package: r-cran-madmmplasso Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 698 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-foreach, r-cran-doparallel, r-cran-class, r-cran-spatstat.sparse Suggests: r-cran-testthat, r-cran-lintr Filename: pool/dists/noble/main/r-cran-madmmplasso_1.0.1-1.ca2404.1_amd64.deb Size: 321192 MD5sum: 48f4f66a5ddf3b68d1730347908cb8fa SHA1: e1f40d0f5285c250e725033ef568af79405b6c95 SHA256: b3f390327ca02452d4ce9530bd5c4d4d48b49d50fe0b745b0da5e85ad80b0842 SHA512: 8da159bcd0f03622f440db85a3e0f7cdfc9f37964f21d19f72f8151c7123125ded25bd3d2e2b817e7f5457805db45461104f11a8af89cc4cfc3b72af934a0406 Homepage: https://cran.r-project.org/package=MADMMplasso Description: CRAN Package 'MADMMplasso' (Multi Variate Multi Response ADMM with Interaction Effects) This system allows one to model a multi-variate, multi-response problem with interaction effects. It combines the usual squared error loss for the multi-response problem with some penalty terms to encourage responses that correlate to form groups and also allow for modeling main and interaction effects that exit within the covariates. The optimization method employed is the Alternating Direction Method of Multipliers (ADMM). The implementation is based on the methodology presented on Quachie Asenso, T., & Zucknick, M. (2023) . Package: r-cran-madpop Architecture: amd64 Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1627 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-rcpp, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-madpop_1.1.7-1.ca2404.1_amd64.deb Size: 625232 MD5sum: 3b025f72fded90ec6672e5fc95a2e448 SHA1: 178445338562002199af51ca8b1233be08c53fd1 SHA256: 6454aa3cb49eb6ec44d0cfdb40d8e7f53e6663e7153bad7ceda71f7d3a859f8e SHA512: c193ffb4bbac4e10638b47209fa2004889b45f82ee58fda91dab968258b938a289f140243ed608daaaba0520b1dea468c4219453e1885e5c62701e0e9c7e3244 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. 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(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: amd64 Version: 2.9.1-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7515 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libmagick++-6.q16-9t64 (>= 8:6.9.12.98+dfsg1), libmagickcore-6.q16-7t64 (>= 8:6.9.10.2), libmagickwand-6.q16-7t64 (>= 8:6.9.12.98+dfsg1-5.2build2), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-magrittr, r-cran-curl Suggests: r-cran-av, r-cran-spelling, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-rsvg, r-cran-webp, r-cran-pdftools, r-cran-ggplot2, r-cran-gapminder, r-cran-irdisplay, r-cran-tesseract, r-cran-gifski Filename: pool/dists/noble/main/r-cran-magick_2.9.1-1.ca2404.2_amd64.deb Size: 4850982 MD5sum: b4c36176406a4167487489e7b067d380 SHA1: 160549005838c8217985083fc430701aebb28316 SHA256: 5f618e000d8d7de4621f2582e98b8966e4f26a322e06f9e6ce5937fa4c5a0e1b SHA512: e07b7063e65343f365039189a5931df2a289ad17a1a473fd112c74a43388cff4ac34563a50af948be8fc566487306008e318ad8b772656a772d73ff7abfad73d Homepage: https://cran.r-project.org/package=magick Description: CRAN Package 'magick' (Advanced Graphics and Image-Processing in R) Bindings to 'ImageMagick': the most comprehensive open-source image processing library available. Supports many common formats (png, jpeg, tiff, pdf, etc) and manipulations (rotate, scale, crop, trim, flip, blur, etc). All operations are vectorized via the Magick++ STL meaning they operate either on a single frame or a series of frames for working with layers, collages, or animation. In RStudio images are automatically previewed when printed to the console, resulting in an interactive editing environment. Also includes a graphics device for creating drawing onto images using pixel coordinates. Package: r-cran-magmaclustr Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1630 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-mvtnorm, r-cran-plyr, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-gganimate, r-cran-gifski, r-cran-gridextra, r-cran-knitr, r-cran-plotly, r-cran-png, r-cran-rmarkdown, r-cran-testthat, r-cran-transformr Filename: pool/dists/noble/main/r-cran-magmaclustr_1.2.1-1.ca2404.1_amd64.deb Size: 1496836 MD5sum: 14ee24d33a291ef929e3b4b06b968a9c SHA1: 56e9223fe471190f37c2a5dc2959ee56f0b1b04e SHA256: 6c0420ec8e3ad815d8ca3094cc5341fa97784cbaf3707b5b548928f3774f5a19 SHA512: eed4faba71317dea3c27bd1b2ca7997474854bc026cd5ab6963348641405de3551655d7ac26f4bfeda30d11c879addd660f8c8d221f1e072f7787c013c726f0a Homepage: https://cran.r-project.org/package=MagmaClustR Description: CRAN Package 'MagmaClustR' (Clustering and Prediction using Multi-Task Gaussian Processeswith Common Mean) An implementation for the multi-task Gaussian processes with common mean framework. Two main algorithms, called 'Magma' and 'MagmaClust', are available to perform predictions for supervised learning problems, in particular for time series or any functional/continuous data applications. The corresponding articles has been respectively proposed by Arthur Leroy, Pierre Latouche, Benjamin Guedj and Servane Gey (2022) , and Arthur Leroy, Pierre Latouche, Benjamin Guedj and Servane Gey (2023) . Theses approaches leverage the learning of cluster-specific mean processes, which are common across similar tasks, to provide enhanced prediction performances (even far from data) at a linear computational cost (in the number of tasks). 'MagmaClust' is a generalisation of 'Magma' where the tasks are simultaneously clustered into groups, each being associated to a specific mean process. User-oriented functions in the package are decomposed into training, prediction and plotting functions. Some basic features (classic kernels, training, prediction) of standard Gaussian processes are also implemented. Package: r-cran-magree Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-magree_1.2-1.ca2404.1_amd64.deb Size: 122526 MD5sum: 5b235075fd61d2533c66761d4adf09a6 SHA1: 32f59a7ee83a4923a40e235cf688e488ed1b54cf SHA256: f04183a029702660f8e1e75d309e13493b21b5514ec20f7225f130cb0368fef9 SHA512: 9cf2cdc109b6b1f186bade1dee72ed15b95cc8a1c788d406d342c6576b9c3165f0b1515c690d1916f8a0559823740f2542ed9cd1f3084066b85a98a29e09c6d8 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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Optimize real- valued functions over manifolds such as Stiefel, Grassmann, and Symmetric Positive Definite matrices. For details see Martin et. al. (2020) . Note that the optional ldr package used in some of this package's examples can be obtained from either JSS or from the CRAN archives . 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We provide a number of algorithms for selected problems in optimization and statistical inference. For general exposition to the topic with focus on statistical context, see the book by Banerjee and Roy (2014, ISBN:9781420095388). Package: r-cran-mapdata Architecture: amd64 Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 34237 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maps Filename: pool/dists/noble/main/r-cran-mapdata_2.3.1-1.ca2404.1_amd64.deb Size: 23448528 MD5sum: 6d5c61ee24ca4fe014db76abf435d355 SHA1: a3adfb4e9f1bcf16f6dc9abda1173c5087330f22 SHA256: 21afbf4445a55e45d0c37d125d0677229df6f5de1d132cc9a8451f39e8c62f36 SHA512: 12b230d9cebbf6fbff8de5da93ba9deade5f2c37455ccebef04ebac8a1527b8459e973d47875373c5c8b3688953450878aab3b5a898ee7b0f5f8e219462e03a7 Homepage: https://cran.r-project.org/package=mapdata Description: CRAN Package 'mapdata' (Extra Map Databases) Supplement to maps package, providing some larger and/or higher-resolution databases. NOTE: this is a legacy package. The world map is out-dated. Package: r-cran-mapdeck Architecture: amd64 Version: 0.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9052 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-colourvalues, r-cran-googlepolylines, r-cran-geojsonsf, r-cran-htmlwidgets, r-cran-jsonify, r-cran-magrittr, r-cran-rcpp, r-cran-shiny, r-cran-sfheaders, r-cran-bh, r-cran-geometries, r-cran-interleave, r-cran-rapidjsonr, r-cran-spatialwidget Suggests: r-cran-covr, r-cran-googleway, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mapdeck_0.3.6-1.ca2404.1_amd64.deb Size: 3429772 MD5sum: 1e557995f1f238ab6af93c43d98488ad SHA1: bfc18537b0906b6d6904275571274f9ee0cc5aff SHA256: c9b12d718266369f666461c3e21dcc60e01d38cdb3f90e275b4d7250886aa51e SHA512: e7bbe902949ca5e9a629bbcdaf6443e8f11a91134cd8447b4944a3c0d7696d63e5dabb86b17d4f1e888c6011db38d7d125df50d72162db0a9ad63979bd42897e Homepage: https://cran.r-project.org/package=mapdeck Description: CRAN Package 'mapdeck' (Interactive Maps Using 'Mapbox GL JS' and 'Deck.gl') Provides a mechanism to plot an interactive map using 'Mapbox GL' (), a javascript library for interactive maps, and 'Deck.gl' (), a javascript library which uses 'WebGL' for visualising large data sets. Package: r-cran-mapfit Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1051 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-deformula, r-cran-matrix, r-cran-rcpp Suggests: r-cran-covr, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mapfit_1.0.0-1.ca2404.1_amd64.deb Size: 673966 MD5sum: 5bbd1080faaf3cfbe61835116bcb7e5a SHA1: 7b6aff82f2113b136a960b08e8d509945b21e36d SHA256: 94e06e136f5e328b6a1c103d7ef955b57d45a8093ed97398c19ea9ccff5ad0b9 SHA512: fa6bed30eb97ed1b8b98ed0fd634ff9243c8ae8978897003d28333187f387d67335de95d1771ad0d2af44dd636993d6989481be5ed9af71ca961390093eaca4d Homepage: https://cran.r-project.org/package=mapfit Description: CRAN Package 'mapfit' (PH/MAP Parameter Estimation) Estimation methods for phase-type distribution (PH) and Markovian arrival process (MAP) from empirical data (point and grouped data) and density function. The tool is based on the following researches: Okamura et al. (2009) , Okamura and Dohi (2009) , Okamura et al. (2011) , Okamura et al. (2013) , Horvath and Okamura (2013) , Okamura and Dohi (2016) . Package: r-cran-mapi Architecture: amd64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2736 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-doparallel, r-cran-fmesher, r-cran-foreach, r-cran-rcpp, r-cran-s2, r-cran-sf Suggests: r-cran-dggridr, r-cran-ggplot2, r-cran-latticeextra, r-cran-progress, r-cran-sp, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mapi_1.1.4-1.ca2404.1_amd64.deb Size: 2604994 MD5sum: 16883abfe5947ffdae7a2e24ebcaa1ea SHA1: f1390cb557ecb525edc2849e5b3911132e0538f4 SHA256: 296d0ab36a77def1b30203458a4d23a67385ec299603744ad128b9fbe01e058f SHA512: 6e479372e5679c95b8ec924ffc1993798458c34425c2c9e829e8d7f99e4371f7dfdcba8faa24c8e02d22f6df279654b6af5909711b511185834361657715ed82 Homepage: https://cran.r-project.org/package=mapi Description: CRAN Package 'mapi' (Mapping Averaged Pairwise Information) Mapping Averaged Pairwise Information (MAPI) is an exploratory method providing graphical representations summarizing the spatial variation of pairwise metrics (eg. distance, similarity coefficient, ...) computed between georeferenced samples. Package: r-cran-mapitr Architecture: amd64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 696 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-rcpp, r-cran-compquadform, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mapitr_1.1.2-1.ca2404.1_amd64.deb Size: 309486 MD5sum: 7b2ffac66589002dd691cb40338f8fd3 SHA1: bc591cf0d1628902d75f104650ba8177f5328599 SHA256: 93d69d998ccb36918b09b25d125fe88124b22bd38fba81f07e0a86596abbe7c9 SHA512: 6699d3e376aed89d592a486fe29565659077e1e2aad84b3b7255c2c353d09bf978bfb28983d93cd550555678c0006ecceae830c234be954f5001f2ca5c4e051e Homepage: https://cran.r-project.org/package=MAPITR Description: CRAN Package 'MAPITR' (MArginal ePIstasis Test for Regions) A genetic analysis tool and variance component model for identifying marginal epistasis between pathways and the rest of the genome. 'MAPITR' uses as input a matrix of genotypes, a vector of phenotypes, and a list of pathways. 'MAPITR' then iteratively tests each pathway for epistasis between any variants within the pathway versus any variants remaining in the rest of the genome. 'MAPITR' returns results in the form of p-values for every pathway indicating whether the null model of there being no epistatic interactions between a pathway and the rest of the genome can be rejected. Package: r-cran-mappoly Architecture: amd64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7604 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcurl, r-cran-fields, r-cran-ggpubr, r-cran-ggsci, r-cran-rstudioapi, r-cran-dplyr, r-cran-crayon, r-cran-cli, r-cran-magrittr, r-cran-reshape2, r-cran-ggplot2, r-cran-smacof, r-cran-princurve, r-cran-dendextend, r-cran-vcfr, r-cran-zoo, r-cran-plotly Suggests: r-cran-updog, r-cran-plot3d, r-cran-fitpoly, r-cran-polymapr, r-cran-aghmatrix, r-cran-gatepoints, r-cran-knitr, r-cran-rmarkdown, r-cran-stringr Filename: pool/dists/noble/main/r-cran-mappoly_0.4.2-1.ca2404.1_amd64.deb Size: 6231556 MD5sum: 36f53c4daa6c0c79c1043b93785638f2 SHA1: be5b97d8c97fdd6e018f889121bc85f10f8e64b4 SHA256: a482548f3ab7a3aad9b314bf7e6df7064c3f02e5b227d9fe88aa8e1548906e33 SHA512: 38ff881f4063b6eb5b6cd3e33eb0106247c6b8fcfde1d4f332387e0356b6a89424155dd85fef5f989c6c1a708f104e783a0f1a7a5ccc1a45ac0d4038c39b417d Homepage: https://cran.r-project.org/package=mappoly Description: CRAN Package 'mappoly' (Genetic Linkage Maps in Autopolyploids) Constructs genetic linkage maps in autopolyploid full-sib populations. Uses pairwise recombination fraction estimation as the first source of information to sequentially position allelic variants in specific homologous chromosomes. For situations where pairwise analysis has limited power, the algorithm relies on the multilocus likelihood obtained through a hidden Markov model (HMM). Methods are described in Mollinari and Garcia (2019) and Mollinari et al. (2020) . Package: r-cran-mapproj Architecture: amd64 Version: 1.2.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-maps Filename: pool/dists/noble/main/r-cran-mapproj_1.2.12-1.ca2404.1_amd64.deb Size: 50352 MD5sum: 14b9a8e48a6e565db3ff8ba5771fced6 SHA1: 7dfd1cd0355229126fcca728abc66b990032e163 SHA256: 55bc5a559fa5b45e6d56e9bef5cefba8ca432667dcf70d5a31435624eff0f2fb SHA512: 41a97d278645966d9c988916bac2205742a1a46c2df93186d1d5a2b2d816d690afe671a4f97fd1176c2ee8c3a935c8494a08fadaa32e66eadcfe0f532b6e05e2 Homepage: https://cran.r-project.org/package=mapproj Description: CRAN Package 'mapproj' (Map Projections) Converts latitude/longitude into projected coordinates. Package: r-cran-maps Architecture: amd64 Version: 3.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3889 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mapproj, r-cran-mapdata, r-cran-sf, r-cran-rnaturalearth Filename: pool/dists/noble/main/r-cran-maps_3.4.3-1.ca2404.1_amd64.deb Size: 2349926 MD5sum: b38b70bff83411318cc86e357c72601d SHA1: 8c979ea1a4ca4628a03460f2412789432d941335 SHA256: cedadd2b1b6cc3ba87173a43485c59882ec0586b1f5dfe5bcc819d7bc2df301e SHA512: 0dad51d7da70fc0a9c46570f8fd6f7aeaeb4dad65e252f86e291b70e1758849572065e817dc9ec4e2e648dd42ca5feb1804b9d96a863aedd8ff0bf24da9b2578 Homepage: https://cran.r-project.org/package=maps Description: CRAN Package 'maps' (Draw Geographical Maps) Display of maps. Projection code and larger maps are in separate packages ('mapproj' and 'mapdata'). Package: r-cran-mapscanner Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3518 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-fs, r-cran-glue, r-cran-magick, r-cran-magrittr, r-cran-memoise, r-cran-pdftools, r-cran-png, r-cran-purrr, r-cran-raster, r-cran-rcpp, r-cran-reproj, r-cran-rniftyreg, r-cran-sf, r-cran-slippymath, r-cran-tibble Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-gibble, r-cran-jpeg, r-cran-knitr, r-cran-lwgeom, r-cran-mapview, r-cran-mmand, r-cran-osmdata, r-cran-polyclip, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mapscanner_0.1.1-1.ca2404.1_amd64.deb Size: 1956366 MD5sum: bf2e8e1ac389027ada89d28017a42da3 SHA1: e750235c6b0530f22047bcaf124fa08a9d0c98a2 SHA256: 2106afed599a5a8fa08b09c4bf0fd3afed0ccac0f6411c2e6baca0f1a87adceb SHA512: 8633ee8b2a604052dace6957bd4eca1240a2608481aca4a1a175e22d61e87d8a0beef75bb43eb77f036bd2e2785e790bafade135ab1d2367445c2eed5df7513a Homepage: https://cran.r-project.org/package=mapscanner Description: CRAN Package 'mapscanner' (Print Maps, Draw on Them, Scan Them Back in) Enables preparation of maps to be printed and drawn on. Modified maps can then be scanned back in, and hand-drawn marks converted to spatial objects. Package: r-cran-maptpx Architecture: amd64 Version: 1.9-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-slam Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-maptpx_1.9-7-1.ca2404.1_amd64.deb Size: 100544 MD5sum: 782598485aba12ac078b3890d9f1a27b SHA1: 8ebcb9a065870564180906c190638929612a06ee SHA256: 8a570565658fe6ad55b45231c1cb859d1183384bae836b34249b2df2e1ba1d2e SHA512: 8b3a9ef6c781d869f22b3dce04e75da066823217a4f78a6f714618c6e4d80926cb9046e0fdd1bea33b6c2dbee145931d6822628031f2138d19209f3eee9b8579 Homepage: https://cran.r-project.org/package=maptpx Description: CRAN Package 'maptpx' (MAP Estimation of Topic Models) Maximum a posteriori (MAP) estimation for topic models (i.e., Latent Dirichlet Allocation) in text analysis, as described in Taddy (2012) 'On estimation and selection for topic models'. Previous versions of this code were included as part of the 'textir' package. If you want to take advantage of openmp parallelization, uncomment the relevant flags in src/MAKEVARS before compiling. 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We propose a novel marginal Bayesian variable selection method for Gene-Environment interactions studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo. The core algorithms of the package have been developed in 'C++'. Package: r-cran-marcox Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen, r-cran-survival, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-marcox_1.0.0-1.ca2404.1_amd64.deb Size: 195992 MD5sum: 1832cf08d4ce73762f2b72c82d827f3f SHA1: 8be56b5e832b306df55c50413b85c38caf2f6513 SHA256: 4d10ac603bb009ed3fade8936ab03f581572dcffd9d6ab68542592410211f577 SHA512: f8ca8e95d36551dcd3cfbf3ebb7154ab673362fbe20e24b388838c1d6c2e5425230c04e52065056818d42535c06839d4f9609432ef0d64de46ddcad599341f2e Homepage: https://cran.r-project.org/package=marcox Description: CRAN Package 'marcox' (Marginal Hazard Ratio Estimation in Clustered Failure Time Data) Estimation of marginal hazard ratios in clustered failure time data. 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Package: r-cran-marelac Architecture: amd64 Version: 2.1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1723 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shape, r-cran-seacarb Filename: pool/dists/noble/main/r-cran-marelac_2.1.11-1.ca2404.1_amd64.deb Size: 1635240 MD5sum: 17167aa0256309e400f9f1e7b05994e1 SHA1: 8e4dd9ab013de637f6fc8d2a7a77c03ae16badf1 SHA256: c64c7d784e5b9615457c4f04c8b1b64c015b91eebe50907df690916d51667c0d SHA512: 5b0def4a0861426a44f0870a3ecaf4f6ee0a3a83b6b3455af112542f50ad2af90e0336ccb3308336709b8738b8a7c363ec600214674b6dde149c2e55af2bc508 Homepage: https://cran.r-project.org/package=marelac Description: CRAN Package 'marelac' (Tools for Aquatic Sciences) Datasets, constants, conversion factors, and utilities for 'MArine', 'Riverine', 'Estuarine', 'LAcustrine' and 'Coastal' science. The package contains among others: (1) chemical and physical constants and datasets, e.g. atomic weights, gas constants, the earths bathymetry; (2) conversion factors (e.g. gram to mol to liter, barometric units, temperature, salinity); (3) physical functions, e.g. to estimate concentrations of conservative substances, gas transfer and diffusion coefficients, the Coriolis force and gravity; (4) thermophysical properties of the seawater, as from the UNESCO polynomial or from the more recent derivation based on a Gibbs function. Package: r-cran-marginaleffects Architecture: amd64 Version: 0.30.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2394 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-data.table, r-cran-generics, r-cran-formula, r-cran-insight, r-cran-rlang Suggests: r-cran-aer, r-cran-amelia, r-cran-afex, r-cran-aod, r-cran-arrow, r-cran-bayestestr, r-cran-bench, r-cran-betareg, r-cran-bh, r-cran-bife, r-cran-biglm, r-cran-blme, r-cran-boot, r-cran-brglm2, r-cran-brms, r-cran-brmsmargins, r-cran-broom, r-cran-car, r-cran-cardata, r-cran-causaldata, r-cran-clarify, r-cran-cjoint, r-cran-cobalt, r-cran-collapse, r-cran-conflicted, r-cran-countrycode, r-cran-covr, r-cran-crch, r-cran-dalextra, r-cran-dcchoice, r-cran-dbarts, r-cran-distributional, r-cran-dfidx, r-cran-dplyr, r-cran-emmeans, r-cran-equivalence, r-cran-estimatr, r-cran-fixest, r-cran-flexsurv, r-cran-fmeffects, r-cran-fontquiver, r-cran-future, r-cran-future.apply, r-cran-fwb, r-cran-gam, r-cran-gamlss, r-cran-gamlss.dist, r-cran-geepack, r-cran-ggdist, r-cran-ggokabeito, r-cran-ggplot2, r-cran-ggrepel, r-cran-glmmtmb, r-cran-glmtoolbox, r-cran-glmx, r-cran-haven, r-cran-here, r-cran-itsadug, r-cran-ivreg, r-cran-kableextra, r-cran-lme4, r-cran-lmertest, r-cran-logistf, r-cran-magrittr, r-cran-margins, r-cran-matchit, r-cran-mass, r-cran-mclogit, r-cran-mcmcglmm, r-cran-mhurdle, r-cran-missranger, r-cran-mgcv, r-cran-mice, r-cran-miceadds, r-cran-mlogit, r-cran-mlr3verse, r-cran-modelbased, r-cran-modelsummary, r-cran-multcomp, r-cran-mvgam, r-cran-mvtnorm, r-cran-nanoparquet, r-cran-nlme, r-cran-nnet, r-cran-numderiv, r-cran-nycflights13, r-cran-optmatch, r-cran-ordbetareg, r-cran-ordinal, r-cran-parameters, r-cran-parsnip, r-cran-partykit, r-cran-patchwork, r-cran-pkgdown, r-cran-phylolm, r-cran-pbapply, r-cran-plm, r-cran-polspline, r-cran-posterior, r-cran-pscl, r-cran-purrr, r-cran-quantreg, r-cran-rchoice, r-cran-rendo, r-cran-rcmdcheck, r-cran-rdatasets, r-cran-remotes, r-cran-reticulate, r-cran-rmarkdown, r-cran-rms, r-cran-robust, r-cran-robustbase, r-cran-robustlmm, r-cran-rsample, r-cran-rstanarm, r-cran-rstantools, r-cran-rstpm2, r-cran-rstudioapi, r-cran-rsvg, r-cran-sampleselection, r-cran-sandwich, r-cran-scam, r-cran-spelling, r-cran-speedglm, r-cran-survey, r-cran-survival, r-cran-svglite, r-cran-systemfit, r-cran-systemfonts, r-cran-tibble, r-cran-tictoc, r-cran-tidymodels, r-cran-tidyr, r-cran-tidyverse, r-cran-tinysnapshot, r-cran-tinytable, r-cran-tinytest, r-cran-titanic, r-cran-truncreg, r-cran-tsmodel, r-cran-withr, r-cran-workflows, r-cran-yaml, r-cran-xgboost, r-cran-altdoc, r-cran-knitr, r-cran-quarto Filename: pool/dists/noble/main/r-cran-marginaleffects_0.30.0-1.ca2404.1_amd64.deb Size: 2187520 MD5sum: 5cccd02fad4fbc50cc57169cba455ad6 SHA1: 59765e5d539173c5fb3a263ea7c6176872f8ca0a SHA256: e3dd3324edf8e4c5e53efd967d2d1ba460937dc6f4ebd4c67f93149d28afcc88 SHA512: 3ca113c69085a85ad6753c9adf734251facc5835eac22da89b7e102c0b8a6902719e5595dd592d0a7449265650beac98ec8414ed31e0e5317e8f197e0d91bb85 Homepage: https://cran.r-project.org/package=marginaleffects Description: CRAN Package 'marginaleffects' (Predictions, Comparisons, Slopes, Marginal Means, and HypothesisTests) Compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and machine learning models in R. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference. Details can be found in Arel-Bundock, Greifer, and Heiss (2024) . Package: r-cran-marginalmaxtest Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-marginalmaxtest_1.0.1-1.ca2404.1_amd64.deb Size: 69620 MD5sum: 6ceb92dad84d5fac823e6a2b6156c5b3 SHA1: 978fc3a95d731677ac92485a221d343d61ba03fe SHA256: b34e1f76bbd80e4202fe74988c8f06e6796afd53f060906e1febf0adec9f4b12 SHA512: 85708a3f28811cef2f4118001ad316d0ec18b3f29409107e77fff16533d6cfeb73ad9bfcf17ac50b9154b1d182c887939b27b31c25999989b5018be691eaa7d7 Homepage: https://cran.r-project.org/package=MarginalMaxTest Description: CRAN Package 'MarginalMaxTest' (Max-Type Test for Marginal Correlation with Bootstrap) Test the marginal correlation between a scalar response variable with a vector of explanatory variables using the max-type test with bootstrap. 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Package: r-cran-marked Architecture: amd64 Version: 1.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1258 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-r2admb, r-cran-truncnorm, r-cran-coda, r-cran-matrix, r-cran-numderiv, r-cran-expm, r-cran-rcpp, r-cran-tmb, r-cran-optimx, r-cran-data.table, r-cran-knitr, r-cran-kableextra, r-cran-bookdown Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-marked_1.2.8-1.ca2404.1_amd64.deb Size: 810704 MD5sum: dde1afb295dc524ac4bafc9f7857ca0c SHA1: beb1cb8585b1eac8ab84c130a5908530cad2a414 SHA256: c6ef4f09e24347b511a409fcc93ee1e8cdd758809faeecff6037afe3fe58876f SHA512: 63f579912f52b55d190cbde16ca563d90fcd885bc30e0f24673af5cea926a92954431aadac4f0701cb388bc98d699fe01b157ef18f47b219f38f9f5d22f0db7f Homepage: https://cran.r-project.org/package=marked Description: CRAN Package 'marked' (Mark-Recapture Analysis for Survival and Abundance Estimation) Functions for fitting various models to capture-recapture data including mixed-effects Cormack-Jolly-Seber(CJS) and multistate models and the multi-variate state model structure for survival estimation and POPAN structured Jolly-Seber models for abundance estimation. There are also Hidden Markov model (HMM) implementations of CJS and multistate models with and without state uncertainty and a simulation capability for HMM models. Package: r-cran-markerpen Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4199 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rspectra, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc, r-cran-scales Filename: pool/dists/noble/main/r-cran-markerpen_0.1.2-1.ca2404.1_amd64.deb Size: 3824778 MD5sum: e81a785933d5088b0ffbaa3f4cbd59a2 SHA1: 1afb47dfb8b764d5e4936489a79c0d88bf614f47 SHA256: 85db5af543feef6be81eebaa77f6590e1cd29fe29a98cf44ee2fefc75721b1fd SHA512: cd81d4985819274335bff4827388d26d00b72931c967b14b49c93be58ef97c6c432293f6081626d3906b86cf0381dace288a045ec0bef9e7700df212c9d4c418 Homepage: https://cran.r-project.org/package=markerpen Description: CRAN Package 'markerpen' (Marker Gene Detection via Penalized Principal Component Analysis) Implementation of the 'MarkerPen' algorithm, short for marker gene detection via penalized principal component analysis, described in the paper by Qiu, Wang, Lei, and Roeder (2021, ). 'MarkerPen' is a semi-supervised algorithm for detecting marker genes by combining prior marker information with bulk transcriptome data. Package: r-cran-markets Architecture: amd64 Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2100 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-formula, r-cran-mass, r-cran-rlang, r-cran-rcpp, r-cran-rcppgsl, r-cran-rcppparallel Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-numderiv, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-markets_1.1.7-1.ca2404.1_amd64.deb Size: 1138036 MD5sum: 674c3b21dd83d775227c246a70538b59 SHA1: 9f24b3edfe50c77479768e3e3bd1cdf7982b94f1 SHA256: 4cb81b96d512141782ce020068baabb858ef57b59ac37b890f8587fd39f1dfdc SHA512: e44cba792bf4a53a16090eb5f7a0acea2d3f74fa4929804b03cb7efbec49bccc19557df78d79fd62a37c1f07da578cbc475d144a22f41266caab9d6e0d071105 Homepage: https://cran.r-project.org/package=markets Description: CRAN Package 'markets' (Estimation Methods for Markets in Equilibrium and Disequilibrium) Provides estimation methods for markets in equilibrium and disequilibrium. Supports the estimation of an equilibrium and four disequilibrium models with both correlated and independent shocks. Also provides post-estimation analysis tools, such as aggregation, marginal effect, and shortage calculations. See Karapanagiotis (2024) for an overview of the functionality and examples. The estimation methods are based on full information maximum likelihood techniques given in Maddala and Nelson (1974) . They are implemented using the analytic derivative expressions calculated in Karapanagiotis (2020) . Standard errors can be estimated by adjusting for heteroscedasticity or clustering. The equilibrium estimation constitutes a case of a system of linear, simultaneous equations. Instead, the disequilibrium models replace the market-clearing condition with a non-linear, short-side rule and allow for different specifications of price dynamics. Package: r-cran-markophylo Architecture: amd64 Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 528 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ape, r-cran-numderiv, r-cran-phangorn, r-cran-geiger, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-testthat, r-cran-markdown Filename: pool/dists/noble/main/r-cran-markophylo_1.0.9-1.ca2404.1_amd64.deb Size: 261884 MD5sum: dd5c4d9e1b6c914e02f270c85d120f44 SHA1: 6fd8ba704604b1a8895bb027a571d96cbe2e3a94 SHA256: 0dc696b1fcf18b0d26697aee68d2ee53e81b9c64f48aca033fdc1c86e28ef004 SHA512: 3721ee071eed6317e1c44b643119a7676ef077e1b4cf327b5682c5d20a3e531459b6745eb0945ac2771053efc94aa9c1c8b2dd589211dfdc42783e841a8ad56c Homepage: https://cran.r-project.org/package=markophylo Description: CRAN Package 'markophylo' (Markov Chain Models for Phylogenetic Trees) Allows for fitting of maximum likelihood models using Markov chains on phylogenetic trees for analysis of discrete character data. Examples of such discrete character data include restriction sites, gene family presence/absence, intron presence/absence, and gene family size data. Hypothesis-driven user- specified substitution rate matrices can be estimated. Allows for biologically realistic models combining constrained substitution rate matrices, site rate variation, site partitioning, branch-specific rates, allowing for non-stationary prior root probabilities, correcting for sampling bias, etc. See Dang and Golding (2016) for more details. Package: r-cran-markovchain Architecture: amd64 Version: 0.10.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2281 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-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/noble/main/r-cran-markovchain_0.10.3-1.ca2404.1_amd64.deb Size: 1286964 MD5sum: 6a2f9b1aab6d9c4f3759756bc1f57798 SHA1: fdd4acacea39ff0758e43c7127bdd435965956e5 SHA256: 7d12d42a30689e01646a05e3fbf13396c32da563e966e8dc33788ec5c9e2f079 SHA512: 3d6da4d244e58434cf6e37bb9a49c6f157bbcdfd78207980a9b82fcbfec14f0cbe46830005a4260c4795cc7026e9ca0da8929680aaf18d4edaac738b93ba40f3 Homepage: https://cran.r-project.org/package=markovchain Description: CRAN Package 'markovchain' (Easy Handling Discrete Time Markov Chains) Functions and S4 methods to create and manage discrete time Markov chains more easily. In addition functions to perform statistical (fitting and drawing random variates) and probabilistic (analysis of their structural proprieties) analysis are provided. See Spedicato (2017) . Some functions for continuous times Markov chains depend on the suggested ctmcd package. Package: r-cran-markovmix Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-pillar, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-bench, r-cran-covr, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-markovmix_0.1.3-1.ca2404.1_amd64.deb Size: 111154 MD5sum: d6efc48cc144c3433d31b6a4adfe6e23 SHA1: 7596f162ddf562c78de44c486ea2e2e367b94a42 SHA256: 09d62e8841550189431686c396ab251301850f2a4af9f2043d08fdcc72245668 SHA512: 95981f865e60b6d1d20812bbd6ff88034aa16ba32c840febcd32c3c42ac6d897bbe9e9b71790fe872ef8ee3fb962085042895c3439b1dca98d3d74f5e92dfb81 Homepage: https://cran.r-project.org/package=markovmix Description: CRAN Package 'markovmix' (Mixture of Markov Chains with Support of Higher Orders andMultiple Sequences) Fit mixture of Markov chains of higher orders from multiple sequences. It is also compatible with ordinary 1-component, 1-order or single-sequence Markov chains. Various utility functions are provided to derive transition patterns, transition probabilities per component and component priors. In addition, print(), predict() and component extracting/replacing methods are also defined as a convention of mixture models. Package: r-cran-markovmsm Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-mstate Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-doparallel, r-cran-devtools, r-cran-bibtex, r-cran-testthat Filename: pool/dists/noble/main/r-cran-markovmsm_0.1.3-1.ca2404.1_amd64.deb Size: 187596 MD5sum: d4643dd70447f9b23d880c894eeec1b4 SHA1: c006c8cc84105e079f0f62266f7d637fa247fdcf SHA256: 608f6833fc74e14e392c86e4e95e2e28c024ddf2b2cfe3d668a6a0cef4f32810 SHA512: ecab68865e689647ec54bf3b06156583973129c54e0b61c41bc97a64d33950a7a3771e73c427e72302bdb27a557e6ee607047d677e9e4b3440b6d22785008189 Homepage: https://cran.r-project.org/package=markovMSM Description: CRAN Package 'markovMSM' (Methods for Checking the Markov Condition in Multi-StateSurvival Data) The inference in multi-state models is traditionally performed under a Markov assumption that claims that past and future of the process are independent given the present state. In this package, we consider tests of the Markov assumption that are applicable to general multi-state models. Three approaches using existing methodology are considered: a simple method based on including covariates depending on the history in Cox models for the transition intensities; methods based on measuring the discrepancy of the non-Markov estimators of the transition probabilities to the Markov Aalen-Johansen estimators; and, finally, methods that were developed by considering summaries from families of log-rank statistics where patients are grouped by the state occupied of the process at a particular time point (see Soutinho G, Meira-Machado L (2021) and Titman AC, Putter H (2020) ). Package: r-cran-marlod Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 573 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-survival, r-cran-quantreg, r-cran-knitr Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-marlod_0.2.0-1.ca2404.1_amd64.deb Size: 462278 MD5sum: 398d00166456f2a691645abb7c4cb5e2 SHA1: d14b955fab8afcb9f20003068ba48c2eaa893365 SHA256: cf3ce1e05cecb156db18c051a466cb354c6b3bb108d1675b351da63aac9de6cd SHA512: 07b271f3f8eb93c6483ec15b4ee12a129d70cf8d2df8a8dae348c247c757494da714029c5e7bed476daae869907d3d18558fd7bff0a7a01d23ebf707cd49cb06 Homepage: https://cran.r-project.org/package=marlod Description: CRAN Package 'marlod' (Marginal Modeling for Exposure Data with Values Below the LOD) Functions of marginal mean and quantile regression models are used to analyze environmental exposure and biomonitoring data with repeated measurements and non-detects (i.e., values below the limit of detection (LOD)), as well as longitudinal exposure data that include non-detects and time-dependent covariates. For more details see Chen IC, Bertke SJ, Curwin BD (2021) , Chen IC, Bertke SJ, Estill CF (2024) , Chen IC, Bertke SJ, Dahm MM (2024) , and Chen IC (2025) . 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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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All of the estimators can be written as a generalized regression estimator with the Horvitz-Thompson, ratio, post-stratified, and regression estimators summarized by Sarndal et al. (1992, ISBN:978-0-387-40620-6). Two of the estimators employ a statistical learning model as the assisting model: the elastic net regression estimator, which is an extension of the lasso regression estimator given by McConville et al. (2017) , and the regression tree estimator described in McConville and Toth (2017) . The variance estimators which approximate the joint inclusion probabilities can be found in Berger and Tille (2009) and the bootstrap variance estimator is presented in Mashreghi et al. (2016) . 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The package also implements a series of recommendations offered in Ho, Imai, King, and Stuart (2007) . (The 'gurobi' package, which is not on CRAN, is optional and comes with an installation of the Gurobi Optimizer, available at .) Package: r-cran-mateable Architecture: amd64 Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 564 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn, r-cran-rcpp, r-cran-sn Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mateable_0.3.3-1.ca2404.1_amd64.deb Size: 317764 MD5sum: 854e452ea585ce6e33c56a97ecf81d6c SHA1: c5c099fa9331f85c503e2d1082efb57a17355049 SHA256: 9c24b90fe9f5fa115ec23fb3b786f50b45d547aacc856561f45ffbb32b6db151 SHA512: 046d457ac1488c06d9c71d0e75295f33cadb7fa1c8d040a03771e567ca0a78aac490f5fac1c3588159bbd4a9ef98b8960411300d6ec49e7d21f87e9753672ec4 Homepage: https://cran.r-project.org/package=mateable Description: CRAN Package 'mateable' (Assess Mating Potential in Space and Time) Simulate, manage, visualize, and analyze spatially and temporally explicit datasets of mating potential. Implements methods to calculate synchrony, proximity, and compatibility.Synchrony calculations are based on methods described in Augspurger (1983) , Kempenaers (1993) , Ison et al. (2014) , and variations on these, as described. Package: r-cran-mates Architecture: amd64 Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 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-ade4, r-cran-mass, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-mates_0.1-1.ca2404.1_amd64.deb Size: 88432 MD5sum: ff6aeeaf4ccd510d2534ad6889c652a6 SHA1: 9426307fe69dfe87240e164eb026c9584f7998f0 SHA256: 8bdb2ea49797f585ccedecc677d132c0453d496af89f4e850379af6d36fd20a5 SHA512: 72d9e364829c7dbe02bd672f38e422c489054c0149265a598c1a09f0765e5917a17eac47483750d2c04cc105ed8f20e053e1c4fbf2ecc0759398747566d0e93f Homepage: https://cran.r-project.org/package=MATES Description: CRAN Package 'MATES' (Multi-View Aggregated Two Sample Tests) Implements the Multi-view Aggregated Two-Sample (MATES) test, a powerful nonparametric method for testing equality of two multivariate distributions. 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Allows to set multiple matrix values at once, by using list of formulae. Provides additional matrix operators and dedicated plotting function. Package: r-cran-matrix Architecture: amd64 Version: 1.7-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7829 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/noble/main/r-cran-matrix_1.7-5-1.ca2404.1_amd64.deb Size: 4220750 MD5sum: b08f7df4175b4878e4ec7f97407f1027 SHA1: 6db9e46a4620c8efba68031477827b0e3e5894ca SHA256: fff25ed9be6d6afd34cb2217403e133488172bb1135fa455996987b3057f99ca SHA512: 15915267230fc71f71150b71aa6afacccbe9d5377c475d5728a51dfcdc299bbdd6c9f6d08bc1fc93f104258ee75765afb67810c567744b39855d0e817fbb0c9c 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: amd64 Version: 0.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-matrixcorrelation_0.10.1-1.ca2404.1_amd64.deb Size: 139520 MD5sum: 4e9382dc14dc4dc0ecb563abd21fae6a SHA1: ce79c14ea3cb5e5905d4c2021e04f32521fb85fb SHA256: 6b691169337ccb0e96e709045cc78d673bc0564d4853fd74d01331de9e36bd48 SHA512: 0374270412a7742ded46af880f77a881b783deeb07fdca77258386d29c0d42916eee8bebb3c6ffbb1a9dd8d5a3971820fdf541e8326ab3b2a0abb58d831d220d Homepage: https://cran.r-project.org/package=MatrixCorrelation Description: CRAN Package 'MatrixCorrelation' (Matrix Correlation Coefficients) Computation and visualization of matrix correlation coefficients. The main method is the Similarity of Matrices Index, while various related measures like r1, r2, r3, r4, Yanai's GCD, RV, RV2, adjusted RV, Rozeboom's linear correlation and Coxhead's coefficient are included for comparison and flexibility. Package: r-cran-matrixdist Architecture: amd64 Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2135 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-nnet, r-cran-reshape2, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-matrixdist_1.1.9-1.ca2404.1_amd64.deb Size: 1096226 MD5sum: c31ffaa3060ed20f6a04cd853495fcb3 SHA1: c5e556a870729eb74585adb25fa59fa0dd3573d9 SHA256: 0780cd230809fa0b89c9f6843d9c9a8d4b8c1ff5784a2ca2b7950205046ce55e SHA512: ca3d62bc0d1c84f1eeb23d9a9ca5533b828faf94aae46782f5f347a30bf752c0a64e313032079a011ee63c29fa0766ddf8cf9ac98e9a5301d2820c6055fe0f4e 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) . 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For a description of the method, see Molstad and Rothman (2018) . 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Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. There are also optimized vector-based methods, e.g. binMeans(), madDiff() and weightedMedian(). Package: r-cran-mattransmix Architecture: amd64 Version: 0.1.18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 617 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-mattransmix_0.1.18-1.ca2404.1_amd64.deb Size: 428624 MD5sum: ac41f57464d65722f59f4ac50c80f542 SHA1: 18e9b6188257ccc3b4149a0557bf76c364ee3903 SHA256: 1f95e2651371a5bc393cb644d600ad3a635e0886ecb437f2bcacfcad3f841d93 SHA512: 3839fa3261cd8a642f5dbf2c7e0cd3480022566d359ab829e856149114990445fe4282bb43e34ab6e10bdea1e266b51f803ca3149749d6a38f70cb15da189c91 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. 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See Bonat (2018) , for more information and examples. 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Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return 'coda' mcmc objects that can then be summarized using the 'coda' package. Some useful utility functions such as density functions, pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization are provided. Package: r-cran-mcmcprecision Architecture: amd64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1047 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-combinat, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-mcmcprecision_0.4.2-1.ca2404.1_amd64.deb Size: 619878 MD5sum: fbbf44cb539b4e72b76485c26459809a SHA1: 04a840f957bab87596ff3f2bfc8a3b23b81abc56 SHA256: d7399c9f456b18bd52626d8838efba6e61925905a6daf83e95c97f0f998c8123 SHA512: b1b5963ee0ec9ecf0b9a22a5f47fd191b8b22fe13681eec47c49c5e2cbc918e65b0552c229782d9dd3a65b8689893f187ed37add07c2fbb6b2540f6b70adabb7 Homepage: https://cran.r-project.org/package=MCMCprecision Description: CRAN Package 'MCMCprecision' (Precision of Discrete Parameters in Transdimensional MCMC) Estimates the precision of transdimensional Markov chain Monte Carlo (MCMC) output, which is often used for Bayesian analysis of models with different dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible jump MCMC) relies on sampling a discrete model-indicator variable to estimate the posterior model probabilities. If only few switches occur between the models, precision may be low and assessment based on the assumption of independent samples misleading. Based on the observed transition matrix of the indicator variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2019, Statistics & Computing, 29, 631-643) draws posterior samples of the stationary distribution to (a) assess the uncertainty in the estimated posterior model probabilities and (b) estimate the effective sample size of the MCMC output. Package: r-cran-mcmcsae Architecture: amd64 Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2268 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-gigrvg, r-cran-loo, r-cran-collapse, r-cran-rcppeigen Suggests: r-cran-dbarts, r-cran-bayeslogit, r-cran-lintools, r-cran-mgcv, r-cran-spdep, r-cran-sf, r-cran-bayesplot, r-cran-coda, r-cran-posterior, r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-survey Filename: pool/dists/noble/main/r-cran-mcmcsae_0.8.0-1.ca2404.1_amd64.deb Size: 1571032 MD5sum: fdfac60ac796bcd12c696900d0eb8001 SHA1: c0506e10440234da87f1bb030e436f428269f889 SHA256: 64c8c1681ace57f47a75137291bbd48c0aeeec99551a1613480d38d9a02bd695 SHA512: 0b3c1be6ee2f4de5481e69611241eca410e7623ee47e773654844259e167151db77abbb28972c700eb22a8ef36a2f86a406f1dd762ef0681fbf20f1270506cd9 Homepage: https://cran.r-project.org/package=mcmcsae Description: CRAN Package 'mcmcsae' (Markov Chain Monte Carlo Small Area Estimation) Fit multi-level models with possibly correlated random effects using Markov Chain Monte Carlo simulation. 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Package: r-cran-mcmcse Architecture: amd64 Version: 1.5-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 693 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ellipse, r-cran-rcpp, r-cran-fftwtools, r-cran-testthat, r-cran-rcpparmadillo Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-mcmcse_1.5-1-1.ca2404.1_amd64.deb Size: 445900 MD5sum: 90018007cbba7d685b2d39a4fda2f683 SHA1: a8a25d2a83568c2f4323bd95665c44ebe8c8d1b4 SHA256: 386ccfe846f4c2ca2b18016457b48eb5a27e98bb9e3d2cbec16a65353470a69f SHA512: e776a9e9e05c448439a4bd96ad72969340326fdd7c1c5e108edecbfb4cccd57287beed73bc35c652d36ebcc0e393a6223bab3867c25c463ca14f9afed618017a Homepage: https://cran.r-project.org/package=mcmcse Description: CRAN Package 'mcmcse' (Monte Carlo Standard Errors for MCMC) Provides tools for computing Monte Carlo standard errors (MCSE) in Markov chain Monte Carlo (MCMC) settings (survey in , Chapter 7). 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Package: r-cran-mco Architecture: amd64 Version: 1.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-scatterplot3d, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mco_1.17-1.ca2404.1_amd64.deb Size: 66488 MD5sum: 592c769334e7cea0a1f20100331646cc SHA1: b4fb9af4890283b45e067228cce0d0ee47e58486 SHA256: efc00e59d0e5259e8be4c1741bb166e9a4fe72bab23cb0f64672d99cb7cdbebb SHA512: 9dcf17392d32372781e81ad5bcef58b9857cb3aecad1e5f712e734cebb2175b86c9bb6660ddda68964146a4ac057708585b9b75cdebdbfaa11acc532ea9f3c7b Homepage: https://cran.r-project.org/package=mco Description: CRAN Package 'mco' (Multiple Criteria Optimization Algorithms and Related Functions) A collection of function to solve multiple criteria optimization problems using genetic algorithms (NSGA-II). Also included is a collection of test functions. 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(2005) ). Package: r-cran-mcr Architecture: amd64 Version: 1.3.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 969 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-robslopes Filename: pool/dists/noble/main/r-cran-mcr_1.3.3.1-1.ca2404.1_amd64.deb Size: 611350 MD5sum: c4f02d4cadd636b31a9afb9a948a33bb SHA1: c65dcab1b5357965d3ddf3616138356e26c4e10b SHA256: 0716bf99eb3a6de61fe7d9fac40a0f127db813ccc3656932ff5f31d7c89b3eab SHA512: 8d07b3b8561323a83d839a4e6aee8f5ae032f4aacc02087ef7526dc557ab67ab941c46d54360ce523c1d490f8aac9cf94c8d8ee494095dd51d9bd9bbf5720171 Homepage: https://cran.r-project.org/package=mcr Description: CRAN Package 'mcr' (Method Comparison Regression) Regression methods to quantify the relation between two measurement methods are provided by this package. In particular it addresses regression problems with errors in both variables and without repeated measurements. 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Hastie, Tibshirani and Friedman (2009) "Elements of Statistical Learning (second edition, chap 12)" Springer, New York. 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The methods are based on Shao and Zhang (2014) . Additionally, introduces a novel hypothesis test for evaluating covariate effects on the cure rate in mixture cure models, using MDC-based statistics. The methodology is described in Monroy-Castillo et al. (2025, manuscript submitted). 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For more details please see Liu A., Mukhopadhyay R., and Markatou M. . 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This package, implements the Method of Direct Estimation and Inference as introduced in "Estimation and Inference on Nonlinear and Heterogeneous Effects" by Ratkovic and Tingley (2023) . The method takes an outcome, variable of theoretical interest (treatment), and set of variables and then returns a partial derivative (marginal effect) of the treatment variable at each point along with uncertainty intervals. The approach offers two advances. First, a split-sample approach is used as a guard against over-fitting. Second, the method uses a data-driven interval derived from conformal inference, rather than relying on a normality assumption on the error terms. Package: r-cran-mdendro Architecture: amd64 Version: 2.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1913 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-ape, r-cran-cluster, r-cran-dendextend, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mdendro_2.2.3-1.ca2404.1_amd64.deb Size: 826860 MD5sum: bc3337a6d28374e358ca941f3981f0b1 SHA1: 381a563b162b56813cca736c9be673159a9c519e SHA256: 55da901421675b06936e548df648320def5a31842af1a84f3154f0aef76ca167 SHA512: 836cd9ed7bd111756d5a83dddb66a4d6b701b7b4ebc76b87da03f87dba181354a54b9dca1b29d8c358591393650119d732c2ac02d19ffc0bb20346d80b1322f1 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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Models under the null hypothesis can either be simple or allow for parameter estimation. p values are found via the parametric bootstrap (simulation). The routine gof_test_adjusted_pvalues() runs several tests and then finds a p value adjusted for simultaneous inference. The routine gof_power() allows the estimation of the power of the tests. hybrid_test() and hybrid_power() do the same by first generating a Monte Carlo data set under the null hypothesis and then running a number of two-sample methods. The routine run.studies() allows a user to quickly study the power of a new method and how it compares to those included in the package via a large number of case studies. For details of the methods and references see the included vignettes. 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We provide a new implementation of a common method for computing sentiment, whereby words are scored as positive or negative according to a dictionary lookup. Then the sum of those scores is returned for the document. We use the 'Hu' and 'Liu' sentiment dictionary ('Hu' and 'Liu', 2004) for determining sentiment. The scoring function is 'vectorized' by document, and scores for multiple documents are computed in parallel via 'OpenMP'. 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This package is a spinoff from 'epiworldR' focusing specifically on measles transmission dynamics. It includes models for school settings with quarantine and isolation policies, mixing models with population groups, and risk-based quarantine strategies. The models use Agent-Based Models (ABM) with a fast 'C++' backend from the 'epiworld' library. Ideal for studying measles outbreaks, vaccination strategies, and intervention policies. 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Diagnostic classification models are confirmatory latent class models, as described by Rupp et al. (2010, ISBN: 978-1-60623-527-0). Automatically generate 'Stan' code for the general loglinear cognitive diagnostic diagnostic model proposed by Henson et al. (2009) and other subtypes that introduce additional model constraints. Using the generated 'Stan' code, estimate the model evaluate the model's performance using model fit indices, information criteria, and reliability metrics. 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(2015) ]. Parallelization is allowed in several simulation functions and simulations may be conducted including spatial processes such as lateral water transfer and seed dispersal. Package: r-cran-mediak Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-mediak_1.0-1.ca2404.1_amd64.deb Size: 45424 MD5sum: 0dac328c603f0712eab6d71c16b95f86 SHA1: 1763cfbb9d908cd02fd643b11d189c1dc7b3021a SHA256: 8d3ab189035dba565c9b00bed83deb13a120abd513d562654860c22f106a1722 SHA512: 02bbbf3e2e7c6efd4b6fbfb1fb03d3785db17c1d04a9cae6b48feaf5f3ddc9b16b13f211991e6cadbaacc24546f5cb184ac07cc93feab20ceaf55bd67c1ebf58 Homepage: https://cran.r-project.org/package=MediaK Description: CRAN Package 'MediaK' (Calculate MeDiA_K Distance) Calculates MeDiA_K (means Mean Distance Association by K-nearest neighbor) in order to detect nonlinear associations. 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The following modules are included in the package: Adaptive designs with data-driven sample size or event count re-estimation, Adaptive designs with data-driven treatment selection, Adaptive designs with data-driven population selection, Optimal selection of a futility stopping rule, Event prediction in event-driven trials, Adaptive trials with response-adaptive randomization (experimental module), Traditional trials with multiple objectives (experimental module). Traditional trials with cluster-randomized designs (experimental module). Package: r-cran-mega2r Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2959 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-skat, r-cran-pedgene, r-bioc-gdsfmt, r-bioc-annotationdbi, r-cran-dbi, r-bioc-genomeinfodb, r-cran-rsqlite, r-cran-famskatrc, r-cran-kinship2, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-formatr, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-org.hs.eg.db Filename: pool/dists/noble/main/r-cran-mega2r_1.1.0-1.ca2404.1_amd64.deb Size: 2385916 MD5sum: d1f54e25a8bdeca186cf7593a53ea21d SHA1: f50ff41b0b22ee0b172511cd4c8394f52c156929 SHA256: c9017a17f2c8b4814e8f345f782b8851c6e6c56eb3061f609d367744e6fc21ba SHA512: fcc9c3dbbe8c34a0adef7334a1c1274c4faad9b4efd2af3d6d14b5c628cd78b271e81686f07b638e56c3f5c1602e6d2d7b1cc9bf09c95b0ce3fde82bf302f232 Homepage: https://cran.r-project.org/package=Mega2R Description: CRAN Package 'Mega2R' (Accessing and Processing a 'Mega2' Genetic Database) Uses as input genetic data that have been reformatted and stored in a 'SQLite' database; this database is initially created by the standalone 'mega2' C++ program (available freely from ). Loads and manipulates data frames containing genotype, phenotype, and family information from the input 'SQLite' database, and decompresses needed subsets of the genotype data, on the fly, in a memory efficient manner. We have also created several more functions that illustrate how to use the data frames as well as perform useful tasks: these permit one to run the 'pedgene' package to carry out gene-based association tests on family data using selected marker subsets, to run the 'SKAT' package to carry out gene-based association tests using selected marker subsets, to run the 'famSKATRC' package to carry out gene-based association tests on families (optionally) and with rare or common variants using selected marker subsets, to output the 'Mega2R' data as a VCF file and related files (for phenotype and family data), and to convert the data frames into CoreArray Genomic Data Structure (GDS) format. 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Song W.-M., Zhang B. (2015) Multiscale Embedded Gene Co-expression Network Analysis. PLoS Comput Biol 11(11): e1004574. . 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Funded by ERC grant 856506 and NIH grant R01ES028804. Package: r-cran-mess Architecture: amd64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3553 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-rcpp, r-cran-clipr, r-cran-geepack, r-cran-geem, r-cran-ggplot2, r-cran-ggformula, r-cran-glmnet, r-cran-kinship2, r-cran-mvtnorm, r-cran-rcpparmadillo, r-cran-rcppparallel Suggests: r-cran-knitr, r-cran-lme4, r-cran-magrittr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mess_0.6.0-1.ca2404.1_amd64.deb Size: 3256840 MD5sum: 6e8472ba1b86ba5ab095f9bc789a6bd5 SHA1: 03ea73ca8c8891e0d5e7d8f2ba99939018f31af5 SHA256: 0232e0207d7c1ff458427d746467bcd941c111b57915cd1da0a8a3dd415fced2 SHA512: 5d94e210199548434c1ac4bbd6420cc517808b579781462ca0555d63307797638d12c391e6c42de6164ff44f5065a60f5f1ea107845201cdfd77fbb3899a8602 Homepage: https://cran.r-project.org/package=MESS Description: CRAN Package 'MESS' (Miscellaneous Esoteric Statistical Scripts) A mixed collection of useful and semi-useful diverse statistical functions, some of which may even be referenced in The R Primer book. See Ekstrøm, C. T. (2016). The R Primer. 2nd edition. Chapman & Hall. Package: r-cran-metabma Architecture: amd64 Version: 0.6.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7410 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bridgesampling, r-cran-coda, r-cran-laplacesdemon, r-cran-logspline, r-cran-mvtnorm, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-metabma_0.6.9-1.ca2404.1_amd64.deb Size: 1734894 MD5sum: cb9c9dd9553a124690d07ed71f718a9d SHA1: be00cb500b0f025a7d566539f78d854c73c9bfe9 SHA256: cd9ba8919100fd252a05a47adb2504bb818b7ffa3bf40858e9b9824c0baf0026 SHA512: 0d2a7f9e08c53c4c47cf73dd630c38232ee0ea4478ae1fdb5774bf4083f57a9e1bdca9e395c389cdbce7b391a69997e7ff035cda97cebc03943088649f8a7451 Homepage: https://cran.r-project.org/package=metaBMA Description: CRAN Package 'metaBMA' (Bayesian Model Averaging for Random and Fixed EffectsMeta-Analysis) Computes the posterior model probabilities for standard meta-analysis models (null model vs. alternative model assuming either fixed- or random-effects, respectively). These posterior probabilities are used to estimate the overall mean effect size as the weighted average of the mean effect size estimates of the random- and fixed-effect model as proposed by Gronau, Van Erp, Heck, Cesario, Jonas, & Wagenmakers (2017, ). The user can define a wide range of non-informative or informative priors for the mean effect size and the heterogeneity coefficient. Moreover, using pre-compiled Stan models, meta-analysis with continuous and discrete moderators with Jeffreys-Zellner-Siow (JZS) priors can be fitted and tested. This allows to compute Bayes factors and perform Bayesian model averaging across random- and fixed-effects meta-analysis with and without moderators. For a primer on Bayesian model-averaged meta-analysis, see Gronau, Heck, Berkhout, Haaf, & Wagenmakers (2021, ). Package: r-cran-metacart Architecture: amd64 Version: 3.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 562 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-rpart, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-metacart_3.0.4-1.ca2404.1_amd64.deb Size: 372736 MD5sum: b83d62dee679355daecdc283784c08f5 SHA1: e20c85105d68874d6a1bd661aab4d600eb75cf74 SHA256: 0b22082b075cb3e3383876eb104a5c2060d54983d1e620621df0f7b5873ee1fc SHA512: d6dd217366be3411243bb0a8e291728d19650bfde2c410d9a8ce88bab8cdbdb9e4b92936e5a012452d3da977284dd7cf54982b527c94985d4e135ff3047a0d70 Homepage: https://cran.r-project.org/package=metacart Description: CRAN Package 'metacart' (Meta-CART: A Flexible Approach to Identify Moderators inMeta-Analysis) Meta-CART integrates classification and regression trees (CART) into meta-analysis. Meta-CART is a flexible approach to identify interaction effects between moderators in meta-analysis. The method is described in Dusseldorp et al. (2014) and Li et al. (2017) . Package: r-cran-metacoder Architecture: amd64 Version: 0.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2866 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-ggplot2, r-cran-igraph, r-cran-taxize, r-cran-seqinr, r-cran-rcurl, r-cran-ape, r-cran-lazyeval, r-cran-dplyr, r-cran-magrittr, r-cran-readr, r-cran-rlang, r-cran-ggfittext, r-cran-vegan, r-cran-cowplot, r-cran-ga, r-cran-rcpp, r-cran-crayon, r-cran-tibble, r-cran-r6 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-biocmanager, r-bioc-phyloseq, r-cran-phylotate, r-cran-traits, r-bioc-biomformat, r-bioc-deseq2 Filename: pool/dists/noble/main/r-cran-metacoder_0.3.9-1.ca2404.1_amd64.deb Size: 2066824 MD5sum: 01c43b5115d3a4f16cea91fc0179e128 SHA1: 6fdd46ee2a8cf862c89790c82073ad65228804c4 SHA256: ae7516b4382726c8b7349d2d80ecb9ce107c19a1b1b0063e5d8b8dd369d2e3cf SHA512: 7fc4ac8b0cd51b8b059977d18aab0c1db7fa8c318143579208ad33a83d49e8b98fb6ee99f10a7ad00c4465e8bbb6a727c25393ac27cd7ad1e2016cfbab43f88a Homepage: https://cran.r-project.org/package=metacoder Description: CRAN Package 'metacoder' (Tools for Parsing, Manipulating, and Graphing TaxonomicAbundance Data) Reads, plots, and manipulates large taxonomic data sets, like those generated from modern high-throughput sequencing, such as metabarcoding (i.e. amplification metagenomics, 16S metagenomics, etc). It provides a tree-based visualization called "heat trees" used to depict statistics for every taxon in a taxonomy using color and size. It also provides various functions to do common tasks in microbiome bioinformatics on data in the 'taxmap' format defined by the 'taxa' package. The 'metacoder' package is described in the publication by Foster et al. (2017) . Package: r-cran-metadynminer3d Architecture: amd64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2203 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-metadynminer, r-cran-rgl, r-cran-rcpp, r-cran-misc3d Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-metadynminer3d_0.0.2-1.ca2404.1_amd64.deb Size: 2046778 MD5sum: a8a27072732d40d5ed24b58ebce00131 SHA1: 017e23b625571dbdf70ed02a82153309aa10ca4b SHA256: e65cf5b7cc77ab1868c77342d5c7be18e917f70820a64280c84602b8f39ac24c SHA512: 8d355df22cf01eb6acb65ba21d8653055543b729cf5087172bac18c481a28c2c07885bc56ed501fea348afba8ccc44c815660eccdf1f5c69781a4e2d7645fa49 Homepage: https://cran.r-project.org/package=metadynminer3d Description: CRAN Package 'metadynminer3d' (Tools to Read, Analyze and Visualize Metadynamics 3D HILLS Filesfrom 'Plumed') Metadynamics is a state of the art biomolecular simulation technique. 'Plumed' Tribello, G.A. et al. (2014) program makes it possible to perform metadynamics using various simulation codes. The results of metadynamics done in 'Plumed' can be analyzed by 'metadynminer'. The package 'metadynminer' reads 1D and 2D metadynamics hills files from 'Plumed' package. As an addendum, 'metadynaminer3d' is used to visualize 3D hills. It uses a fast algorithm by Hosek, P. and Spiwok, V. (2016) to calculate a free energy surface from hills. Minima can be located and plotted on the free energy surface. Free energy surfaces and minima can be plotted to produce publication quality images. Package: r-cran-metadynminer Architecture: amd64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2754 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-metadynminer_0.1.7-1.ca2404.1_amd64.deb Size: 2590178 MD5sum: 55e37a6426ce15e456b0b30133df393a SHA1: 2b6d05bec0ef11ff812e620b0e88113678cd74b6 SHA256: a241b9b6e398dc90e443bb6c97c6ff152904fc337396a7f020cec47b10517f31 SHA512: c53db95bb4703a2383ea1efceeef57f68b189fef5d06da329c9b277edf0d3c96368d6f361ce1d498795608119f72c3f34cde815ce2e8a517d5b53fecaf187a72 Homepage: https://cran.r-project.org/package=metadynminer Description: CRAN Package 'metadynminer' (Tools to Read, Analyze and Visualize Metadynamics HILLS Filesfrom 'Plumed') Metadynamics is a state of the art biomolecular simulation technique. 'Plumed' Tribello, G.A. et al. (2014) program makes it possible to perform metadynamics using various simulation codes. The results of metadynamics done in 'Plumed' can be analyzed by 'metadynminer'. The package 'metadynminer' reads 1D and 2D metadynamics hills files from 'Plumed' package. It uses a fast algorithm by Hosek, P. and Spiwok, V. (2016) to calculate a free energy surface from hills. Minima can be located and plotted on the free energy surface. Transition states can be analyzed by Nudged Elastic Band method by Henkelman, G. and Jonsson, H. (2000) . Free energy surfaces, minima and transition paths can be plotted to produce publication quality images. Package: r-cran-metafolio Architecture: amd64 Version: 0.1.2-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-plyr, r-cran-colorspace, r-cran-mass, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-teachingdemos, r-cran-rcolorbrewer, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-metafolio_0.1.2-1.ca2404.1_amd64.deb Size: 981480 MD5sum: d1cabb210c3cce09915e2396d3f5e8b7 SHA1: 8a4d313426961343df7a25d466bfe7c7926b3e0c SHA256: 34c42760e7751d5ac73efcd39763a9c2e322f26d4bba423158ae5bbc36dd68b2 SHA512: 6c4560e9ae490e823b55dd5bb0781c982f742fad2b1979ba48fa12904a7b7c4f9e69679b4cbf7fa82682cae376717654085c6b35cf94dc8c896fe67e5eb33116 Homepage: https://cran.r-project.org/package=metafolio Description: CRAN Package 'metafolio' (Metapopulation Simulations for Conserving Salmon ThroughPortfolio Optimization) A tool to simulate salmon metapopulations and apply financial portfolio optimization concepts. The package accompanies the paper Anderson et al. (2015) . Package: r-cran-metahd Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-metafor, r-cran-corpcor, r-cran-nloptr, r-cran-matrix, r-cran-matrixcalc, r-cran-rcpp, r-cran-dynamictreecut, r-cran-future.apply, r-cran-metapro, r-cran-metap, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-metahd_0.1.4-1.ca2404.1_amd64.deb Size: 216788 MD5sum: e8d1229fa8baef5d4e65e203111ce5aa SHA1: 2aafe8edc2c35791cb62879e8ff5d73d84610a74 SHA256: b97b34e724fe300909070768147583684e62eda6c5150992590730336b3bc2b3 SHA512: 6894f4100063787794f6373f95f634d35676f75bbdec4221df019f8528088b241f2db46902c49f9f2a044ed9517480c0819771398d746886d1817f062cdf95e7 Homepage: https://cran.r-project.org/package=MetaHD Description: CRAN Package 'MetaHD' (A Multivariate Meta-Analysis Model for High-Dimensional Data) Performs multivariate meta-analysis for high-dimensional data to integrate and collectively analyse individual-level data from multiple studies, as well as to combine summary estimates. This approach accounts for correlation between outcomes, incorporates within‑ and between‑study variability, handles missing values, and uses shrinkage estimation to accommodate high dimensionality. The 'MetaHD' R package provides access to our multivariate meta-analysis approach, along with a comprehensive suite of existing meta-analysis methods, including fixed-effects and random-effects models, Fisher’s method, Stouffer’s method, the weighted Z method, Lancaster’s method, the weighted Fisher’s method, and vote-counting approach. A detailed vignette with example datasets and code for data preparation and analysis is available at . Package: r-cran-metapack Architecture: amd64 Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2275 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-gridextra, r-cran-formula, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metapack_0.3-1.ca2404.1_amd64.deb Size: 815200 MD5sum: 0f1e0a321c6c39a5925911cc53b8f98b SHA1: f9476fe1a542181794edae43322c98be581dbd64 SHA256: e571732b03cc9edb048aa1252601957d84675d5e356c188a36cbd03e8731a53f SHA512: c8074cc905fdfc70c1a724a082210ec716aa7abb23b30751111d533d06063293f6b4bbd0fcd762798a973b0bfb5f3485e29e162a31e6c1bacfb4f833af6e75a0 Homepage: https://cran.r-project.org/package=metapack Description: CRAN Package 'metapack' (Bayesian Meta-Analysis and Network Meta-Analysis) Contains functions performing Bayesian inference for meta-analytic and network meta-analytic models through Markov chain Monte Carlo algorithm. Currently, the package implements Hui Yao, Sungduk Kim, Ming-Hui Chen, Joseph G. Ibrahim, Arvind K. Shah, and Jianxin Lin (2015) and Hao Li, Daeyoung Lim, Ming-Hui Chen, Joseph G. Ibrahim, Sungduk Kim, Arvind K. Shah, Jianxin Lin (2021) . For maximal computational efficiency, the Markov chain Monte Carlo samplers for each model, written in C++, are fine-tuned. This software has been developed under the auspices of the National Institutes of Health and Merck & Co., Inc., Kenilworth, NJ, USA. Package: r-cran-metarange Architecture: amd64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2127 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-terra, r-cran-r6, r-cran-checkmate, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-metarange_1.1.4-1.ca2404.1_amd64.deb Size: 742842 MD5sum: 02551c746fa6f33ad81356922973ce17 SHA1: 2264a9968d1ed502d55f9a00347708a40d4ee73e SHA256: 475ded8c8495427c589a2b4524b6258dba54abd4e620831be63e3a1e15481311 SHA512: a6f92a3f967b1186755d63c4674678e2cf0fdf18ffe43f4c40433e6b118700cda35f56a6cb851e377e0ac876910fb0936b4d71eed209517289359c1a660e4abd Homepage: https://cran.r-project.org/package=metaRange Description: CRAN Package 'metaRange' (Framework to Build Mechanistic and Metabolic Constrained SpeciesDistribution Models) Build spatially and temporally explicit process-based species distribution models, that can include an arbitrary number of environmental factors, species and processes including metabolic constraints and species interactions. The focus of the package is simulating populations of one or multiple species in a grid-based landscape and studying the meta-population dynamics and emergent patterns that arise from the interaction of species under complex environmental conditions. It provides functions for common ecological processes such as negative exponential, kernel-based dispersal (see Nathan et al. (2012) ), calculation of the environmental suitability based on cardinal values ( Yin et al. (1995) , simplified by Yan and Hunt (1999) see eq: 4), reproduction in form of an Ricker model (see Ricker (1954) and Cabral and Schurr (2010) ), as well as metabolic scaling based on the metabolic theory of ecology (see Brown et al. (2004) and Brown, Sibly and Kodric-Brown (2012) ). Package: r-cran-metarep Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-meta Suggests: r-cran-metafor, r-cran-lme4, r-cran-numderiv, r-cran-biasedurn, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metarep_1.2.1-1.ca2404.1_amd64.deb Size: 189038 MD5sum: e08baf2b2fba5167269120f6bb57cecc SHA1: 5d5b2b57c9b0d06b2e0c7a2dd8360309897f6ffe SHA256: c403e2733d87a33da17f4a869842fd208e769d52a6ef632298ce93a43c11998b SHA512: 863fa8e31f9392ddeee4f5433b4f5f47ec00e3012c32062be4cbf040742fc882063951a483bd3b0188bb99a5450e0f7c307d466e4e4ed2fe03933434ccd4c591 Homepage: https://cran.r-project.org/package=metarep Description: CRAN Package 'metarep' (Replicability-Analysis Tools for Meta-Analysis) User-friendly package for reporting replicability-analysis methods, affixed to meta-analyses summary. The replicability-analysis output provides an assessment of the investigated intervention, where it offers quantification of effect replicability and assessment of the consistency of findings. - Replicability-analysis for fixed-effects and random-effect meta analysis: - r(u)-value; - lower bounds on the number of studies with replicated positive and\or negative effect; - Allows detecting inconsistency of signals; - forest plots with the summary of replicability analysis results; - Allows Replicability-analysis with or without the common-effect assumption. Package: r-cran-metaskat Architecture: amd64 Version: 0.90-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-skat Filename: pool/dists/noble/main/r-cran-metaskat_0.90-1.ca2404.1_amd64.deb Size: 371112 MD5sum: e08f1768a36181fe3ead63523b8ce1ae SHA1: 15de9bde0695136abc747f4c487ec5d5c64d6c0f SHA256: 1492bebdec6387282371ba3e6277bcbbb7d0b66e0fb001608072253b7883b853 SHA512: 84ac1416d609f9047d19502bb2f9959c3fab18d9810ffecf95f72bbfeae166bca4da7df8b1c8eb2631b4330b71331df216d89f15cd329215972ba7ed2c84e329 Homepage: https://cran.r-project.org/package=MetaSKAT Description: CRAN Package 'MetaSKAT' (Meta Analysis for SNP-Set (Sequence) Kernel Association Test) Functions for Meta-analysis Burden Test, Sequence Kernel Association Test (SKAT) and Optimal SKAT (SKAT-O) by Lee et al. (2013) . These methods use summary-level score statistics to carry out gene-based meta-analysis for rare variants. Package: r-cran-metastan Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3945 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-loo, r-cran-forestplot, r-cran-metafor, r-cran-hdinterval, r-cran-coda, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-shinystan, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-metastan_1.0.0-1.ca2404.1_amd64.deb Size: 1119350 MD5sum: b0745a991db9b75f45505182df1ee230 SHA1: ac4cd481414ff9849d5d804c0be097065b91ce4e SHA256: c1a94e93d95a94051a906d3ff66a627c335f880bcdd850197408f23baf8abfd5 SHA512: c7d27342d8c5d60f05f990c56e15cc74bf104fdc69724f36d045fc8805792166d4c2ab6a0d41cda0bf15932b60c6e258906e802ec41ab5c7fd0a44afbb0629c2 Homepage: https://cran.r-project.org/package=MetaStan Description: CRAN Package 'MetaStan' (Bayesian Meta-Analysis via 'Stan') Performs Bayesian meta-analysis, meta-regression and model-based meta-analysis using 'Stan'. Includes binomial-normal hierarchical models and option to use weakly informative priors for the heterogeneity parameter and the treatment effect parameter which are described in Guenhan, Roever, and Friede (2020) . Package: r-cran-meteoland Architecture: amd64 Version: 2.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1526 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-stars, r-cran-rcpp, r-cran-units, r-cran-lifecycle, r-cran-cli, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-assertthat, r-cran-purrr, r-cran-ncdfgeom, r-cran-ncmeta, r-cran-lubridate, r-cran-cubelyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-meteospain, r-cran-worldmet, r-cran-tibble Filename: pool/dists/noble/main/r-cran-meteoland_2.2.7-1.ca2404.1_amd64.deb Size: 872292 MD5sum: 781df1df1f9741425bd45b435f0a2d4e SHA1: 863ac6225455379e7cc12d1cb3b724e4d0836fd0 SHA256: 5e5679cbe7126f5c4ae3fb3e28e7093de61c23ae6e047c95bf33b272bd9bb091 SHA512: 763b4d72f19fbf0597815c554ed45a26358186f3c682de7a138abb39b3c7d8dd6e91ed7cd9ef6f4208eb17a06bc773a9d0f8f7279051c4a6333de5696045dea2 Homepage: https://cran.r-project.org/package=meteoland Description: CRAN Package 'meteoland' (Landscape Meteorology Tools) Functions to estimate weather variables at any position of a landscape [De Caceres et al. (2018) ]. Package: r-cran-meteor Architecture: amd64 Version: 0.4-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2375 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-terra Filename: pool/dists/noble/main/r-cran-meteor_0.4-5-1.ca2404.1_amd64.deb Size: 799364 MD5sum: 4ee15b6ccea2dda7c02510e1e2963dd5 SHA1: d524557f29f64f8659dc5a886c6aea8f4232b123 SHA256: c77f82691766fc792d874890acfe357dd4960dc61fac1042d55522170b2d96d3 SHA512: 865a74450fa8b9deb4e20e0dc984a891caade01102c2338ecc4e401301c0de479475815150bda643fbd02c09d2b9cbea027f24ef34b49b4fa4f3f3cc59eebb5a Homepage: https://cran.r-project.org/package=meteor Description: CRAN Package 'meteor' (Meteorological Data Manipulation) A set of functions for weather and climate data manipulation, and other helper functions, to support dynamic ecological modeling, particularly crop and crop disease modeling. Package: r-cran-meteorits Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4853 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-meteorits_0.1.1-1.ca2404.1_amd64.deb Size: 3754688 MD5sum: 28e0f73b65d8c6339e86f4bba54a8ad3 SHA1: 386a2fc788973f2a73e8a46ed19f0a5a0034a070 SHA256: 778db5823cff059139a7359341c89699670e22093485905a47f9f5ccb5ec34ec SHA512: c61a5ef0d94acdb2b76dfb241265728f29434bf086ac5330871c25e1e4b975186ec8039cc796c10d1b6e080369d0fb0d08504c3d3ff57991bd9b71568c50f972 Homepage: https://cran.r-project.org/package=meteorits Description: CRAN Package 'meteorits' (Mixture-of-Experts Modeling for Complex Non-Normal Distributions) Provides a unified mixture-of-experts (ME) modeling and estimation framework with several original and flexible ME models to model, cluster and classify heterogeneous data in many complex situations where the data are distributed according to non-normal, possibly skewed distributions, and when they might be corrupted by atypical observations. Mixtures-of-Experts models for complex and non-normal distributions ('meteorits') are originally introduced and written in 'Matlab' by Faicel Chamroukhi. The references are mainly the following ones. The references are mainly the following ones. Chamroukhi F., Same A., Govaert, G. and Aknin P. (2009) . Chamroukhi F. (2010) . Chamroukhi F. (2015) . Chamroukhi F. (2015) . Chamroukhi F. (2016) . Chamroukhi F. (2016) . Chamroukhi F. (2017) . Package: r-cran-methfuse Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4393 Depends: libc6 (>= 2.29), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-bioc-bsseq, r-bioc-methrix, r-bioc-beachmat, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-delayedarray, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-methfuse_1.1.0-1.ca2404.1_amd64.deb Size: 4280316 MD5sum: 107e0b97fd488dea95551b54b82df7d5 SHA1: 4d74e9285dc65933fc8a51b574d8afe892ee6a09 SHA256: 31a11d3ea787471eaff5caca91b267b809f31447ed9a4088228ecca7efe8af72 SHA512: 22dd1a32132b34d447c5580fedb74e38678492aee6c1e390cd8eca315803776ebd2d498750de0db6c06ca11de86ccfe00f253e0158715f5c9acbb94e122562e9 Homepage: https://cran.r-project.org/package=methFuse Description: CRAN Package 'methFuse' (Functional Segmentation of the Methylome) Implements FUSE (Functional Segmentation of DNA methylation data), a data-driven method for identifying spatially coherent DNA methylation segments from whole-genome bisulfite sequencing (WGBS) count data. The method performs hierarchical clustering of CpG sites based on methylated and unmethylated read counts across multiple samples and determines the optimal number of segments using an information criterion (AIC or BIC). Resulting segments represent regions with homogeneous methylation profiles across the input cohort while allowing sample-specific methylation levels. The package provides functions for clustering, model selection, tree cutting, segment-level summarization, and visualization. Input can be supplied as count matrices or extracted directly from 'BSseq' and 'methrix' objects. Package: r-cran-methscope Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7111 Depends: libc6 (>= 2.38), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-xgboost, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-caret, r-cran-doparallel, r-cran-ggplot2, r-cran-uwot, r-cran-magrittr, r-cran-fnn, r-cran-data.table, r-cran-nnls Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-methscope_1.0.1-1.ca2404.1_amd64.deb Size: 7125096 MD5sum: 6df27f6dc66db3f17f741f4c58ef6ef2 SHA1: b17b0c6eeb07d15361fbf9350926041abc4d566f SHA256: 238c3344c069a074d433465f0c7ed74273a252d35d80d28802b5e60a6b9ab815 SHA512: 4f3fb29cccde085acfac549dae3f1f933f3c904895211ae34771e5df295a81f4e9e04fd34840adab70551716c0fc725909628b4cee9bb5e5bb7cfe8f9c4e8045 Homepage: https://cran.r-project.org/package=MethScope Description: CRAN Package 'MethScope' (Ultra-Fast Analysis of Sparse DNA Methylome via RecurrentPattern Encoding) Methods for analyzing DNA methylation data via Most Recurrent Methylation Patterns (MRMPs). Supports cell-type annotation, spatial deconvolution, unsupervised clustering, and cancer cell-of-origin inference. Includes C-backed summaries for YAME “.cg/.cm” files (overlap counts, log2 odds ratios, beta/depth aggregation), an XGBoost classifier, NNLS deconvolution, and plotting utilities. Scales to large spatial and single-cell methylomes and is robust to extreme sparsity. Package: r-cran-metricgraph Architecture: amd64 Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2520 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rann, r-cran-ggplot2, r-cran-igraph, r-cran-sf, r-cran-rspde, r-cran-matrix, r-cran-rcpp, r-cran-r6, r-cran-lifecycle, r-cran-sp, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-broom, r-cran-zoo, r-cran-ggnewscale, r-cran-rlang, r-cran-foreach, r-cran-doparallel, r-cran-spatstat.geom, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-testthat, r-cran-inlabru, r-cran-osmdata, r-cran-sn, r-cran-plotly, r-cran-optimparallel, r-cran-numderiv, r-cran-ssn2, r-cran-cowplot, r-cran-leaflet, r-cran-mapview, r-cran-viridis, r-cran-fmesher, r-cran-data.table, r-cran-spatstat.data Filename: pool/dists/noble/main/r-cran-metricgraph_1.6.0-1.ca2404.1_amd64.deb Size: 2014850 MD5sum: e8e167820a201889f4a7d1007e68f383 SHA1: 8cd32724a7d365d4e4bfb126d065ca5d17fea510 SHA256: f20b9f69d17cb8aeb58aa1781aa5f06f8340addb874053c29072f3af1be3157d SHA512: 5d4533659474ec768c72f433af169931ca9c9870bcff214edbbd47ba1613701b4c4b051ad9b93e5c5b9e4199acc9e1ea73e573d3c3e1a612ac11b7b5f3cbaf1b Homepage: https://cran.r-project.org/package=MetricGraph Description: CRAN Package 'MetricGraph' (Random Fields on Metric Graphs) Facilitates creation and manipulation of metric graphs, such as street or river networks. Further facilitates operations and visualizations of data on metric graphs, and the creation of a large class of random fields and stochastic partial differential equations on such spaces. These random fields can be used for simulation, prediction and inference. In particular, linear mixed effects models including random field components can be fitted to data based on computationally efficient sparse matrix representations. Interfaces to the R packages 'INLA' and 'inlabru' are also provided, which facilitate working with Bayesian statistical models on metric graphs. The main references for the methods are Bolin, Simas and Wallin (2024) , Bolin, Kovacs, Kumar and Simas (2023) and Bolin, Simas and Wallin (2023) and . Package: r-cran-mets Architecture: amd64 Version: 1.3.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7716 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-lava, r-cran-mvtnorm, r-cran-numderiv, r-cran-survival, r-cran-timereg Suggests: r-cran-cmprsk, r-cran-icenreg, r-cran-kernsmooth, r-cran-knitr, r-cran-optimx, r-cran-prodlim, r-cran-riskregression, r-cran-rmarkdown, r-cran-tinytest, r-cran-ucminf Filename: pool/dists/noble/main/r-cran-mets_1.3.10-1.ca2404.1_amd64.deb Size: 4420798 MD5sum: c42e6c7713b4dac876310e836652d37f SHA1: f6b517f3ce4e02b295f6d584ce0c55c182918252 SHA256: 299a8ea1832c58de5ef9388bfd1f9a201d39501d427b31127e502cf52b65589b SHA512: 4fffa0dd5418240d5e1990d993c674c4f53089829ed63ab8f1b8b5d7c9b7c1f793d09e634c46e1818c45789389130f49f23e0e9984c7aa8c617b341901655b46 Homepage: https://cran.r-project.org/package=mets Description: CRAN Package 'mets' (Analysis of Multivariate Event Times) Implementation of various statistical models for multivariate event history data . Including multivariate cumulative incidence models , and bivariate random effects probit models (Liability models) . Modern methods for survival analysis, including regression modelling (Cox, Fine-Gray, Ghosh-Lin, Binomial regression) with fast computation of influence functions. 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Exact simulation from max-stable processes (Dombry, Engelke and Oesting, 2016, , R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, ) and Extremal Student (Thibaud and Opitz, 2015, ). Threshold selection methods, including Wadsworth (2016) , and Northrop and Coleman (2014) . Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) . Package: r-cran-mewavg Architecture: amd64 Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mewavg_0.3.1-1.ca2404.1_amd64.deb Size: 149390 MD5sum: e4470358bdb2fcd00e1298de1de215f4 SHA1: 01b95e403fe36c179d78d0af4893cf50495f439a SHA256: 234ed6eee49333f5e0ccb57df73f65c8ef1b958df7d7076ea35d85e2f19b1366 SHA512: 5cef47f3691603ae6e02fdaaaf55238bc0480cb3e37817d1b64d89a690ffba980319e5b4fc46880b1b09adcc152f2e181247ec42592f914aba8920670d079487 Homepage: https://cran.r-project.org/package=mewAvg Description: CRAN Package 'mewAvg' (A Fixed Memeory Moving Expanding Window Average) Compute the average of a sequence of random vectors in a moving expanding window using a fixed amount of memory. 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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-mfp2 Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1504 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-survival Suggests: r-cran-knitr, r-cran-testthat, r-cran-xfun, r-cran-rmarkdown, r-cran-formatr, r-cran-patchwork, r-cran-spelling Filename: pool/dists/noble/main/r-cran-mfp2_1.0.0-1.ca2404.1_amd64.deb Size: 779510 MD5sum: 13e39836238a67ff1b667402a52f9207 SHA1: 5412f32e40221fc1885c3cd25d7e9b3d0dd96096 SHA256: 830fb8b1a03c582ba9a31512db58dc0c3871b6f1de0cc91924142ead7ffb57ec SHA512: 3398318197527a59449d29c873671f8f4abdfcd1b0810ad59f3ffbfc30947cce01a73576e06cdad6d3a8bb608f0faa53b96de6bfe4cf3095f144fbf467d53df5 Homepage: https://cran.r-project.org/package=mfp2 Description: CRAN Package 'mfp2' (Multivariable Fractional Polynomial Models with Extensions) Multivariable fractional polynomial algorithm simultaneously selects variables and functional forms in both generalized linear models and Cox proportional hazard models. Key references for this algorithm are Royston and Altman (1994) and Sauerbrei and Royston (2008, ISBN:978-0-470-02842-1). In addition, it can model a 'sigmoid' relationship between variable x and an outcome variable y using the approximate cumulative distribution transformation proposed by Royston (2014) . This feature distinguishes it from a standard fractional polynomial function, which lacks the ability to achieve such modeling. Package: r-cran-mfpca Architecture: amd64 Version: 1.3-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: libfftw3-double3 (>= 3.3.10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fundata, r-cran-abind, r-cran-foreach, r-cran-irlba, r-cran-matrix, r-cran-mgcv, r-cran-plyr Suggests: r-cran-covr, r-cran-fda, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mfpca_1.3-11-1.ca2404.1_amd64.deb Size: 256058 MD5sum: 6a3efb6cb87a8acf080988997d2c098e SHA1: e24696dc5b54a0039ccfb0df65c32ca1141e21ca SHA256: da27ac2018e4ad53ee896fbcb9dcd916511ec94073accb9c8e3136e2c9016624 SHA512: c1976008133badd9f2cd34967abb0e2db3e1ae5abb2151c4c5a2c8e712d03067d49d19b11f1f2deaa6dbf8bdda113434ab8659677ab7c44caa3eb155e8879d9e Homepage: https://cran.r-project.org/package=MFPCA Description: CRAN Package 'MFPCA' (Multivariate Functional Principal Component Analysis for DataObserved on Different Dimensional Domains) Calculate a multivariate functional principal component analysis for data observed on different dimensional domains. The estimation algorithm relies on univariate basis expansions for each element of the multivariate functional data (Happ & Greven, 2018) . Multivariate and univariate functional data objects are represented by S4 classes for this type of data implemented in the package 'funData'. For more details on the general concepts of both packages and a case study, see Happ-Kurz (2020) . Package: r-cran-mfrmr Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5768 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-purrr, r-cran-stringr, r-cran-psych, r-cran-lifecycle, r-cran-rlang, r-cran-cpp11 Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-igraph, r-cran-lme4, r-cran-digest, r-cran-kableextra, r-cran-flextable, r-cran-future.apply, r-cran-mirt, r-cran-tam, r-cran-erm Filename: pool/dists/noble/main/r-cran-mfrmr_0.2.0-1.ca2404.1_amd64.deb Size: 5189848 MD5sum: e31e4d3d2049bc8a388437a36766a758 SHA1: 8194510dfbbe07d86cae4178d59db90acf7fa4c7 SHA256: baa8bbf9b40ac388a73e05b4a518cd6993d924f3bb9a5bb95d076c6cbf8bcefa SHA512: debd9d391e0931fd3ba40ab91995443ec6b9ecbe2602c79dd4f697c33e4c9af72320809a691e65726ff1ca1ec4862bcd82a2a0f1e14a30d2e7a4623253b2fa3a Homepage: https://cran.r-project.org/package=mfrmr Description: CRAN Package 'mfrmr' (Estimation and Diagnostics for Many-Facet Measurement Models) Native R implementation of many-facet ordered-response measurement models with arbitrary facet counts, rating-scale and partial-credit parameterizations, a bounded generalized partial-credit extension, and both marginal and joint maximum likelihood estimation. The package provides a fit / diagnose / report pipeline covering anchoring, linking, bias and differential-functioning screening, and publication-oriented reporting summaries, with reproducibility manifests for replay. See 'Andrich' (1978) , 'Masters' (1982) , and 'Muraki' (1992) for the underlying ordered-response models. Package: r-cran-mfsd Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2418 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mfsd_0.1.1-1.ca2404.1_amd64.deb Size: 2349880 MD5sum: b82c5282f78d6129f7ae194a95636fae SHA1: b8d496d72037931f21290d6937dbe03715624876 SHA256: 480b0eaac8000bd8666d8b42a8d727e9eaa79337f8df58f3e7d7696d1cecdb9f SHA512: e5cac1338025fdf607e26d6dee1c9509f407b3b8ca232c2c303d1bcdcc8a3eedd6edcedc17c56ae23382e196120084226a91c5262d635f169a8feffe56b1ac93 Homepage: https://cran.r-project.org/package=MFSD Description: CRAN Package 'MFSD' (Multivariate Functional Spatial Data) Analysis of multivariate functional spatial data, including spectral multivariate functional principal component analysis and related statistical procedures (Si-Ahmed, Idris, et al. "Principal component analysis of multivariate spatial functional data." Big Data Research 39 (2025) 100504). (Kuenzer, T., Hörmann, S., & Kokoszka, P. (2021). "Principal component analysis of spatially indexed functions." Journal of the American Statistical Association, 116(535), 1444-1456.) (Happ, C., & Greven, S. (2018). "Multivariate functional principal component analysis for data observed on different (dimensional) domains." Journal of the American Statistical Association, 113(522), 649-659.) Package: r-cran-mgarchbekk Architecture: amd64 Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: libc6 (>= 2.3.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tseries, r-cran-mvtnorm Suggests: r-cran-testthat, r-cran-devtools, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-mgarchbekk_0.0.5-1.ca2404.1_amd64.deb Size: 78174 MD5sum: 8b70da8fed98d7198bd64b6e3f8ca703 SHA1: c3ad0a1fac840802151ed294c6d98369ade0b0af SHA256: 4bd7d7be2735a816a69a7124d1fcc2ae65f99ef47fcfc532a82d331537894a29 SHA512: 9f7617b4ba50372343f778be31ca1d9a28d6207942bc37bf7756283ece3b3a4e53f3542f38d10186e96580acbe7dfcfc5601b8146e32f72fd2230d44886f5e46 Homepage: https://cran.r-project.org/package=mgarchBEKK Description: CRAN Package 'mgarchBEKK' (Simulating, Estimating and Diagnosing MGARCH (BEKK and mGJR)Processes) Procedures to simulate, estimate and diagnose MGARCH processes of BEKK and multivariate GJR (bivariate asymmetric GARCH model) specification. Package: r-cran-mgc Architecture: amd64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1104 Depends: r-base-core (>= 4.4.0), 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/noble/main/r-cran-mgc_2.0.2-1.ca2404.1_amd64.deb Size: 782070 MD5sum: ff1e8b0ce832d02e1b12b4329fb9dea8 SHA1: 9e7ff9d22916e1e3394b521d32af670b01c2e510 SHA256: 93d4016562a5b4b0e0d476372f61bde49021cc24b7719b824c08e7916d3b46f9 SHA512: 019b21e7944864542651f3f4a71eeeb7999e9d166211a475b95688665be6efac1383b966c8ba46f0de8813f95f7a78c4822329ae91156c6c980992aa9bcab621 Homepage: https://cran.r-project.org/package=mgc Description: CRAN Package 'mgc' (Multiscale Graph Correlation) Multiscale Graph Correlation (MGC) is a framework developed by Vogelstein et al. 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Package: r-cran-mgcv Architecture: amd64 Version: 1.9-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4053 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/noble/main/r-cran-mgcv_1.9-4-1.ca2404.1_amd64.deb Size: 3560338 MD5sum: cd75285a0b1e99df1a7c4a4e2ef8f73f SHA1: 51427ed04f55b2c0dfd6a8c92ef79770c7a59a96 SHA256: 4f9586411e13fa8731f52f466c347e55d617d18f1a0dd8853aeb194b6b4740fe SHA512: c7eeb0e48acf05b50bb7466cd0f6f554104a36a167b9ccd0141ab9c69e96dd706ebaffb54e43a89fe77d1bc81c6373c426d3ad40512bb191c1b53f94831af8a4 Homepage: https://cran.r-project.org/package=mgcv Description: CRAN Package 'mgcv' (Mixed GAM Computation Vehicle with Automatic SmoothnessEstimation) Generalized additive (mixed) models, some of their extensions and other generalized ridge regression with multiple smoothing parameter estimation by (Restricted) Marginal Likelihood, Cross Validation and similar, or using iterated nested Laplace approximation for fully Bayesian inference. See Wood (2025) for an overview. Includes a gam() function, a wide variety of smoothers, 'JAGS' support and distributions beyond the exponential family. Package: r-cran-mgdrive Architecture: amd64 Version: 1.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1907 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rcpp, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-mgdrive_1.6.2-1.ca2404.1_amd64.deb Size: 1199960 MD5sum: 7349c5252629076965d98dea971ca64a SHA1: 8ad78b2cb5cba8fa88aabedc33f6985b7c6088c2 SHA256: ae85b652376dbb0118ae7fe33f6d8e40647136b490888f6aee389223440655ec SHA512: 15c7118d7a47afb3ba6102b788ed58161cf62aafd260e2728ba89ad1b43c8869f06ffaa76c4a8926c23753ec497ffca7ec5fc0870507c311258251cde8334a1e Homepage: https://cran.r-project.org/package=MGDrivE Description: CRAN Package 'MGDrivE' (Mosquito Gene Drive Explorer) Provides a model designed to be a reliable testbed where various gene drive interventions for mosquito-borne diseases control. It is being developed to accommodate the use of various mosquito-specific gene drive systems within a population dynamics framework that allows migration of individuals between patches in landscape. Previous work developing the population dynamics can be found in Deredec et al. (2001) and Hancock & Godfray (2007) , and extensions to accommodate CRISPR homing dynamics in Marshall et al. (2017) . Package: r-cran-mgee2 Architecture: amd64 Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-ggplot2 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-mgee2_0.6-1.ca2404.1_amd64.deb Size: 212018 MD5sum: 6aa6a2df77de712df2a8cf5577fb5b30 SHA1: 3ff5e5dbf65287e09eb3e29233ad6979e123e7e3 SHA256: d38a86791e431b705de93e39c9b4b23f2fd4c80a2dff24a0160d57b6902c6d6c SHA512: 119691d2a96e5e34d5dbd61b4dc4a47e8545bc0e618b5bca9c0b33f2dd842ca43c4f524eac4c6d5291631cb5139b773348ddc8e5a5ee787be117c944e9005ce5 Homepage: https://cran.r-project.org/package=mgee2 Description: CRAN Package 'mgee2' (Marginal Analysis of Misclassified Longitudinal Ordinal Data) Three estimating equation methods are provided in this package for marginal analysis of longitudinal ordinal data with misclassified responses and covariates. The naive analysis which is solely based on the observed data without adjustment may lead to bias. The corrected generalized estimating equations (GEE2) method which is unbiased requires the misclassification parameters to be known beforehand. The corrected generalized estimating equations (GEE2) with validation subsample method estimates the misclassification parameters based on a given validation set. This package is an implementation of Chen (2013) . Package: r-cran-mgl Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mgl_1.1-1.ca2404.1_amd64.deb Size: 17178 MD5sum: 7608161e7428fe9c8319a513335b8539 SHA1: c76153e487d3f7617edfd4d10c1e51ef33f0ef7f SHA256: 184e48677096411d7ff2bb3f01f97456d58478c5c6720566fb12a7e5ee63ac64 SHA512: ca40eb8e02210ee42dbe81bdc2a96372fbb8f8f86f361d3eb0a469687ab5bd51ba8e1000bd065995d59c530f3f2c761d8bcffbe2d94c80723f6ac5a159ae6492 Homepage: https://cran.r-project.org/package=MGL Description: CRAN Package 'MGL' (Module Graphical Lasso) An aggressive dimensionality reduction and network estimation technique for a high-dimensional Gaussian graphical model (GGM). Please refer to: Efficient Dimensionality Reduction for High-Dimensional Network Estimation, Safiye Celik, Benjamin A. Logsdon, Su-In Lee, Proceedings of The 31st International Conference on Machine Learning, 2014, p. 1953--1961. Package: r-cran-mgmm Architecture: amd64 Version: 1.0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 844 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-glue, r-cran-mvnfast, r-cran-plyr, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/noble/main/r-cran-mgmm_1.0.1.3-1.ca2404.1_amd64.deb Size: 639062 MD5sum: 4ca9e6c46411c7a302d46ab6c53875c0 SHA1: 7ede4fa4eca63aba9639ed40abfc5a6e1ecf9f42 SHA256: 6c3089ff2fb0e3c584ea10618e1c1a7b6a46ecc4985a58dd3eca3ff9e10626a9 SHA512: 3c96e849aa1ab9128ce27335d19ec64609b46bc85c8437516134d2003f4276b4a4d1c71800e43eef6dff51efef6ff51333908a17f6b630bfd6aa02a229989d2f Homepage: https://cran.r-project.org/package=MGMM Description: CRAN Package 'MGMM' (Missingness-Aware Gaussian Mixture Models) Parameter estimation and classification for Gaussian Mixture Models (GMMs) in the presence of missing data. This package complements existing implementations by allowing for both missing elements in the input vectors and full (as opposed to strictly diagonal) covariance matrices. Estimation is performed using an expectation conditional maximization algorithm that accounts for missingness of both the cluster assignments and the vector components. The output includes the marginal cluster membership probabilities; the mean and covariance of each cluster; the posterior probabilities of cluster membership; and a completed version of the input data, with missing values imputed to their posterior expectations. For additional details, please see McCaw ZR, Julienne H, Aschard H. "Fitting Gaussian mixture models on incomplete data." . Package: r-cran-mgsda Architecture: amd64 Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-mgsda_1.6.1-1.ca2404.1_amd64.deb Size: 40740 MD5sum: 566e44fe946607aec3452fa0c70ff866 SHA1: 17c0eafe2b14a8222809c124dc3bcac78d4f6a44 SHA256: 506f59d8aeda050716cde5dc383b64601b8dfe2791a235f477d933aefaa2676e SHA512: 10d6e183b074a8b58187b6ab3fc505779d5b67aa34a38bd93b4824e6a67d85b697b038e128a380fe13f6d07cb38e196d8d404222107b1a0ef26ff257822b02a5 Homepage: https://cran.r-project.org/package=MGSDA Description: CRAN Package 'MGSDA' (Multi-Group Sparse Discriminant Analysis) Implements Multi-Group Sparse Discriminant Analysis proposal of I.Gaynanova, J.Booth and M.Wells (2016), Simultaneous sparse estimation of canonical vectors in the p>>N setting, JASA . Package: r-cran-mgsfpca Architecture: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2145 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-fda, r-cran-pracma, r-cran-rcpp, r-cran-metrics, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-mgsfpca_0.2.2-1.ca2404.1_amd64.deb Size: 1865558 MD5sum: 20390922c38993999ad58d47f95afe9c SHA1: ce33d5c93690f4f10f54c77ad336d86a88f4a75c SHA256: 14b7d4749a6194e54f934f01b4178d0a1a70c8f078892089d090900bcfc114ca SHA512: 32e6a632e10e11cd3e0a1a495c156d86aa92666d3782875891e3941adbbaf4d641c205352ab4d87a3fab74a23c1d1aa655132a4d5d9b380d543a24515d7bcb16 Homepage: https://cran.r-project.org/package=mGSFPCA Description: CRAN Package 'mGSFPCA' (Estimate Functional Principal Components from Sparse Data) Implements functional principal component analysis (FPCA) for univariate and multivariate sparse functional data. The package estimates eigenfunctions, eigenvalues, and error variance simultaneously via maximum likelihood estimation (MLE), using a spline basis representation of the eigenfunctions. Orthonormality of the estimated eigenfunctions is enforced through a modified Gram-Schmidt (MGS) orthogonalization procedure applied iteratively during estimation, avoiding direct optimization over the Stiefel manifold and improving numerical stability. The optimal number of basis functions and principal components is selected via an Akaike Information Criterion (AIC)-type criterion, supporting both a full grid-search strategy and a computationally efficient sequential selection approach. Principal component scores are estimated by conditional expectation, enabling reconstruction of individual trajectories over the entire domain from sparse observations. Pointwise confidence intervals for reconstructed trajectories are also provided. Methods are described in Mbaka, Cao and Carey (2026) and Mbaka and Carey (2026) . Package: r-cran-mgss Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-combinat, r-cran-statmod, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mgss_1.2-1.ca2404.1_amd64.deb Size: 122006 MD5sum: 797df584e07931f55b94e0eabdeb2e6c SHA1: 4cfece55755a79a28f14c929dd9c0fa214014018 SHA256: 9e0b70e7928d649d74347b1f18215b6aba74513a84eb96fdb1a4b8b704886900 SHA512: 91401bd037dd4fe3ca1b8e7ca1f999afacf299d600badd228dc452d1ebb3dcfc4a4835cdc72ac9fc4574304b7be541fae2db37189b950e7d0a422738091001ed Homepage: https://cran.r-project.org/package=mgss Description: CRAN Package 'mgss' (A Matrix-Free Multigrid Preconditioner for Spline Smoothing) Data smoothing with penalized splines is a popular method and is well established for one- or two-dimensional covariates. The extension to multiple covariates is straightforward but suffers from exponentially increasing memory requirements and computational complexity. This toolbox provides a matrix-free implementation of a conjugate gradient (CG) method for the regularized least squares problem resulting from tensor product B-spline smoothing with multivariate and scattered data. It further provides matrix-free preconditioned versions of the CG-algorithm where the user can choose between a simpler diagonal preconditioner and an advanced geometric multigrid preconditioner. The main advantage is that all algorithms are performed matrix-free and therefore require only a small amount of memory. For further detail see Siebenborn & Wagner (2021). Package: r-cran-mgsub Architecture: amd64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mgsub_2.0.0-1.ca2404.1_amd64.deb Size: 63790 MD5sum: 385338d05ecd3823ccbd8751886400b0 SHA1: bda99ab6a79c044ee5c0f4403f561bd224c6d34f SHA256: d538a9a41b7bea65f3c436e0d45338efd6331b31727ed6a0654c46d9baf299ad SHA512: e9b48d18284de4e61030de6d42265082e4a0728b54ce3b347fe8ddc9173d1575454c91b8c2261c807efb6865e97be6da36bef22caef972ac87b2ab2fe98b2ba6 Homepage: https://cran.r-project.org/package=mgsub Description: CRAN Package 'mgsub' (Safe, Multiple, Simultaneous String Substitution) Designed to enable simultaneous substitution in strings in a safe fashion. Safe means it does not rely on placeholders (which can cause errors in same length matches). 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Implements Multiscale Geographically Weighted Regression with Top-Down Scale approaches (Geniaux 2026) . Package: r-cran-mhazard Architecture: amd64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-boot, r-cran-plot3d, r-cran-survival, r-cran-rootsolve, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-mhazard_0.2.3-1.ca2404.1_amd64.deb Size: 186980 MD5sum: 2249811203b1cd2b063cb30f0bbc8d40 SHA1: 30672106cab1cd7fbbef28ab34e08db4bb9623a1 SHA256: 4a3799a2f4cfd8dc4413ce9e77c0ae9d6b57c48338437115825dffcb876d5491 SHA512: ef790d86c95815902495b15a368d560d5ecc410e9c24d68f30948e9534ee615233dfa85d55b8acfa701be7d7c56b05a311f95f4e6afbcc1bbe6b74a2d086250a Homepage: https://cran.r-project.org/package=mhazard Description: CRAN Package 'mhazard' (Nonparametric and Semiparametric Methods for MultivariateFailure Time Data) Nonparametric survival function estimates and semiparametric regression for the multivariate failure time data with right-censoring. For nonparametric survival function estimates, the Volterra, Dabrowska, and Prentice-Cai estimates for bivariate failure time data may be computed as well as the Dabrowska estimate for the trivariate failure time data. Bivariate marginal hazard rate regression can be fitted for the bivariate failure time data. Functions are also provided to compute (bootstrap) confidence intervals and plot the estimates of the bivariate survival function. For details, see "The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach", Prentice, R., Zhao, S. (2019, ISBN: 978-1-4822-5657-4), CRC Press. Package: r-cran-mhd Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-manifold, r-cran-nloptr, r-cran-distory, r-cran-plyr Suggests: r-cran-foreach Filename: pool/dists/noble/main/r-cran-mhd_0.1.3-1.ca2404.1_amd64.deb Size: 155356 MD5sum: 6077b40f0ab83dcb6c849e4bd682ebbe SHA1: ce2b4779e2c92ea545a4d7278c100884794242ef SHA256: b3c56f52e52cac3355021ee3b78713ac991044f5031bac4386e1769d293e15e2 SHA512: dbfbf1ceb4dc73aedd224d52054fbc8c5d0393a643c841a9dead08bd1047c795e486220a2a6d1990ea151f3d4a2bb2855d9603f59ca406dda475a3c1304ca346 Homepage: https://cran.r-project.org/package=MHD Description: CRAN Package 'MHD' (Metric Halfspace Depth) Metric halfspace depth for object data, generalizing Tukey's depth for Euclidean data. Implementing the method described in Dai and Lopez-Pintado (2022) . Package: r-cran-mhmmbayes Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1184 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-mvtnorm, r-cran-rdpack, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-alluvial, r-cran-rcolorbrewer, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mhmmbayes_1.1.1-1.ca2404.1_amd64.deb Size: 704588 MD5sum: dfdd2e0a15ca883a7e67bd7289c04c0b SHA1: b972196de8439c7da1264155eafde9d1e2552a04 SHA256: 9b4b8ce65479fe9d31f70fbb61f0b0fa90fbe558dcdf0c4b7608fd88b8319b59 SHA512: 5b88195a9373daee90011ed1c40da474cb424917be23d8cfc50f9fb03f48d7acc82389818dcb748a7a1bdd5a3fcfb59efae4624aedba70fed1b6d28852ccaf60 Homepage: https://cran.r-project.org/package=mHMMbayes Description: CRAN Package 'mHMMbayes' (Multilevel Hidden Markov Models Using Bayesian Estimation) An implementation of the multilevel (also known as mixed or random effects) hidden Markov model using Bayesian estimation in R. The multilevel hidden Markov model (HMM) is a generalization of the well-known hidden Markov model, for the latter see Rabiner (1989) . The multilevel HMM is tailored to accommodate (intense) longitudinal data of multiple individuals simultaneously, see e.g., de Haan-Rietdijk et al. . Using a multilevel framework, we allow for heterogeneity in the model parameters (transition probability matrix and conditional distribution), while estimating one overall HMM. The model can be fitted on multivariate data with either a categorical, normal, or Poisson distribution, and include individual level covariates (allowing for e.g., group comparisons on model parameters). Parameters are estimated using Bayesian estimation utilizing the forward-backward recursion within a hybrid Metropolis within Gibbs sampler. Missing data (NA) in the dependent variables is accommodated assuming MAR. The package also includes various visualization options, a function to simulate data, and a function to obtain the most likely hidden state sequence for each individual using the Viterbi algorithm. Package: r-cran-mhorseshoe Architecture: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mhorseshoe_0.1.5-1.ca2404.1_amd64.deb Size: 122418 MD5sum: 2989c10c8a5cc89cb3b91e6ccc73f986 SHA1: e5ebbfeea134a50167507040b45108e8c2e6ec0f SHA256: bb785b763e830811c53add4cc6030366bbe5568fdbeb74974aea6ca8f89971bb SHA512: dcee163fadaa7f6db8fafa23f7dcb23db2ffd0f67a362246f228806030a7b5349b92168d2834ce35c7eb80a6491bbe589e174be798dca8b6be5095781c942d49 Homepage: https://cran.r-project.org/package=Mhorseshoe Description: CRAN Package 'Mhorseshoe' (Approximate Algorithm for Horseshoe Prior) Provides exact and approximate algorithms for the horseshoe prior in linear regression models, which were proposed by Johndrow et al. (2020) . Package: r-cran-mhpfilter Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 935 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-data.table, r-cran-collapse, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse, r-cran-fastverse Filename: pool/dists/noble/main/r-cran-mhpfilter_0.1.0-1.ca2404.1_amd64.deb Size: 531336 MD5sum: 5fad7c38ca408f252164e731094912e5 SHA1: 9a1d3e211340381d7b197a2c622d902af82474ff SHA256: ea215de8b0f77c4cefb340aa847fb537a5954f84a991ab565adc68b2eda2c93c SHA512: f3b3bba6a253a897c78456a698d9f066a485b24016a13e34e6d4288fb5f1b04db92541907a35a1144c3bc19a1176a8c83dd4834302e7924738c108520eb1079c Homepage: https://cran.r-project.org/package=mhpfilter Description: CRAN Package 'mhpfilter' (Modified Hodrick-Prescott Filter with Optimal SmoothingParameter Selection) High-performance implementation of the Modified Hodrick-Prescott (HP) Filter for decomposing macroeconomic time series into trend and cyclical components. Based on the methodology of Choudhary, Hanif and Iqbal (2014) "On smoothing macroeconomic time series using the modified HP filter", which uses generalized cross-validation (GCV) to automatically select the optimal smoothing parameter lambda, following McDermott (1997) "An automatic method for choosing the smoothing parameter in the HP filter" (as described in Coe and McDermott (1997) ). Unlike the standard HP filter that uses fixed lambda values (1600 for quarterly, 100 for annual data), this package estimates series-specific lambda values that minimize the GCV criterion. Implements efficient C++ routines via 'RcppArmadillo' for fast computation, supports batch processing of multiple series, and provides comprehensive visualization tools using 'ggplot2'. Particularly useful for cross-country macroeconomic comparisons, business cycle analysis, and when the appropriate smoothing parameter is uncertain. Package: r-cran-mhsmm Architecture: amd64 Version: 0.4.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-mhsmm_0.4.21-1.ca2404.1_amd64.deb Size: 479984 MD5sum: 5e58b513452d4cf7d9d1dbc7acb06d30 SHA1: dc112fbe1f497a0809987d8d3f50ab53a2465db3 SHA256: e45cb7b3d2241ba7fd66489d7daa67232313154c60f44afb8f5e7e615afbf858 SHA512: 07cb12a123bc356770ac61f4709cffeac8dde1177c0b8d86f3cb207f67476bb0aabe169738d6d7ceefc6c2d067ad3ac9af74c33f1181068fdac28bc915a65d44 Homepage: https://cran.r-project.org/package=mhsmm Description: CRAN Package 'mhsmm' (Inference for Hidden Markov and Semi-Markov Models) Parameter estimation and prediction for hidden Markov and semi-Markov models for data with multiple observation sequences. Suitable for equidistant time series data, with multivariate and/or missing data. Allows user defined emission distributions. Package: r-cran-mhtmult Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mhtdiscrete, r-cran-fixseqmtp Filename: pool/dists/noble/main/r-cran-mhtmult_0.1.0-1.ca2404.1_amd64.deb Size: 52178 MD5sum: fcb073786fde4b4577da491f997f25a7 SHA1: 774cfabb94f17bc3bd81a69b72f451e8e4c080ed SHA256: 414ab9ca610371e3d24e0e7fd995ae9fd627fa426b10b37071d3a10eb5d92fed SHA512: 5fa3a0b535beebb33e246ebdd8410eb380dcfe3a5a1ba707f905f17b3c05f5806f93ad1e14dfae0e684ff01cbb6e64d2aff1d4f6ac44687691de921a0427d801 Homepage: https://cran.r-project.org/package=MHTmult Description: CRAN Package 'MHTmult' (Multiple Hypotheses Testing for Multiple Families/GroupsStructure) A Comprehensive tool for almost all existing multiple testing methods for multiple families. The package summarizes the existing methods for multiple families multiple testing procedures (MTPs) such as double FDR, group Benjamini-Hochberg (GBH) procedure and average FDR controlling procedure. The package also provides some novel multiple testing procedures using selective inference idea. Package: r-cran-mhurdle Architecture: amd64 Version: 1.3-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 665 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-truncreg, r-cran-maxlik, r-cran-survival, r-cran-rdpack, r-cran-prediction, r-cran-margins, r-cran-generics, r-cran-numderiv, r-cran-sandwich, r-cran-nonnest2, r-cran-compquadform Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lmtest, r-cran-bookdown, r-cran-testthat, r-cran-modelsummary, r-cran-tibble, r-cran-broom Filename: pool/dists/noble/main/r-cran-mhurdle_1.3-2-1.ca2404.1_amd64.deb Size: 528048 MD5sum: e17f904e23924acc194fdfe915ac7383 SHA1: c3503b500279f406e8e6d37b185df21d8a3c92b5 SHA256: 28c8a014e0ebbad0599afd56ab514fd4da37c8adaeabfa8e3573d7ad88f30c32 SHA512: f16bd4db510874cb75dd0e041d01ee6314966877642d894ba5f64f3f03b20248da06a21cab50140eae59f2a565655b83833867981c58ddaeb6ea47c0e8c20b84 Homepage: https://cran.r-project.org/package=mhurdle Description: CRAN Package 'mhurdle' (Multiple Hurdle Tobit Models) Estimation of models with dependent variable left-censored at zero. Null values may be caused by a selection process Cragg (1971) , insufficient resources Tobin (1958) , or infrequency of purchase Deaton and Irish (1984) . Package: r-cran-mic Architecture: amd64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1329 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-amr, r-cran-glue, r-cran-readr, r-cran-dplyr, r-cran-rcpp, r-cran-data.table, r-bioc-biostrings, r-cran-stringr, r-cran-rlang, r-cran-tidyr, r-cran-future.apply, r-cran-progressr, r-cran-lemon, r-cran-ggplot2, r-cran-forcats, r-cran-purrr, r-cran-tibble, r-cran-curl Suggests: r-cran-testthat, r-cran-xgboost, r-cran-flextable, r-cran-caret, r-cran-lifecycle, r-cran-future Filename: pool/dists/noble/main/r-cran-mic_1.2.0-1.ca2404.1_amd64.deb Size: 989592 MD5sum: d8aee7da970af11eecfc23d6104941c9 SHA1: d28394808a9062dce8696d2cf5d9d0d5dc56ca80 SHA256: 0ad5c3014d350e50291db94f0be3ab5308f6b8406465ff864f9ab9f256ce62cd SHA512: d85ec9e7822a7158127a5ea8a982ac7e7c90e7e92906d91bc20fa96ac73c73387594a65aab22f09375c9ed1fcc225a10d559e78c42389a589d5387c227fa6fe9 Homepage: https://cran.r-project.org/package=MIC Description: CRAN Package 'MIC' (Analysis of Antimicrobial Minimum Inhibitory Concentration Data) Analyse, plot, and tabulate antimicrobial minimum inhibitory concentration (MIC) data. Validate the results of an MIC experiment by comparing observed MIC values to a gold standard assay, in line with standards from the International Organization for Standardization (2021) . Perform MIC prediction from whole genome sequence data stored in the Pathosystems Resource Integration Center (2013) database or locally. Package: r-cran-mice Architecture: amd64 Version: 3.19.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1684 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-glmnet, r-cran-lattice, r-cran-mitml, r-cran-nnet, r-cran-rcpp, r-cran-rpart, r-cran-tidyr, r-cran-cpp11 Suggests: r-cran-broom.mixed, r-cran-future, r-cran-furrr, r-cran-haven, r-cran-knitr, r-cran-literanger, r-cran-lme4, r-cran-mass, r-cran-miceadds, r-cran-pan, r-cran-parallelly, r-cran-purrr, r-cran-ranger, r-cran-randomforest, r-cran-rmarkdown, r-cran-rstan, r-cran-survival, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mice_3.19.0-1.ca2404.1_amd64.deb Size: 1473472 MD5sum: 805309557cfcdcb349526a9dcd635d31 SHA1: 450fce89d79613fb96bf1e577a65db88df7ffb88 SHA256: 29976f4066c6f34977a4cc779d1572fdd995d17a82515b982ad7ad5ca42fac6f SHA512: 3feb5c73eae0ecf16ec58b33c3c3b741b50bf983854abcf8a3191f55260b70e62c17080f366497643b841c6bc938ff30827c7705d467c4f9dc887fdf31796c72 Homepage: https://cran.r-project.org/package=mice Description: CRAN Package 'mice' (Multivariate Imputation by Chained Equations) Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) . Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations. Package: r-cran-miceadds Architecture: amd64 Version: 3.19-16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2121 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mice, r-cran-mitools, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-bifiesurvey, r-cran-blme, r-cran-car, r-cran-cdm, r-cran-coda, r-cran-foreign, r-cran-inline, r-cran-lme4, r-cran-mass, r-cran-matrix, r-cran-mbess, r-cran-mcmcglmm, r-cran-mdmb, r-cran-pls, r-cran-numderiv, r-cran-readxl, r-cran-sandwich, r-cran-sirt, r-cran-sjlabelled, r-cran-synthpop, r-cran-tam Filename: pool/dists/noble/main/r-cran-miceadds_3.19-16-1.ca2404.1_amd64.deb Size: 1585968 MD5sum: eaca4f3b7d69c067073bc86943ba76ab SHA1: ab3b0d898b33f35cbc7b69dec870b743b6d73690 SHA256: 72e24a790bc6444716faf8dc6dfa1ee11300f1661f3d53ef007a8119ea32a224 SHA512: a902ad8876d69cd63f43f12871ae7a178411cadc575be68ba20a669d5f5cb7b4236a5253e2a5de9bd16723a4a3415582c88fef8eed4dbfa4f7b41148cfe18468 Homepage: https://cran.r-project.org/package=miceadds Description: CRAN Package 'miceadds' (Some Additional Multiple Imputation Functions, Especially for'mice') Contains functions for multiple imputation which complements existing functionality in R. In particular, several imputation methods for the mice package (van Buuren & Groothuis-Oudshoorn, 2011, ) are implemented. Main features of the miceadds package include plausible value imputation (Mislevy, 1991, ), multilevel imputation for variables at any level or with any number of hierarchical and non-hierarchical levels (Grund, Luedtke & Robitzsch, 2018, ; van Buuren, 2018, Ch.7, ), imputation using partial least squares (PLS) for high dimensional predictors (Robitzsch, Pham & Yanagida, 2016), nested multiple imputation (Rubin, 2003, ), substantive model compatible imputation (Bartlett et al., 2015, ), and features for the generation of synthetic datasets (Reiter, 2005, ; Nowok, Raab, & Dibben, 2016, ). Package: r-cran-micefast Architecture: amd64 Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2665 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mice, r-cran-magrittr, r-cran-ggplot2, r-cran-upsetr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-micefast_0.9.1-1.ca2404.1_amd64.deb Size: 888472 MD5sum: 0983a2b0378df9ec2143b18f8e1a27d3 SHA1: e2d9bf741e3424cf222983ae535a018d46343737 SHA256: 8492da91e3f39be48160c8c3835deb08bcb1d3755795b528a054e741c47282ae SHA512: f1c508c363bab20462ba6db651fbe9b48524606bf249713426bf113d209b3a967cfc43e48df64985ec0524aece4c4d615f2089fae29ea02c6aad6694f19c66f5 Homepage: https://cran.r-project.org/package=miceFast Description: CRAN Package 'miceFast' (Fast Imputations Using 'Rcpp' and 'Armadillo') Fast imputations under the object-oriented programming paradigm. Moreover there are offered a few functions built to work with popular R packages such as 'data.table' or 'dplyr'. The biggest improvement in time performance can be achieved for a calculation where a grouping variable is used. A single evaluation of a quantitative model for the multiple imputations is another major enhancement. A new major improvement is one of the fastest predictive mean matching in the R world because of presorting and binary search. Package: r-cran-microbenchmark Architecture: amd64 Version: 1.5.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-multcomp, r-cran-runit Filename: pool/dists/noble/main/r-cran-microbenchmark_1.5.0-1.ca2404.2_amd64.deb Size: 65844 MD5sum: ebc58d3600aa9160964c3a67595dd061 SHA1: 3e04dfaac6ab01485e77e90a9e9d9de1c8b0bbf7 SHA256: 250931dcb49d59705c2e1e0283a2d6dd2fb7cd3aaed225756bea1a628dd21aea SHA512: b8f04c36aab0fc69878cb74f3cc97c6656524523b69797bee400159a83416706c4866ff4cbc3c41b1759acf6728a2be2b3e2b633a9b9b622a1075a91152c2667 Homepage: https://cran.r-project.org/package=microbenchmark Description: CRAN Package 'microbenchmark' (Accurate Timing Functions) Provides infrastructure to accurately measure and compare the execution time of R expressions. Package: r-cran-microbiomestat Architecture: amd64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-matrixstats, r-cran-matrix, r-cran-statmod, r-cran-mass, r-cran-ggrepel, r-cran-lmertest, r-cran-foreach, r-cran-modeest, r-cran-dplyr, r-cran-rcpp, r-cran-mlr3, r-cran-mlr3mbo, r-cran-bbotk, r-cran-paradox, r-cran-rcpparmadillo Suggests: r-cran-dicekriging, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-microbiomestat_1.4-1.ca2404.1_amd64.deb Size: 375824 MD5sum: 642be38c5dc857af9e40dfe6ade40be9 SHA1: 7f7e7b70c1e16fc56b797615c75b489a4bb254b9 SHA256: 268c8d570043ca9449c790f8a3fd66cf9170ac35075871d1d4beea85e979855f SHA512: f72dea4bd9e603a1b93d68b378704c1d45a41f6675b51df862d87274e29c3e1fb8d49c412482bfbe783c6e69c6006af3e948b54d1e6545e8456fca966ba611ab Homepage: https://cran.r-project.org/package=MicrobiomeStat Description: CRAN Package 'MicrobiomeStat' (Statistical Methods for Microbiome Compositional Data) A suite of methods for powerful and robust microbiome data analysis addressing zero-inflation, phylogenetic structure and compositional effects. Includes the LinDA method for differential abundance analysis (Zhou et al. (2022)), the BMDD (Bimodal Dirichlet Distribution) method for accurate modeling and imputation of zero-inflated microbiome sequencing data (Zhou et al. (2025)) and compositional sparse CCA methods for microbiome multi-omics data integration (Deng et al. (2024) ). Package: r-cran-microclass Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-microseq, r-cran-microcontax, r-cran-dplyr, r-cran-stringr, r-cran-rlang, r-cran-rcpp, r-cran-rcppparallel, r-cran-tibble, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-microclass_1.2-1.ca2404.1_amd64.deb Size: 214254 MD5sum: 6298d3a672f9dac7e23fccc69a16f4ab SHA1: c5c6af689cf950000876eb872e7bb00097cfdbad SHA256: fbcd4748645adafe06f611c938654e99b58ee3da7113b26a29c6c9124f470fed SHA512: 4b7bf91d27155a7e6a633ffa2a83dae6345e88d0bd1a176031ac030d270106108f3f8e6e12d94b3580747404f132d98d491325124db4b5e564ab2329968e2cf3 Homepage: https://cran.r-project.org/package=microclass Description: CRAN Package 'microclass' (Methods for Taxonomic Classification of Prokaryotes) Functions for assigning 16S sequence data to a taxonomic level in the tree-of-life for prokaryotes. Package: r-cran-micromob Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4381 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-data.table, r-cran-callr, r-cran-httr, r-cran-readr, r-cran-withr, r-cran-plumber Filename: pool/dists/noble/main/r-cran-micromob_0.1.2-1.ca2404.1_amd64.deb Size: 2918590 MD5sum: 441da3751e36b02f3fae787ec5ae73a0 SHA1: 80a898fabb1eff077d2a0bca858b7c7721936ef5 SHA256: eb9aa9002a0e77606b57d8ff6df0f99344745708ba51e9936e08f53cbf92b37e SHA512: 4cc9a6da6d14941d9357a9b8a4f5886bde84e3e6021f9928f0f87923005d50130f4880bed46e654d39dae725adf351e70901fc38d61ed23d139df80fe0f02967 Homepage: https://cran.r-project.org/package=MicroMoB Description: CRAN Package 'MicroMoB' (Discrete Time Simulation of Mosquito-Borne Pathogen Transmission) Provides a framework based on S3 dispatch for constructing models of mosquito-borne pathogen transmission which are constructed from submodels of various components (i.e. immature and adult mosquitoes, human populations). A consistent mathematical expression for the distribution of bites on hosts means that different models (stochastic, deterministic, etc.) can be coherently incorporated and updated over a discrete time step. Package: r-cran-microseq Architecture: amd64 Version: 2.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-stringr, r-cran-dplyr, r-cran-rlang, r-cran-rcpp, r-cran-data.table Suggests: r-cran-r.utils Filename: pool/dists/noble/main/r-cran-microseq_2.1.7-1.ca2404.1_amd64.deb Size: 185042 MD5sum: 39253a34f457866c302ef3d06a96ac65 SHA1: bdca9764bd3d0fd258448ed421bb18bff8cc8bf1 SHA256: 4d0e943bbda833cc970454b022fb1bc618132bb6120705d4df6b77b2e321b338 SHA512: 22c1a55f2baac619480a22bd5c4a100a6903a894812f9e05538ce84e322aaaf4badb009252d59f56f608568d177f6249f74ec71c913f91c8575c1272acd1fc78 Homepage: https://cran.r-project.org/package=microseq Description: CRAN Package 'microseq' (Basic Biological Sequence Handling) Basic functions for microbial sequence data analysis. The idea is to use generic R data structures as much as possible, making R data wrangling possible also for sequence data. Package: r-cran-microsimulation Architecture: amd64 Version: 1.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7313 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ascii, r-cran-survival, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-microsimulation_1.4.5-1.ca2404.1_amd64.deb Size: 1032050 MD5sum: 8846e8638afa7feccee067c90a75f65c SHA1: 94a0a6c6c11a6c0c3c1ed017fe07b51a00fb63cc SHA256: 0fda4e555fe17573e3987261c7b2c45b1ae00f71fea64e4a70d5a7a805bc1979 SHA512: 8343537a7d6d7ffad1f473b17c740425ece1331042b2c6e869ba280350bd0a2a899722e20bfa4d788db1eb15a3099d02e064b1a3f1b346e11d0829308f80adbb Homepage: https://cran.r-project.org/package=microsimulation Description: CRAN Package 'microsimulation' (Discrete Event Simulation in R and C++, with Tools forCost-Effectiveness Analysis) Discrete event simulation using both R and C++ (Karlsson et al 2016; ). The C++ code is adapted from the SSIM library , allowing for event-oriented simulation. The code includes a SummaryReport class for reporting events and costs by age and other covariates. The C++ code is available as a static library for linking to other packages. A priority queue implementation is given in C++ together with an S3 closure and a reference class implementation. Finally, some tools are provided for cost-effectiveness analysis. Package: r-cran-micsplines Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-micsplines_1.0-1.ca2404.1_amd64.deb Size: 28264 MD5sum: c46d8c0617524a21a87662bf7491306c SHA1: c334d3489bca2a54f868a84359a72cb0743655b5 SHA256: 984a5a5731d8b864352c61507be4d77e1df6b2132f8642873e8a6b33c6e217d8 SHA512: 4f5f975c745d3305656842fb0d8bef9704918bdccd9610f276604097a547ff3788a387fe978e11809eba8cac8eeb3f8baa55a3250b9cf6776ac89451be85640a Homepage: https://cran.r-project.org/package=MICsplines Description: CRAN Package 'MICsplines' (The Computing of Monotonic Spline Bases and ConstrainedLeast-Squares Estimates) Providing C implementation for the computing of monotonic spline bases, including M-splines, I-splines, and C-splines, denoted by MIC splines. The definitions of the spline bases are described in Meyer (2008) . The package also provides the computing of constrained least-squares estimates when a subset of or all of the regression coefficients are constrained to be non-negative. Package: r-cran-micsr Architecture: amd64 Version: 0.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2010 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-rdpack, r-cran-sandwich, r-cran-generics, r-cran-numderiv, r-cran-survival, r-cran-rcpp, r-cran-compquadform, r-cran-dfidx Suggests: r-cran-quarto, r-cran-aer, r-cran-censreg, r-cran-sampleselection, r-cran-mlogit, r-cran-mass, r-cran-lmtest, r-cran-tinytest, r-cran-ggplot2, r-cran-modelsummary Filename: pool/dists/noble/main/r-cran-micsr_0.1-4-1.ca2404.1_amd64.deb Size: 1727680 MD5sum: 0d0c1448d07a624f821ff7b4c65a8fe3 SHA1: 580335b0a35bd9b5908f383dbddbbb31cbac8954 SHA256: ee8a339616101f3a40d225a2a8a0dd40f9f57e4c1faa98990087c58a0566f072 SHA512: 3affe77fb313f872a97145ce6ce998696c779b9f5cbffff8dcdf6ee863848c98dbcc009f783a9a99948e9fa9cb210f0a0f74a9eff0f4eaf7a28225fc36d6e369 Homepage: https://cran.r-project.org/package=micsr Description: CRAN Package 'micsr' (Microeconometrics with R) Functions, data sets and examples for the book: Yves Croissant (2025) "Microeconometrics with R", Chapman and Hall/CRC The R Series . The package includes a set of estimators for models used in microeconometrics, especially for count data and limited dependent variables. Test functions include score test, Hausman test, Vuong test, Sargan test and conditional moment test. A small subset of the data set used in the book is also included. Package: r-cran-midasml Architecture: amd64 Version: 0.1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 976 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-dorng, r-cran-doparallel, r-cran-foreach, r-cran-randtoolbox, r-cran-snow, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-midasml_0.1.11-1.ca2404.1_amd64.deb Size: 936684 MD5sum: 6aeaae3b79af5e35ba6263c5b1202648 SHA1: 0f97e7c7192d7cd9197f0bf3f434bd41007316ee SHA256: debaf3c827ea61501a1df9f8e80887cdd552b4caf0a7f582119c629daecd053f SHA512: 10529359968a96c00ee8a1818662a2242b246d6ae72381bc99cb1020d3dc55224cda4c28c946c90b7fd20d959799bff369233291f58e62aab0b09a2d6b64f6ee Homepage: https://cran.r-project.org/package=midasml Description: CRAN Package 'midasml' (Estimation and Prediction Methods for High-Dimensional MixedFrequency Time Series Data) The 'midasml' package implements estimation and prediction methods for high-dimensional mixed-frequency (MIDAS) time-series and panel data regression models. The regularized MIDAS models are estimated using orthogonal (e.g. Legendre) polynomials and sparse-group LASSO (sg-LASSO) estimator. For more information on the 'midasml' approach see Babii, Ghysels, and Striaukas (2021, JBES forthcoming) . The package is equipped with the fast implementation of the sg-LASSO estimator by means of proximal block coordinate descent. High-dimensional mixed frequency time-series data can also be easily manipulated with functions provided in the package. Package: r-cran-midnight Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 863 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-midr, r-cran-rlang, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-colormap, r-cran-dalex, r-cran-ggbeeswarm, r-cran-ggforce, r-cran-ggplot2, r-cran-metbrewer, r-cran-parsnip Filename: pool/dists/noble/main/r-cran-midnight_0.2.0-1.ca2404.1_amd64.deb Size: 557460 MD5sum: 459091d6ed30cc6456003737ad9cd531 SHA1: 53cb677e3ed4a30d1a275aacc773706f9a20ef41 SHA256: bda906e7c1e4c0376707fe229ae6b366aaf0a506d786f5dc5fe7246a3e8b1373 SHA512: 852bfce9827fbc4984217161983b0b95167edb9b729fd2b799ead3d7601f35e208e45c6dc1d02f790fa5c511d8ccfae3073feb185549afe817445438e784bb8a Homepage: https://cran.r-project.org/package=midnight Description: CRAN Package 'midnight' (A 'tidymodels' Engine and Other Extensions for the 'midr'Package) Provides a 'parsnip' engine for the 'midr' package, enabling users to fit, tune, and evaluate Maximum Interpretation Decomposition (MID) models within the 'tidymodels' framework. Developed through research by the Moonlight Seminar 2025, a study group of actuaries from the Institute of Actuaries of Japan, to enhance practical applications of interpretable modeling. Detailed methodology is available in Asashiba et al. (2025) . Package: r-cran-midr Architecture: amd64 Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 903 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-khroma, r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-midr_0.6.1-1.ca2404.1_amd64.deb Size: 781762 MD5sum: 49f29cc2f8a55ee78c6c04f347592580 SHA1: faceb3d35ce33be05a85ee1ac46a2c202172192b SHA256: 47b7d20170d27b9517668729f7fa7025e9b6c53ef0a9f8c727ff7fd58096277b SHA512: 4ea5a14689506b688db44d6dbbfeefc977f4df3e7b117244916e81dc6bcbf0867e63ca9fb611413368c396ed443088f47a3bb74df6e94d3d0939d3e6ca3ef1ba Homepage: https://cran.r-project.org/package=midr Description: CRAN Package 'midr' (Learning from Black-Box Models by Maximum InterpretationDecomposition) The goal of 'midr' is to provide a model-agnostic method for interpreting and explaining black-box predictive models by creating a globally interpretable surrogate model. The package implements 'Maximum Interpretation Decomposition' (MID), a functional decomposition technique that finds an optimal additive approximation of the original model. This approximation is achieved by minimizing the squared error between the predictions of the black-box model and the surrogate model. The theoretical foundations of MID are described in Iwasawa & Matsumori (2025) [Forthcoming], and the package itself is detailed in Asashiba et al. (2025) . Package: r-cran-mig Architecture: amd64 Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-statmod, r-cran-truncatednormal, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-numderiv, r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown, r-cran-minqa Filename: pool/dists/noble/main/r-cran-mig_2.0-1.ca2404.1_amd64.deb Size: 222886 MD5sum: 25944b1b6b66f89c1c65106897e734dc SHA1: 54a68d76b2ebe10695618469e1cd62ae2f643195 SHA256: c8fd46b0a7364e7ed2e59778b7f2d1034a56d381f08841d4ec6a32e2414cf5e9 SHA512: fdb90c817097f66230c9f28da22a7cec9a9d6c42165d0122c857902b777b6d73924e065c2979d16c1a2e6b360b29078e50418ac163f9eb13add4c9c27d9afb0c Homepage: https://cran.r-project.org/package=mig Description: CRAN Package 'mig' (Multivariate Inverse Gaussian Distribution) Provides utilities for estimation for the multivariate inverse Gaussian distribution of Minami (2003) , including random vector generation and explicit estimators of the location vector and scale matrix. The package implements kernel density estimators discussed in Belzile, Desgagnes, Genest and Ouimet (2024) for smoothing multivariate data on half-spaces. Package: r-cran-miic Architecture: amd64 Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 826 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ppcor, r-cran-rcpp, r-cran-scales Suggests: r-cran-igraph, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-miic_2.0.3-1.ca2404.1_amd64.deb Size: 517034 MD5sum: 23e7dbd916fe1e1bfb3d9554572e532c SHA1: a9187e395eb8022ead884719209567049faee8d9 SHA256: d338292bb4a87f54ac6c42f4a2375d0fe7835a164be088ac1a4188e6990344f5 SHA512: 97fc6f4ac6142391dd72d51236a9561aed324142a430d79eff35b7d4c33b9dd2669bbaf8324d2d2259cdc9ef361e6ba1354babd7527bcda41c656eafbdbace31 Homepage: https://cran.r-project.org/package=miic Description: CRAN Package 'miic' (Learning Causal or Non-Causal Graphical Models Using InformationTheory) Multivariate Information-based Inductive Causation, better known by its acronym MIIC, is a causal discovery method, based on information theory principles, which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. The recent more interpretable MIIC extension (iMIIC) further distinguishes genuine causes from putative and latent causal effects, while scaling to very large datasets (hundreds of thousands of samples). Since the version 2.0, MIIC also includes a temporal mode (tMIIC) to learn temporal causal graphs from stationary time series data. MIIC has been applied to a wide range of biological and biomedical data, such as single cell gene expression data, genomic alterations in tumors, live-cell time-lapse imaging data (CausalXtract), as well as medical records of patients. MIIC brings unique insights based on causal interpretation and could be used in a broad range of other data science domains (technology, climatology, economy, ...). For more information, you can refer to: Simon et al., eLife 2024, , Ribeiro-Dantas et al., iScience 2024, , Cabeli et al., NeurIPS 2021, , Cabeli et al., Comput. Biol. 2020, , Li et al., NeurIPS 2019, , Verny et al., PLoS Comput. Biol. 2017, , Affeldt et al., UAI 2015, . Changes from the previous 1.5.3 release on CRAN are available at . Package: r-cran-milorgwas Architecture: amd64 Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1057 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gaston, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-png Filename: pool/dists/noble/main/r-cran-milorgwas_0.7.1-1.ca2404.1_amd64.deb Size: 528472 MD5sum: 297017958725b30c30d87c30d3b8ff5f SHA1: bed906b713510722974141a34416a1cc40b9c86f SHA256: c7fd64e9d43582a06f9bd380dfc9c019d5ecadd3cf71760a40c355bdb96ae7a8 SHA512: 6591103e7cb5ecabe75363e45bea12bf080c490d1fc6f1d9e549a673b1835ab69184006d4949bf3e9a28b99ea7afa10f3d6bb41a2eed3becb9908441daf16552 Homepage: https://cran.r-project.org/package=milorGWAS Description: CRAN Package 'milorGWAS' (Mixed Logistic Regression for Genome-Wide Analysis Studies(GWAS)) Fast approximate methods for mixed logistic regression in genome-wide analysis studies (GWAS). Two computationnally efficient methods are proposed for obtaining effect size estimates (beta) in Mixed Logistic Regression in GWAS: the Approximate Maximum Likelihood Estimate (AMLE), and the Offset method. The wald test obtained with AMLE is identical to the score test. Data can be genotype matrices in plink format, or dosage (VCF files). The methods are described in details in Milet et al (2020) . Package: r-cran-milr Architecture: amd64 Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-piper, r-cran-numderiv, r-cran-glmnet, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-hmisc, r-cran-rmarkdown, r-cran-data.table, r-cran-ggplot2, r-cran-plyr Filename: pool/dists/noble/main/r-cran-milr_0.4.1-1.ca2404.1_amd64.deb Size: 141944 MD5sum: a03d6cd9f0d303d00e288da6a6be3f04 SHA1: db243da697fac45f4f0f0ea78b712692e23fcefb SHA256: cbf62246bb80a5d969ee01a7842ddb8aacdcb8f1e538392156286632946603b2 SHA512: c3930e94004498f325afa0b2984d031fe9d3fc57ac89d6a1c59bc6d860641bf6d524152e4ce197e033bf799a0e511b3295317fbce53055442b2346db374302d2 Homepage: https://cran.r-project.org/package=milr Description: CRAN Package 'milr' (Multiple-Instance Logistic Regression with LASSO Penalty) The multiple instance data set consists of many independent subjects (called bags) and each subject is composed of several components (called instances). The outcomes of such data set are binary or categorical responses, and, we can only observe the subject-level outcomes. For example, in manufacturing processes, a subject is labeled as "defective" if at least one of its own components is defective, and otherwise, is labeled as "non-defective". The 'milr' package focuses on the predictive model for the multiple instance data set with binary outcomes and performs the maximum likelihood estimation with the Expectation-Maximization algorithm under the framework of logistic regression. Moreover, the LASSO penalty is attached to the likelihood function for simultaneous parameter estimation and variable selection. Package: r-cran-mime Architecture: amd64 Version: 0.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mime_0.13-1.ca2404.1_amd64.deb Size: 45378 MD5sum: e3d424ba2e247f23de63e688e9e7612c SHA1: 7916323e471acdf6ac93e312cbd21ae8d80de055 SHA256: e5cc1db91141ae4c6a51170c95eafe7db69259be6aac937dd5afd624d160c7f1 SHA512: fff3a206fa8d3d578849f502a94d86fef7ffa1fd129a3bef2adb795a35cf10355a858f3a3ce4d3409c3b9885147bd220c2a6ff58afd4bcfe652e20c0bb7b0162 Homepage: https://cran.r-project.org/package=mime Description: CRAN Package 'mime' (Map Filenames to MIME Types) Guesses the MIME type from a filename extension using the data derived from /etc/mime.types in UNIX-type systems. Package: r-cran-mined Architecture: amd64 Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-mined_1.0-3-1.ca2404.1_amd64.deb Size: 154940 MD5sum: 0d96cacd2c6f674aede713b0d74eaf12 SHA1: 8698bbeec040ce35e44a375a5d66429964b752c4 SHA256: f8d670bb0ad2650999f4013b5325d7ca500126e8830626ad19b0e082aa19f2d0 SHA512: 5ae0694b7e613a951f7df0b252c34d0ab5d8eb445ff04461f8c85a49afa69f589a4eafd449515264b93b25df2a97e2398f622a1fdfbd08367271b3997e1c7925 Homepage: https://cran.r-project.org/package=mined Description: CRAN Package 'mined' (Minimum Energy Designs) This is a method (MinED) for mining probability distributions using deterministic sampling which is proposed by Joseph, Wang, Gu, Lv, and Tuo (2019) . The MinED samples can be used for approximating the target distribution. They can be generated from a density function that is known only up to a proportionality constant and thus, it might find applications in Bayesian computation. Moreover, the MinED samples are generated with much fewer evaluations of the density function compared to random sampling-based methods such as MCMC and therefore, this method will be especially useful when the unnormalized posterior is expensive or time consuming to evaluate. This research is supported by a U.S. National Science Foundation grant DMS-1712642. Package: r-cran-minerva Architecture: amd64 Version: 1.5.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 482 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-minerva_1.5.10-1.ca2404.1_amd64.deb Size: 336760 MD5sum: f3bd8b39fdee5cf13dff1db418585bb2 SHA1: b860a2ee0b8582c18996dcc3765c1b71c8e5ed06 SHA256: 769b29e928974b63a737850e16e410bba53b4121cc50342e0d18e11784ca666a SHA512: cee6936f27109a18889c27312ee19dfdae2546a3fdb8a60fd88440fbe1b6677587f0b6505850c4a03e6fb8a82b18f5b1a3b2514a267310e243204dd4fafffc9f Homepage: https://cran.r-project.org/package=minerva Description: CRAN Package 'minerva' (Maximal Information-Based Nonparametric Exploration for VariableAnalysis) Wrapper for 'minepy' implementation of Maximal Information-based Nonparametric Exploration statistics (MIC and MINE family). Detailed information of the ANSI C implementation of 'minepy' can be found at . Package: r-cran-minic Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 364 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-minic_1.0.3-1.ca2404.1_amd64.deb Size: 145500 MD5sum: 14477b897ef571eae0b35cf05377c525 SHA1: 9043e2b1400aa91d5eb0b32bacf206a8b18c1c45 SHA256: 7b5d1b889761f9e072b453844a5dff299f4994061ec592c93b4496969ffb4d48 SHA512: 09ba3b6155b47050f76e9b4ca7b3fe4078af999614cce523ba09b3481dd5142ff0e7f262a5b55afd9acf062c7444a78fed7eaded43b6c0e0da7d27b020fc4fc4 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. 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This package gives a simple implementation with a 30 line 'Stan' script. This lightweight implementation makes it an easy starting point for other projects, in particular for downstream tasks that require analysis of "compositional" data. It can be applied whenever a multinomial probability parameter is thought to depend linearly on inputs in a transformed, log ratio space. Additional utilities make it easy to inspect, create predictions, and draw samples using the fitted models. More about the LNM can be found in Xia et al. (2013) "A Logistic Normal Multinomial Regression Model for Microbiome Compositional Data Analysis" and Sankaran and Holmes (2023) "Generative Models: An Interdisciplinary Perspective" . 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A derivation of the used algorithms can be found in my masters thesis . Package: r-cran-minired Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42812 Depends: libc6 (>= 2.34), libgcc-s1 (>= 7), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cpp11 Filename: pool/dists/noble/main/r-cran-minired_1.0.1-1.ca2404.1_amd64.deb Size: 11548386 MD5sum: 5aeedff233b4b0e9b99b10a44f991290 SHA1: 40fe149611c0c3f261070971c042afefa982c7a5 SHA256: e989d790739b791232c3bd2bdbc26cbd6837724e41ae24a65e95164ba8642c04 SHA512: 34d1b156567aa43c9aee372eb8b9f88e2c01b6e5807b84e61bfbc7ae8204e56c6cf1a27bcbed9069d07acc50f57c32fe62d1b6513a0ca2db85990ccd99e71ea9 Homepage: https://cran.r-project.org/package=minired Description: CRAN Package 'minired' (R Interface to 'Redatam' Library) This package is deprecated. Please use 'redatamx' instead. 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Powell. 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Package: r-cran-minty Architecture: amd64 Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 732 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tzdb, r-cran-cpp11 Suggests: r-cran-knitr, r-cran-stringi, r-cran-testthat, r-cran-withr, r-cran-hms, r-cran-readr Filename: pool/dists/noble/main/r-cran-minty_0.0.6-1.ca2404.1_amd64.deb Size: 300042 MD5sum: 9591178e48e77994ba3cecc2c7a2e2b6 SHA1: fb9b1fb4f8e6bc4fb5825d87328751026c52a1be SHA256: 661b48f10665aa7cbe29f51cb1c4628a587c9933e4b703da81c67e84b7cf17e4 SHA512: 5c14c3d6ab492e802e9352927fd0596e9a16393ffac2c5a55395818dce20a38d837ee54a447faa97d0b6c00af8bc780e9469363a91693386ea7c5fac7ba0bbc0 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: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7387 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-formula, r-cran-mvtnorm, r-cran-dirichletprocess, r-cran-truncnorm, r-cran-pracma, r-cran-cubature, r-cran-logspline, r-cran-nlme, r-cran-hdinterval, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mires_0.1.1-1.ca2404.1_amd64.deb Size: 1829330 MD5sum: 544b1b5e525fd616beded4a2f939711d SHA1: 05ffc070ab38d579c0dd68609bb8eec8d82a7389 SHA256: 53e125bc160c57a66a7cdb5a65b393caeb4d05491cfd8ec09bbeb4c8b51095db SHA512: c1a8d00b06e0961b0af50f6b1cc11fd97f2ac2d4f57f0a0f618f437e5e742002f4c0bc21bef96e92206e3ba0943b3158189529aaa13475c8cecedff6c71a0013 Homepage: https://cran.r-project.org/package=MIRES Description: CRAN Package 'MIRES' (Measurement Invariance Assessment Using Random Effects Modelsand Shrinkage) Estimates random effect latent measurement models, wherein the loadings, residual variances, intercepts, latent means, and latent variances all vary across groups. The random effect variances of the measurement parameters are then modeled using a hierarchical inclusion model, wherein the inclusion of the variances (i.e., whether it is effectively zero or non-zero) is informed by similar parameters (of the same type, or of the same item). This additional hierarchical structure allows the evidence in favor of partial invariance to accumulate more quickly, and yields more certain decisions about measurement invariance. Martin, Williams, and Rast (2020) . Package: r-cran-mirnass Architecture: amd64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-corelearn, r-cran-rspectra Filename: pool/dists/noble/main/r-cran-mirnass_1.5-1.ca2404.1_amd64.deb Size: 360852 MD5sum: 85842b208657d2655bcf36a0923120f7 SHA1: 2f2b96cff3b133e7ed24496b4609094b9bd3e3c0 SHA256: 0f30e213b1249f3e1982665b3bdae86b5dad2126a30b380c361f9988e64879f5 SHA512: e97afd7a827b5c97e868ff8145b52ada4d3d771c09085a0ce63282b3cb2b27c6d975a77e1eb044ba4803ae408b25f05d5a9b5dab1a8b5f96a454497b0073c418 Homepage: https://cran.r-project.org/package=miRNAss Description: CRAN Package 'miRNAss' (Genome-Wide Discovery of Pre-miRNAs with few Labeled Examples) Machine learning method specifically designed for pre-miRNA prediction. It takes advantage of unlabeled sequences to improve the prediction rates even when there are just a few positive examples, when the negative examples are unreliable or are not good representatives of its class. Furthermore, the method can automatically search for negative examples if the user is unable to provide them. MiRNAss can find a good boundary to divide the pre-miRNAs from other groups of sequences; it automatically optimizes the threshold that defines the classes boundaries, and thus, it is robust to high class imbalance. Each step of the method is scalable and can handle large volumes of data. Package: r-cran-mirt Architecture: amd64 Version: 1.46.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3002 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-gparotation, r-cran-gridextra, r-cran-matrix, r-cran-rcpp, r-cran-mgcv, r-cran-vegan, r-cran-deriv, r-cran-splines2, r-cran-pbapply, r-cran-dcurver, r-cran-simdesign, r-cran-rcpparmadillo Suggests: r-cran-boot, r-cran-mirai, r-cran-latticeextra, r-cran-directlabels, r-cran-shiny, r-cran-knitr, r-cran-markdown, r-cran-rsolnp, r-cran-nloptr, r-cran-sirt, r-cran-plink, r-cran-mirtcat, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mirt_1.46.1-1.ca2404.1_amd64.deb Size: 2251690 MD5sum: 4fb125ac1c0242be168dd1468f3b6913 SHA1: a418e7390853059dc6ca6ed7508be6ffc527b6c3 SHA256: 718ce88529ec7419fe8d1f795b416368937f240340013555894e9c3a8549a123 SHA512: edae2f104b3366a885913565dce1ea21f56efd8865d2d5fdfce162e84dfc3d1610ff6e08f6925678911bbbd2958a013a981ecd662965a54b4c822489634b7486 Homepage: https://cran.r-project.org/package=mirt Description: CRAN Package 'mirt' (Multidimensional Item Response Theory) Analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) ). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture IRT models, and zero-inflated response models are supported, as well as a wide family of probabilistic unfolding models. Package: r-cran-mirtcat Architecture: amd64 Version: 1.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 612 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mirt, r-cran-shiny, r-cran-lattice, r-cran-rcpp, r-cran-markdown, r-cran-pbapply, r-cran-lpsolve, r-cran-rcpparmadillo Suggests: r-cran-shinythemes, r-cran-knitr Filename: pool/dists/noble/main/r-cran-mirtcat_1.14-1.ca2404.1_amd64.deb Size: 434772 MD5sum: be85c90d66131cf392ca8a18361c0254 SHA1: 4bed2b2fe711e90fa5d533abfbc500416c99b8df SHA256: c9800a9294a36590298d319e7553c1184fc9c8957275d13fc1324c7e04f155d2 SHA512: 148bae0e5a967f92942f878f39603931cce3c7b0a99670acdaf346006e8b11b026395461ce18738db18a1df21d23c522674d94ef08e3298ef1976d793f5a32f0 Homepage: https://cran.r-project.org/package=mirtCAT Description: CRAN Package 'mirtCAT' (Computerized Adaptive Testing with Multidimensional ItemResponse Theory) Provides tools to generate HTML interfaces for adaptive and non-adaptive tests using the shiny package (Chalmers (2016) ). Suitable for applying unidimensional and multidimensional computerized adaptive tests (CAT) using item response theory methodology and for creating simple questionnaires forms to collect response data directly in R. Additionally, optimal test designs (e.g., "shadow testing") are supported for tests that contain a large number of item selection constraints. Finally, package contains tools useful for performing Monte Carlo simulations for studying test item banks. Package: r-cran-mirtjml Architecture: amd64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-gparotation, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-mirtjml_1.4.0-1.ca2404.1_amd64.deb Size: 180450 MD5sum: b5031853e65a199a810f8ee1fd55e523 SHA1: a84ed3d2eca5a04bd2cdf0b033232f32e11e698d SHA256: 3af67ba6917f2e709b6c11531b896a3aa004049e18b4029f5d965c453bd6b181 SHA512: 89443e1f757f1b0db5569adaa15df9868e86145cb01d8f365e64138a741949cca8febe42f2b8820f833a35f306d68bd53c86dd518152f45d531796de08a43b04 Homepage: https://cran.r-project.org/package=mirtjml Description: CRAN Package 'mirtjml' (Joint Maximum Likelihood Estimation for High-Dimensional ItemFactor Analysis) Provides constrained joint maximum likelihood estimation algorithms for item factor analysis (IFA) based on multidimensional item response theory models. So far, we provide functions for exploratory and confirmatory IFA based on the multidimensional two parameter logistic (M2PL) model for binary response data. Comparing with traditional estimation methods for IFA, the methods implemented in this package scale better to data with large numbers of respondents, items, and latent factors. The computation is facilitated by multiprocessing 'OpenMP' API. For more information, please refer to: 1. Chen, Y., Li, X., & Zhang, S. (2018). Joint Maximum Likelihood Estimation for High-Dimensional Exploratory Item Factor Analysis. Psychometrika, 1-23. ; 2. Chen, Y., Li, X., & Zhang, S. (2019). Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications. Journal of the American Statistical Association, . 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The models require side information from a secondary data set on the misclassification process, i.e. some sort of misclassification probabilities conditional on some common covariates. A detailed description of the algorithm can be found in Dlugosz, Mammen and Wilke (2015) . 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The 'misha' package implements an efficient data structure for storing genomic data, and provides a set of functions for data extraction, manipulation and analysis. Some of the 2D genome algorithms were described in Yaffe and Tanay (2011) . Package: r-cran-mispu Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-vegan, r-cran-ape, r-cran-aspu, r-cran-cluster, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ade4 Filename: pool/dists/noble/main/r-cran-mispu_1.0-1.ca2404.1_amd64.deb Size: 158414 MD5sum: fbccda03e557345371a194357cc07f5c SHA1: a1066d56e432affa780275d3a805680116e8e7ce SHA256: a1c28da0ae94a06d1dba3126255a662aa1d70a4b5f3393bbbac0b57221dafa50 SHA512: cd42ad8235df1b9618bc5345da58a7d9f64a286337825246207e4c6fa91de7ff39d5e3c86724be22795450c681dc48bd569a3c4a7f372d60fcb79bdfe00ae16d Homepage: https://cran.r-project.org/package=MiSPU Description: CRAN Package 'MiSPU' (Microbiome Based Sum of Powered Score (MiSPU) Tests) There is an increasing interest in investigating how the compositions of microbial communities are associated with human health and disease. In this package, we present a novel global testing method called aMiSPU, that is highly adaptive and thus high powered across various scenarios, alleviating the issue with the choice of a phylogenetic distance. Our simulations and real data analysis demonstrated that aMiSPU test was often more powerful than several competing methods while correctly controlling type I error rates. Package: r-cran-misscp Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-factoextra, r-cran-rcpp, r-cran-ggplot2, r-cran-glmnet, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-misscp_0.1.1-1.ca2404.1_amd64.deb Size: 184750 MD5sum: f3d00e994b2f174c04b61bce4f0db052 SHA1: 3e34e47874244233ce60960741c884fb8240fd2b SHA256: 847cc44e1a41d2e1f8bd0c00c1e29a212b76fee92387268d6e028d5fc72352ae SHA512: 149df8418d8342f2b688a8be769c6553290d8a55143262bd13c6c649cc252d2d893770f469ebdb18b17bdb78e0282109875ac40942c055abd44779f6c18e1634 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) . 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Package: r-cran-missonet Architecture: amd64 Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1944 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-circlize, r-bioc-complexheatmap, r-cran-glassofast, r-cran-mvtnorm, r-cran-pbapply, r-cran-rcpp, r-cran-scatterplot3d, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-glasso, r-cran-gridextra, r-cran-igraph, r-cran-knitr, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-missonet_1.5.1-1.ca2404.1_amd64.deb Size: 1038882 MD5sum: e55bb1ad9aa9bbcc23d4ba5bd5e9dacf SHA1: a626c4e2133c6dff4aaad6571e3f2a319efb5c85 SHA256: 2e226e79d6cf48c3a78ed844a15c836be1526d52885b08b5f6a1e20fda74a76f SHA512: de7b3dacd96a2f50a09eae7423dbea16553b0c53a9e7a5e42e6bb4c8f3662c55a56c7d7f5e70f61992499ec5f6fb4b88aa24cef50e6a9068209e4900f2209bec Homepage: https://cran.r-project.org/package=missoNet Description: CRAN Package 'missoNet' (Joint Sparse Regression & Network Learning with Missing Data) Simultaneously estimates sparse regression coefficients and response network structure in multivariate models with missing data. Unlike traditional approaches requiring imputation, handles missingness natively through unbiased estimating equations (MCAR/MAR compatible). Employs dual L1 regularization with automated selection via cross-validation or information criteria. Includes parallel computation, warm starts, adaptive grids, publication-ready visualizations, and prediction methods. Ideal for genomics, neuroimaging, and multi-trait studies with incomplete high-dimensional outcomes. See Zeng et al. (2025) . Package: r-cran-misssbm Architecture: amd64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2411 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-igraph, r-cran-nloptr, r-cran-ggplot2, r-cran-future.apply, r-cran-r6, r-cran-rlang, r-cran-sbm, r-cran-magrittr, r-cran-matrix, r-cran-rspectra, r-cran-rcpparmadillo Suggests: r-cran-aricode, r-cran-blockmodels, r-cran-corrplot, r-cran-future, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-misssbm_1.0.5-1.ca2404.1_amd64.deb Size: 1970196 MD5sum: 8e9e25fcee3a12fd1337975906d80462 SHA1: f9a37d786d35315e1ce6dcffcdeb669b32edaab9 SHA256: 20b8bcff4c9dd665fde02d715f66636e01ae13a3dfcbf81029e7a3adc8fed505 SHA512: a1a1dedba65fa807f9c40b4ddac0bb4e3187e98e44c1dd1b0bbc9d21af01d8757635bc0093878a6cf574ba79341b2ed3d1d4beea73199eaab616fc23031856d7 Homepage: https://cran.r-project.org/package=missSBM Description: CRAN Package 'missSBM' (Handling Missing Data in Stochastic Block Models) When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0 due to missing information between node pairs), it is possible to account for the underlying process that generates those NAs. 'missSBM', presented in 'Barbillon, Chiquet and Tabouy' (2022) , adjusts the popular stochastic block model from network data sampled under various missing data conditions, as described in 'Tabouy, Barbillon and Chiquet' (2019) . Package: r-cran-misssom Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 463 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-kpodclustr Filename: pool/dists/noble/main/r-cran-misssom_1.0.1-1.ca2404.1_amd64.deb Size: 355114 MD5sum: c5b291293a7d848456d46c6bfe947e98 SHA1: 40e8c7961a104af5a0fc95d82403261f2dacb8c1 SHA256: 39106461f7040f3fa399571d54fab8d9b102c428b950887a186735f9c9248349 SHA512: b059ec305c147d4c90977c9679fb1ac314ec62866d118a136518f40157a4de1c7044ac397c771e91dc9aea86dae63f331973d0af2ed29671e1be5eafa28d47e1 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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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) . 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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) . 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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. 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Bridge functions are provided to connect Kendall's tau to latent correlation under the Gaussian copula model. The methods are described in Yoon, Carroll and Gaynanova (2020) and Yoon, Mueller and Gaynanova (2021) . 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The test statistics are based on the multilinear empirical copula and multipliers are used to compute P-values. The test of independence between random variables appeared in Genest, Nešlehová, Rémillard & Murphy (2019) and the test of randomness appeared in Nasri (2022). Package: r-cran-mixedmem Architecture: amd64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 878 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-gtools, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-xtable Filename: pool/dists/noble/main/r-cran-mixedmem_1.1.2-1.ca2404.1_amd64.deb Size: 546826 MD5sum: a2fe29e2214b12d2179bef26dbc4425f SHA1: a2959d532b086445d104dff1d3426b05599b43f4 SHA256: 7fd48007621e0a352b4695126860549fda16ded877baa7b31ec96e104c71f54f SHA512: 9628f95d576c20c7f6051328d72c3ac179733506ab5d11fcfcf7074276ffacdcde50e55201b0b68818a5f8e1ccc78ae2b1b23781381303a989c15a83c1f3b730 Homepage: https://cran.r-project.org/package=mixedMem Description: CRAN Package 'mixedMem' (Tools for Discrete Multivariate Mixed Membership Models) Fits mixed membership models with discrete multivariate data (with or without repeated measures) following the general framework of Erosheva et al (2004). This package uses a Variational EM approach by approximating the posterior distribution of latent memberships and selecting hyperparameters through a pseudo-MLE procedure. Currently supported data types are Bernoulli, multinomial and rank (Plackett-Luce). The extended GoM model with fixed stayers from Erosheva et al (2007) is now also supported. See Airoldi et al (2014) for other examples of mixed membership models. Package: r-cran-mixexp Architecture: amd64 Version: 1.2.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-daewr Filename: pool/dists/noble/main/r-cran-mixexp_1.2.7.1-1.ca2404.1_amd64.deb Size: 170910 MD5sum: 973b2d5f7743f2f080fd2bdb8c17567f SHA1: ab2df12394ee03555a0fd00a06172e795a7cda27 SHA256: efebf02a766daaf6230d2ce7fc0fc9a1c69f54e595ecd4ea977066418984fb96 SHA512: 1fba4e7ec535f07bc991755c52362c6293ea36a7d934db2dc1c964cab25806d727ea157f0a28dd01c005a62f7cddf1162debbc8517adf2525f439c0443130e68 Homepage: https://cran.r-project.org/package=mixexp Description: CRAN Package 'mixexp' (Design and Analysis of Mixture Experiments) Functions for creating designs for mixture experiments, making ternary contour plots, and making mixture effect plots. Package: r-cran-mixfmri Architecture: amd64 Version: 0.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1801 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-rcolorbrewer, r-cran-fftw, r-cran-mixsim, r-cran-emcluster Filename: pool/dists/noble/main/r-cran-mixfmri_0.1-4-1.ca2404.1_amd64.deb Size: 1416206 MD5sum: f0ce0d0e20038fdb0f222f184f895afb SHA1: 72824623c9742ebd4b43a27276334c6f2de0a6db SHA256: cdcc18ef0ab48da4c13e9efa809d3adffd7dad49a2d7a16adcde0cdd80935e3e SHA512: bfbe3b8c3b20ffaf552281ec6e3eeb3e998002efa68689ae1b33e51e5cc81257f63069eb86322c005aba06482b3705734e880ebba975891d9a55d41121d8cdfd Homepage: https://cran.r-project.org/package=MixfMRI Description: CRAN Package 'MixfMRI' (Mixture fMRI Clustering Analysis) Utilizing model-based clustering (unsupervised) for functional magnetic resonance imaging (fMRI) data. The developed methods (Chen and Maitra (2023) ) include 2D and 3D clustering analyses (for p-values with voxel locations) and segmentation analyses (for p-values alone) for fMRI data where p-values indicate significant level of activation responding to stimulate of interesting. The analyses are mainly identifying active voxel/signal associated with normal brain behaviors. Analysis pipelines (R scripts) utilizing this package (see examples in 'inst/workflow/') is also implemented with high performance techniques. Package: r-cran-mixgb Architecture: amd64 Version: 2.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1323 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-matrix, r-cran-mice, r-cran-rcpp, r-cran-rfast, r-cran-xgboost, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mixgb_2.2.3-1.ca2404.1_amd64.deb Size: 1004368 MD5sum: a85a437cb140b62288509f42ea88555c SHA1: cd2073c5dc02a8ca1c07deff4adc53855264d345 SHA256: db27b53afe17991e3d9fa91a660dc350cca3631b2320726872f2ebffda900f3c SHA512: 7906e01a736c674408fcb71e759772ba4420b9d9c60bd254238b1c3b976366805bd7adeab90e997b6eb637cb0e1121b8614285feeb2eb9691053bf53f79ebc63 Homepage: https://cran.r-project.org/package=mixgb Description: CRAN Package 'mixgb' (Multiple Imputation Through 'XGBoost') Multiple imputation using 'XGBoost', subsampling, and predictive mean matching as described in Deng and Lumley (2024) . The package supports various types of variables, offers flexible settings, and enables saving an imputation model to impute new data. Data processing and memory usage have been optimised to speed up the imputation process. Package: r-cran-mixl Architecture: amd64 Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maxlik, r-cran-numderiv, r-cran-randtoolbox, r-cran-rcpp, r-cran-readr, r-cran-sandwich, r-cran-stringr Suggests: r-cran-knitr, r-cran-mlogit, r-cran-rmarkdown, r-cran-testthat, r-cran-texreg, r-cran-xtable Filename: pool/dists/noble/main/r-cran-mixl_1.3.5-1.ca2404.1_amd64.deb Size: 105340 MD5sum: aa912aec83142ec5a25982a898d56bb9 SHA1: a3bba6036a88bccd133c72b2e0a87fa6116d3ba4 SHA256: f56206b651b6644d0f1a1949349edaebbb1ec314e7b5eafcfdfface97d6fbda6 SHA512: 9bc589a7a0f5c7d1c38503b4422432bcead491a6ebae998103f7f58f453002e57c9470596f789e6e020c496ec31295000652031ad36d94c3bb0a889978797973 Homepage: https://cran.r-project.org/package=mixl Description: CRAN Package 'mixl' (Simulated Maximum Likelihood Estimation of Mixed Logit Modelsfor Large Datasets) Specification and estimation of multinomial logit models. Large datasets and complex models are supported, with an intuitive syntax. Multinomial Logit Models, Mixed models, random coefficients and Hybrid Choice are all supported. For more information, see Molloy et al. (2021) . Package: r-cran-mixlfa Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 752 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-pheatmap, r-cran-ggally, r-cran-dplyr, r-cran-gparotation, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mixlfa_1.0.0-1.ca2404.1_amd64.deb Size: 326958 MD5sum: a27a52dde0aa333101cc75b6ecbc7589 SHA1: e25f6b57ea64c2ba2e2c68cf70d8520e33cf1448 SHA256: 473f7a043a2b3096086fb8900b5d5515bfd293a9bfb50d308bc3e4f4cafae6fb SHA512: 92a825d4035d2275829c26c00b06536d5208f140b9a1545f1f643c54ad3afd29e6880a857cd646a5a0fd1d1cd3344b776348730d542334c0fc73febcb43334f7 Homepage: https://cran.r-project.org/package=MixLFA Description: CRAN Package 'MixLFA' (Mixture of Longitudinal Factor Analysis Methods) Provides a function for the estimation of mixture of longitudinal factor analysis models using the iterative expectation-maximization algorithm (Ounajim, Slaoui, Louis, Billot, Frasca, Rigoard (2023) ) and several tools for visualizing and interpreting the models' parameters. Package: r-cran-mixmatrix Architecture: amd64 Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 754 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cholwishart, r-cran-rcpp, r-cran-glue, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-ggplot2, r-cran-dplyr, r-cran-magrittr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-mixmatrix_0.2.8-1.ca2404.1_amd64.deb Size: 325326 MD5sum: 2511f1856bfa13953911dac78dc94b48 SHA1: ce2b0243a1cad6dba81ed82112bbae8d5d817bf4 SHA256: 1f834c01fb6d83ba5d6e29f08b63e3446a55243f4d6b7ea1295e2fd70af33477 SHA512: 002e9880effe505e8b7f7e5904e0d68d785051d175dc1446aaae52732fa4b4522ace1f027887dfec48ed91de1ecb2f6a1f914121ad0eab352afdaaef2d753124 Homepage: https://cran.r-project.org/package=MixMatrix Description: CRAN Package 'MixMatrix' (Classification with Matrix Variate Normal and t Distributions) Provides sampling and density functions for matrix variate normal, t, and inverted t distributions; ML estimation for matrix variate normal and t distributions using the EM algorithm, including some restrictions on the parameters; and classification by linear and quadratic discriminant analysis for matrix variate normal and t distributions described in Thompson et al. (2019) . Performs clustering with matrix variate normal and t mixture models. Package: r-cran-mixr Architecture: amd64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 521 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-mockery Filename: pool/dists/noble/main/r-cran-mixr_0.2.1-1.ca2404.1_amd64.deb Size: 264580 MD5sum: 53fed1af892dd79d6bfeb69b18ebfd52 SHA1: 44333847d965397027295ae09d9be40c051cc0f1 SHA256: a81bbef4d8eeaf8a46505cbf72792a492794a9646b7699c80ef0abde21ad764c SHA512: 819bebe6188587e566d28351cca435f8cdb4881d9c0488f87a79e32706e31e96099663aad285924038289b6963f108cc5eccb3d2cf83cc49c60e929b41425552 Homepage: https://cran.r-project.org/package=mixR Description: CRAN Package 'mixR' (Finite Mixture Modeling for Raw and Binned Data) Performs maximum likelihood estimation for finite mixture models for families including Normal, Weibull, Gamma and Lognormal by using EM algorithm, together with Newton-Raphson algorithm or bisection method when necessary. It also conducts mixture model selection by using information criteria or bootstrap likelihood ratio test. The data used for mixture model fitting can be raw data or binned data. The model fitting process is accelerated by using R package 'Rcpp'. Package: r-cran-mixsim Architecture: amd64 Version: 1.1-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-mixsim_1.1-8-1.ca2404.1_amd64.deb Size: 118236 MD5sum: fbfb8db53fc7901f72fe9f23a533ed59 SHA1: 373d91befd6cddadc793b13006f17d322825b71d SHA256: b30ea57db0fe1e0131de6ba59265880ad802d76ea46abddf7340213b0d6a084f SHA512: da0c315d4c1dfef7a0c843cfc01cf9ac91ecfab8d48996628e7cb813a0bdbec5e6f69d8aea019416d4f85b63d9ea600400d549fcec3498a989d8cfc8a3a5c515 Homepage: https://cran.r-project.org/package=MixSim Description: CRAN Package 'MixSim' (Simulating Data to Study Performance of Clustering Algorithms) The utility of this package is in simulating mixtures of Gaussian distributions with different levels of overlap between mixture components. Pairwise overlap, defined as a sum of two misclassification probabilities, measures the degree of interaction between components and can be readily employed to control the clustering complexity of datasets simulated from mixtures. These datasets can then be used for systematic performance investigation of clustering and finite mixture modeling algorithms. Among other capabilities of 'MixSim', there are computing the exact overlap for Gaussian mixtures, simulating Gaussian and non-Gaussian data, simulating outliers and noise variables, calculating various measures of agreement between two partitionings, and constructing parallel distribution plots for the graphical display of finite mixture models. Package: r-cran-mixsqp Architecture: amd64 Version: 0.3-54-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-irlba, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mixsqp_0.3-54-1.ca2404.1_amd64.deb Size: 192274 MD5sum: 579533bd4745524ab12c021e57174bec SHA1: 18c139b6c35851f50e276fdf1a31ee6397f7a711 SHA256: c9a3272ddcf05aedcc4abbd3782264666bf344d08344631286115e2bf76bc9b2 SHA512: a887cc6205a2ac735a13554d27d8d46d5e0f462417973beefe2379088e82be1e8b7c1a333a58b60a8a09976305703600dd6234da3a3f6f3c41bdf9934f924a1b Homepage: https://cran.r-project.org/package=mixsqp Description: CRAN Package 'mixsqp' (Sequential Quadratic Programming for Fast Maximum-LikelihoodEstimation of Mixture Proportions) Provides an optimization method based on sequential quadratic programming (SQP) for maximum likelihood estimation of the mixture proportions in a finite mixture model where the component densities are known. The algorithm is expected to obtain solutions that are at least as accurate as the state-of-the-art MOSEK interior-point solver (called by function "KWDual" in the 'REBayes' package), and they are expected to arrive at solutions more quickly when the number of samples is large and the number of mixture components is not too large. This implements the "mix-SQP" algorithm, with some improvements, described in Y. Kim, P. Carbonetto, M. Stephens & M. Anitescu (2020) . Package: r-cran-mixtime Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1434 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13), r-base-core (>= 4.6.0), r-api-4.0, r-cran-lifecycle, r-cran-vctrs, r-cran-rlang, r-cran-cli, r-cran-s7, r-cran-vecvec, r-cran-tzdb, r-cran-cpp11 Suggests: r-cran-tsibble, r-cran-testthat, r-cran-pillar, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mixtime_0.1.0-1.ca2404.1_amd64.deb Size: 847978 MD5sum: 1fa33d1b2e59a26c27bef05ca7204ae4 SHA1: 3ce9642b65d8ed6b085d321f033b66f4b659cc85 SHA256: 3ee8aa97ca90c71256e771be89d880c0d461ff3dac0d01b2b80afe0347be4e9b SHA512: 7718f0b8b3299927d2eca1797167199d8c05bd2966f283c38255a2c9438afc37a6c5134e96508cf9b37ce5445be13d065ae2e96842c65fd680a304c4fd507971 Homepage: https://cran.r-project.org/package=mixtime Description: CRAN Package 'mixtime' (Mixed Temporal Vectors and Operations) Flexible time classes for time series analysis and forecasting with mixed temporal granularities. Supports linear and cyclical time representations in discrete and continuous forms, with timezone support, across multiple calendar systems including Gregorian and ISO week date calendars. Time points are stored numerically relative to a chronon; an atomic time granule defined by time units of a calendar. Calendrical arithmetic enables conversion between time granules (e.g. days to months) and calendar systems. Multi-unit arithmetic allows for temporal analysis with other granules of common calendars (e.g. fortnights are 2-week units). Time vectors of different granularities (e.g. monthly and quarterly) can be combined in a single vector, making 'mixtime' ideal for data that changes observation frequency over time or requires temporal reconciliation across scales. The package is extensible, allowing users to define custom calendars that build upon civil and astronomical time systems. Package: r-cran-mixtools Architecture: amd64 Version: 2.0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1571 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernlab, r-cran-mass, r-cran-plotly, r-cran-scales, r-cran-segmented, r-cran-survival Filename: pool/dists/noble/main/r-cran-mixtools_2.0.0.1-1.ca2404.1_amd64.deb Size: 1416842 MD5sum: 856e09518a2ef4dadbbe8859cf5cafc6 SHA1: 7bda5e753e667bb3605df931eab337775534e481 SHA256: 2f9a0986fe7d79b89b6707e0210a0dc563aee56aa2dbf45f1593f1a9192b7329 SHA512: 8559b63ae95440fd1f5de27137f819238742fcabb470b00803d66a6b6bf386f7534eae456578594133311b3073cf1cf222d09d013b337c2c60b918d3a8c049ce Homepage: https://cran.r-project.org/package=mixtools Description: CRAN Package 'mixtools' (Tools for Analyzing Finite Mixture Models) Analyzes finite mixture models for various parametric and semiparametric settings. This includes mixtures of parametric distributions (normal, multivariate normal, multinomial, gamma), various Reliability Mixture Models (RMMs), mixtures-of-regressions settings (linear regression, logistic regression, Poisson regression, linear regression with changepoints, predictor-dependent mixing proportions, random effects regressions, hierarchical mixtures-of-experts), and tools for selecting the number of components (bootstrapping the likelihood ratio test statistic, mixturegrams, and model selection criteria). Bayesian estimation of mixtures-of-linear-regressions models is available as well as a novel data depth method for obtaining credible bands. This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772 and the Chan Zuckerberg Initiative: Essential Open Source Software for Science (Grant No. 2020-255193). Package: r-cran-mixture Architecture: amd64 Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1480 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl27 (>= 2.7.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-bh, r-cran-rcppgsl Filename: pool/dists/noble/main/r-cran-mixture_2.2.0-1.ca2404.1_amd64.deb Size: 621028 MD5sum: a33d3d35a0dd3706b9ca6452fac31733 SHA1: c63a4f4833b73af2d918db6866e4e91e9ea74f96 SHA256: 486bc695ae991cd655f1f06dc2ffb793779906ca9131d15047fe96a6450fae71 SHA512: 57594796b50f648a2e920eac9aaabd0299975fc4d4aebee6771107aacae606747f57e76bbd4cc4ad8f240a775b8aaa4414b4bbb292307a4f7958d21ad784dc62 Homepage: https://cran.r-project.org/package=mixture Description: CRAN Package 'mixture' (Mixture Models for Clustering and Classification) An implementation of 14 parsimonious mixture models for model-based clustering or model-based classification. Gaussian, Student's t, generalized hyperbolic, variance-gamma or skew-t mixtures are available. All approaches work with missing data. Celeux and Govaert (1995) , Browne and McNicholas (2014) , Browne and McNicholas (2015) . Package: r-cran-mixturefitting Architecture: amd64 Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-sn Filename: pool/dists/noble/main/r-cran-mixturefitting_0.8.0-1.ca2404.1_amd64.deb Size: 281500 MD5sum: 47fedaa29298f8ac647bca96edbb41a9 SHA1: d94db1e24858cfe700b13ba95cb13cd6462936f5 SHA256: 1aed82372c176c9a6d850b9ae254c23a06d4af94e34ac4284975a2ce179b077e SHA512: 1bf3e78691cd9fd3e6a6268b1efa6ca69e39f582cef25cf6d4dc2b62ba95c874041f93c1d0ef717960cf5dc2f177cc110fe0d54346536e88fd40d5ae578f2b89 Homepage: https://cran.r-project.org/package=MixtureFitting Description: CRAN Package 'MixtureFitting' (Fitting of Univariate Mixture Distributions to Data usingVarious Approaches) Methods for fitting mixture distributions to univariate data using expectation maximization, HWHM and other methods. Supports Gaussian, Cauchy, Student's t, skew-normal and von Mises mixtures. For more details see Merkys (2018) . Package: r-cran-mixvlmc Architecture: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3912 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-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/noble/main/r-cran-mixvlmc_0.2.2-1.ca2404.1_amd64.deb Size: 3087134 MD5sum: 05530db18eb274f63036311601622efe SHA1: d6c8017071c60d6d39189d78e88c5576a24aa56e SHA256: 1cfec5e3c3a0fb32bc2ed1a05ff3684fb695213e0a3665405f9811a8d6f9fcf6 SHA512: 633789b9e71ed979f6c5bab0587da7ad98ecffa2dcb11c7c5d1c4b92395a51b823ba620fe57c1e74c48235be78ee987c509bdf6ac1873be139f01ee71acf7b72 Homepage: https://cran.r-project.org/package=mixvlmc Description: CRAN Package 'mixvlmc' (Variable Length Markov Chains with Covariates) Estimates Variable Length Markov Chains (VLMC) models and VLMC with covariates models from discrete sequences. Supports model selection via information criteria and simulation of new sequences from an estimated model. See Bühlmann, P. and Wyner, A. J. (1999) for VLMC and Zanin Zambom, A., Kim, S. and Lopes Garcia, N. (2022) for VLMC with covariates. 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Implementation for Volkmann, Umlauf, Greven (2023) . 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Package: r-cran-ml Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 661 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-withr Suggests: r-cran-testthat, r-cran-xgboost, r-cran-ranger, r-cran-rpart, r-cran-e1071, r-cran-kknn, r-cran-glmnet, r-cran-naivebayes, r-cran-lightgbm, r-cran-tm, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown, r-cran-caret, r-cran-rsample Filename: pool/dists/noble/main/r-cran-ml_0.1.2-1.ca2404.1_amd64.deb Size: 583650 MD5sum: 0b0b3faa4a6949e975421128d717a4de SHA1: e781e3069f691e6d935d6690799462f36eededf4 SHA256: 744aac729489c12aebf9edfd38e98ac33e881ea34e6308efbbf940a2afe61453 SHA512: 50d9abbc9136170bede3c5307c99cbcc407c292029d612916349ab7b87a34ec95395b418d946aaa1c07bf9b6ad7bebbb529ea46120ef308e4126b588b5fcfb54 Homepage: https://cran.r-project.org/package=ml Description: CRAN Package 'ml' (Supervised Learning with Mandatory Splits and Seeds) Implements the split-fit-evaluate-assess workflow from Hastie, Tibshirani, and Friedman (2009, ISBN:978-0-387-84857-0) "The Elements of Statistical Learning", Chapter 7. Provides three-way data splitting with automatic stratification, mandatory seeds for reproducibility, automatic data type handling, and 10 algorithms out of the box. Uses 'Rust' backend for cross-language deterministic splitting. Designed for tabular supervised learning with minimal ceremony. Polyglot parity with the 'Python' 'mlw' package on 'PyPI'. Package: r-cran-mlbc Architecture: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1197 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tmb, r-cran-mass, r-cran-numderiv, r-cran-rcppeigen Suggests: r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-mlbc_0.2.2-1.ca2404.1_amd64.deb Size: 569042 MD5sum: f6679ba4d9ed890103f2ec4300c9f3fc SHA1: ca5bdac366fed44f435d9e064336042dca138061 SHA256: d0be620251c8b4d03bd8f6c794ac5140b9740f3047418761ea8c8141fe54658d SHA512: f068d9bc577c244e1b80f083dcaa24600ef6aadf35b7a8e1e796bf583a7ca0cad790f50e6bd5b9e7f65717b8584703907f95c4e05b20d57bad731d82388f15e4 Homepage: https://cran.r-project.org/package=MLBC Description: CRAN Package 'MLBC' (Bias Correction Methods for Models Using Synthetic Data) Implements three bias-correction techniques from Battaglia et al. (2025 ) to improve inference in regression models with covariates generated by AI or machine learning. Package: r-cran-mlbench Architecture: amd64 Version: 2.1-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1129 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-mlbench_2.1-8-1.ca2404.1_amd64.deb Size: 1070048 MD5sum: 57a990b6d41ef1904323a2354d507ebb SHA1: 5144471bbfcb26c97574c35ba82f1c806b65ad2d SHA256: fe3f01c0431a166915cb6f8db270aea5c2038370bf8ff2a45af2df38b80c677d SHA512: cb020403bb9f10ea51249986ab77357385f9e8c6eedba3590211d46db1b8f4aeda4d3c2dbd412c8c5b1d9560a1b89195b651a33695745c8b5dd10af502660956 Homepage: https://cran.r-project.org/package=mlbench Description: CRAN Package 'mlbench' (Machine Learning Benchmark Problems) A collection of artificial and real-world machine learning benchmark problems, including, e.g., several data sets from the UCI repository. Package: r-cran-mlecens Architecture: amd64 Version: 0.1-7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mlecens_0.1-7.1-1.ca2404.1_amd64.deb Size: 136592 MD5sum: a533c605a86256a6baeec94e10de4a6f SHA1: a5804d2844a7e8ddf6c280c3ea190290ba30dd27 SHA256: 9c6ff9885d7059923fc46e4488b71f77e3e7e6f246fa17fba244b8943a660287 SHA512: 13e75320dea902efc9c7c652511625a74af944f1afa6f14f4346471bc122e3d2e41f8f4ea6abb2cff5604cf74061c190b6b7a730297e4adb4a2b04cbcf3d31e0 Homepage: https://cran.r-project.org/package=MLEcens Description: CRAN Package 'MLEcens' (Computation of the MLE for Bivariate Interval Censored Data) We provide functions to compute the nonparametric maximum likelihood estimator (MLE) for the bivariate distribution of (X,Y), when realizations of (X,Y) cannot be observed directly. To be more precise, we consider the situation where we observe a set of rectangles in R^2 that are known to contain the unobservable realizations of (X,Y). We compute the MLE based on such a set of rectangles. The methods can also be used for univariate censored data (see data set 'cosmesis'), and for censored data with competing risks (see data set 'menopause'). We also provide functions to visualize the observed data and the MLE. Package: r-cran-mlegp Architecture: amd64 Version: 3.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-snowfall Filename: pool/dists/noble/main/r-cran-mlegp_3.1.10-1.ca2404.1_amd64.deb Size: 268132 MD5sum: 5791d1d15bcf652a4652615e4d4d81db SHA1: a8aecabe1cbd646def0aeab0ba16ec42fb53d2e2 SHA256: 3a2ec22edd47d22f4f4ec11441f3995bf2ae1ef8a90e4a9697c54b903e9b4c9e SHA512: 0dc0ba052bdc9e06d101f2d4fc5f741b1da129f423c53e0836aac1c005b58f3e4790a324a03f6bcfc6bcf36dd1e7fc96cc8f7be2da4d684880ac85581bd157fc Homepage: https://cran.r-project.org/package=mlegp Description: CRAN Package 'mlegp' (Maximum Likelihood Estimates of Gaussian Processes) Maximum likelihood Gaussian process modeling for univariate and multi-dimensional outputs with diagnostic plots following Santner et al (2003) . Contact the maintainer for a package version that includes sensitivity analysis. Package: r-cran-mlmc Architecture: amd64 Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-mlmc_2.1.1-1.ca2404.1_amd64.deb Size: 379898 MD5sum: 0b6a4e3f0eeebda84d2344bdd75f1233 SHA1: 51c57513b6057960b0c5a4faa1eb68d7da84c4f5 SHA256: f679cde61138a3c0b88742f2b347bcd16689fd70e2526529b79dd1b7484baaaf SHA512: 3bf8e9f742b1918b0dd0f626d436c12e8827a1025c38d917041147ec196f24e1a3d83fdd81f6f0f8dce65c511161faea0ed977bd3206f006941b1a6a4344d2e5 Homepage: https://cran.r-project.org/package=mlmc Description: CRAN Package 'mlmc' (Multi-Level Monte Carlo) An implementation of MLMC (Multi-Level Monte Carlo), Giles (2008) , Heinrich (1998) , for R. This package builds on the original 'Matlab' and 'C++' implementations by Mike Giles to provide a full MLMC driver and example level samplers. Multi-core parallel sampling of levels is provided built-in. Package: r-cran-mlmodelselection Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlmodelselection_1.0-1.ca2404.1_amd64.deb Size: 145800 MD5sum: 7f1a1291af28ac8907cc997cf25195da SHA1: 9ee548363b90ee2de476b7ec8f8f919edae35844 SHA256: 172759bcd50e6fb8c7a5307d20cf9b7fbba798514c112cc3e79c6d686e426e12 SHA512: 671fc3fe27dc3cdc1df72815043e64f9f3bad62790868a6985236ff0c9793c349171d1f96b347d7eee10a2ba76bbdd20b9fda5fa6c71374f8f8a399995ac4d1f Homepage: https://cran.r-project.org/package=MLModelSelection Description: CRAN Package 'MLModelSelection' (Model Selection in Multivariate Longitudinal Data Analysis) An efficient Gibbs sampling algorithm is developed for Bayesian multivariate longitudinal data analysis with the focus on selection of important elements in the generalized autoregressive matrix. It provides posterior samples and estimates of parameters. In addition, estimates of several information criteria such as Akaike information criterion (AIC), Bayesian information criterion (BIC), deviance information criterion (DIC) and prediction accuracy such as the marginal predictive likelihood (MPL) and the mean squared prediction error (MSPE) are provided for model selection. Package: r-cran-mlogitbma Architecture: amd64 Version: 0.1-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: libc6 (>= 2.27), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bma, r-cran-abind, r-cran-maxlik Suggests: r-cran-mlogit Filename: pool/dists/noble/main/r-cran-mlogitbma_0.1-9-1.ca2404.1_amd64.deb Size: 387564 MD5sum: 0739ce792d06f3debbe63330fed7a81d SHA1: a985d2fc0e92b56bddb0aafbd007b72d5a9fe402 SHA256: db0c35a412ac08435cb4026025d8611019c0d1aebc04dc244413147d50f50847 SHA512: 53b626d8e7b423587336429d2655beb330ed6c4784fda9bd25e754e18c382eaa49c8eb6d2560d0319a0e8ca0282e02af8ffee4dd6babcfaf2c6fcfc0663e656d Homepage: https://cran.r-project.org/package=mlogitBMA Description: CRAN Package 'mlogitBMA' (Bayesian Model Averaging for Multinomial Logit Models) Provides a modified function bic.glm of the BMA package that can be applied to multinomial logit (MNL) data. The data is converted to binary logit using the Begg & Gray approximation. The package also contains functions for maximum likelihood estimation of MNL. Package: r-cran-mlpack Architecture: amd64 Version: 4.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 27556 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppensmallen Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlpack_4.7.0-1.ca2404.1_amd64.deb Size: 4861070 MD5sum: 3dce77c4df20208978e3faa897abd931 SHA1: 43e17e6d6e83e3ea8125fea815392a7f859722c4 SHA256: 5038affcded6dd5789520fc375099bae382909b4766aac5a82c0c86ff9259c03 SHA512: 83c7a9ff325c7b461d708d963b10b8ffa4431c84d85a2e6c31a5f0679a2d63b68c25567951fd361df36686f3c320a4893056d0c7ed2a39bf2869ee6c5ee478c2 Homepage: https://cran.r-project.org/package=mlpack Description: CRAN Package 'mlpack' ('Rcpp' Integration for the 'mlpack' Library) A fast, flexible machine learning library, written in C++, that aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. See also Curtin et al. (2023) . 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Extends 'mlr3' with interfaces to essential machine learning packages on CRAN. This includes, but is not limited to: (penalized) linear and logistic regression, linear and quadratic discriminant analysis, k-nearest neighbors, naive Bayes, support vector machines, and gradient boosting. Package: r-cran-mlr3mbo Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 932 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-mlr3tuning, r-cran-bbotk, r-cran-checkmate, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-paradox, r-cran-spacefillr, r-cran-r6 Suggests: r-cran-dicekriging, r-cran-emoa, r-cran-fastghquad, r-cran-lhs, r-cran-mlr3learners, r-cran-mirai, r-cran-mlr3pipelines, r-cran-nloptr, r-cran-processx, r-cran-ranger, r-cran-rgenoud, r-cran-rpart, r-cran-redux, r-cran-rush, r-cran-stringi, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3mbo_1.1.1-1.ca2404.1_amd64.deb Size: 765446 MD5sum: ee13adab6d04114207d7982fff75c8f9 SHA1: 38f20a6d22b8ed060b0066c231da1ba27f9cba7f SHA256: cd95438eff1f128c4cffc6e65dc616dd7572d27d00e03c00d7ea8fcc8042cbb4 SHA512: 6d22e1dff0d2a95f682d3188d2c05886226135acb0df02786f09d1cac9bf2d477c372856fdf7dc47ce35e4b9bc82b0672cda50e68237130a4f3f4de12c04d81b Homepage: https://cran.r-project.org/package=mlr3mbo Description: CRAN Package 'mlr3mbo' (Flexible Bayesian Optimization) A modern and flexible approach to Bayesian Optimization / Model Based Optimization building on the 'bbotk' package. 'mlr3mbo' is a toolbox providing both ready-to-use optimization algorithms as well as their fundamental building blocks allowing for straightforward implementation of custom algorithms. Single- and multi-objective optimization is supported as well as mixed continuous, categorical and conditional search spaces. Moreover, using 'mlr3mbo' for hyperparameter optimization of machine learning models within the 'mlr3' ecosystem is straightforward via 'mlr3tuning'. Examples of ready-to-use optimization algorithms include Efficient Global Optimization by Jones et al. (1998) , ParEGO by Knowles (2006) and SMS-EGO by Ponweiser et al. (2008) . Package: r-cran-mlr3misc Architecture: amd64 Version: 0.21.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 536 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-digest, r-cran-r6 Suggests: r-cran-callr, r-cran-evaluate, r-cran-mirai, r-cran-paradox, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3misc_0.21.0-1.ca2404.1_amd64.deb Size: 450146 MD5sum: 6d573766ece4009a7fd675f669ddaa1c SHA1: cc024971d1e5fc9ff957775e257d618fb2ea148b SHA256: 60eaabe5d4baab47a7f3c2326b0840ae7f747f4047190da1880c9c59946f766d SHA512: b44131dacd4af9194e1dc1321eddbb3c99a5b0813ff19787d6c2489dbde60d1305171c36d13fb5a661a6a0e7c21def34622a1527abbe41b1d20d2bc5d5b88ace Homepage: https://cran.r-project.org/package=mlr3misc Description: CRAN Package 'mlr3misc' (Helper Functions for 'mlr3') Frequently used helper functions and assertions used in 'mlr3' and its companion packages. Comes with helper functions for functional programming, for printing, to work with 'data.table', as well as some generally useful 'R6' classes. This package also supersedes the package 'BBmisc'. Package: r-cran-mlr3oml Architecture: amd64 Version: 0.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-bit64, r-cran-checkmate, r-cran-curl, r-cran-data.table, r-cran-jsonlite, r-cran-lgr, r-cran-mlr3, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-stringi, r-cran-uuid, r-cran-withr Suggests: r-cran-dbi, r-cran-duckdb, r-cran-mlr3db, r-cran-qs2, r-cran-rweka, r-cran-testthat, r-cran-xml2, r-cran-httr Filename: pool/dists/noble/main/r-cran-mlr3oml_0.12.0-1.ca2404.1_amd64.deb Size: 300448 MD5sum: 1c360476ff0598698338fdf829d7d2c2 SHA1: 1e775bb59d878207c3ba022320ac33b61d049117 SHA256: 076c04ceeca7321f137c461db11c52440a53ffd006a7332e0c2b4b7f19932790 SHA512: 2ffbce927a3675c391e0cee93e5a4dc8bb645223d4460bed63a9ee0f7dc3cc94dd66f3e51e39aad558d547bd565f6a6ffb69394d440f00d98637c9c181b91abd Homepage: https://cran.r-project.org/package=mlr3oml Description: CRAN Package 'mlr3oml' (Connector Between 'mlr3' and 'OpenML') Provides an interface to 'OpenML.org' to list and download machine learning data, tasks and experiments. The 'OpenML' objects can be automatically converted to 'mlr3' objects. For a more sophisticated interface with more upload options, see the 'OpenML' package. Package: r-cran-mlr3resampling Architecture: amd64 Version: 2026.5.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 849 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-r6, r-cran-checkmate, r-cran-paradox, r-cran-mlr3, r-cran-mlr3misc, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-pbdmpi, r-cran-geepack, r-cran-ggplot2, r-cran-mlr3tuning, r-cran-lgr, r-cran-future, r-cran-future.apply, r-cran-testthat, r-cran-weightedroc, r-cran-nc, r-cran-rpart, r-cran-directlabels, r-cran-mlr3pipelines, r-cran-glmnet, r-cran-mlr3learners, r-cran-mlr3torch, r-cran-torch, r-cran-batchtools, r-cran-mlr3batchmark, r-cran-litedown Filename: pool/dists/noble/main/r-cran-mlr3resampling_2026.5.19-1.ca2404.1_amd64.deb Size: 522574 MD5sum: b04d651e0926c010291d86309414832b SHA1: 0f8c3a6d95c5b6724de3c320173edad6a2830cf1 SHA256: 8fe05370737c77b122531e97a7f458cc2870c9384ba7e9e2874dcfdae1997f72 SHA512: 4bf2ad607df434fc5288c1561439112bfc8713fd70fba9045332bf54366845d2d88ef9704d5817d18ca0d342aa97c19aa40a0b4a470fd76d0fb0e4ef549e2f7a Homepage: https://cran.r-project.org/package=mlr3resampling Description: CRAN Package 'mlr3resampling' (Resampling Algorithms for 'mlr3' Framework) A supervised learning algorithm inputs a train set, and outputs a prediction function, which can be used on a test set. If each data point belongs to a subset (such as geographic region, year, etc), then how do we know if subsets are similar enough so that we can get accurate predictions on one subset, after training on Other subsets? And how do we know if training on All subsets would improve prediction accuracy, relative to training on the Same subset? SOAK, Same/Other/All K-fold cross-validation, can be used to answer these questions, by fixing a test subset, training models on Same/Other/All subsets, and then comparing test error rates (Same versus Other and Same versus All). Also provides code for estimating how many train samples are required to get accurate predictions on a test set. Package: r-cran-mlr Architecture: amd64 Version: 2.19.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5027 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-paramhelpers, r-cran-backports, r-cran-bbmisc, r-cran-checkmate, r-cran-data.table, r-cran-ggplot2, r-cran-parallelmap, r-cran-stringi, r-cran-survival, r-cran-xml Suggests: r-cran-ada, r-cran-adabag, r-cran-batchtools, r-cran-bit64, r-cran-brnn, r-cran-bst, r-cran-c50, r-cran-care, r-cran-caret, r-cran-class, r-cran-clue, r-cran-cluster, r-cran-clusterr, r-cran-clustersim, r-cran-cmaes, r-cran-cowplot, r-cran-crs, r-cran-cubist, r-cran-deepnet, r-cran-dicekriging, r-cran-e1071, r-cran-earth, r-cran-elasticnet, r-cran-emoa, r-cran-evtree, r-cran-fda.usc, r-cran-fdboost, r-cran-fnn, r-cran-forecast, r-cran-fpc, r-cran-frbs, r-cran-fselector, r-cran-fselectorrcpp, r-cran-gbm, r-cran-gensa, r-cran-ggpubr, r-cran-glmnet, r-cran-gpfit, r-cran-h2o, r-cran-hmisc, r-cran-irace, r-cran-kernlab, r-cran-kknn, r-cran-klar, r-cran-knitr, r-cran-lagp, r-cran-liblinear, r-cran-lintr, r-cran-mass, r-cran-mboost, r-cran-mco, r-cran-mda, r-cran-memoise, r-cran-mlbench, r-cran-mldr, r-cran-mlrmbo, r-cran-modeltools, r-cran-mrmre, r-cran-neuralnet, r-cran-nnet, r-cran-numderiv, r-cran-pamr, r-cran-pander, r-cran-party, r-cran-pec, r-cran-penalized, r-cran-pls, r-cran-pmcmrplus, r-cran-praznik, r-cran-randomforest, r-cran-ranger, r-cran-rappdirs, r-cran-refund, r-cran-rex, r-cran-rferns, r-cran-rgenoud, r-cran-rmarkdown, r-cran-rmpi, r-cran-rocr, r-cran-rotationforest, r-cran-rpart, r-cran-rrf, r-cran-rsm, r-cran-rsnns, r-cran-rucrdtw, r-cran-rweka, r-cran-sda, r-cran-sf, r-cran-smoof, r-cran-sparselda, r-cran-stepplr, r-cran-survauc, r-cran-svglite, r-cran-testthat, r-cran-tgp, r-cran-th.data, r-cran-tidyr, r-cran-tsfeatures, r-cran-vdiffr, r-cran-wavelets, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-mlr_2.19.3-1.ca2404.1_amd64.deb Size: 4793732 MD5sum: b0a83a7ee42b5cefcb309817816937b2 SHA1: f330c506abaa021e2dcd27d11233f3ad3f6e051a SHA256: 5692cb160dfcc608ad5bdcceabdbc2a3ed0faf0651a499de17e134ab3d1edd2c SHA512: 50a63a97afc2ef9284f4351106081556f059dab7432a4ef57eaef9dba59a016cc85bdbbfa1b7a54123f2fea1672cce0bdfce337f85b50bd640507a1bd65a8412 Homepage: https://cran.r-project.org/package=mlr Description: CRAN Package 'mlr' (Machine Learning in R) Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling. Most operations can be parallelized. Package: r-cran-mlrmbo Architecture: amd64 Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1554 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr, r-cran-paramhelpers, r-cran-smoof, r-cran-backports, r-cran-bbmisc, r-cran-checkmate, r-cran-data.table, r-cran-lhs, r-cran-parallelmap Suggests: r-cran-akima, r-cran-cmaesr, r-cran-covr, r-cran-dicekriging, r-cran-earth, r-cran-emoa, r-cran-ggally, r-cran-ggplot2, r-cran-gridextra, r-cran-interp, r-cran-kernlab, r-cran-kknn, r-cran-knitr, r-cran-mco, r-cran-nnet, r-cran-party, r-cran-randomforest, r-cran-reshape2, r-cran-rgenoud, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlrmbo_1.1.6-1.ca2404.1_amd64.deb Size: 950092 MD5sum: e70b560ce7fdd58193767090cf87e8d5 SHA1: 73469c573777e76ab576060343810331e581ec80 SHA256: 16934863f2fadc15dbb64aa17176124e1ec46f5f614da715c17a32ea1cee7ad0 SHA512: 5386a77e6573cd2c834c138538383445b7445c48b6f005dd7e464a2821a65004ba8a01db514bc966075d576bb47ffbaa03cd206058fbd2b2c918a55bf72fac9f Homepage: https://cran.r-project.org/package=mlrMBO Description: CRAN Package 'mlrMBO' (Bayesian Optimization and Model-Based Optimization of ExpensiveBlack-Box Functions) Flexible and comprehensive R toolbox for model-based optimization ('MBO'), also known as Bayesian optimization. It implements the Efficient Global Optimization Algorithm and is designed for both single- and multi- objective optimization with mixed continuous, categorical and conditional parameters. The machine learning toolbox 'mlr' provide dozens of regression learners to model the performance of the target algorithm with respect to the parameter settings. It provides many different infill criteria to guide the search process. Additional features include multi-point batch proposal, parallel execution as well as visualization and sophisticated logging mechanisms, which is especially useful for teaching and understanding of algorithm behavior. 'mlrMBO' is implemented in a modular fashion, such that single components can be easily replaced or adapted by the user for specific use cases. Package: r-cran-mlrv Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 640 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-numderiv, r-cran-magrittr, r-cran-foreach, r-cran-doparallel, r-cran-rcpparmadillo, r-cran-mathjaxr, r-cran-xtable Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlrv_0.1.2-1.ca2404.1_amd64.deb Size: 299050 MD5sum: b645417af7f8a4d4c8327a9c77682234 SHA1: 4d71d0e86ac572726e4d9cbb0d271045895f0fa7 SHA256: 028050b4463183aa02e9605dc85bcfc0a9ffcb075a202c2af9061e208a2baecb SHA512: f1e2dc040efaffd5f25c9336553f376fb6638dbc08988d9bb8edea013cdd878387a7b09712db5b1e63d2254eb1286f666dc4d72383a988634e8761f437b5b3da Homepage: https://cran.r-project.org/package=mlrv Description: CRAN Package 'mlrv' (Long-Run Variance Estimation in Time Series Regression) Plug-in and difference-based long-run covariance matrix estimation for time series regression. Two applications of hypothesis testing are also provided. The first one is for testing for structural stability in coefficient functions. The second one is aimed at detecting long memory in time series regression. Lujia Bai and Weichi Wu (2024) Zhou Zhou and Wei Biao Wu(2010) Jianqing Fan and Wenyang Zhang Lujia Bai and Weichi Wu(2024) Dimitris N. Politis, Joseph P. Romano, Michael Wolf(1999) Weichi Wu and Zhou Zhou(2018). Package: r-cran-mlsbm Architecture: amd64 Version: 0.99.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-mlsbm_0.99.2-1.ca2404.1_amd64.deb Size: 97736 MD5sum: 4c62a453b561ae23874b5a81d6a10ff7 SHA1: 664310ac66aee68b93e1aa7a933b2c778a967be1 SHA256: a1f609d36caeaa830a085ad60880760b4c4a2632147a116b112f416f62815374 SHA512: 436475f369f0159f8fc5a4d9ff2eb9bf7d453f3fd63401fbe897d00a39dcde036e981fbc095c318ac635900cd594a2662b30d7255c629fb97491ac02b3fd7387 Homepage: https://cran.r-project.org/package=mlsbm Description: CRAN Package 'mlsbm' (Efficient Estimation of Bayesian SBMs & MLSBMs) Fit Bayesian stochastic block models (SBMs) and multi-level stochastic block models (MLSBMs) using efficient Gibbs sampling implemented in 'Rcpp'. The models assume symmetric, non-reflexive graphs (no self-loops) with unweighted, binary edges. Data are input as a symmetric binary adjacency matrix (SBMs), or list of such matrices (MLSBMs). Package: r-cran-mlstm Architecture: amd64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-data.table, r-cran-rcppparallel, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlstm_0.1.7-1.ca2404.1_amd64.deb Size: 201330 MD5sum: e5f2f7bff58d877c99c63418adafc55c SHA1: 52d2805275d307e1313d747f3d1b059681c30cc2 SHA256: 6a533df6d2256b5f26f123fd29ea29c91e4ece1e144f34dd8f15a2f22aa701b2 SHA512: 3af337c781cdd0027783cafb47fd828f2f3b76e185a68caae22c7f57729b0d4c87dcdc66ff7de5105c3d1bf4aedbc72a90eb60e183eda6628ccf2f19803dd36a Homepage: https://cran.r-project.org/package=mlstm Description: CRAN Package 'mlstm' (Multilevel Supervised Topic Models with Multiple Outcomes) Fits latent Dirichlet allocation (LDA), supervised topic models, and multilevel supervised topic models for text data with multiple outcome variables. Core estimation routines are implemented in C++ using the 'Rcpp' ecosystem. For topic models, see Blei et al. (2003) . For supervised topic models, see Blei and McAuliffe (2007) . Package: r-cran-mlt Architecture: amd64 Version: 1.8-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-basefun, r-cran-variables, r-cran-bb, r-cran-alabama, r-cran-quadprog, r-cran-coneproj, r-cran-sandwich, r-cran-numderiv, r-cran-survival, r-cran-matrix, r-cran-nloptr, r-cran-mvtnorm, r-cran-icenreg Suggests: r-cran-mass, r-cran-nnet, r-cran-th.data, r-cran-multcomp, r-cran-qrng, r-cran-bibtex Filename: pool/dists/noble/main/r-cran-mlt_1.8-0-1.ca2404.1_amd64.deb Size: 377530 MD5sum: 476a641f7b1d164da3f52eef38df13df SHA1: 47667506106a27501c908ef58b656b9001f49c4c SHA256: 2644bb7d78d02b8adc7b2d0f4bfdc32f952ceaf740d7f443d8880408e1aed36a SHA512: dd89fd83d124dce75677845bdc0dfd868bbbcdcc5e25856f33b35cb5c41967b1b08688aa648e44d2c2bef27f5e198e4df7eccd46c2ff8e837de9cdeff511969a Homepage: https://cran.r-project.org/package=mlt Description: CRAN Package 'mlt' (Most Likely Transformations) Likelihood-based estimation of conditional transformation models via the most likely transformation approach described in Hothorn et al. (2018) and Hothorn (2020) . Shift-scale (Siegfried et al, 2023, ) and multivariate (Klein et al, 2022, ) transformation models are part of this package. A package vignette is available from and more convenient user interfaces to many models from . Package: r-cran-mlts Architecture: amd64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8514 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-mvtnorm, r-cran-pdftools, r-cran-rcpp, r-cran-rlang, r-cran-rmarkdown, r-cran-rstan, r-cran-rstantools, r-cran-shape, r-cran-diagram, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlts_2.0.1-1.ca2404.1_amd64.deb Size: 2644406 MD5sum: f716fb4650b875e14fba6ab6ce985582 SHA1: d5d920a23a73b6bef274b4c63a61c508d5211da8 SHA256: 0aa8cf860edcb6f4bba3b99cc51a1b4117a39fc05a7bc6148f4a0fb4528ca737 SHA512: 052a1d60c97cbaed7aa0a2dafdcf3e142c81a6f5845e16b45154aa43d077a494895071c1c24f65f5690005186627dea2e4b2a4a780052e9d29d0c85a21b4db1c Homepage: https://cran.r-project.org/package=mlts Description: CRAN Package 'mlts' (Multilevel Latent Time Series Models with 'R' and 'Stan') Fit multilevel manifest or latent time-series models, including popular Dynamic Structural Equation Models (DSEM). The models can be set up and modified with user-friendly functions and are fit to the data using 'Stan' for Bayesian inference. Path models and formulas for user-defined models can be easily created with functions using 'knitr'. Asparouhov, Hamaker, & Muthen (2018) . Package: r-cran-mlumr Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6450 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-randtoolbox, r-cran-copula, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-posterior, r-cran-bayesplot, r-cran-loo, r-cran-matrix, r-cran-withr Filename: pool/dists/noble/main/r-cran-mlumr_0.1.0-1.ca2404.1_amd64.deb Size: 1786110 MD5sum: 221f49882e9de155d958e40f08269201 SHA1: 1314e8aca89d5686129ae905f707dc48a1375985 SHA256: 7619891907af63ddcfe2b6c19b02dd158b434a2d098eae07e5b847fb44ca328b SHA512: 74506a2ab72714133b93c9aaddaa86241069b0a3f9d2ee10d1dc82cd6643ca2ac263ffc1e3d77a5bac0dfe1f7c473e4c9d0b58401c251ec28784e5c278f4820b Homepage: https://cran.r-project.org/package=mlumr Description: CRAN Package 'mlumr' (Multilevel Unanchored Meta-Regression for Indirect TreatmentComparisons) Bayesian multilevel unanchored meta-regression (ML-UMR) for indirect treatment comparisons using individual patient data (IPD) and aggregate data (AgD). 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Package: r-cran-mlz Architecture: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1896 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-gplots, r-cran-ggplot2, r-cran-reshape2, r-cran-tmb, r-cran-rcppeigen Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mlz_0.1.5-1.ca2404.1_amd64.deb Size: 813708 MD5sum: 352c7fa6e62d5d876783f8c373af062d SHA1: 993cbc051cbde5ec062e2257c4bed02cf41af4e6 SHA256: fc9cf65e891d9cb89927e3736b140f6002c688b01a67ad90c6c2aec260af6f65 SHA512: e32b9ae805c5d6a070ca0a6845d1d60153ff704d2bf8654fae39ff192e0c8094681be466422bb6211c5cb651f183c0049d2289d41ada3a421046f98ce9e1d9f8 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) . 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The estimation method supports computation in parallel using a shared memory C++ implementation. A sandwich estimator of the covariance matrix is available. Natural cubic splines are used to provide a flexible model for the cumulative incidence functions. 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(1997) ): Functions mmcm.mvt() and mcm.mvt() give P-value by using randomized quasi-Monte Carlo method with pmvt() function of package 'mvtnorm', and mmcm.resamp() gives P-value by using a permutation method. Package: r-cran-mmconvert Architecture: amd64 Version: 0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3543 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-devtools, r-cran-roxygen2, r-cran-qtl2 Filename: pool/dists/noble/main/r-cran-mmconvert_0.12-1.ca2404.1_amd64.deb Size: 3521906 MD5sum: 076f078084e04f0919ea6fe7e4704a6a SHA1: 346e5009bd5730122042b956c2c0be1f0c7146d6 SHA256: bab50d6e35fac860667f1e34866815a681c11ae05e67f8b9bb989939e4c27128 SHA512: 81bcd84b91058f5989390020c250530e7e609a61beb9fe64c4bf79c9e78eab46395dfd8d32be1ffcd01fc180d5ed2409a8d8b3658376151e3c1c6323c4783caa Homepage: https://cran.r-project.org/package=mmconvert Description: CRAN Package 'mmconvert' (Mouse Map Converter) Convert mouse genome positions between the build 39 physical map and the genetic map of Cox et al. (2009) . Package: r-cran-mmeta Architecture: amd64 Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-aod, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmeta_3.0.2-1.ca2404.1_amd64.deb Size: 187740 MD5sum: 93c50e21eb15247cbc0aa49cc269f23f SHA1: 60c3daf2b040a0bc2a7da8237010d01384830a8d SHA256: da1a29170f2b89286400f4dbb69f8f4d504263fad59fd7d00f9b55852e25b1a2 SHA512: f2d54dc84800d421b89ea7bea8be1307df3952a1fbba554ee008eb8300fa2dfe8be9d919b83652a4cc074315bdd325a897b033fe0b9eeb1700c5590808e59b23 Homepage: https://cran.r-project.org/package=mmeta Description: CRAN Package 'mmeta' (Multivariate Meta-Analysis) Multiple 2 by 2 tables often arise in meta-analysis which combines statistical evidence from multiple studies. Two risks within the same study are possibly correlated because they share some common factors such as environment and population structure. This package implements a set of novel Bayesian approaches for multivariate meta analysis when the risks within the same study are independent or correlated. The exact posterior inference of odds ratio, relative risk, and risk difference given either a single 2 by 2 table or multiple 2 by 2 tables is provided. Luo, Chen, Su, Chu, (2014) , Chen, Luo, (2011) , Chen, Chu, Luo, Nie, Chen, (2015) , Chen, Luo, Chu, Su, Nie, (2014) , Chen, Luo, Chu, Wei, (2013) . 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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: amd64 Version: 0.3.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6391 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-generics, r-cran-lifecycle, r-cran-mass, r-cran-matrix, r-cran-nlme, r-cran-rcpp, r-cran-rdpack, r-cran-stringr, r-cran-tibble, r-cran-tmb, r-cran-rcppeigen, r-cran-testthat Suggests: r-cran-broom, r-cran-broom.helpers, r-cran-car, r-cran-cli, r-cran-clubsandwich, r-cran-clustergeneration, r-cran-dplyr, r-cran-emmeans, r-cran-estimability, r-cran-ggplot2, r-cran-glmmtmb, r-cran-hardhat, r-cran-knitr, r-cran-lme4, r-cran-lmertest, r-cran-microbenchmark, r-cran-mockery, r-cran-parallelly, r-cran-parsnip, r-cran-purrr, r-cran-rmarkdown, r-cran-sasr, r-cran-scales, r-cran-tidymodels, r-cran-withr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-mmrm_0.3.17-1.ca2404.1_amd64.deb Size: 2181112 MD5sum: 62a52c8bb877ccd7fa4d42685699ee32 SHA1: 204f785b6aee2eb84c5e411d58cae347fcd584bf SHA256: 92931d9880fac768bee308de25fb93a846c002bc72ff2b868833580db81102bf SHA512: a968f10428e6ffc31217c4346482d6f090012c3d4d4c867115df51938eecdcf2f8956cd2f674176e08a33b749e2d846abc1611f01a6d87377c1a6f9995986a95 Homepage: https://cran.r-project.org/package=mmrm Description: CRAN Package 'mmrm' (Mixed Models for Repeated Measures) Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see Cnaan, Laird and Slasor (1997) for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008) for a review. This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using 'emmeans'. Package: r-cran-mmsample Architecture: amd64 Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-mmsample_0.1-1.ca2404.1_amd64.deb Size: 49308 MD5sum: e4591231a9653e25a0be7a35d966e104 SHA1: ad9c422902a5712c957d7c1d12f15f0c310aaeea SHA256: 61616bddc17711f1dcc67b687a12a51f593d8a3013ea4ecdbaf3b4b286320d36 SHA512: 84fab6f7db09529780be48cffffcbfd57270b585441743e23e972172dec1bd8d5a62fe9bccc1b8cf2c0d88e34b5855aa84bc7240e28da2ecd643e383fff585a2 Homepage: https://cran.r-project.org/package=mmsample Description: CRAN Package 'mmsample' (Multivariate Matched Sampling) Subset a control group to match an intervention group on a set of features using multivariate matching and propensity score calipers. Based on methods in Rosenbaum and Rubin (1985). 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Visualization functions show the posterior distribution of gamma (inclusion variables) and beta (coefficients). Users can also visualize the heatmap of the posterior mean of covariance matrix. Kim, T. Nicolae, D. (2019) . Guan, Y. Stephens, M. (2011) . Package: r-cran-mnarclust Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-sn, r-cran-rmutil, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-mnarclust_1.1.0-1.ca2404.1_amd64.deb Size: 84136 MD5sum: 8ac2a129664cdb7fdd657285ecf656c7 SHA1: 68b1b80ea47868ce7e957fed4b00a54481bc997d SHA256: 19805237c870e29ad8f889a83bed1464a3fff0e12f95853f721c86933767723a SHA512: 5faf2ecbc74519d6b70e98b86a46e777419e09caf794b97ffc1dc24f23f83a3e9d82d084401e2b5525e356791bcfcd047eba4d22dcec218823e981aa3617d769 Homepage: https://cran.r-project.org/package=MNARclust Description: CRAN Package 'MNARclust' (Clustering Data with Non-Ignorable Missingness usingSemi-Parametric Mixture Models) Clustering of data under a non-ignorable missingness mechanism. 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Core calculations are implemented in a portable (header-only) C++ library, with matrix manipulations using the 'Eigen' library for linear algebra. Also provided is a Gibbs sampler for Bayesian inference on a random-effects model with multivariate normal observations. Package: r-cran-mnlfa Architecture: amd64 Version: 0.3-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cdm, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-mnlfa_0.3-4-1.ca2404.1_amd64.deb Size: 97340 MD5sum: 43c909a9527ba9d9357a627fcd98ef65 SHA1: 7229c28e9d9d088333feb22e9b04b89918c1c03c SHA256: fc0300184de32e8c2fa49849961ed5d3b78146384e5f7901d6d1633a6463e1c3 SHA512: c778f342554beb61b6dd322980d2d9a3c34b42146b6bff8ac88376105b0d5d9a178480f6f0f597f4cdf987ee294f0d4845edf0864e35e173e723ffe4850e338c Homepage: https://cran.r-project.org/package=mnlfa Description: CRAN Package 'mnlfa' (Moderated Nonlinear Factor Analysis) Conducts moderated nonlinear factor analysis (e.g., Curran et al., 2014, ). Regularization methods are implemented for assessing non-invariant items. Currently, the package includes dichotomous items and unidimensional item response models. Extensions will be included in future package versions. Package: r-cran-mnorm Architecture: amd64 Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 985 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-hpa, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-mnorm_1.2.3-1.ca2404.1_amd64.deb Size: 392468 MD5sum: 46731c98446b7d847d5771e4aa68dcec SHA1: 49a6c44c87a13b8ebd346cafae9e78f858de04e2 SHA256: f043c1945be4d32c871846a122482d7689336d8ef0fc8437ab2b1bc35f657ebe SHA512: a50a04c3bfd3ae653f52a3f5e976deb4ad5e61b2f2379e26f17b5852126e62f815a5e8ff882b6e23f33b9963999337f61764b877d02896cd8537712238456373 Homepage: https://cran.r-project.org/package=mnorm Description: CRAN Package 'mnorm' (Multivariate Normal Distribution) Calculates and differentiates probabilities and density of (conditional) multivariate normal distribution and Gaussian copula (with various marginal distributions) using methods described in A. 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Package: r-cran-mnp Architecture: amd64 Version: 3.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1253 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mnp_3.1-5-1.ca2404.1_amd64.deb Size: 1112236 MD5sum: 7e714162f97d18bbe659ba4cb919be5c SHA1: f3437a30d7da7a4ee6c3e4b48d0774e3d5db3711 SHA256: 8db943dc53fae0fcb6bdf8fd82814787ec4009799a72904a447e940cb0f1ccd9 SHA512: d8d1934cb7addbb934633c1659f92a72ec278e2bbc6aaf1ce66d0bf6237b43a3f3ebb2634ca1afee6597168f5ad6d767b819297c195581938c6b40d3d0cf533e Homepage: https://cran.r-project.org/package=MNP Description: CRAN Package 'MNP' (Fitting the Multinomial Probit Model) Fits the Bayesian multinomial probit model via Markov chain Monte Carlo. The multinomial probit model is often used to analyze the discrete choices made by individuals recorded in survey data. Examples where the multinomial probit model may be useful include the analysis of product choice by consumers in market research and the analysis of candidate or party choice by voters in electoral studies. The MNP package can also fit the model with different choice sets for each individual, and complete or partial individual choice orderings of the available alternatives from the choice set. The estimation is based on the efficient marginal data augmentation algorithm that is developed by Imai and van Dyk (2005). "A Bayesian Analysis of the Multinomial Probit Model Using the Data Augmentation." Journal of Econometrics, Vol. 124, No. 2 (February), pp. 311-334. Detailed examples are given in Imai and van Dyk (2005). "MNP: R Package for Fitting the Multinomial Probit Model." Journal of Statistical Software, Vol. 14, No. 3 (May), pp. 1-32. . Package: r-cran-mobsim Architecture: amd64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1256 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-vegan, r-cran-sads Suggests: r-cran-rmarkdown, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-testthat, r-cran-mockery, r-cran-knitr, r-cran-vctrs Filename: pool/dists/noble/main/r-cran-mobsim_0.3.2-1.ca2404.1_amd64.deb Size: 766166 MD5sum: 2ebf938918352ede4739ad469c3f30ca SHA1: ae14f3ef9d468d204f12476e4ba11ce0a0f65389 SHA256: 700c095a08b438ce7432abadc4710e0609f98b5cc7d6050a2b196f453db52cc4 SHA512: 29580d9be89ee900852f9cf5beb6b723fc11e880a21788f83693272219bc465355181c0d6322de3dd290c0185e8213039473081270598420c2ce4ac04025fba2 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) . 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Package: r-cran-modelltest Architecture: amd64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 396 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-survival, r-cran-coxrobust, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-modelltest_1.0.5-1.ca2404.1_amd64.deb Size: 192410 MD5sum: 0a948b09004d7a884d6e57afcf18f1ca SHA1: 82be85250fbd569bd45e2eda40fbb91050d1198b SHA256: ce348a07a51894df5c8e68eddda248382b557050c32a4dc92c2efbadc8aef17e SHA512: 678034f55a6cd93e5e55f37accf9390bd2e399b6b89a640b3860106db87fe6e0e0e8f7c39f244066770dfab669c35329dcf184fe280537888ed70f0244bf9652 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. 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Popular metrics include area under the curve, log loss, root mean square error, etc. Package: r-cran-modelselection Architecture: amd64 Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2316 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-glmnet, r-cran-intervals, r-cran-l0learn, r-cran-matrix, r-cran-mclust, r-cran-mgcv, r-cran-mvtnorm, r-cran-ncvreg, r-cran-pracma, r-bioc-sparsematrixstats, r-cran-survival, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-patrick Filename: pool/dists/noble/main/r-cran-modelselection_1.0.7-1.ca2404.1_amd64.deb Size: 1381762 MD5sum: 6f2d4e82892da6640c9984ab7217b443 SHA1: 3aff0203b1e4c1bbeca90e3f681f9fa8fef65d40 SHA256: 52fe4212bfb7a7efdf1dc96ce5eb848e706b0bf8da9792dc9db3150c5f19a215 SHA512: 3b12e9c3b67ebb665767ef0751eb4384fad2a6d63a13a1c6f7abf4b1886e465f39fd2482e433a08d7a4b87863b50844958239a50b233a55e1b7b751287807830 Homepage: https://cran.r-project.org/package=modelSelection Description: CRAN Package 'modelSelection' (High-Dimensional Model Selection) Model selection and averaging for regression, generalized linear models, generalized additive models, graphical models and mixtures, focusing on Bayesian model selection and information criteria (Bayesian information criterion etc.). See Rossell (2025) (see the URL field below for its URL) for a hands-on book describing the methods, examples and suggested citations if you use the package. Package: r-cran-modernva Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-modernva_0.1.3-1.ca2404.1_amd64.deb Size: 60608 MD5sum: a4d889aef2b6feb025d03129e83ffef8 SHA1: 083c9781d61378dc2c0cd4c6da584b627862a8f6 SHA256: b476ce6704ec2b8d019ba5372ff969d727afbe3ca5c69f118c8d797ca86ae832 SHA512: 370cdd9db9de293315e6ebfc8f441de452313a2e84da813e283e991b3a3e29bca2cae26bf1d97b1dfd097782baf81479a66a53f73af8b5e2832108d8f337f63e Homepage: https://cran.r-project.org/package=modernVA Description: CRAN Package 'modernVA' (An Implementation of Two Modern Education-Based Value-AddedModels) Provides functions that fit two modern education-based value-added models. One of these models is the quantile value-added model. This model permits estimating a school's value-added based on specific quantiles of the post-test distribution. Estimating value-added based on quantiles of the post-test distribution provides a more complete picture of an education institution's contribution to learning for students of all abilities. See Page, G.L.; San Martín, E.; Orellana, J.; Gonzalez, J. (2017) for more details. The second model is a temporally dependent value-added model. This model takes into account the temporal dependence that may exist in school performance between two cohorts in one of two ways. The first is by modeling school random effects with a non-stationary AR(1) process. The second is by modeling school effects based on previous cohort's post-test performance. In addition to more efficiently estimating value-added, this model permits making statements about the persistence of a schools effectiveness. The standard value-added model is also an option. Package: r-cran-modesto Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-markovchain, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-modesto_0.1.4-1.ca2404.1_amd64.deb Size: 53230 MD5sum: 6e609b154ef7b577c359d9073122eb7f SHA1: 2c67e33535f4a786b2af48548b4a67feac3f8682 SHA256: 0cd90ac8e09ce7303a2f3233ca34d7c38f4ffa122be7652f12e396b420c47c54 SHA512: 69df35d4e5e8c5cdcb1f5268e757442537c6e0fc373d277bf112c029742ea9f7225c14ed2e2bfbd7c6fdef50a2586de48ff9b5903f5c4b7f34d32a9749bb763f Homepage: https://cran.r-project.org/package=modesto Description: CRAN Package 'modesto' (Modeling and Analysis of Stochastic Systems) Compute important quantities when we consider stochastic systems that are observed continuously. Such as, Cost model, Limiting distribution, Transition matrix, Transition distribution and Occupancy matrix. The methods are described, for example, Ross S. (2014), Introduction to Probability Models. Eleven Edition. Academic Press. Package: r-cran-modsem Architecture: amd64 Version: 1.0.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3473 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-purrr, r-cran-stringr, r-cran-lavaan, r-cran-rlang, r-cran-mplusautomation, r-cran-nlme, r-cran-dplyr, r-cran-mvnfast, r-cran-fastghquad, r-cran-mvtnorm, r-cran-ggplot2, r-cran-plotly, r-cran-deriv, r-cran-mass, r-cran-amelia, r-cran-cli, r-cran-rhpcblasctl, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggpubr, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-modsem_1.0.19-1.ca2404.1_amd64.deb Size: 2581278 MD5sum: f9a06f9588240e752f13edfca5f0867d SHA1: 7ec5e2910874aedac86ad07295fb1df7ee19c39a SHA256: 82be56c24865a873eb8e8eb2b4fea18ef51f135a978ad6a1f96f8e388517c58f SHA512: 25efcdab5ebce438a89c91c26d0d7e2558766823666fab078bd6c71acd1523beb47963cfb3578ed84b5e2509a1565c712184dcf38090aee3d77e156719cf04fe Homepage: https://cran.r-project.org/package=modsem Description: CRAN Package 'modsem' (Latent Interaction (and Moderation) Analysis in StructuralEquation Models (SEM)) Estimation of interaction (i.e., moderation) effects between latent variables in structural equation models (SEM). The supported methods are: The constrained approach (Algina & Moulder, 2001). The unconstrained approach (Marsh et al., 2004). The residual centering approach (Little et al., 2006). The double centering approach (Lin et al., 2010). The latent moderated structural equations (LMS) approach (Klein & Moosbrugger, 2000). The quasi-maximum likelihood (QML) approach (Klein & Muthén, 2007) The constrained- unconstrained, residual- and double centering- approaches are estimated via 'lavaan' (Rosseel, 2012), whilst the LMS- and QML- approaches are estimated via 'modsem' it self. Alternatively model can be estimated via 'Mplus' (Muthén & Muthén, 1998-2017). References: Algina, J., & Moulder, B. C. (2001). . "A note on estimating the Jöreskog-Yang model for latent variable interaction using 'LISREL' 8.3." Klein, A., & Moosbrugger, H. (2000). . "Maximum likelihood estimation of latent interaction effects with the LMS method." Klein, A. G., & Muthén, B. O. (2007). . "Quasi-maximum likelihood estimation of structural equation models with multiple interaction and quadratic effects." Lin, G. C., Wen, Z., Marsh, H. W., & Lin, H. S. (2010). . "Structural equation models of latent interactions: Clarification of orthogonalizing and double-mean-centering strategies." Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). . "On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables." Marsh, H. W., Wen, Z., & Hau, K. T. (2004). . "Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction." Muthén, L.K. and Muthén, B.O. (1998-2017). "'Mplus' User’s Guide. Eighth Edition." . Rosseel Y (2012). . "'lavaan': An R Package for Structural Equation Modeling." Package: r-cran-mokken Architecture: amd64 Version: 3.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 779 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-polca, r-cran-rcpp Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-mokken_3.1.2-1.ca2404.1_amd64.deb Size: 682530 MD5sum: 47f4a4af6be0e288a329c1c83c6b5cb7 SHA1: 02c61616528517c230feb31008a9af11ea02aa1d SHA256: ad4062cad8d976cc08529933daa3fd7893b390a3aaa5ca16bddd0a6bf417911b SHA512: fe9009b8e5527baa11dd6ef518a9235db6b2c03edee9db975f95d1ca728a7f14ce0a2d43f4e8917c79a47e70aa0aeb549bb3164590b65729eb8d5217f3db980d Homepage: https://cran.r-project.org/package=mokken Description: CRAN Package 'mokken' (Conducts Mokken Scale Analysis) Contains functions for performing Mokken scale analysis on test and questionnaire data. It includes an automated item selection algorithm, and various checks of model assumptions. Package: r-cran-molhd Architecture: amd64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields, r-cran-arrangements Filename: pool/dists/noble/main/r-cran-molhd_0.2-1.ca2404.1_amd64.deb Size: 97186 MD5sum: 761750652dc35bc25898427bddfd1054 SHA1: 46feef3e886b88e110a750efc147e9e76f13215a SHA256: ee76ee95f0c3488be64202a51f4d1bf2d571d7e84b4ef86e75b687a536387a62 SHA512: f8ffe86879577fda7e6e7a4d81dd3ab9ae180e9e6b0168e6dacdd61f1f4f08772502b119c4d3d14c831e6fdecc0a25e2b6dc85da34602b0ae88acb5e0e2a82c9 Homepage: https://cran.r-project.org/package=MOLHD Description: CRAN Package 'MOLHD' (Multiple Objective Latin Hypercube Design) Generate the optimal maximin distance, minimax distance (only for low dimensions), and maximum projection designs within the class of Latin hypercube designs efficiently for computer experiments. Generate Pareto front optimal designs for each two of the three criteria and all the three criteria within the class of Latin hypercube designs efficiently. Provide criterion computing functions. References of this package can be found in Morris, M. D. and Mitchell, T. J. (1995) , Lu Lu and Christine M. Anderson-CookTimothy J. Robinson (2011) , Joseph, V. R., Gul, E., and Ba, S. (2015) . Package: r-cran-mombf Architecture: amd64 Version: 3.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2391 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-ncvreg, r-cran-mgcv, r-cran-rcpp, r-cran-dplyr, r-cran-glasso, r-cran-glmnet, r-cran-intervals, r-cran-matrix, r-cran-mclust, r-cran-pracma, r-bioc-sparsematrixstats, r-cran-survival, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-patrick Filename: pool/dists/noble/main/r-cran-mombf_3.5.4-1.ca2404.1_amd64.deb Size: 1590942 MD5sum: 71b3c09b8e9f333d36bf0a4fa2494e18 SHA1: 75c651ba37416c9df419a39061d4ddfee15e2e05 SHA256: e79cdaf42d143df3b37e73b3cb75a6e0f7005ff681f0e3ed5a316383b32c9839 SHA512: f42977027403702d9cf7dd18812a2a6606a0a0a1d03ca963dab341f8ea1c0b4351c1ea42e4702c69c625562be4034b43f1dfd44fcd2c4d24e22515dc19478bf6 Homepage: https://cran.r-project.org/package=mombf Description: CRAN Package 'mombf' (Model Selection with Bayesian Methods and Information Criteria) Model selection and averaging for regression and mixtures, inclusing Bayesian model selection and information criteria (BIC, EBIC, AIC, GIC). Package: r-cran-momentfit Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2539 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-sandwich Suggests: r-cran-lmtest, r-cran-knitr, r-cran-texreg, r-cran-rmarkdown, r-cran-ivmodel, r-cran-nloptr Filename: pool/dists/noble/main/r-cran-momentfit_1.0-1.ca2404.1_amd64.deb Size: 1999488 MD5sum: f4187f55795794f0feab196de14d90bb SHA1: 1cd556ce40fb356064322a51b27d6cb406929d52 SHA256: 1cf3f4b5e0692b91bb7cc802187f7d2ea8b366c5435e6a2164176c7937578ee6 SHA512: 558203b3c7baf6e267aa12aa0a88d441cfe2281fee872688bfb42edd9ecd22b17b4a76c66e117c5320685ace65d6909b4ab54dbe712d6a2a6a4f86ce95ad4871 Homepage: https://cran.r-project.org/package=momentfit Description: CRAN Package 'momentfit' (Methods of Moments) Several classes for moment-based models are defined. The classes are defined for moment conditions derived from a single equation or a system of equations. The conditions can also be expressed as functions or formulas. Several methods are also offered to facilitate the development of different estimation techniques. The methods that are currently provided are the Generalized method of moments (Hansen 1982; ), for single equations and systems of equation, and the Generalized Empirical Likelihood (Smith 1997; , Kitamura 1997; , Newey and Smith 2004; , and Anatolyev 2005 ). Some work is being done to add tools to deal with weak and/or many instruments. This includes K-Class estimators (Limited Information Maximum Likelihood and Fuller), Anderson and Rubin statistic test, etc. Package: r-cran-momentuhmm Architecture: amd64 Version: 1.5.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3955 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-numderiv, r-cran-circstats, r-cran-crawl, r-cran-mvtnorm, r-cran-sp, r-cran-mass, r-cran-brobdingnag, r-cran-dorng, r-cran-rlang, r-cran-raster, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-setrng, r-cran-splines2, r-cran-r.rsp, r-cran-conicfit, r-cran-ggplot2, r-cran-ggmap, r-cran-lubridate, r-cran-dplyr, r-cran-magrittr, r-cran-scatterplot3d, r-cran-bb, r-cran-expm, r-cran-matrixcalc, r-cran-movehmm, r-cran-extradistr, r-cran-data.tree, r-cran-geosphere, r-cran-mitools, r-cran-dofuture, r-cran-future, r-cran-car, r-cran-survival, r-cran-prodlim, r-cran-nleqslv, r-cran-qdapregex Filename: pool/dists/noble/main/r-cran-momentuhmm_1.5.8-1.ca2404.1_amd64.deb Size: 3626566 MD5sum: dd4123dd4139173c70ce99060e19d12a SHA1: dea86f2a2b936992f7ea636efef5ba053568d743 SHA256: 5b1acc92f1fc077b4c6414b5a40666f5f7a8dfd9b182d2dd94636531a4ebb177 SHA512: e65b197c77c999e76951f9d4a6572c80aca84e84279c12fb87a2ac840ca5fcb56df92ad5bf9c98f5946069bde4914b993d90422e0d0b92eda931a6d01ce73b97 Homepage: https://cran.r-project.org/package=momentuHMM Description: CRAN Package 'momentuHMM' (Maximum Likelihood Analysis of Animal Movement Behavior UsingMultivariate Hidden Markov Models) Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) . Package: r-cran-momtrunc Architecture: amd64 Version: 6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1002 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-tlrmvnmvt, r-cran-hypergeo, r-cran-rcpparmadillo Suggests: r-cran-tmvtnorm Filename: pool/dists/noble/main/r-cran-momtrunc_6.1-1.ca2404.1_amd64.deb Size: 679686 MD5sum: 228a65b314642de235fcf7bd6f12b4ee SHA1: 90ae4487010ce9d0e0895360bf02aa9e2f89c46d SHA256: 651a180f3648a90e18b76758bf2eb398f38bc69d3f33112ecec2b66a5d36db4e SHA512: 2b15fd5de2d9c730dab61547ad75a5b8e565a6ea95cad2df39685e774883b98ee15a5649a5f9389eb1113a8aff700b5e49a5b6eb075c687f7fa7e8e6154dd218 Homepage: https://cran.r-project.org/package=MomTrunc Description: CRAN Package 'MomTrunc' (Moments of Folded and Doubly Truncated MultivariateDistributions) It computes arbitrary products moments (mean vector and variance-covariance matrix), for some double truncated (and folded) multivariate distributions. These distributions belong to the family of selection elliptical distributions, which includes well known skewed distributions as the unified skew-t distribution (SUT) and its particular cases as the extended skew-t (EST), skew-t (ST) and the symmetric student-t (T) distribution. Analogous normal cases unified skew-normal (SUN), extended skew-normal (ESN), skew-normal (SN), and symmetric normal (N) are also included. Density, probabilities and random deviates are also offered for these members. Package: r-cran-monetdb.r Architecture: amd64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-digest, r-cran-testthat, r-cran-codetools Filename: pool/dists/noble/main/r-cran-monetdb.r_2.0.0-1.ca2404.1_amd64.deb Size: 227072 MD5sum: ee0d47e9579128d004d50ba655b42a78 SHA1: e0d4d59206fc595d51afeb697c60043b5ff4d50c SHA256: 91454d25086dd1d086c1124695c2cca9c5ce3cd3875c21d9a14fa010bd26d8df SHA512: 3745f978f9c18e85f1a6612a7146ba64b555c054e2671d2f16646236cc8b4c033088d10c27d51002f86ee42ee29ccdee81959455a92f954fb57299b6dd32c670 Homepage: https://cran.r-project.org/package=MonetDB.R Description: CRAN Package 'MonetDB.R' (Connect MonetDB to R) Allows to pull data from MonetDB into R. Package: r-cran-mongolite Architecture: amd64 Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1626 Depends: libc6 (>= 2.38), libsasl2-2 (>= 2.1.28+dfsg1), libssl3t64 (>= 3.0.0), zlib1g (>= 1:1.2.0), r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-openssl, r-cran-mime Suggests: r-cran-curl, r-cran-spelling, r-cran-nycflights13, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-mongolite_4.0.0-1.ca2404.1_amd64.deb Size: 561510 MD5sum: d28c925351820026cbce92da4e7f0333 SHA1: 065a0f5a4aca9ec32e61ab2af05b4884a75fb7c2 SHA256: 150862b2fad7b234c86f7438c4372145672357661e98fe9f9d5b17eb56b9a1d8 SHA512: 42c76a9c17855af2a6ba5a4b856c86cfafc4f9b474b204a27279f085a71c5899e5e0a22b1a81069ec2c6f9737d880a56d2e04738422791adc93ad134a09674ed Homepage: https://cran.r-project.org/package=mongolite Description: CRAN Package 'mongolite' (Fast and Simple 'MongoDB' Client for R) High-performance MongoDB client based on 'mongo-c-driver' and 'jsonlite'. Includes support for aggregation, indexing, map-reduce, streaming, encryption, enterprise authentication, and GridFS. The online user manual provides an overview of the available methods in the package: . Package: r-cran-monolix2rx Architecture: amd64 Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3894 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-checkmate, r-cran-cli, r-cran-dparser, r-cran-withr, r-cran-ggplot2, r-cran-ggforce, r-cran-stringi, r-cran-crayon, r-cran-lotri, r-cran-magrittr, r-cran-rxode2 Suggests: r-cran-devtools, r-cran-testthat, r-cran-xgxr, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-monolix2rx_0.0.6-1.ca2404.1_amd64.deb Size: 1026822 MD5sum: b391065dacb1fc7181a4f059a6964aaa SHA1: f2ab52cd6cb49c9794da8e4ef9932d4e41cf824e SHA256: 39bd60300088bd713cc4cde4d2c15c90eb75ecb6f16a380f71aaf7d6e62c7488 SHA512: 02938736994d054354f4482d50ae85e43b1eba76c9d6a55bc6b2b1959d9a9001f8afe511d246cf15b9eeb729d702a2de694552f740710595b424a64aa5257304 Homepage: https://cran.r-project.org/package=monolix2rx Description: CRAN Package 'monolix2rx' (Converts 'Monolix' Models to 'rxode2') 'Monolix' is a tool for running mixed effects model using 'saem'. This tool allows you to convert 'Monolix' models to 'rxode2' (Wang, Hallow and James (2016) ) using the form compatible with 'nlmixr2' (Fidler et al (2019) ). If available, the 'rxode2' model will read in the 'Monolix' data and compare the simulation for the population model individual model and residual model to immediately show how well the translation is performing. This saves the model development time for people who are creating an 'rxode2' model manually. Additionally, this package reads in all the information to allow simulation with uncertainty (that is the number of observations, the number of subjects, and the covariance matrix) with a 'rxode2' model. This is complementary to the 'babelmixr2' package that translates 'nlmixr2' models to 'Monolix' and can convert the objects converted from 'monolix2rx' to a full 'nlmixr2' fit. While not required, you can get/install the 'lixoftConnectors' package in the 'Monolix' installation, as described at the following url . When 'lixoftConnectors' is available, 'Monolix' can be used to load its model library instead manually setting up text files (which only works with old versions of 'Monolix'). Package: r-cran-monomvn Architecture: amd64 Version: 1.9-21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1264 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-pls, r-cran-lars, r-cran-mass, r-cran-quadprog, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-monomvn_1.9-21-1.ca2404.1_amd64.deb Size: 1177766 MD5sum: 0ae24ab3a091cee7328204687ebe5b8e SHA1: de819c7d27a66e24ab51f71c9549befdd0c5bf1c SHA256: e15ef8b0c662852f409619badbcaa9c0cccfdf3a71fc6b59ea11397e17336688 SHA512: d31996c4ed778ada28861c5633fa21b3800e9873ed6def83e107bf8988772faedbe4b31ecbd34b9f935d2072f0c7d59f6b3c3302fa28871eb2970594551d1970 Homepage: https://cran.r-project.org/package=monomvn Description: CRAN Package 'monomvn' (Estimation for MVN and Student-t Data with Monotone Missingness) Estimation of multivariate normal (MVN) and student-t data of arbitrary dimension where the pattern of missing data is monotone. See Pantaleo and Gramacy (2010) . Through the use of parsimonious/shrinkage regressions (plsr, pcr, lasso, ridge, etc.), where standard regressions fail, the package can handle a nearly arbitrary amount of missing data. The current version supports maximum likelihood inference and a full Bayesian approach employing scale-mixtures for Gibbs sampling. Monotone data augmentation extends this Bayesian approach to arbitrary missingness patterns. A fully functional standalone interface to the Bayesian lasso (from Park & Casella), Normal-Gamma (from Griffin & Brown), Horseshoe (from Carvalho, Polson, & Scott), and ridge regression with model selection via Reversible Jump, and student-t errors (from Geweke) is also provided. Package: r-cran-monopoly Architecture: amd64 Version: 0.3-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-monopoly_0.3-10-1.ca2404.1_amd64.deb Size: 417314 MD5sum: 4ead274d0f17361607d7d314fbd17009 SHA1: 9232c808a1e4b30a8e8c804ebf521ec75772a4b3 SHA256: 438a13d0ebfe3ed934e049dc70a6df7ea44e0df2a6f10363c38c3307612fec9d SHA512: bdd29a07d6892f6af5a542b241e5b72adf5b3a37549c7a94aa4d13e405a5e02a39f1fac152588a77fa9dcf65f69d90c6f556e908d399dd16f469a88d7c155aad 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) . 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(2000) Includes tools for visualizing results using Nadaraya-Watson kernel regression and supports efficient computation with 'C++'. Tutorials and shiny application demo are available at and . 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Package: r-cran-move Architecture: amd64 Version: 4.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4071 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-geosphere, r-cran-sp, r-cran-raster, r-cran-httr, r-cran-memoise, r-cran-terra, r-cran-xml2, r-cran-rcpp Suggests: r-cran-adehabitathr, r-cran-adehabitatlt, r-cran-markdown, r-cran-rmarkdown, r-cran-circular, r-cran-ggmap, r-cran-mapproj, r-cran-testthat, r-cran-knitr, r-cran-ggplot2, r-cran-leaflet, r-cran-lubridate, r-cran-ctmm, r-cran-amt, r-cran-bcpa, r-cran-embc, r-cran-solartime Filename: pool/dists/noble/main/r-cran-move_4.2.7-1.ca2404.1_amd64.deb Size: 2934810 MD5sum: e40083483dbd744a1741de7ffd1f8264 SHA1: f9f385399697cb57a199d85bb21d51e4ad941bfc SHA256: ef71d84ddf79a3ba6350ef969e60d7ee044ca9e208ae6516148d08978db6f4d3 SHA512: 889194c50b460c6e81d4d471e62ca0d4e7242b5c3254b03df36b5e36be160c32fe8a14e4e6401eb19ec148ba357723355a7ba3626970bced8f7f9e7a4cdfbd78 Homepage: https://cran.r-project.org/package=move Description: CRAN Package 'move' (Visualizing and Analyzing Animal Track Data) Contains functions to access movement data stored in 'movebank.org' as well as tools to visualize and statistically analyze animal movement data, among others functions to calculate dynamic Brownian Bridge Movement Models. 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Package: r-cran-mpboost Architecture: amd64 Version: 0.1-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-knitr, r-cran-pinp, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mpboost_0.1-6-1.ca2404.1_amd64.deb Size: 276464 MD5sum: 9e91a21b8ca4debb14cd6dd5b5c78fa1 SHA1: 72108d510ab6748996170aa2920df5f05498195d SHA256: 9c4c133eedc8aa57d7f168cc455ea6ffcf3aabc2930185b0fe73dcc486043a4b SHA512: 699a7bde9bd5db01fb6d057aeddefbc144c82b52f7b9f12b93c95fb373b93aaf9505cf91842886263668b6546b7235b318fdab66833391a6c750db212cf74941 Homepage: https://cran.r-project.org/package=MPBoost Description: CRAN Package 'MPBoost' (Treatment Allocation in Clinical Trials by the Maximal Procedure) Performs treatment allocation in two-arm clinical trials by the maximal procedure described by Berger et al. (2003) . 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Package: r-cran-mpmi Architecture: amd64 Version: 0.43.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgfortran5 (>= 10), libgomp1 (>= 6), r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernsmooth Filename: pool/dists/noble/main/r-cran-mpmi_0.43.2.1-1.ca2404.1_amd64.deb Size: 225954 MD5sum: 768756afbf511f311edacad419730ffe SHA1: d47ce2a97ae9d958143e938de81b33eca13a0621 SHA256: 8f131b81cbfaacb298ab2b1158f1fb153770cc271670a1849aa7e00270691886 SHA512: aaadbb6fb95a00e18b1b64cdebad878db3ebbce4427c5e3f3736e8241a12f4cc7fb68032bc478e2434d2646edc45e21422dcab2e55331322f7d3486a88b7014b Homepage: https://cran.r-project.org/package=mpmi Description: CRAN Package 'mpmi' (Mixed-Pair Mutual Information Estimators) Uses a kernel smoothing approach to calculate Mutual Information for comparisons between all types of variables including continuous vs continuous, continuous vs discrete and discrete vs discrete. 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Package: r-cran-mptinr Architecture: amd64 Version: 1.14.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1055 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv, r-cran-brobdingnag, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-snowfall, r-cran-knitr Filename: pool/dists/noble/main/r-cran-mptinr_1.14.1-1.ca2404.1_amd64.deb Size: 814832 MD5sum: 49f90bf148d9a046a21a457f0266cf2d SHA1: 3a33033380ea86b2d030f03e0cebe5ed2ef31b71 SHA256: 96f8cd93baea1c8382b3ddaa1036f387c7f5d1223aee0cc173f285ad41f555cc SHA512: c50e834b1d5c438f19f96f454179a9db129f01449999cb5491c287a0ef33669f7260acf7e0dd81074d4476f5082c44021dda79d9260e2f5af5cc4f8f1991fb6d Homepage: https://cran.r-project.org/package=MPTinR Description: CRAN Package 'MPTinR' (Analyze Multinomial Processing Tree Models) Provides a user-friendly way for the analysis of multinomial processing tree (MPT) models (e.g., Riefer, D. M., and Batchelder, W. H. [1988]. Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318-339) for single and multiple datasets. The main functions perform model fitting and model selection. Model selection can be done using AIC, BIC, or the Fisher Information Approximation (FIA) a measure based on the Minimum Description Length (MDL) framework. The model and restrictions can be specified in external files or within an R script in an intuitive syntax or using the context-free language for MPTs. The 'classical' .EQN file format for model files is also supported. Besides MPTs, this package can fit a wide variety of other cognitive models such as SDT models (see fit.model). It also supports multicore fitting and FIA calculation (using the snowfall package), can generate or bootstrap data for simulations, and plot predicted versus observed data. Package: r-cran-mr.mashr Architecture: amd64 Version: 0.3.44-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1808 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcppparallel, r-cran-mvtnorm, r-cran-matrixstats, r-cran-mashr, r-cran-ebnm, r-cran-flashier, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-varbvs, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mr.mashr_0.3.44-1.ca2404.1_amd64.deb Size: 823720 MD5sum: 52108057403ac76f36ea68137b15ebff SHA1: dfe96b67229fa4ae79b66bab88e2b4e451503a88 SHA256: b17e04a99c924e4daedab853f79e3a5bc3c3570b2dabe1601e00a199a4adf0c8 SHA512: b9364f6f35b580e715a71c4e7ea95f62c5453ae1f502b3a8b2e38faabf0b48c9a41cabb97374b648010531254dfe7fa3910c522681a484949831bfb34279acde Homepage: https://cran.r-project.org/package=mr.mashr Description: CRAN Package 'mr.mashr' (Multiple Regression with Multivariate Adaptive Shrinkage) Provides an implementation of methods for multivariate multiple regression with adaptive shrinkage priors as described in F. Morgante et al (2023) . Package: r-cran-mr.rgm Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2408 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-igraph, r-cran-gigrvg, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-mr.rgm_0.1.0-1.ca2404.1_amd64.deb Size: 871354 MD5sum: e90ec4b0406df871bbd01922ba39e758 SHA1: 1e2544dfda29b8b9b6421fc42fae5b02bb872628 SHA256: a4069ee02e6204b44f394519de188d6fed075ed41ec17e905d76d8ef7d5d0dd0 SHA512: e76b30bdb0a1954c4d43a07d0802a7ecfe6907c6820735462d421405ae43c8172ad161da462cbfbd813985ba0df9326f03460efb3393edbacdb1ef0f79a0b42b Homepage: https://cran.r-project.org/package=MR.RGM Description: CRAN Package 'MR.RGM' (Fitting Multivariate Bidirectional Mendelian RandomizationNetworks Using Bayesian Directed Cyclic Graphical Models) Addressing a central challenge encountered in Mendelian randomization (MR) studies, where MR primarily focuses on discerning the effects of individual exposures on specific outcomes and establishes causal links between them. Using a network-based methodology, the intricacy involving interdependent outcomes due to numerous factors has been tackled through this routine. Based on Ni et al. (2018) , 'MR.RGM' extends to a broader exploration of the causal landscape by leveraging on network structures and involves the construction of causal graphs that capture interactions between response variables and consequently between responses and instrument variables. The resulting Graph visually represents these causal connections, showing directed edges with effect sizes labeled. 'MR.RGM' facilitates the navigation of various data availability scenarios effectively by accommodating three input formats, i.e., individual-level data and two types of summary-level data. The method also optionally incorporates measured covariates (when available) and allows flexible modeling of the error variance structure, including correlated errors that may reflect unmeasured confounding among responses. In the process, causal effects, adjacency matrices, and other essential parameters of the complex biological networks, are estimated. Besides, 'MR.RGM' provides uncertainty quantification for specific network structures among response variables. Parts of the Inverse Wishart sampler are adapted from the econ722 repository by DiTraglia (GPL-2.0). Package: r-cran-mra Architecture: amd64 Version: 2.16.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 820 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-mra_2.16.11-1.ca2404.1_amd64.deb Size: 673954 MD5sum: fb22cddcb8a3ae207d3d41d7acd0f478 SHA1: 561656700c207fd75ff670f3dbb9a1489d79c316 SHA256: f48003d455e4f47cd04d00539c40b4012ede3f04b8fcb6c86507353e38deb6e7 SHA512: 56256fa4414d507b1b29e21a2d729cac4eba566d83466c8e3ffb616551bee51f1920b3b043a00a8d5c791f571fe52a9676e62289f89baf9900f23340f7b25691 Homepage: https://cran.r-project.org/package=mra Description: CRAN Package 'mra' (Mark-Recapture Analysis) Accomplishes mark-recapture analysis with covariates. Models available include the Cormack-Jolly-Seber open population (Cormack (1972) ; Jolly (1965) ; Seber (1965) ) and Huggin's (1989) closed population. Link functions include logit, sine, and hazard. Model selection, model averaging, plot, and simulation routines included. Open population size by the Horvitz-Thompson (1959) estimator. 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Package: r-cran-mrf2d Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1107 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-glue Filename: pool/dists/noble/main/r-cran-mrf2d_1.0-1.ca2404.1_amd64.deb Size: 818858 MD5sum: dfe5153f21cf8371c1d89bf4bd299c3e SHA1: 9998f5dc486dfde5b4eeb9539d8f0a72a5322b79 SHA256: 7e9a0e7c3e648eaf09a38a9cc23bb9337f538d89d221a30ecbf91dfd214f34d2 SHA512: b6c0224d92664b7b2ab839552f69f2bd1aec2aea2c73fffd77fcd4e802e192360405a5dd5ed10b1b9ebbf87dae37ef86e1ad3ed82228c277b0fa6518366705df Homepage: https://cran.r-project.org/package=mrf2d Description: CRAN Package 'mrf2d' (Markov Random Field Models for Image Analysis) Model fitting, sampling and visualization for the (Hidden) Markov Random Field model with pairwise interactions and general interaction structure from Freguglia, Garcia & Bicas (2020) , which has many popular models used in 2-dimensional lattices as particular cases, like the Ising Model and Potts Model. A complete manuscript describing the package is available in Freguglia & Garcia (2022) . 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The package incorporates the partition algorithm, which offers a flexible framework for agglomerative partitioning based on the Direct-Measure-Reduce approach. This method ensures that each reduced variable maintains a user-specified minimum level of information while remaining interpretable, as each maps uniquely to one variable in the reduced dataset. The partition framework is described in Millstein et al. (2020) . The package allows customization in variable selection, measurement of information loss, and data reduction methods for neuroimaging analysis and machine learning workflows. Package: r-cran-mritc Architecture: amd64 Version: 0.5-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1379 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-misc3d, r-cran-oro.nifti Suggests: r-cran-tkrplot Filename: pool/dists/noble/main/r-cran-mritc_0.5-3-1.ca2404.1_amd64.deb Size: 1257920 MD5sum: 50a975ac10327ed53275da8c0aa4634b SHA1: 32de39c2a10fec12940419fad028b5243b2e46bc SHA256: 7d7d582089778d39f1fc9cfd14d4dba49dc111b1a3c2a9a56d558779567491bc SHA512: 2e9633781939ce93d9b14bcf729ded5123d03a3f9264000cfc2720271a8586bf86ea20f38897eab187c93d1e4b21aa1708dad34d274e5011d0f4620eb44953e9 Homepage: https://cran.r-project.org/package=mritc Description: CRAN Package 'mritc' (MRI Tissue Classification) Implements various methods for tissue classification in magnetic resonance (MR) images of the brain, including normal mixture models and hidden Markov normal mixture models, as outlined in Feng & Tierney (2011) . These methods allow a structural MR image to be classified into gray matter, white matter and cerebrospinal fluid tissue types. Package: r-cran-mrmlm.gui Architecture: amd64 Version: 4.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2938 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-lars, r-cran-rcpp, r-cran-foreach, r-cran-ncvreg, r-cran-coin, r-cran-shinyjs, r-cran-data.table, r-cran-doparallel, r-cran-sampling, r-cran-bigmemory, r-cran-mrmlm, r-cran-sbl, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-mrmlm.gui_4.0.2-1.ca2404.1_amd64.deb Size: 1290296 MD5sum: 6f3c5a394a8a373bcae652c779be3892 SHA1: a5d7fa40c5ac9b84a0d0e5878cae95754224398f SHA256: 593f15d61bae107212bd7e0adb372cc3b63f80ce81a2a5747e463e5de42bb6c6 SHA512: cb9b02dc85113978d550166633764d657981962693378f780ee6baf3bdc0cb1500cde7f22a4f2280a255c3f2a015a63ae5a8d6f03b87cea6b935e91f9b03d736 Homepage: https://cran.r-project.org/package=mrMLM.GUI Description: CRAN Package 'mrMLM.GUI' (Multi-Locus Random-SNP-Effect Mixed Linear Model Tools forGenome-Wide Association Study with Graphical User Interface) Conduct multi-locus genome-wide association study under the framework of multi-locus random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes and all the nonzero effects were further identified by likelihood ratio test for true QTL. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018) . Package: r-cran-mrmlm Architecture: amd64 Version: 5.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2983 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lars, r-cran-rcpp, r-cran-foreach, r-cran-ncvreg, r-cran-coin, r-cran-sampling, r-cran-data.table, r-cran-doparallel, r-cran-sbl, r-cran-bedmatrix, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-mrmlm_5.0.1-1.ca2404.1_amd64.deb Size: 1317070 MD5sum: 8e2ebfaf655711bd67596b186aa425f6 SHA1: 14d6fedca2555e921774b87d9e5ad23bd5c99f04 SHA256: f19cab1706b0916db0668ebd8af5a86376546713afbfe975306ebe916c6d0cca SHA512: 3e4129b4dc1fc645c3ca7e30cb808bb840923711533b15e4f2629cb71602865dc00d6d8271d47d163b14f8281d60a0ab3212a4ce7f55e6b08fb5309007c56e1d Homepage: https://cran.r-project.org/package=mrMLM Description: CRAN Package 'mrMLM' (Multi-Locus Random-SNP-Effect Mixed Linear Model Tools for GWAS) Conduct multi-locus genome-wide association study under the framework of multi-locus random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes, and all the nonzero effects were further identified by likelihood ratio test for significant QTL. The program may run on a desktop or laptop computers. If marker genotypes in association mapping population are almost homozygous, these methods in this software are very effective. If there are many heterozygous marker genotypes, the IIIVmrMLM software is recommended. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018, ), and Li M, Zhang YW, Zhang ZC, Xiang Y, Liu MH, Zhou YH, Zuo JF, Zhang HQ, Chen Y, Zhang YM (2022, ). Package: r-cran-mrmre Architecture: amd64 Version: 2.1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1886 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-igraph Filename: pool/dists/noble/main/r-cran-mrmre_2.1.2.2-1.ca2404.1_amd64.deb Size: 1771730 MD5sum: b10adc20f8fffc450c31d488570e6873 SHA1: 6ef887e871aabb9406fcd3c88c0002fab4704da8 SHA256: 875b6291ec06182cc64e31871f910f144b4626689e29e8448ff530977675af2c SHA512: 433e0fef47261d13a368f065e99d8f9ff74d2f8dec675e8de79eb28c49eb30f2572bf0026701d591eff94f2e1be09867329c30486eb5d1aca27b97947c4feb31 Homepage: https://cran.r-project.org/package=mRMRe Description: CRAN Package 'mRMRe' (Parallelized Minimum Redundancy, Maximum Relevance (mRMR)) Computes mutual information matrices from continuous, categorical and survival variables, as well as feature selection with minimum redundancy, maximum relevance (mRMR) and a new ensemble mRMR technique. Published in De Jay et al. (2013) . Package: r-cran-mrs Architecture: amd64 Version: 1.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 369 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-igraph, r-cran-rcpparmadillo Suggests: r-cran-kernsmooth Filename: pool/dists/noble/main/r-cran-mrs_1.2.6-1.ca2404.1_amd64.deb Size: 173860 MD5sum: 733320071bc1ce3a72b6a35ddeae0b92 SHA1: 368994602f6307af02a2b237760f701de9f4b8ac SHA256: 116bdfb0b19040c8b50f0b5e07af4ae00cebd368f8f35d02fa22dc2af4ac1a73 SHA512: cd560b4dc0203e31c571dcb6cfc966df6e67220576feda5057ae7350232ceffddb3a33afa2c82417accc543430edf36119ae8b689c86dffc5bb1edc6d6a131af Homepage: https://cran.r-project.org/package=MRS Description: CRAN Package 'MRS' (Multi-Resolution Scanning for Cross-Sample Differences) An implementation of the MRS algorithm for comparison across distributions, as described in Jacopo Soriano, Li Ma (2017) . The model is based on a nonparametric process taking the form of a Markov model that transitions between a "null" and an "alternative" state on a multi-resolution partition tree of the sample space. MRS effectively detects and characterizes a variety of underlying differences. These differences can be visualized using several plotting functions. Package: r-cran-mrtssphere Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2277 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rspectra, r-cran-rcppeigen, r-cran-rcppnumerical Suggests: r-cran-fields, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mrtssphere_0.1.2-1.ca2404.1_amd64.deb Size: 1969426 MD5sum: a7b5ed42bb36bcd11c89805aeb6463e3 SHA1: cb086468bb79a3d951db86a826abd6af931772e5 SHA256: 77dde9f0ef2457ac2f044c2b2c0272d58e3e8f09a1352d2aa8c1ef307218c667 SHA512: 7fef088925b6ffe29a2c87a76a50be81489641bc1c5ea444b3bb2a1c105694b5ac6ef523ae1eebe4deb4007f60c13dc6d974c6ba529b3f9b9a1455046acc6bb4 Homepage: https://cran.r-project.org/package=mrtsSphere Description: CRAN Package 'mrtsSphere' (Multi-Resolution Thin-Plate Splines on the Sphere) Constructs multi-resolution thin-plate spline basis functions on the sphere for use in spatial regression and large-scale spatial prediction problems. Implements the basis system described in Huang, Huang, and Ing (2025) "Multi-Resolution Spatial Methods on the Sphere: Efficient Prediction for Global Data", Environmetrics, . Heavy computations are written in 'C++' via 'Rcpp' with optional 'OpenMP' parallelism. Package: r-cran-msbp Architecture: amd64 Version: 1.4-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: libc6 (>= 2.29), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-msbp_1.4-1-1.ca2404.1_amd64.deb Size: 356616 MD5sum: 0ee48b3bc3a3fbe2eddf911b8efa519a SHA1: dbf2f77a1b1d4d4bd9d2e5a765af83ae67fa08ad SHA256: 4e48806ba6463b62b481ddca7ede386940880ee360eaf0e39ebf51de7880fb40 SHA512: b992254d30cd949d70fdc9a991a6e46c4e82ee33704534ceda7a3d26be095df222a0180722747a42ac94a0dec0cd22efb54da6299ae2d3b249e8edac208a5b85 Homepage: https://cran.r-project.org/package=msBP Description: CRAN Package 'msBP' (Multiscale Bernstein Polynomials for Densities) Performs Bayesian nonparametric multiscale density estimation and multiscale testing of group differences with multiscale Bernstein polynomials (msBP) mixtures as in Canale and Dunson (2016). Package: r-cran-msca Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1667 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-fastkmedoids, r-cran-rcppparallel, r-cran-data.table, r-cran-dplyr, r-cran-matrix, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-cluster, r-cran-fastcluster Filename: pool/dists/noble/main/r-cran-msca_1.2.1-1.ca2404.1_amd64.deb Size: 1522998 MD5sum: a1134bdf2148830d89471459d5316434 SHA1: 05e9c151ecc6aa1d55ed41e90469e5641f58e594 SHA256: 53d00b97a175ef17932c0f92d5d08c36a4288e02108e9fd1d44444fa97bafa90 SHA512: 6cb5a213d806c3f20c1d9c97229be1518856a1d24c0108a8b3c4bef7031708ffafd4e3073610b7514a58426ee4619ddeccc58959fe90ce3e45390493061c6475 Homepage: https://cran.r-project.org/package=MSCA Description: CRAN Package 'MSCA' (Unsupervised Clustering of Multiple Censored Time-to-EventEndpoints) Provides basic tools and wrapper functions for computing clusters of instances described by multiple time-to-event censored endpoints. From long-format datasets, where one instance is described by one or more dated records, the main function, `make_state_matrices()`, creates state matrices. Based on these matrices, optimised procedures using the Jaccard distance between instances enable the construction of longitudinal typologies. The package is under active development, with additional tools for graphical representation of typologies planned. For methodological details, see our accompanying paper: `Delord M, Douiri A (2025) `. Package: r-cran-msce Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-msce_1.0.2-1.ca2404.1_amd64.deb Size: 166056 MD5sum: f2586ef8270cf139e60cb7545a4482c6 SHA1: 18d5685f39fc79f1d8751682f2543587f6a17571 SHA256: d1a5ae4f886d52d1735ae7921de5b608bb8d7ca2c9d7bbca6628ce8af6635775 SHA512: 3d06a27e2fae65ac23db8cca7526b684b488426851781451092c567188ad92348fea77d3d6606f980eac87a1c12f9ea114a6ddb26122aafdb8c42486f31412dc Homepage: https://cran.r-project.org/package=msce Description: CRAN Package 'msce' (Hazard of Multi-Stage Clonal Expansion Models) Functions to calculate hazard and survival function of Multi-Stage Clonal Expansion Models used in cancer epidemiology. For the Two-Stage Clonal Expansion Model an exact solution is implemented assuming piecewise constant parameters, see Heidenreich, Luebeck, Moolgavkar (1997) . Numerical solutions are provided for its extensions, see also Little, Vineis, Li (2008) . Package: r-cran-msclassifr Architecture: amd64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3817 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cp4p, r-cran-caret, r-cran-statmod, r-cran-maldiquant, r-cran-maldirppa, r-cran-reshape2, r-cran-ggplot2, r-cran-dplyr, r-bioc-limma, r-cran-car, r-cran-rcpp, r-cran-matrix Suggests: r-cran-doparallel, r-cran-foreach, r-cran-ranger, r-cran-randomforest, r-bioc-mixomics, r-cran-vsurf, r-cran-vita, r-cran-boruta, r-cran-glmnet, r-cran-e1071, r-cran-xgboost, r-cran-nnet, r-cran-mclust, r-cran-mltools, r-cran-metap, r-cran-maldiquantforeign, r-cran-matrixstats, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-msclassifr_0.5.0-1.ca2404.1_amd64.deb Size: 3776048 MD5sum: 2639ccc2d9e4d9f77dd53da84f376b32 SHA1: a51f242257eee86f1d191e906830776a3c88de92 SHA256: b51f7cd23ade9b9bf6bd01281eabea5e0cb76a61ef748028651ab7bf51fd40d9 SHA512: 1f4738b7e7683bc5bdfc4f1624487c15169b6652c674b23eea6b85f3a362c4491f23fc746d143a1f1e07c146c6f9ceb7297e7e2acca4fffc9608cfee2dc47c75 Homepage: https://cran.r-project.org/package=MSclassifR Description: CRAN Package 'MSclassifR' (Automated Classification of Mass Spectra) Functions to classify mass spectra in known categories and to determine discriminant mass-to-charge values (m/z). Includes easy-to-use preprocessing pipelines for Matrix Assisted Laser Desorption Ionisation - Time Of Flight Mass Spectrometry (MALDI-TOF) mass spectra, methods to select discriminant m/z from labelled libraries, and tools to predict categories (species, phenotypes, etc.) from selected features. Also provides utilities to build design matrices from peak intensities and labels. While this package was developed with the aim of identifying very similar species or phenotypes of bacteria from MALDI-TOF MS, the functions of this package can also be used to classify other categories associated to mass spectra; or from mass spectra obtained with other mass spectrometry techniques. Parallelized processing and optional C++-accelerated functions are available (notably to deal with large datasets) from version 0.5.0. If you use this package in your research, please cite the associated publication (). For a comprehensive guide, additional applications, and detailed examples, see . Package: r-cran-mscmt Architecture: amd64 Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1146 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.6.0), r-api-4.0, r-cran-lpsolve, r-cran-ggplot2, r-cran-lpsolveapi, r-cran-rglpk, r-cran-rdpack Suggests: r-cran-synth, r-cran-deoptim, r-cran-rgenoud, r-cran-deoptimr, r-cran-gensa, r-cran-ga, r-cran-soma, r-cran-cmaes, r-cran-rmalschains, r-cran-nmof, r-cran-nloptr, r-cran-pso, r-cran-lowrankqp, r-cran-kernlab, r-cran-reshape, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mscmt_1.4.2-1.ca2404.1_amd64.deb Size: 801220 MD5sum: a1c0e805de4670248906d2bfad4f4e40 SHA1: 4df3ada3f0e1b8f6dbed4f3d2afb09bea4b7c819 SHA256: 33d1811ef311b4e30d8df888997eed20474eba6ace57aa212696d8ea145d2f85 SHA512: 7229c1a76d267dd2df9a1d1a153a5c76958b9bd9225583c842873ea5617aa1a84b224d9b03456a5ce4e2d43ae74011cc25fb7ae4dd887c0cb0f193c2f83252de Homepage: https://cran.r-project.org/package=MSCMT Description: CRAN Package 'MSCMT' (Multivariate Synthetic Control Method Using Time Series) Three generalizations of the synthetic control method (which has already an implementation in package 'Synth') are implemented: first, 'MSCMT' allows for using multiple outcome variables, second, time series can be supplied as economic predictors, and third, a well-defined cross-validation approach can be used. Much effort has been taken to make the implementation as stable as possible (including edge cases) without losing computational efficiency. A detailed description of the main algorithms is given in Becker and Klößner (2018) . Package: r-cran-mscquartets Architecture: amd64 Version: 3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4770 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-phangorn, r-cran-zipfr, r-cran-rdpack, r-cran-foreach, r-cran-doparallel, r-cran-rcpp, r-cran-igraph, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mscquartets_3.2-1.ca2404.1_amd64.deb Size: 2364920 MD5sum: 0752265ad27be32d68aff91d48e815c9 SHA1: 3e834671ba4e01a65aef8202bce98870638402fd SHA256: 8bd8be8101a282af7a1c05e60985a95496dc0067ba1b988000f84168652e9547 SHA512: 182cc87db9e3b5e46641796555fb9f698d0ff139a75fb8c1476e194b3192c9b6a4e05b55172a3acc9dfcc4cf8ff9f617f843e2c3cd407eb9a19dbf21d456b0b7 Homepage: https://cran.r-project.org/package=MSCquartets Description: CRAN Package 'MSCquartets' (Analyzing Gene Tree Quartets under the Multi-Species Coalescent) Methods for analyzing and using quartets displayed on a collection of gene trees, primarily to make inferences about the species tree or network under the multi-species coalescent model. These include quartet hypothesis tests for the model, as developed by Mitchell et al. (2019) , simplex plots of quartet concordance factors as presented by Allman et al. (2020) , species tree inference methods based on quartet distances of Rhodes (2019) and Yourdkhani and Rhodes (2019) , the NANUQ algorithm for inference of level-1 species networks of Allman et al. (2019) , the TINNIK algorithm for inference of the tree of blobs of an arbitrary network of Allman et al.(2022) , and NANUQ+ routines for resolving multifurcations in the tree of blobs to cycles as in Allman et al.(2024) (forthcoming). Software announcement by Rhodes et al. (2020) . Package: r-cran-msda Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mass Filename: pool/dists/noble/main/r-cran-msda_1.0.4-1.ca2404.1_amd64.deb Size: 181068 MD5sum: 227898eb04bc76e0cbce5189ba84e5c1 SHA1: d14dcd690357bb6e9a0dc8d545cff057056dc080 SHA256: 6e0cab1617f15da02fec580fd9baa70ea5449aec6ff58e759279fc55ab1be83c SHA512: f04cd6ce6890984d6850800f2dc61449f0daac5700c6a9a23596c65caa610cffd5cd12043cfa461a2b19ffc64896c161f58993f3ff55eb019b9c78643ef997c4 Homepage: https://cran.r-project.org/package=msda Description: CRAN Package 'msda' (Multi-Class Sparse Discriminant Analysis) Efficient procedures for computing a new Multi-Class Sparse Discriminant Analysis method that estimates all discriminant directions simultaneously. It is an implementation of the work proposed by Mai, Q., Yang, Y., and Zou, H. (2019) . Package: r-cran-msde Architecture: amd64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1181 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-whisker, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-msde_1.0.5-1.ca2404.1_amd64.deb Size: 409250 MD5sum: fe4e2a7ea0b4c11109b487f0c26e66fd SHA1: 149880959254a72c7dfb420c5794635d210fca8d SHA256: 18e59204acc64f519fd06b74a66959201d0e8ec35d45937545734b857e4e8596 SHA512: aeae22886c0c6c59e1c53ffae60ec3b99b11a53efecf9fa12d1473c52c11f188eb45533ff2095816a2b5c3cbe791acd390f1e2c60a2a32c829e5efca2ac17bdc 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'. 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The entropy similarity is a novel similarity measure for MS/MS spectra which outperform the widely used dot product similarity in compound identification. For more details, please refer to the paper: Yuanyue Li et al. (2021) "Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification" . Package: r-cran-msetool Architecture: amd64 Version: 3.7.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7906 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-snowfall, r-cran-abind, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-boot, r-cran-broom, r-cran-covr, r-cran-crayon, r-cran-dt, r-cran-fmsb, r-cran-kableextra, r-cran-knitr, r-cran-ggrepel, r-cran-gridextra, r-cran-mass, r-cran-mvtnorm, r-cran-openxlsx, r-cran-pak, r-cran-pbapply, r-cran-r4ss, r-cran-readxl, r-cran-reshape2, r-cran-rfishbase, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat, r-cran-tidyr, r-cran-tmb, r-cran-usethis Filename: pool/dists/noble/main/r-cran-msetool_3.7.5-1.ca2404.1_amd64.deb Size: 7468018 MD5sum: edda09f4239f4e7fc0273282386417b9 SHA1: 6286b9d4757ee1ead170eec4cbf4cc46d92c9b0c SHA256: a12b42675bbd5b60c6d14ff6764ca76863b4f5a1f7c1f13e18580fb5886a6c32 SHA512: 7c63f250650ceef56bb0846d49613b88540af4fedef372da3317986c814b663c841aed46ab3493b3d66a9f413e04a2f8623ec936a629cef008cf7c95485c91f2 Homepage: https://cran.r-project.org/package=MSEtool Description: CRAN Package 'MSEtool' (Management Strategy Evaluation Toolkit) Development, simulation testing, and implementation of management procedures for fisheries (see Carruthers & Hordyk (2018) ). 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(2019) . Package: r-cran-msgps Architecture: amd64 Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-msgps_1.3.5-1.ca2404.1_amd64.deb Size: 99820 MD5sum: e4da835f471657edb366ccf20126978b SHA1: db60ba635131b13905350758efab7ae0d521f2e7 SHA256: fdc83fe233fd7564183070ace455a6b45cdb58e24312d56cd1b40ade757ce1f3 SHA512: 439418109c8ee254e0b0247500297e011e82ec237c48014c6deabc97de38dd5db636f79edac4182a86a2ce9f7a64fa970583b0d5be14d8d93f9784ed3e2175f2 Homepage: https://cran.r-project.org/package=msgps Description: CRAN Package 'msgps' (Degrees of Freedom of Elastic Net, Adaptive Lasso andGeneralized Elastic Net) Computes the degrees of freedom of the lasso, elastic net, generalized elastic net and adaptive lasso based on the generalized path seeking algorithm. 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Package: r-cran-mtdesign Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 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-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/noble/main/r-cran-mtdesign_0.1.4-1.ca2404.1_amd64.deb Size: 272256 MD5sum: 398905d1f65a1fe201f103f3127ffc29 SHA1: 35f2e04dfee8a13fa9150e18cf2c1fcf8c66c82f SHA256: cb25753300ba06f5d4e7652f37651a62fefd55f0ba83b1a0719bab582e852fae SHA512: 4cb439788cb88287620cf72607a0532568ec3fb5b2d373732b2c283451533cf0ad66c55223fe6cab1c76d10563d3ccdd988972a1e24977320b56251f617c6e09 Homepage: https://cran.r-project.org/package=mtdesign Description: CRAN Package 'mtdesign' (Mander and Thompson Designs) Implements Mander & Thompson's (2010) methods for two-stage designs optimal under the alternative hypothesis for phase II [cancer] trials. 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Package: r-cran-mtlr Architecture: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1032 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-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/noble/main/r-cran-mtlr_0.2.2-1.ca2404.1_amd64.deb Size: 732736 MD5sum: 8d2901125b01e6a0c0a12ad5eeaa2d07 SHA1: fd5bfced6781bb7ad4c76f5666cfc2f1d9a1ca74 SHA256: 0b342e2a9a899170319b9c9bacc37da41c327ba1bcdfbcd7058213f71af8f383 SHA512: ad023944b4302bba34e9dd7978a9098751c09fb4d54a6781aee24646fce114c3f1b97e07c9718d485c9a5d11b8e832cf83129eef0572c5bc054feab4956b2b45 Homepage: https://cran.r-project.org/package=MTLR Description: CRAN Package 'MTLR' (Survival Prediction with Multi-Task Logistic Regression) An implementation of Multi-Task Logistic Regression (MTLR) for R. This package is based on the method proposed by Yu et al. (2011) which utilized MTLR for generating individual survival curves by learning feature weights which vary across time. This model was further extended to account for left and interval censored data. Package: r-cran-mts Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1059 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-fgarch, r-cran-fbasics, r-cran-mvtnorm, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-mts_1.2.1-1.ca2404.1_amd64.deb Size: 911120 MD5sum: 51ee22afca0372a821d9b9feb7d71176 SHA1: 46102b80640198a8d7ce406b7e0c852dcd49346d SHA256: ff64646b30a526aef4177ff9da36f12adfe3001263281a2815df6a31ddf614c0 SHA512: 9b5cf19518946d535443d008ac937f19bbc1fe61f5bb6f6d0ec562a1fbab3a04523fc3593c1d00603b525b704faecb989d03aaf0ccb3267eb079d323332b5a15 Homepage: https://cran.r-project.org/package=MTS Description: CRAN Package 'MTS' (All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS)and Estimating Multivariate Volatility Models) Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis. (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models. (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics. (c) Finally, the MTS package also performs forecasting using diffusion index , transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model. Package: r-cran-muchpoint Architecture: amd64 Version: 0.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-capushe, r-cran-shiny, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-muchpoint_0.6.4-1.ca2404.1_amd64.deb Size: 249714 MD5sum: 334fefcc57c106d671bbc777241fb774 SHA1: 7787211a722c92cc43ba322ae591797acc57e60c SHA256: 1216809bd236a0080254b1ada6a4cb5e1d2b47004c306013f0bb529bfdffc32e SHA512: 52c172b5aafa1e526177177f809bb738ddab5cee745e5fe86e2200f2210eaa8c76943cfb565c67dc5e51b71d753c06648d12729d214d51af1b3d740005fb6c25 Homepage: https://cran.r-project.org/package=MuChPoint Description: CRAN Package 'MuChPoint' (Multiple Change Point) Nonparametric approach to estimate the location of block boundaries (change-points) of non-overlapping blocks in a random symmetric matrix which consists of random variables whose distribution changes from block to block. BRAULT Vincent, OUADAH Sarah, SANSONNET Laure and LEVY-LEDUC Celine (2017) . Package: r-cran-mufimeshgp Architecture: amd64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 414 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lhs, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-mufimeshgp_0.0.1-1.ca2404.1_amd64.deb Size: 229284 MD5sum: 7d0cdf5764ce38e6031dbbb623d26319 SHA1: d209288aaee8ca1d065feb73944502b581eaf1f8 SHA256: 66351a2f3ab18a97f882800a44e5a0d4486cb2b1e7921f16645d76e083244258 SHA512: 1ade95f5ef02aa6e5215fd91486642fa16825c4e66ae04048060133536d2e66bb95e8f51a175c98da0d7f954b19a14b8119a0709810890903732c6e1da5c3d7b Homepage: https://cran.r-project.org/package=MuFiMeshGP Description: CRAN Package 'MuFiMeshGP' (Multi-Fidelity Emulator for Computer Experiments with TunableFidelity Levels) Multi-Fidelity emulator for data from computer simulations of the same underlying system but at different input locations and fidelity level, where both the input locations and fidelity level can be continuous. Active Learning can be performed with an implementation of the Integrated Mean Square Prediction Error (IMSPE) criterion developed by Boutelet and Sung (2025, ). Package: r-cran-muhaz Architecture: amd64 Version: 1.2.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-muhaz_1.2.6.4-1.ca2404.1_amd64.deb Size: 65580 MD5sum: 9d6d84b05f6daae6a81f7e917a753a3f SHA1: 3a5807495c1a2b4a3bc2de608b670c154930b494 SHA256: bf4f8eb11a02e48e42cc547add47261046dc47993c8642829428cd2a4853ca52 SHA512: d68ee3dbd55244a66319dd982ad3faa194fd1b1cb3de326e4c460c1a742871ebdddaa4e22195551aa274e8506891b0928766db6ae7067761d51cbfc03bd2fe1f Homepage: https://cran.r-project.org/package=muhaz Description: CRAN Package 'muhaz' (Hazard Function Estimation in Survival Analysis) Produces a smooth estimate of the hazard function for censored data. Package: r-cran-mulea Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2438 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-bioc-fgsea, r-cran-ggplot2, r-cran-ggraph, r-cran-magrittr, r-cran-plyr, r-cran-rcpp, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-stringi, r-cran-tibble, r-cran-tidygraph, r-cran-tidyverse Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mulea_1.1.1-1.ca2404.1_amd64.deb Size: 920152 MD5sum: 9628961a1837a7432c8b35799015868b SHA1: 537b3819c2cbe5be9e554c13cb48fb41526fc3ac SHA256: f2316f1182a520b9b3f92fae414ec88b890f8a2e429fea3c8c26799924d5a398 SHA512: c405853cf960a1140144f7415bad8e9aac3cf76591ca36ebb5fda7d90ce24314b48469f5ebeb62670c49fbe04c516ad1bc5e3cc2bf8149fc0ede4a1e3e4211da 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. Package: r-cran-multbxxc Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmumps, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-slam, r-cran-testthat Filename: pool/dists/noble/main/r-cran-multbxxc_1.0.3-1.ca2404.1_amd64.deb Size: 111988 MD5sum: 0b81c6999fdacaea0e6ebbdcb66d492b SHA1: a84deed95cd46a8def89b117d0cd76905c2b3822 SHA256: 38e2d67a19e4d6c98e357164428550039b6c6a9104c99858cb955f181e499d3b SHA512: 5f0a0a220211c1d78ef3e40091fb7f1e6471f77aa9209b49c9e1432482613f4d252e3d1d33b12d7063f3fd052cfa378dd780b334575de3deea01903d118e1eb9 Homepage: https://cran.r-project.org/package=multbxxc Description: CRAN Package 'multbxxc' (Auxiliary Routines for Influx Software) Contains auxiliary routines for influx software. This packages is not intended to be used directly. Influx was published here: Sokol et al. (2012) . 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Such processes range from a simple Yule model to the complex susceptible-infectious-removed model in disease dynamics. Efficient likelihood evaluation facilitates maximum likelihood estimation and Bayesian inference. 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This implements and imports a large collection of methods for multiblock data analysis with common interfaces, result- and plotting functions, several real data sets and six vignettes covering a range different applications. Package: r-cran-multibridge Architecture: amd64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2598 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libmpfr6 (>= 3.1.3), libstdc++6 (>= 13.1), 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/noble/main/r-cran-multibridge_1.3.0-1.ca2404.1_amd64.deb Size: 760664 MD5sum: b2eb192476c6dc0b2860cd60da1162ae SHA1: fac5d66664ef8773761cc2920180dca1709b3a19 SHA256: dfe0ce6532493d6123b4cd3d97c3a2d957da5dfd5eb859650275b87762df54b5 SHA512: 69c34362a89825070b246bfc0228f2c2ee4951a67fc4e0879ac644857140dadbd721e733adf2ac99a0c4d41e76539176ca1a8fbc320c304f65f33f45b2274fcf Homepage: https://cran.r-project.org/package=multibridge Description: CRAN Package 'multibridge' (Evaluating Multinomial Order Restrictions with Bridge Sampling) Evaluate hypotheses concerning the distribution of multinomial proportions using bridge sampling. The bridge sampling routine is able to compute Bayes factors for hypotheses that entail inequality constraints, equality constraints, free parameters, and mixtures of all three. These hypotheses are tested against the encompassing hypothesis, that all parameters vary freely or against the null hypothesis that all category proportions are equal. For more information see Sarafoglou et al. (2020) . Package: r-cran-multicoap Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-irlba, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-multicoap_1.1-1.ca2404.1_amd64.deb Size: 171804 MD5sum: 3e0c59a65bd4ae69ed1bc7df01bd014a SHA1: 0921f14389ed5f42c7c22997cfc0f29eb780f77e SHA256: cfea3c62dfcf2bdf936427ba08b77db2e36c5616912df33f982aaa0494fd341c SHA512: 0698d186ac3e097dce6359a36828e99ab70982b00513841123df731a06de96fa948de0b53ed995ae42f745606b9ace5ccd1349b5ef0bf642bf1e7aae3b1ca13e Homepage: https://cran.r-project.org/package=MultiCOAP Description: CRAN Package 'MultiCOAP' (High-Dimensional Covariate-Augmented Overdispersed Multi-StudyPoisson Factor Model) We introduce factor models designed to jointly analyze high-dimensional count data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among counts with augmented covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of factors and the rank of regression coefficient matrix. More details can be referred to Liu et al. (2024) . Package: r-cran-multicool Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-multicool_1.0.1-1.ca2404.1_amd64.deb Size: 104796 MD5sum: a04929f75e4536308f1a373b91880596 SHA1: a1ba6fa8d0fa9c472ba71709cededcdc3a11d061 SHA256: 71d76d609ad93b39c0cbc0dbc6620bb4b27d10d9431c6f514f754274bbf8dfe0 SHA512: a393a9dbd2d2ec10e379d97d0c2b6ffa982f2d4148134720f4f649e51644f8b6f7cc08ba58646b8a4a3e97a5d9dfa5addce5f170b4aeb829de95440a7382a0d3 Homepage: https://cran.r-project.org/package=multicool Description: CRAN Package 'multicool' (Permutations of Multisets in Cool-Lex Order) A set of tools to permute multisets without loops or hash tables and to generate integer partitions. The permutation functions are based on C code from Aaron Williams. Cool-lex order is similar to colexicographical order. The algorithm is described in Williams, A. Loopless Generation of Multiset Permutations by Prefix Shifts. SODA 2009, Symposium on Discrete Algorithms, New York, United States. The permutation code is distributed without restrictions. The code for stable and efficient computation of multinomial coefficients comes from Dave Barber. The code can be download from and is distributed without conditions. The package also generates the integer partitions of a positive, non-zero integer n. The C++ code for this is based on Python code from Jerome Kelleher which can be found here . The C++ code and Python code are distributed without conditions. Package: r-cran-multifit Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 698 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-rcpparmadillo Suggests: r-cran-png, r-cran-qgraph, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-multifit_1.1.1-1.ca2404.1_amd64.deb Size: 429826 MD5sum: dc72992d65eabce3a5cb0d32047173b0 SHA1: 13aa111ba38f5a3611975c8f6e3c9e83088d8c83 SHA256: 71364ec7d49332a47f8787f916726b8f6f47a93cdec69326e83fedddd750b80e SHA512: 71b36c7633dffa6599f75818bfd16dff12efe51541a19d4e15bddf62aa710770b150a847b8e935e9646db553c1d24dffccb8434ef7bb4df6650180ded07dbfe8 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). 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For more technical details, see Lyrvall et al. (2025) . 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It handles the general problem of multifile record linkage and duplicate detection, where any number of files are to be linked, and any of the files may have duplicates. Package: r-cran-multimark Architecture: amd64 Version: 2.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 654 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-coda, r-cran-statmod, r-cran-rmark, r-cran-brobdingnag, r-cran-mvtnorm, r-cran-prodlim, r-cran-sp, r-cran-raster Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-multimark_2.1.7-1.ca2404.1_amd64.deb Size: 542730 MD5sum: e320e224ee2c4cc0cfa6656c27d9e6f8 SHA1: 3baaecb8288d1202bbaf8ea291130d3f29b5fa68 SHA256: f3a3c7c73e988a4221b614093ea715628d88760e9f86875687655ddf0a11ab4f SHA512: 969b30cce9468bea7370443437edb9f52d215cd0ba51986c8d14177b59d72962125dbd77290c849c88a2ec000bad6c7929d90ac56bf0c91a0ba27482e35580ed Homepage: https://cran.r-project.org/package=multimark Description: CRAN Package 'multimark' (Capture-Mark-Recapture Analysis using Multiple Non-InvasiveMarks) Traditional and spatial capture-mark-recapture analysis with multiple non-invasive marks. The models implemented in 'multimark' combine encounter history data arising from two different non-invasive "marks", such as images of left-sided and right-sided pelage patterns of bilaterally asymmetrical species, to estimate abundance and related demographic parameters while accounting for imperfect detection. Bayesian models are specified using simple formulae and fitted using Markov chain Monte Carlo. Addressing deficiencies in currently available software, 'multimark' also provides a user-friendly interface for performing Bayesian multimodel inference using non-spatial or spatial capture-recapture data consisting of a single conventional mark or multiple non-invasive marks. See McClintock (2015) and Maronde et al. (2020) . Package: r-cran-multimode Architecture: amd64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: libc6 (>= 2.2.5), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-diptest, r-cran-ks, r-cran-rootsolve Suggests: r-cran-nor1mix Filename: pool/dists/noble/main/r-cran-multimode_1.5-1.ca2404.1_amd64.deb Size: 229564 MD5sum: 06c0e450ee92fbbe75263b6bd8559bb4 SHA1: 77262f60ec0d53dc149550090050e625d37b0085 SHA256: bfc3fd54f864661abf86a3fbeebbdd9bd9347adb0082bc696f53624bc612d1e3 SHA512: 2ef1d6e208459b10b40ec1e03868fcb38cec37376d5d206fcbccbeae4c728f6d4aa0f67097b47a1100931f94166fb7e57090d1a52d3022e97d68150e9c53826a Homepage: https://cran.r-project.org/package=multimode Description: CRAN Package 'multimode' (Mode Testing and Exploring) Different examples and methods for testing (including different proposals described in Ameijeiras-Alonso et al., 2019 ) and exploring (including the mode tree, mode forest and SiZer) the number of modes using nonparametric techniques . Package: r-cran-multinet Architecture: amd64 Version: 4.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2882 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-rcpp, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-multinet_4.3.4-1.ca2404.1_amd64.deb Size: 820472 MD5sum: f00292273d64eef89ca53cacf9943c63 SHA1: f2d9878a1d24ec74e0513bec59ad892a80052988 SHA256: fcce8b868e5088c8b882e6fcd35a532f5f535b2b180b06d6ed6909a4fc9ff40d SHA512: 945b291c6f0f484f3f23a3adb8c02b2993aa73e2b714ddb2fa719d4786a56cd27b91ca06119ad7b91e39fced6dea7ff087b5b400fef25a08d07bef5efa9f72d7 Homepage: https://cran.r-project.org/package=multinet Description: CRAN Package 'multinet' (Analysis and Mining of Multilayer Social Networks) Functions for the creation/generation and analysis of multilayer social networks . Package: r-cran-multinets Architecture: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-rcpp Suggests: r-cran-sand, r-cran-statnet, r-cran-data.table, r-cran-testthat, r-cran-igraphdata Filename: pool/dists/noble/main/r-cran-multinets_0.2.2-1.ca2404.1_amd64.deb Size: 131296 MD5sum: 2fe1e6bf30ca8f289573f45b42013312 SHA1: f4f1e2cc2c8196818d3d8dac9aed5be4a0a8423d SHA256: b5968e74b1b08478ea51bbc026fdcb273bd064ed72005a382d3b9a807587c740 SHA512: b8855f56062f6436450f1602407af5517c96534dff0a11389d4cade8374c8de05e1d60a22a3ce3bcefe5345ce39759105832578d600f0a0d21a9ac1cba4de5db Homepage: https://cran.r-project.org/package=multinets Description: CRAN Package 'multinets' (Multilevel Networks Analysis) Analyze multilevel networks as described in Lazega et al (2008) and in Lazega and Snijders (2016, ISBN:978-3-319-24520-1). The package was developed essentially as an extension to 'igraph'. Package: r-cran-multinma Architecture: amd64 Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 19990 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-rdpack, r-cran-tibble, r-cran-dplyr, r-cran-rlang, r-cran-purrr, r-cran-forcats, r-cran-glue, r-cran-randtoolbox, r-cran-copula, r-cran-tidyr, r-cran-stringr, r-cran-matrix, r-cran-igraph, r-cran-ggraph, r-cran-ggplot2, r-cran-ggdist, r-cran-truncdist, r-cran-bayesplot, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-withr, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-rprojroot, r-cran-loo, r-cran-crayon, r-cran-tidygraph, r-cran-pkgdown, r-cran-splines2, r-cran-flexsurv, r-cran-rstpm2 Filename: pool/dists/noble/main/r-cran-multinma_0.9.1-1.ca2404.1_amd64.deb Size: 7054942 MD5sum: 0a0db99692fb29040bf865c85316fe08 SHA1: 9101bb1f8be161ce5fff0b3a36098edd31c184b3 SHA256: 6a49329fe48b108357e34245ba705785f995c22f0af4fc638dca66ef6047a154 SHA512: 4a0f7819c66fda4ffe004f816d61d4b185e778718b9280d7f092d59e3a8ef2fe5bcf9c2eb2d849556180a7381d337b0ea249baf6a6fe8c9aa828f765fcaecf41 Homepage: https://cran.r-project.org/package=multinma Description: CRAN Package 'multinma' (Bayesian Network Meta-Analysis of Individual and Aggregate Data) Network meta-analysis and network meta-regression models for aggregate data, individual patient data, and mixtures of both individual and aggregate data using multilevel network meta-regression as described by Phillippo et al. (2020) . Models are estimated in a Bayesian framework using 'Stan'. Package: r-cran-multinomiallogitmix Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-doparallel, r-cran-foreach, r-cran-label.switching, r-cran-ggplot2, r-cran-coda, r-cran-matrixstats, r-cran-mvtnorm, r-cran-rcolorbrewer, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-multinomiallogitmix_1.1-1.ca2404.1_amd64.deb Size: 186996 MD5sum: f57efd6f69e620819495982e7264ee0e SHA1: 99ab1128c02cc3fb2b91eca092b090ac7733bc66 SHA256: 797583f77657e1cb98ecbf6ca6bc721df4b506b48de58417c6198bfe6cd0bc96 SHA512: a0410d365a877283c70567d1bd47642526c17c9a11ede2a6ee141c3bed146cb1bae26dffdecaaa67daf3955f54f5fa3107936efd431e669a4f5aaf8f8df18eb9 Homepage: https://cran.r-project.org/package=multinomialLogitMix Description: CRAN Package 'multinomialLogitMix' (Clustering Multinomial Count Data under the Presence ofCovariates) Methods for model-based clustering of multinomial counts under the presence of covariates using mixtures of multinomial logit models, as implemented in Papastamoulis (2023) . These models are estimated under a frequentist as well as a Bayesian setup using the Expectation-Maximization algorithm and Markov chain Monte Carlo sampling (MCMC), respectively. The (unknown) number of clusters is selected according to the Integrated Completed Likelihood criterion (for the frequentist model), and estimating the number of non-empty components using overfitting mixture models after imposing suitable sparse prior assumptions on the mixing proportions (in the Bayesian case), see Rousseau and Mengersen (2011) . In the latter case, various MCMC chains run in parallel and are allowed to switch states. The final MCMC output is suitably post-processed in order to undo label switching using the Equivalence Classes Representatives (ECR) algorithm, as described in Papastamoulis (2016) . Package: r-cran-multinomineq Architecture: amd64 Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1710 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rglpk, r-cran-quadprog, r-cran-coda, r-cran-rcppxptrutils, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-multinomineq_0.2.6-1.ca2404.1_amd64.deb Size: 1344604 MD5sum: f7ed1e6f159f6d2aa08158d0d64bdc17 SHA1: 643590cc2b84a8b83da80a4520b1e0e18b1d4ce8 SHA256: 1e0dbcf5555704ded33f523bfcac549bd085694116d06076727c9c3644a5ed1b SHA512: 1cb92a824b88e48dd17aa8c43b924c0f731b11f3ff45245bcdc1e22cbc02a4a566782c8d50bf4c48ee4172357bb06af95306a8a3aaef4dfb886ef3aa62ac3c89 Homepage: https://cran.r-project.org/package=multinomineq Description: CRAN Package 'multinomineq' (Bayesian Inference for Multinomial Models with InequalityConstraints) Implements Gibbs sampling and Bayes factors for multinomial models with linear inequality constraints on the vector of probability parameters. As special cases, the model class includes models that predict a linear order of binomial probabilities (e.g., p[1] < p[2] < p[3] < .50) and mixture models assuming that the parameter vector p must be inside the convex hull of a finite number of predicted patterns (i.e., vertices). A formal definition of inequality-constrained multinomial models and the implemented computational methods is provided in: Heck, D.W., & Davis-Stober, C.P. (2019). Multinomial models with linear inequality constraints: Overview and improvements of computational methods for Bayesian inference. Journal of Mathematical Psychology, 91, 70-87. . Inequality-constrained multinomial models have applications in the area of judgment and decision making to fit and test random utility models (Regenwetter, M., Dana, J., & Davis-Stober, C.P. (2011). Transitivity of preferences. Psychological Review, 118, 42–56, ) or to perform outcome-based strategy classification to select the decision strategy that provides the best account for a vector of observed choice frequencies (Heck, D.W., Hilbig, B.E., & Moshagen, M. (2017). From information processing to decisions: Formalizing and comparing probabilistic choice models. Cognitive Psychology, 96, 26–40. ). Package: r-cran-multipledl Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1600 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-sparsem, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/noble/main/r-cran-multipledl_1.0.0-1.ca2404.1_amd64.deb Size: 607066 MD5sum: 6ce7b3ae534ab29d9386cb15c7885365 SHA1: c9d0ebb118e5462ea3e772bd36f1bb014429c2a0 SHA256: e1f3ba246b94f6190b07a92a2b934817af9f63d86e59bdd38897b8635c3a401e SHA512: b65f057246da8be7c37e2a5807b4b6f9b9339bf888e8ae3cb51d40d89db9199ee2915c6e2efdc5c5b43ef5b4391af28aab1b26b8858b7e6bffde7aaec6a810fe 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". . 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Implements PATED (Prognostic Assisted Treatment Effect Detection), a randomized-trial method that exploits balanced prognostic covariates to tighten standard errors and increase statistical power without introducing bias. Package: r-cran-multirfm Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 396 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-irlba, r-cran-laplacesdemon, r-cran-mixtools, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-multirfm_1.1.0-1.ca2404.1_amd64.deb Size: 135244 MD5sum: 2c7af8e7c1d01740d2ff7146552c91bf SHA1: 059c59472e1606dfc92195c9afe7c27e6dfa1220 SHA256: c7d1edc21325ca1a9f72758a8e9d1319b2255a77abd346f1b607fdff9bad2868 SHA512: 9106f8c8acefe5ff87a79adc796ed1b7e09a30ace5b9d2ff2254f7ad47bc5c48740c26433d47894fda7ecf92e4c0d107f9c409b92ed6a09cd86e2297dc9f3479 Homepage: https://cran.r-project.org/package=MultiRFM Description: CRAN Package 'MultiRFM' (High-Dimensional Multi-Study Robust Factor Model) We introduce a high-dimensional multi-study robust factor model, which learns latent features and accounts for the heterogeneity among source. It could be used for analyzing heterogeneous RNA sequencing data. More details can be referred to Jiang et al. (2025) . Package: r-cran-multirl Architecture: amd64 Version: 0.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 954 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-future, r-cran-dofuture, r-cran-foreach, r-cran-dorng, r-cran-progressr, r-cran-ggplot2, r-cran-scales Suggests: r-cran-gensa, r-cran-ga, r-cran-deoptim, r-cran-pso, r-cran-mlrmbo, r-cran-mlr, r-cran-paramhelpers, r-cran-smoof, r-cran-lhs, r-cran-dicekriging, r-cran-rgenoud, r-cran-cmaes, r-cran-nloptr, r-cran-abc, r-cran-pls, r-cran-reticulate, r-cran-keras, r-cran-keras3 Filename: pool/dists/noble/main/r-cran-multirl_0.3.7-1.ca2404.1_amd64.deb Size: 750926 MD5sum: c24e6fba8cd90e6bb9cd34deda1567bb SHA1: 9e79ad0209dc82139e57f5f7c01ca8e7ccba7cc3 SHA256: 96c704559d2cd88636362d2978a9782da372991ace17171a5c0bfa960edf57d0 SHA512: 6e92c05fada44a09e29c1902a5cf27c47760951f758eb4d904d6c31548adec178b7db08b19e03164322c866362a9d19fb5989619143df2b45c4108d8dc5e5bd6 Homepage: https://cran.r-project.org/package=multiRL Description: CRAN Package 'multiRL' (Reinforcement Learning Tools for Multi-Armed Bandit) A flexible general-purpose toolbox for implementing Rescorla-Wagner models in multi-armed bandit tasks. As the successor and functional extension of the 'binaryRL' package, 'multiRL' modularizes the Markov Decision Process (MDP) into six core components. This framework enables users to construct custom models via intuitive if-else syntax and define latent learning rules for agents. For parameter estimation, it provides both likelihood-based inference (MLE and MAP) and simulation-based inference (ABC and RNN), with full support for parallel processing across subjects. The workflow is highly standardized, featuring four main functions that strictly follow the four-step protocol (and ten rules) proposed by Wilson & Collins (2019) . Beyond the three built-in models (TD, RSTD, and Utility), users can easily derive new variants by declaring which variables are treated as free parameters. 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Specifically, the scale parameter of a distance-weighted kernel distribution is identified for all environmental layers included in the model. Includes functions to assist in model selection, model evaluation, efficient transformation of raster surfaces using fast Fourier transformation, and projecting models. For more details see Peterman (2026) . Package: r-cran-multiscape Architecture: amd64 Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8240 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-multiscape_1.0.7-1.ca2404.1_amd64.deb Size: 7097518 MD5sum: 5080a88faf8c99fb1e6c4174e0a6d0ad SHA1: 1e7c4f35950ea8bef1df5f505b0ced357372556a SHA256: e5e0b34c206eb298d66a54995fb9909ce94939d2db6dad97a101757a0e5aeb67 SHA512: fe22d68d963e21d5dbd7fbe7563334bdb32f858685b4bf9d02f446005db4c733ef4e884695be68bedf58f3d73df89527ba22edee569e1eb2aaa9ac1829cbf809 Homepage: https://cran.r-project.org/package=multiscape Description: CRAN Package 'multiscape' (Multi-Objective Spatial Planning) Provides a modular framework for exact multi-objective spatial planning using mixed-integer programming. The package supports the definition of planning problems through planning units, features, management actions, action effects, spatial relations, targets, constraints, and objective functions. It enables the optimisation of spatial planning portfolios under considerations such as boundary structure, connectivity, and fragmentation. Supported multi-objective methods include weighted-sum aggregation, epsilon-constraint, and the augmented epsilon-constraint method. Problems can be solved with several commercial and open-source optimisation solvers. Optional solver backends include the 'gurobi' R package, which is distributed with the Gurobi Optimizer installation , and the 'rcbc' R package, available from GitHub at . For background on multi-objective optimisation methods, see Halffmann et al. (2022) ; for the augmented epsilon-constraint method, see Mavrotas (2009) . Package: r-cran-multispatialccm Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-multispatialccm_1.3-1.ca2404.1_amd64.deb Size: 53378 MD5sum: 7050b7b5a4431d2774dc0d52f30fff4b SHA1: db8f4b34c919356844ee82e80d3279bb1a6063aa SHA256: 05cb566fe772961a940647b650832b33e6c1d725f8393b826fda9a6ac9f0a9ef SHA512: bf160848c266d84ad7f9fc9da758e4bddf11b6f92d5da28af74c02a9df71905e52a42f2a09d3019c612116ddc72e82df7826070228982ab8b32725eba390c73d Homepage: https://cran.r-project.org/package=multispatialCCM Description: CRAN Package 'multispatialCCM' (Multispatial Convergent Cross Mapping) The multispatial convergent cross mapping algorithm can be used as a test for causal associations between pairs of processes represented by time series. This is a combination of convergent cross mapping (CCM), described in Sugihara et al., 2012, Science, 338, 496-500, and dew-drop regression, described in Hsieh et al., 2008, American Naturalist, 171, 71–80. The algorithm allows CCM to be implemented on data that are not from a single long time series. Instead, data can come from many short time series, which are stitched together using bootstrapping. Package: r-cran-multistatm Architecture: amd64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrangements, r-cran-matrix, r-cran-eql, r-cran-mvtnorm, r-cran-rcpp Suggests: r-cran-mass, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-multistatm_2.1.0-1.ca2404.1_amd64.deb Size: 376004 MD5sum: e0a8cbc49e08a85251312e0bf45648d2 SHA1: 1666c02acc8a3f7b190dba3583b6ed7eed4df787 SHA256: e6107059289441ab9e514700c7717a37ac53de593b10766aec241321822834a6 SHA512: 523057d803981009569eb448d835674ae9423a49460b96d2891d87d9a01ecad3add8bb8a4ee014479647a109632ca485beade990c47f7c4c3bbb77bd3a6f72f7 Homepage: https://cran.r-project.org/package=MultiStatM Description: CRAN Package 'MultiStatM' (Multivariate Statistical Methods) Algorithms to build set partitions and commutator matrices and their use in the construction of multivariate d-Hermite polynomials; estimation and derivation of theoretical vector moments and vector cumulants of multivariate distributions; conversion formulae for multivariate moments and cumulants. Applications to estimation and derivation of multivariate measures of skewness and kurtosis; estimation and derivation of asymptotic covariances for d-variate Hermite polynomials, multivariate moments and cumulants and measures of skewness and kurtosis. The formulae implemented are discussed in Terdik (2021, ISBN:9783030813925), "Multivariate Statistical Methods". Package: r-cran-multitaper Architecture: amd64 Version: 1.0-17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-psd, r-cran-fftwtools, r-cran-slp Filename: pool/dists/noble/main/r-cran-multitaper_1.0-17-1.ca2404.1_amd64.deb Size: 284538 MD5sum: 3979c1a71ffa4e5509b583f89ddac064 SHA1: 69a0a7a7dc1540cee97c4f973a2f0945beb1a617 SHA256: d0d8f07bfc9e6b8062f33f06bcfa78ba7286585a651a9e1cb34c15bddbc46f7f SHA512: 6d2009a6d91f6d1aebd4c0822933eba8e2e335769648379d764e504c165541a7bb1bd99bc46aa974ee3d9e02b831b125791a60050a80f67372a439e90bcb74cc Homepage: https://cran.r-project.org/package=multitaper Description: CRAN Package 'multitaper' (Spectral Analysis Tools using the Multitaper Method) Implements multitaper spectral analysis using discrete prolate spheroidal sequences (Slepians) and sine tapers. 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Package: r-cran-multivar Architecture: amd64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 961 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-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/noble/main/r-cran-multivar_1.4.0-1.ca2404.1_amd64.deb Size: 747636 MD5sum: a6e608c0df04f010da47ddce5d8e5c20 SHA1: 371aeeca1b61b709115b398fdf261a18458a16c2 SHA256: 49e17ef776d82ec26792c1bd2c08033accbf389ba86fa1a4fdc8948b2d86e47f SHA512: 5f273a384e1fabae1185ab3c14c1d65facfe0173023f23ed0175e59330724472ee5042dadb17e02e14b9178b1b6699f36019a81a58f776f310508219cd57217e Homepage: https://cran.r-project.org/package=multivar Description: CRAN Package 'multivar' (Penalized Estimation of Multiple-Subject Vector AutoregressiveModels) Simulate, estimate, and forecast vector autoregressive (VAR) models for multiple-subject data using structured penalization. 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The necessary functions are implemented in this packages and examples are given. It includes: distance multivariance, distance multicorrelation, dependence structure detection, tests of independence and copula versions of distance multivariance based on the Monte Carlo empirical transform. Detailed references are given in the package description, as starting point for the theoretic background we refer to: B. Böttcher, Dependence and Dependence Structures: Estimation and Visualization Using the Unifying Concept of Distance Multivariance. Open Statistics, Vol. 1, No. 1 (2020), . Package: r-cran-multivariaterandomforest Architecture: amd64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bootstrap Filename: pool/dists/noble/main/r-cran-multivariaterandomforest_1.1.5-1.ca2404.1_amd64.deb Size: 82008 MD5sum: 6388c0a23548b67ce296583c92287e18 SHA1: b8acd3ee9b6979464ab838ede3f293343e06e407 SHA256: 73226020b46b9a0cb8195dff155285740fdbbb3200f553cd2a1639be406b8376 SHA512: efda6aac585717861ba4062e089653ff8c39111655c7c4d2a6d2d4aca0769d6dde146fd475cdb8b8e7571c1ec3cf52f497adadce0b12547ee597a20fe4dee4f8 Homepage: https://cran.r-project.org/package=MultivariateRandomForest Description: CRAN Package 'MultivariateRandomForest' (Models Multivariate Cases Using Random Forests) Models and predicts multiple output features in single random forest considering the linear relation among the output features, see details in Rahman et al (2017). 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By varying the weight of the agreement penalty, we get a continuum of solutions that include the well-known early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or cross-validation to estimate test set prediction error. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty (Ding, D., Li, S., Narasimhan, B., Tibshirani, R. (2021) ). Package: r-cran-multnonparam Architecture: amd64 Version: 1.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-icsnp Filename: pool/dists/noble/main/r-cran-multnonparam_1.3.9-1.ca2404.1_amd64.deb Size: 159276 MD5sum: 3a831f6e6d10439066c6596e21a9e961 SHA1: b55566763312552bf8a18864ca498ab25396e85c SHA256: 98d026d00ed06b0490676b90d920d6cdf046799d049910dfd1103c4762e664a4 SHA512: 2acd9b567f6d323fac6f17dbd9a75e07859e33a4a3f1e1d27cfeab1e49b424c055b2a5f07fe289215f3ce0e6cb1c606068f917c1d90790b3a5c059acd11ceecb Homepage: https://cran.r-project.org/package=MultNonParam Description: CRAN Package 'MultNonParam' (Multivariate Nonparametric Methods) A collection of multivariate nonparametric methods, selected in part to support an MS level course in nonparametric statistical methods. Methods include adjustments for multiple comparisons, implementation of multivariate Mann-Whitney-Wilcoxon testing, inversion of these tests to produce a confidence region, some permutation tests for linear models, and some algorithms for calculating exact probabilities associated with one- and two- stage testing involving Mann-Whitney-Wilcoxon statistics. Supported by grant NSF DMS 1712839. See Kolassa and Seifu (2013) . Package: r-cran-multordrs Architecture: amd64 Version: 0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-statmod, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-multordrs_0.1-3-1.ca2404.1_amd64.deb Size: 212384 MD5sum: 755aa39cc3553e45dd7c606e25f9b2ee SHA1: 5b6e02125c8b83d0391a014a26024bb674352f75 SHA256: 53e7ab166113446406457c24bf83d450c7e122c02dff03e3469a810f3c03a44b SHA512: 5f0ad478a19d2e436de023f68398edb1dab6840cb5fc3e73b2ce41fc1bb69acd9fb6e77efc2bf7db99e6ffb7be1c1e391879d58dc3f590f5d7b85c39af5236b7 Homepage: https://cran.r-project.org/package=MultOrdRS Description: CRAN Package 'MultOrdRS' (Model Multivariate Ordinal Responses Including Response Styles) In the case of multivariate ordinal responses, parameter estimates can be severely biased if personal response styles are ignored. 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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Package: r-cran-mutualinf Architecture: amd64 Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 652 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-runner, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-mutualinf_2.0.4-1.ca2404.1_amd64.deb Size: 495762 MD5sum: e32bcb719df4164c851de8a84a5be859 SHA1: 08e90d608b8c2f50d61e5fc363b56deffd696d9a SHA256: 49bafd1ff8840a37f29ffb950dcae9010b532baa7ff998edc3126bfe86a5e744 SHA512: 9ad040fa7028e2b8ca80f41da65d1a4ec3273b1b612226a4f5fb34d5f3815211eac1c6ab3475f33a9586d0925a164affcc707db57354feecc7626cb3765c086c Homepage: https://cran.r-project.org/package=mutualinf Description: CRAN Package 'mutualinf' (Computation and Decomposition of the Mutual Information Index) The Mutual Information Index (M) introduced to social science literature by Theil and Finizza (1971) is a multigroup segregation measure that is highly decomposable and that according to Frankel and Volij (2011) and Mora and Ruiz-Castillo (2011) satisfies the Strong Unit Decomposability and Strong Group Decomposability properties. This package allows computing and decomposing the total index value into its "between" and "within" terms. These last terms can also be decomposed into their contributions, either by group or unit characteristics. The factors that produce each "within" term can also be displayed at the user's request. The results can be computed considering a variable or sets of variables that define separate clusters. 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See 'mvabund-package.Rd' for details of overall package organization. The package is implemented with the Gnu Scientific Library () and 'Rcpp' () 'R' / 'C++' classes. Package: r-cran-mvar.pt Architecture: amd64 Version: 2.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 449 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-mvar.pt_2.2.9-1.ca2404.1_amd64.deb Size: 396580 MD5sum: ab0e02d205ebb620a861e643bee1e206 SHA1: 88757b1d54a60e42e64b9f56422d66edcd1c863e SHA256: 8419bc23a5a64289a662b95f5c8fd992bbeca48b7bf19874f3ef6cddd09336e0 SHA512: bbae559a5f9e72bdea228475a5f7c07dce71a6fc617e9ce1817b1f783f0a3264d56f2cbd000a79b28ef32c9264ed8a37942c2c7b1e940364f6a4fd5346a1b836 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: amd64 Version: 2.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-mvar_2.2.9-1.ca2404.1_amd64.deb Size: 396086 MD5sum: 5fbfaa80352170b06c1f039e11004192 SHA1: 6afc78aae260924f5bf92f3c679e7ec5bb03f34e SHA256: bd475ef7f0e8e832b9897baf0f07ca2ab5829aa7f9c89907e2dde6263ca0c47e SHA512: 4474cd434dcd84a934cd325fb81e19abd80414fb125d0b7430bcb424ffd78d62209ba26b3bbac9b07575cc8df4e0b380279b861a6a0e2c6ed8c6240f0467ad38 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: amd64 Version: 1.1.594-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10454 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-brms, r-cran-mgcv, r-cran-insight, r-cran-marginaleffects, r-cran-rcpp, r-cran-rstan, r-cran-posterior, r-cran-loo, r-cran-rstantools, r-cran-bayesplot, r-cran-ggplot2, r-cran-mvnfast, r-cran-purrr, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-generics, r-cran-tibble, r-cran-patchwork, r-cran-rcpparmadillo Suggests: r-cran-scoringrules, r-cran-matrixstats, r-cran-tweedie, r-cran-splines2, r-cran-extradistr, r-cran-corpcor, r-cran-wrswor, r-cran-ggrepel, r-cran-ggpp, r-cran-ggarrow, r-cran-xts, r-cran-lubridate, r-cran-knitr, r-cran-collapse, r-cran-rmarkdown, r-cran-rjags, r-cran-coda, r-cran-runjags, r-cran-usethis, r-cran-testthat, r-cran-colorspace Filename: pool/dists/noble/main/r-cran-mvgam_1.1.594-1.ca2404.1_amd64.deb Size: 9087072 MD5sum: a9293214768e695def009201fbc9eba6 SHA1: 9cb7ff5536738bf3e15661dfc05367291f82a24b SHA256: 8c5a5089d4cd22284570f50ebeb5c18734567a453031010b215624de6620032f SHA512: 9bcd82e8856841726c3a0611fe613976460a5147b0b8b7a011c42e879b6496c920f260841e91455b623640e4c2c3a7756691221563e98c167191faedbee1d257 Homepage: https://cran.r-project.org/package=mvgam Description: CRAN Package 'mvgam' (Multivariate (Dynamic) Generalized Additive Models) Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2023) . Package: r-cran-mvgb Architecture: amd64 Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mvgb_0.0.6-1.ca2404.1_amd64.deb Size: 35950 MD5sum: 74738bcb4fed21453ebb27ff4c4f4cf0 SHA1: 8454c5a62202878fe055102bdc41034534ceed88 SHA256: e8e3ffda5b864d164a8ce2496c06fe711fee6e2cf61e83e1fb34607dd0c1692e SHA512: 012fee687b598a0c8efb61dfa6cc18cd7227892c87623565077a51e2ead248ee38e9eb32820264044321c82c5fd13517b74b54b3eed6689a443d126ebfaf68a1 Homepage: https://cran.r-project.org/package=mvgb Description: CRAN Package 'mvgb' (Multivariate Probabilities of Scale Mixtures of MultivariateNormal Distributions via the Genz and Bretz (2002) QRSVN Method) Generates multivariate subgaussian stable probabilities using the QRSVN algorithm as detailed in Genz and Bretz (2002) but by sampling positive stable variates not chi/sqrt(nu). Package: r-cran-mvlsw Architecture: amd64 Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1309 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields, r-cran-wavethresh, r-cran-xts, r-cran-zoo Filename: pool/dists/noble/main/r-cran-mvlsw_1.2.5-1.ca2404.1_amd64.deb Size: 1206512 MD5sum: b573ecac366a123eb12a3505e25716e4 SHA1: a01e8119353ba986cd841f5673468c6cc67d687e SHA256: c9916ab118393648f7b966f246ac1f54f8838098c0a8e556bd2d78c5216ef7b2 SHA512: e8c7308e178b898d6e857fea6b8306f888cda174f5b6cabbf305d853ed060fb506ed4253d4ae00d6c5b0dc32b6eb44a1e264b56214e076a384c2c610620933a4 Homepage: https://cran.r-project.org/package=mvLSW Description: CRAN Package 'mvLSW' (Multivariate, Locally Stationary Wavelet Process Estimation) Tools for analysing multivariate time series with wavelets. This includes: simulation of a multivariate locally stationary wavelet (mvLSW) process from a multivariate evolutionary wavelet spectrum (mvEWS); estimation of the mvEWS, local coherence and local partial coherence. See Park, Eckley and Ombao (2014) for details. Package: r-cran-mvmapit Architecture: amd64 Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2168 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-compquadform, r-cran-dplyr, r-cran-foreach, r-cran-harmonicmeanp, r-cran-logging, r-cran-mvtnorm, r-cran-rcpp, r-cran-tidyr, r-cran-truncnorm, r-cran-rcpparmadillo, r-cran-rcppparallel, r-cran-rcppprogress, r-cran-rcppspdlog, r-cran-testthat Suggests: r-cran-ggally, r-cran-ggplot2, r-cran-ggrepel, r-cran-kableextra, r-cran-knitr, r-cran-markdown, r-cran-rcppalgos, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mvmapit_2.0.4-1.ca2404.1_amd64.deb Size: 1087980 MD5sum: 6b57b823edf1c58b4749399c27f53bfb SHA1: 08c9aaa7a14864b490c04632b1561fd9863882cf SHA256: 4b88e96015967167201f2113dc406ad664a348e7506e80a58c8cd9672b067766 SHA512: cf62ae597ab90954129c1dffa05095ad546f6214c715ee72a6f2ec1a05d24136ac38b36f1cf7afdb64ffeec2186bb50460daa78fea96f38762512909f7c07a88 Homepage: https://cran.r-project.org/package=mvMAPIT Description: CRAN Package 'mvMAPIT' (Multivariate Genome Wide Marginal Epistasis Test) Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this package, we present the 'multivariate MArginal ePIstasis Test' ('mvMAPIT') – a multi-outcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact – thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search based methods. Our proposed 'mvMAPIT' builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate 'mvMAPIT' as a multivariate linear mixed model and develop a multi-trait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. Crawford et al. (2017) . Stamp et al. (2023) . Stamp et al. (2025) . 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'mvMORPH' also proposes high-dimensional multivariate comparative tools (linear models using Generalized Least Squares and multivariate tests) based on penalized likelihood. See Clavel et al. (2015) , Clavel et al. (2019) , and Clavel & Morlon (2020) . Package: r-cran-mvna Architecture: amd64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Filename: pool/dists/noble/main/r-cran-mvna_2.0.1-1.ca2404.1_amd64.deb Size: 97918 MD5sum: 601c15c75e02f7c229888995adfebb36 SHA1: d180ead69f63a199464b23f30b15be8ced1e6483 SHA256: f51fbcc4820ad17bfcccd76f0d5957f85c820a4709f602be6ae16d8dbcf2dabc SHA512: eb3ef072400900e0d75a9af3c773281a33bfdaa6f5c269e340e2f51a3844ac8ee52b46f2efe98c4ab3f026f763c389d937a83962104d5cf8a75b2bc4697df718 Homepage: https://cran.r-project.org/package=mvna Description: CRAN Package 'mvna' (Nelson-Aalen Estimator of the Cumulative Hazard in MultistateModels) Computes the Nelson-Aalen estimator of the cumulative transition hazard for arbitrary Markov multistate models . 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The main functionalities are: simulating multivariate random vectors, evaluating multivariate normal or Student's t densities and Mahalanobis distances. These tools are very efficient thanks to the use of C++ code and of the OpenMP API. 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The multiple imputation algorithm is based on the data augmentation algorithm proposed by Tanner and Wong (1987). The Gibbs sampling algorithm is adopted to to update the model parameters and draw imputations of the coarse data. Package: r-cran-mvnmle Architecture: amd64 Version: 0.1-11.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mvnmle_0.1-11.2-1.ca2404.1_amd64.deb Size: 37604 MD5sum: 14ef706ac97332bde43a60c5b8767fed SHA1: 838b922e668d13470aa19aecf65ce67d0f594e0e SHA256: d3469dac93e3fd30da704a53e9cc3744da161ccdffef5d75393825e0e4b21707 SHA512: ddfbc831bc249954d65f9024e53ba8deaff6c645bc8579ecacc771a32978c20dcde956123c9106d5f78664126d04094eafd8a5a78e2281c7b75137e3a12bd67b Homepage: https://cran.r-project.org/package=mvnmle Description: CRAN Package 'mvnmle' (ML Estimation for Multivariate Normal Data with Missing Values) Finds the Maximum Likelihood (ML) Estimate of the mean vector and variance-covariance matrix for multivariate normal data with missing values. Package: r-cran-mvord Architecture: amd64 Version: 1.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1957 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-minqa, r-cran-bb, r-cran-ucminf, r-cran-dfoptim, r-cran-mass, r-cran-pbivnorm, r-cran-optimx, r-cran-mnormt, r-cran-numderiv, r-cran-matrix, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-xtable, r-cran-colorspace, r-cran-vgam Filename: pool/dists/noble/main/r-cran-mvord_1.2.6-1.ca2404.1_amd64.deb Size: 1725436 MD5sum: 53f7ee2ed7e358469157ea8f942a92f5 SHA1: e597aa92b2ee663ba4f2b1e52a6225cde789ec24 SHA256: b59f293ad23fc70912726984d244c2086e9711c3f3dcbd15c22ac9da55d77f82 SHA512: 7eae9a14a2b404dc8a9d3224732bbd14e6662d6b0d3abab92dbe4e29216902e236ce5a4ad8556240e4fd82d6b1e0993c3700cd5038a5e851348bc76a8cc6270d Homepage: https://cran.r-project.org/package=mvord Description: CRAN Package 'mvord' (Multivariate Ordinal Regression Models) A flexible framework for fitting multivariate ordinal regression models with composite likelihood methods. Methodological details are given in Hirk, Hornik, Vana (2020) . Package: r-cran-mvp Architecture: amd64 Version: 1.0-18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 687 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-partitions, r-cran-magic, r-cran-digest, r-cran-disordr, r-cran-numbers, r-cran-mpoly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark, r-cran-testthat, r-cran-spray, r-cran-magrittr, r-cran-covr Filename: pool/dists/noble/main/r-cran-mvp_1.0-18-1.ca2404.1_amd64.deb Size: 403886 MD5sum: e2440f6ec7effae9d8f89d87949b95c4 SHA1: 37585a64433720251a205a277e66418bf84f1c6c SHA256: 35117ebb79a828f154400b71401b5087c47c0b7c0dde2161fb001ac576318bbb SHA512: 606304f2cb21a30a826d7e142b951276e18b8c002e55928c79c5f7146a514abd46d453b27d2ba4eb51ce2017ffa2576ec2fb1bf644a60e1c62baac87b324b15e Homepage: https://cran.r-project.org/package=mvp Description: CRAN Package 'mvp' (Fast Symbolic Multivariate Polynomials) Fast manipulation of symbolic multivariate polynomials using the 'Map' class of the Standard Template Library. The package uses print and coercion methods from the 'mpoly' package but offers speed improvements. It is comparable in speed to the 'spray' package for sparse arrays, but retains the symbolic benefits of 'mpoly'. To cite the package in publications, use Hankin 2022 . Uses 'disordR' discipline. Package: r-cran-mvpot Architecture: amd64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-evd, r-cran-numbers, r-cran-gmp Filename: pool/dists/noble/main/r-cran-mvpot_0.1.7-1.ca2404.1_amd64.deb Size: 121092 MD5sum: b54c0fbba368b80c08f22ded162779f1 SHA1: 5bc164127a7a660b23a7ad6f488b3a294be97416 SHA256: 039f5d4a65bf1a5e7bec8943b0e7f3780840351ce24b5b17135354167efafc59 SHA512: 4674428a54545da29948338e394434b58348bdf3067039a6ead9e0bd420675c82b70fd16e8013ea72bb99d288d515911fc07bbf800be8c6472b01b8fe79cc69a Homepage: https://cran.r-project.org/package=mvPot Description: CRAN Package 'mvPot' (Multivariate Peaks-over-Threshold Modelling for Spatial ExtremeEvents) Tools for high-dimensional peaks-over-threshold inference and simulation of Brown-Resnick and extremal Student spatial extremal processes. These include optimization routines based on censored likelihood and gradient scoring, and exact simulation algorithms for max-stable and multivariate Pareto distributions based on rejection sampling. Fast multivariate Gaussian and Student distribution functions using separation-of-variable algorithm with quasi Monte Carlo integration are also provided. Key references include de Fondeville and Davison (2018) , Thibaud and Opitz (2015) , Wadsworth and Tawn (2014) and Genz and Bretz (2009) . Package: r-cran-mvr Architecture: amd64 Version: 1.33.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 766 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.3), r-base-core (>= 4.4.0), r-api-4.0, r-cran-statmod Filename: pool/dists/noble/main/r-cran-mvr_1.33.0-1.ca2404.1_amd64.deb Size: 686992 MD5sum: 553a64d01f4c1b84ce309ed412a9b3a6 SHA1: e36de99617763d8b8ada98bd9bb93f99113c1413 SHA256: 8ede8942d59f7e3d9eb8a332b841ca2c65474db35d385f345d2191eb31b92915 SHA512: 2a2fb985e076229189ed814bd36f18bca09aa7d65e00e4079945003f8eccc97109bca7ea00a02754419355591eff81007f9f62c5cde3474a100d8734a2a1480c Homepage: https://cran.r-project.org/package=MVR Description: CRAN Package 'MVR' (Mean-Variance Regularization) This is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data. It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm). Among those are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. Key features include: (i) Normalization and/or variance stabilization of the data, (ii) Computation of mean-variance-regularized t-statistics (F-statistics to follow), (iii) Generation of diverse diagnostic plots, (iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment. 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May be useful for multidimensional predictions (such as a multinomial model) or calculating goodness of fit from latent variable models such as probabilistic topic models like latent Dirichlet allocation or deterministic topic models like latent semantic analysis. Based on Jones (2019) . Package: r-cran-mvst Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: libc6 (>= 2.29), libgsl27 (>= 2.7.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack, r-cran-mvtnorm, r-cran-mnormt Filename: pool/dists/noble/main/r-cran-mvst_1.1.1-1.ca2404.1_amd64.deb Size: 211508 MD5sum: 46b310a61994fcb0d31a64e725160aac SHA1: 9aacb742fa9c5d051de6e47c8f7ac8d43e82a794 SHA256: 70150244c67c90186a4e18b9c5ae578ff971ce2d5cbc65fe3b4ecae07cce91d5 SHA512: 6ebf3c3d703da684190b7d564ebf1dd4b99b2422868510094a7ba850a4e34982aeab08dc1ca1c87b3275593946078e71053eed4218b1bd17925d89c343e1bc82 Homepage: https://cran.r-project.org/package=mvst Description: CRAN Package 'mvst' (Bayesian Inference for the Multivariate Skew-t Model) Estimates the multivariate skew-t and nested models, as described in the articles Liseo, B., Parisi, A. (2013). Bayesian inference for the multivariate skew-normal model: a population Monte Carlo approach. Comput. Statist. Data Anal. and in Parisi, A., Liseo, B. (2017). Objective Bayesian analysis for the multivariate skew-t model. Statistical Methods & Applications . 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Currently, the following methodologies are implemented: multivariate mean and covariance estimation, hypothesis testing about equicorrelation and homogeneity of variances, the Wilson-Hilferty transformation, QQ-plots with envelopes and random variate generation. Package: r-cran-mvtnorm Architecture: amd64 Version: 1.3-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1566 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-qrng, r-cran-numderiv, r-cran-bibtex Filename: pool/dists/noble/main/r-cran-mvtnorm_1.3-7-1.ca2404.1_amd64.deb Size: 1014610 MD5sum: 04f9451529137f30cc06e9804eca9f64 SHA1: e80f6d4bc85f46e6b62bc281d78a3c38eaa3a452 SHA256: ef8c9c59c849f53551281d81048a50ed8898bd8ab538546af01824c48c33a00d SHA512: 40e1eb7955c3956e0bb9189f0abccd3351c2ff81f95d795c3def7b4858a6404c6b2a6c2cbc78d2465dc564a4f68212ef43f5f26ebd804de8005286770cb6f9bd Homepage: https://cran.r-project.org/package=mvtnorm Description: CRAN Package 'mvtnorm' (Multivariate Normal and t Distributions) Computes multivariate normal and t probabilities, quantiles, random deviates, and densities. Log-likelihoods for multivariate Gaussian models and Gaussian copulae parameterised by Cholesky factors of covariance or precision matrices are implemented for interval-censored and exact data, or a mix thereof. Score functions for these log-likelihoods are available. A class representing multiple lower triangular matrices and corresponding methods are part of this package. Package: r-cran-mwcsr Architecture: amd64 Version: 0.1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3647 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-igraph, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mathjaxr, r-cran-testthat, r-bioc-bionet, r-cran-roxygen2, r-bioc-dlbcl Filename: pool/dists/noble/main/r-cran-mwcsr_0.1.11-1.ca2404.1_amd64.deb Size: 2816396 MD5sum: 108ece5fb76e399fa3ec64fe922d9685 SHA1: 1dd74de643fc949f3a9894b85d31cef5636d95e1 SHA256: 7147c1e7e71ac1dc06f51e6df7d260a4b644151e054cafca2916afa0076f262b SHA512: a52883e44cbe8aab4b67a5893cb33fa664cb7fa6686810e544ce0b56166d5b773acf726e2757680091121ceb9c1fb5645765b551a587a13292801de9e350573c Homepage: https://cran.r-project.org/package=mwcsr Description: CRAN Package 'mwcsr' (Solvers for Maximum Weight Connected Subgraph Problem and ItsVariants) Algorithms for solving various Maximum Weight Connected Subgraph Problems, including variants with budget constraints, cardinality constraints, weighted edges and signals. The package represents an R interface to high-efficient solvers based on relax-and-cut approach (Álvarez-Miranda E., Sinnl M. (2017) ) mixed-integer programming (Loboda A., Artyomov M., and Sergushichev A. (2016) ) and simulated annealing. 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Package: r-cran-mytai Architecture: amd64 Version: 2.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7954 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-s7, r-cran-patchwork, r-cran-purrr, r-cran-tidyr, r-cran-rcpp, r-cran-memoise, r-cran-fitdistrplus, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-ggplot2, r-cran-ggforce, r-cran-ggridges, r-cran-ggtext, r-cran-readr, r-cran-tibble, r-cran-ggplotify, r-cran-ggrepel, r-cran-matrix, r-cran-pheatmap, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mgcv, r-cran-seurat, r-cran-seuratobject, r-cran-uwot, r-cran-decor, r-bioc-deseq2, r-cran-gganimate, r-cran-taxize Filename: pool/dists/noble/main/r-cran-mytai_2.3.5-1.ca2404.1_amd64.deb Size: 5483140 MD5sum: 63b2ed8c6df9a42887f479f58e297eef SHA1: 09b004a4f8b9651a209b69ecccd1ab30fa3ba843 SHA256: 82e81de01b2a947743a51986aab1aeba8a9f44f18056c1d69ef5dc9ccd3bc066 SHA512: 1b1efd5d94d8dd699993b889a148099a358b9bae25c1ede07da4cce04ef1c2627f5487c256052731aefe5119d644342ba498e1d54065901bcb9a8d7a94d1d23a Homepage: https://cran.r-project.org/package=myTAI Description: CRAN Package 'myTAI' (Evolutionary Transcriptomics) Investigate the evolution of biological processes by capturing evolutionary signatures in transcriptomes (Drost et al. (2018) ). This package aims to provide a transcriptome analysis environment to quantify the average evolutionary age of genes contributing to a transcriptome of interest. 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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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Algorithm based on the 'N2' implementation of an approximate nearest neighbor search using hierarchical Navigable Small World (NSW) graphs. The original algorithm is described in "Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs", Y. Malkov and D. Yashunin, , . Package: r-cran-nabor Architecture: amd64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-testthat, r-cran-rann Filename: pool/dists/noble/main/r-cran-nabor_0.5.0-1.ca2404.1_amd64.deb Size: 155706 MD5sum: 4fa41bc7dc549098969a626690b230b4 SHA1: 5af595cb5dae52b87415a769dac71f40baa8d964 SHA256: 51ab68a0dae14525bd10efd7ea70dd3e453842f3be0c4494f1d4d6bca36a96d1 SHA512: 187837310af53c1d0c8f06bef4ca30c8f6998e155493ae2ec171c8ed3d647a8a470f1d4a4c2bbe0e963724d7808cac2401a66cd940e214d7994c33acf9a19ed9 Homepage: https://cran.r-project.org/package=nabor Description: CRAN Package 'nabor' (Wraps 'libnabo', a Fast K Nearest Neighbour Library for LowDimensions) An R wrapper for 'libnabo', an exact or approximate k nearest neighbour library which is optimised for low dimensional spaces (e.g. 3D). 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Package: r-cran-nadiv Architecture: amd64 Version: 2.18.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1048 Depends: libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-nadiv_2.18.0-1.ca2404.1_amd64.deb Size: 967886 MD5sum: c8092f2989dad3d80c7c2d752414e796 SHA1: 5e493f59630f85b0106254a773577bc2a15885e9 SHA256: c8e7f5b43a27df87584d9de09042b0ee95924658045122845acd2f03d4da2ce7 SHA512: 257a6e5d407dfc6a3a2fe85fac0199933fd4427eaca9be325745276b8da8539d588bb9a299c81e4f70119239faa9c2b906d67e203303b44790b758d5a7d1313e Homepage: https://cran.r-project.org/package=nadiv Description: CRAN Package 'nadiv' ((Non)Additive Genetic Relatedness Matrices) Constructs (non)additive genetic relationship matrices, and their inverses, from a pedigree to be used in linear mixed effect models (A.K.A. the 'animal model'). Also includes other functions to facilitate the use of animal models. Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (). 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Package: r-cran-nam Architecture: amd64 Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2111 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 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bwgr Filename: pool/dists/noble/main/r-cran-nam_1.8.0-1.ca2404.1_amd64.deb Size: 1690864 MD5sum: 6d018b5719d006e7dc54fb00ebeaf44f SHA1: 86017b1096550605e3bc9cfcbdad54cc4b6b6590 SHA256: 9b51609765924e9314da5bb9ee7c841e90e7b37f71854de4ab8d7c1dbbbb29e4 SHA512: ee014aba1a62ac9405384defec2c8833c65bf86ee8082dc3b61db6ae6117d57d6f1cd9ff32800d52ee0eb3dcf247dee3d83058696e44620bce36383682558de6 Homepage: https://cran.r-project.org/package=NAM Description: CRAN Package 'NAM' (Nested Association Mapping) Designed for association studies in nested association mapping (NAM) panels, experimental and random panels. The method is described by Xavier et al. (2015) . It includes tools for genome-wide associations of multiple populations, marker quality control, population genetics analysis, genome-wide prediction, solving mixed models and finding variance components through likelihood and Bayesian methods. Package: r-cran-nametagger Architecture: amd64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4817 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-udpipe Filename: pool/dists/noble/main/r-cran-nametagger_0.1.7-1.ca2404.1_amd64.deb Size: 3684958 MD5sum: 19308b4468ef043a2dbb43161025feca SHA1: fca95009871581bf46d7026899962a57c6c6cc18 SHA256: bff13dad57afd94454088443c90faed2875c6dc118f89fe3753c9314f1f37709 SHA512: ae9035cde940d2d9e01dc25171fd292824db61ab523e857cdae7e3ca6cbb7e86f1f442fa9268fbe3ba2787e986871e9474dd51a33f413928fce68babfaf56ec7 Homepage: https://cran.r-project.org/package=nametagger Description: CRAN Package 'nametagger' (Named Entity Recognition in Texts using 'NameTag') Wraps the 'nametag' library , allowing users to find and extract entities (names, persons, locations, addresses, ...) in raw text and build your own entity recognition models. 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Package: r-cran-nandb Architecture: amd64 Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1137 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-autothresholdr, r-cran-bbmisc, r-cran-checkmate, r-cran-detrendr, r-cran-dplyr, r-cran-filesstrings, r-cran-ggplot2, r-cran-glue, r-cran-ijtiff, r-cran-magrittr, r-cran-purrr, r-cran-rcpp, r-cran-reshape2, r-cran-rlang, r-cran-stringr, r-cran-viridis, r-cran-withr Suggests: r-cran-abind, r-cran-covr, r-cran-gridextra, r-cran-knitr, r-cran-magick, r-cran-matrixstats, r-cran-pacman, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-nandb_2.1.1-1.ca2404.1_amd64.deb Size: 892650 MD5sum: a4c52a0a806d83ffcbb19698a554c37c SHA1: 708cb8655c8c38580d2a3bb22a555f3989089ff7 SHA256: 4674390a3081a0d6a9db1acc46932d6d0849479cf7c91f83646d7903ca1180c3 SHA512: 59c695c7464f6c6b02f2f2128dc12cef86eb02a503862fb7be99beb4b97c1b5c9a5a8a1bb2e89e6b1b80176ef6b02ba71c92da63af41b51a49586e7957c6768b Homepage: https://cran.r-project.org/package=nandb Description: CRAN Package 'nandb' (Number and Brightness Image Analysis) Calculation of molecular number and brightness from fluorescence microscopy image series. 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Package: r-cran-nanonext Architecture: amd64 Version: 1.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1543 Depends: libc6 (>= 2.38), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-later, r-cran-litedown Filename: pool/dists/noble/main/r-cran-nanonext_1.9.0-1.ca2404.1_amd64.deb Size: 708962 MD5sum: 7c9f167c0005ac40dd55db7dc6a7ef20 SHA1: b91fc159d5477fb274754a181fb1aa57e4c2ec9b SHA256: 9eff56299b06ee62728e7388f6192a4cdfbe5f39a629f60b1c5eed7a140a55a7 SHA512: 65128ec709c9d06fbb7ab8191bfc6230d8ce9cc5aa5dae0618963f922c0e4e51943970cc7ee6b00aeee36b057bbf653eff5f3420e93557cfd3a8f6633e1b109f Homepage: https://cran.r-project.org/package=nanonext Description: CRAN Package 'nanonext' (Lightweight Toolkit for Messaging, Concurrency and the Web) R binding for NNG (Nanomsg Next Gen), a successor to ZeroMQ. A toolkit for messaging, concurrency and the web. 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Package: r-cran-nativeort Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-digest, r-cran-glue Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nativeort_0.1.2-1.ca2404.1_amd64.deb Size: 176554 MD5sum: 9615b31c00cb7f820fed74d598d67d75 SHA1: 23d101f837cd34d53da3a28037e445020d92d782 SHA256: f6ab5860f658d4dc05893776bfbd7a00882bc4b67279c7a2a5da85b2e21dc999 SHA512: f65ab99a340ba0e40f5fc77eef0ea4e33fc19b67e68fedb80d0dda30a27174ff8e15982a304ae170a96ac70f9ece054afb69b7866dff1a01ec893d6fc7c9f3aa Homepage: https://cran.r-project.org/package=nativeORT Description: CRAN Package 'nativeORT' (Native 'R' 'ONNX' Runtime) Provides 'R' native 'ONNX' model inference without requiring 'Python', 'reticulate' bindings, or 'TensorFlow'. This package directly binds the 'ONNX Runtime' C API via 'Rcpp', enabling real-time inference for '.onnx' engines, all within 'R'. Standard CPU execution is supported as well as the 'CoreML' Execution Provider (CEP) for Apple Silicon, all without external bindings. This package handles OS detection, linking 'ONNX' libraries, and inference. For more information about 'ONNX Runtime' see . Package: r-cran-natural Architecture: amd64 Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-glmnet Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-natural_0.9.0-1.ca2404.1_amd64.deb Size: 170298 MD5sum: 0677631e2f0134dc741432197be9890d SHA1: 1dfc4a446424300b0e249fe9e1256e4a820c48f1 SHA256: 1ec66ab4e7b9889e6715f0a1e8e8e22510feeef6ecf1cd8041f9fe594967e0d3 SHA512: f0b12d25d5519fa9ac315823fe33ab07be294a965375c2f1dca92878cefa4fd938b323ed21d16a413bbaf43673b651f107671b923163a3fe20f528d7ca6636d0 Homepage: https://cran.r-project.org/package=natural Description: CRAN Package 'natural' (Estimating the Error Variance in a High-Dimensional Linear Model) Implementation of the two error variance estimation methods in high-dimensional linear models of Yu, Bien (2017) . Package: r-cran-navigation Architecture: amd64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8982 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-plotly, r-cran-magrittr, r-cran-simts, r-cran-expm, r-cran-rbenchmark, r-cran-leaflet, r-cran-mass, r-cran-pbmcapply, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-navigation_0.0.1-1.ca2404.1_amd64.deb Size: 3589320 MD5sum: 9a70db65e6a13e42cc2e159c565944aa SHA1: ed3c5cebb9069d3522a830248c533daff13cc0b5 SHA256: 5f780131c2f37efe24343e95ecfd8b634535d188c0462c9c3672af1e96387b59 SHA512: 8d83bea1b0405e121f7f997a5a76defdd14aea3658bf73d27729c81e63c57f1d56b457b1caf772646d1433b4ab8a20ee6121a91809390e106cbbb329874df842 Homepage: https://cran.r-project.org/package=navigation Description: CRAN Package 'navigation' (Analyze the Impact of Sensor Error Modelling on NavigationPerformance) Implements the framework presented in Cucci, D. A., Voirol, L., Khaghani, M. and Guerrier, S. (2023) which allows to analyze the impact of sensor error modeling on the performance of integrated navigation (sensor fusion) based on inertial measurement unit (IMU), Global Positioning System (GPS), and barometer data. The framework relies on Monte Carlo simulations in which a Vanilla Extended Kalman filter is coupled with realistic and user-configurable noise generation mechanisms to recover a reference trajectory from noisy measurements. The evaluation of several statistical metrics of the solution, aggregated over hundreds of simulated realizations, provides reasonable estimates of the expected performances of the system in real-world conditions. Package: r-cran-nbfar Architecture: amd64 Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 797 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-magrittr, r-cran-rrpack, r-cran-glmnet, r-cran-rcppparallel, r-cran-mpath, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-nbfar_0.1-1.ca2404.1_amd64.deb Size: 446108 MD5sum: 2e300b9c53e73031e1ac9ca08589340e SHA1: 15a11a222887e75975834d10711d34489f513a5e SHA256: 1866a893bef3a6de149ea633ca893fbe783822f6c84033744ca155685ebd047c SHA512: 9ab9553c91f51de7c87ceae9ca0773a1ec913f3cd89aa01b260a8556fc972a05892cb05b523ae18014595f6329d22944031cf17d82fb3194b27df661b23704a8 Homepage: https://cran.r-project.org/package=nbfar Description: CRAN Package 'nbfar' (Negative Binomial Factor Regression Models ('nbfar')) We developed a negative binomial factor regression model to estimate structured (sparse) associations between a feature matrix X and overdispersed count data Y. With 'nbfar', microbiome count data Y can be used, for example, to associate host or environmental covariates with microbial abundances. Currently, two models are available: a) Negative Binomial reduced rank regression (NB-RRR), b) Negative Binomial co-sparse factor regression (NB-FAR). Please refer the manuscript 'Mishra, A. K., & Müller, C. L. (2021). Negative Binomial factor regression with application to microbiome data analysis. bioRxiv.' for more details. Package: r-cran-nbody Architecture: amd64 Version: 1.41-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-magicaxis, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-nbody_1.41-1.ca2404.1_amd64.deb Size: 118524 MD5sum: c5b6156f4d11ed78e6c1d24e277f78ea SHA1: 3d84c1fe50795a21a47d6caddf89cf783ba4d00e SHA256: fec2f7a10b4951b8c069740186feae82fba7ced056c5de5cbfbd976415423b45 SHA512: 08d65e545a5d1a718b3dc216d785d55750858df8a24d92b0ed15e6c8cca14c58dcc49a1933782a345bc8a0c34415610e04754c51de22d3a9decd74bcd1f25540 Homepage: https://cran.r-project.org/package=nbody Description: CRAN Package 'nbody' (Gravitational N-Body Simulation) Run simple direct gravitational N-body simulations. The package can access different external N-body simulators (e.g. GADGET-4 by Springel et al. (2021) ), but also has a simple built-in simulator. This default simulator uses a variable block time step and lets the user choose between a range of integrators, including 4th and 6th order integrators for high-accuracy simulations. Basic top-hat smoothing is available as an option. The code also allows the definition of background particles that are fixed or in uniform motion, not subject to acceleration by other particles. Package: r-cran-nbpmatching Architecture: amd64 Version: 1.5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-mass Filename: pool/dists/noble/main/r-cran-nbpmatching_1.5.6-1.ca2404.1_amd64.deb Size: 235670 MD5sum: 7b4eba2d37da69eabfeb474d84373b82 SHA1: b9371db72d648d9af3c7b9160263768baa4298c2 SHA256: d1af4d291794e9f131340a412f6f759ebc3630b96c40abcf7539e254d261fb46 SHA512: f36b77843e4674b078e7512cb361771d6d7979e644e0946f0699dece0514504af66048feef466855c2748eff3bf8df627f7a637aea827c3db2b7c3faa966efa2 Homepage: https://cran.r-project.org/package=nbpMatching Description: CRAN Package 'nbpMatching' (Functions for Optimal Non-Bipartite Matching) Perform non-bipartite matching and matched randomization. A "bipartite" matching utilizes two separate groups, e.g. smokers being matched to nonsmokers or cases being matched to controls. A "non-bipartite" matching creates mates from one big group, e.g. 100 hospitals being randomized for a two-arm cluster randomized trial or 5000 children who have been exposed to various levels of secondhand smoke and are being paired to form a greater exposure vs. lesser exposure comparison. At the core of a non-bipartite matching is a N x N distance matrix for N potential mates. The distance between two units expresses a measure of similarity or quality as mates (the lower the better). The 'gendistance()' and 'distancematrix()' functions assist in creating this. The 'nonbimatch()' function creates the matching that minimizes the total sum of distances between mates; hence, it is referred to as an "optimal" matching. The 'assign.grp()' function aids in performing a matched randomization. Note bipartite matching can be performed using the prevent option in 'gendistance()'. Package: r-cran-nbpseq Architecture: amd64 Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-bioc-qvalue Filename: pool/dists/noble/main/r-cran-nbpseq_0.3.1-1.ca2404.1_amd64.deb Size: 327368 MD5sum: 3af0766fd3c42e80e887bae7d353cbd8 SHA1: 075b126161faafa096096706da896d8cb9bfa3f3 SHA256: f0eae6b8760525f1451af247d7a4a1770d4ff50dbd0ffdaa98583ec46e1544d6 SHA512: dd990f8a0494c9649a90fb479803b5bb624158e1fe896b0999701c797f856a4df4840d4409be91433026041a00eea6b1e6382421582862c05512b1bd250496f3 Homepage: https://cran.r-project.org/package=NBPSeq Description: CRAN Package 'NBPSeq' (Negative Binomial Models for RNA-Sequencing Data) Negative Binomial (NB) models for two-group comparisons and regression inferences from RNA-Sequencing Data. Package: r-cran-ncdf4 Architecture: amd64 Version: 1.24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: libc6 (>= 2.14), libnetcdf19t64 (>= 4.0.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ncdf4_1.24-1.ca2404.1_amd64.deb Size: 278416 MD5sum: ecedb4bf3c2240636d83796bec3145b2 SHA1: 7be9306cb07d17b83c7e532580d3a8e861c93ce9 SHA256: bc6514c0fa6fa8c9700e1c1811b60b7d360bee9574e2c4dc5439397391c3529c SHA512: a499ce38004294b1bbc6ed2f48787bbf2bac56bab95bd49bcb9c1a8e0134f207c13b66f8929788caf5dc01688324005770363decb0447b8621a31756207c14fe Homepage: https://cran.r-project.org/package=ncdf4 Description: CRAN Package 'ncdf4' (Interface to Unidata netCDF (Version 4 or Earlier) Format DataFiles) Provides a high-level R interface to data files written using Unidata's netCDF library (version 4 or earlier), which are binary data files that are portable across platforms and include metadata information in addition to the data sets. Using this package, netCDF files (either version 4 or "classic" version 3) can be opened and data sets read in easily. It is also easy to create new netCDF dimensions, variables, and files, in either version 3 or 4 format, and manipulate existing netCDF files. This package replaces the former ncdf package, which only worked with netcdf version 3 files. For various reasons the names of the functions have had to be changed from the names in the ncdf package. The old ncdf package is still available at the URL given below, if you need to have backward compatibility. It should be possible to have both the ncdf and ncdf4 packages installed simultaneously without a problem. However, the ncdf package does not provide an interface for netcdf version 4 files. Package: r-cran-ncpen Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 605 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-ncpen_1.0.0-1.ca2404.1_amd64.deb Size: 314476 MD5sum: 7c490490dad94d29049ed67711f9d31e SHA1: c9525033067e5633785621ef7cda2de90eb4dbeb SHA256: 022a5635f9a6d797be30d1b579335bd051d6db9bbf7052226667e4c5e778f4f9 SHA512: 8972acb59366e212a2d34360afa19c54dc68ac20a86a8318e4f068a1c2d9ded3bee66dd3a14381600778c4fe9861452a611cb44092cc20b52ffb764dc9d6221e Homepage: https://cran.r-project.org/package=ncpen Description: CRAN Package 'ncpen' (Unified Algorithm for Non-convex Penalized Estimation forGeneralized Linear Models) An efficient unified nonconvex penalized estimation algorithm for Gaussian (linear), binomial Logit (logistic), Poisson, multinomial Logit, and Cox proportional hazard regression models. The unified algorithm is implemented based on the convex concave procedure and the algorithm can be applied to most of the existing nonconvex penalties. The algorithm also supports convex penalty: least absolute shrinkage and selection operator (LASSO). Supported nonconvex penalties include smoothly clipped absolute deviation (SCAD), minimax concave penalty (MCP), truncated LASSO penalty (TLP), clipped LASSO (CLASSO), sparse ridge (SRIDGE), modified bridge (MBRIDGE) and modified log (MLOG). For high-dimensional data (data set with many variables), the algorithm selects relevant variables producing a parsimonious regression model. Kim, D., Lee, S. and Kwon, S. (2018) , Lee, S., Kwon, S. and Kim, Y. (2016) , Kwon, S., Lee, S. and Kim, Y. (2015) . (This research is funded by Julian Virtue Professorship from Center for Applied Research at Pepperdine Graziadio Business School and the National Research Foundation of Korea.) Package: r-cran-ncutyx Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2520 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-mass, r-cran-mvtnorm, r-cran-fields, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ncutyx_0.1.0-1.ca2404.1_amd64.deb Size: 2186680 MD5sum: 41f6fd2d727676023683b7fb67129cdc SHA1: cf0863eff3f904d009eaddbbc4db7ba9025a6b17 SHA256: a6ef68909828ad293d3c76b4d9f7936e71460e88a788dab6e4d6d659eed52919 SHA512: ef4264f0c203a85432c47d43b679ef8acc80ffa0561e871b8a59e24d81318984a0783fd5f8691582c8add862e0a887f08dad823fadcbc99c28527a6eef196e04 Homepage: https://cran.r-project.org/package=NCutYX Description: CRAN Package 'NCutYX' (Clustering of Omics Data of Multiple Types with a MultilayerNetwork Representation) Omics data come in different forms: gene expression, methylation, copy number, protein measurements and more. 'NCutYX' allows clustering of variables, of samples, and both variables and samples (biclustering), while incorporating the dependencies across multiple types of Omics data. (SJ Teran Hidalgo et al (2017), ). Package: r-cran-ncvreg Architecture: amd64 Version: 3.16.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 521 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ashr, r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-ncvreg_3.16.0-1.ca2404.1_amd64.deb Size: 370426 MD5sum: ddba644cfd3e706caecd36b550ac3bc7 SHA1: 58f36cce1c9d67883aa2b0ffb1806e37026c293c SHA256: e964bf16a9e51abb9668f2f0a9a335d1cdc07c7ec847c1c11de7fbede9d3b22f SHA512: edc2df41155cf062d5b446a42f7f2e842cf8dde8742099c3e771951a9917c4098e839a1b5fd08bfe579cba2af0f6d9ca6d03706a703f6d0ea58d041852413e41 Homepage: https://cran.r-project.org/package=ncvreg Description: CRAN Package 'ncvreg' (Regularization Paths for SCAD and MCP Penalized RegressionModels) Fits regularization paths for linear regression, GLM, and Cox regression models using lasso or nonconvex penalties, in particular the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalty, with options for additional L2 penalties (the "elastic net" idea). Utilities for carrying out cross-validation as well as post-fitting visualization, summarization, inference, and prediction are also provided. For more information, see Breheny and Huang (2011) or visit the ncvreg homepage . 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These constructs may not be columnar in nature, but it is often useful to read in these files and "flatten" the structure out to enable working with the data in an R 'data.frame'-like context. Functions are provided that make it possible to read in plain 'ndjson' files or compressed ('gz') 'ndjson' files and either validate the format of the records or create "flat" 'data.table' structures from them. Package: r-cran-ndl Architecture: amd64 Version: 0.2.18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 605 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-ndl_0.2.18-1.ca2404.1_amd64.deb Size: 446868 MD5sum: 17a5a76bdb0836320b71cf9ff9cf9702 SHA1: be8676cb7686b34af144e7294f51aa3aabb59e74 SHA256: b175079258da248a65c5fd6ef494f173f507b9e27d815ad5b15898bbb17bf3e5 SHA512: 4cebe8fb0e6f40c70341e1cc878a55f76a4798395bfe69926de4bdd913bcbb86d3e300aa7e4b53f8ad8380bda3cdbc5b2e936cf221550bb4d246c0cc2441474f Homepage: https://cran.r-project.org/package=ndl Description: CRAN Package 'ndl' (Naive Discriminative Learning) Naive discriminative learning implements learning and classification models based on the Rescorla-Wagner equations and their equilibrium equations. Package: r-cran-neatranges Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-neatranges_0.1.4-1.ca2404.1_amd64.deb Size: 83490 MD5sum: ba7799403f9380b3695fd5b587fb5d06 SHA1: 46a9a1ae9e190ed9f159582f90313fba74127673 SHA256: 3c4d8113ede652f67ea4e53a0182033e7542c0969d0420d048c421790b98cb55 SHA512: 414008ce177b022538dfd49b4a8892e19b3c9d5218bc4267331de87138c62eded436cbebb85b56b55274d330159a16e98c6760f320dea34ae9083d3d9aa929ee Homepage: https://cran.r-project.org/package=neatRanges Description: CRAN Package 'neatRanges' (Tidy Up Date/Time Ranges) Collapse, partition, combine, fill gaps in and expand date/time ranges. 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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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Package: r-cran-neojags Architecture: amd64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: jags, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-runjags, r-cran-rjags, r-cran-coda Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-neojags_0.1.7-1.ca2404.1_amd64.deb Size: 186470 MD5sum: a0461d20cf2059007d11bc22ff649289 SHA1: 8d1032d6cee8b556ed2a40d6647a6889a91deaeb SHA256: cd97748d3cbc7812790d8e77fe05b49bb3d43242a0f2f381f17f3c9767555f34 SHA512: 09fb0da32d9f94e0786ce259249556931b72a015d87dd972ab033944ce382ffaefb797939b07ba60a8ce2ed93d514db05c72a4641eaba7f64158fc3b84567220 Homepage: https://cran.r-project.org/package=neojags Description: CRAN Package 'neojags' (Neo-Normal Distributions Family for Markov Chain Monte Carlo(MCMC) Models in 'JAGS') A 'JAGS' extension module provides neo-normal distributions family including MSNBurr, MSNBurr-IIa, GMSNBurr, Lunetta Exponential Power, Fernandez-Steel Skew t, Fernandez-Steel Skew Normal, Fernandez-Osiewalski-Steel Skew Exponential Power, Jones Skew Exponential Power. References: Choir, A. S. (2020). "The New Neo-Normal Distributions and Their Properties".Unpublished Dissertation. Denwood, M.J. (2016) . Fernandez, C., Osiewalski, J., & Steel, M. F. (1995) . Fernandez, C., & Steel, M. F. (1998) . Iriawan, N. (2000). "Computationally Intensive Approaches to Inference in NeoNormal Linear Models".Unpublished Dissertation. Mineo, A., & Ruggieri, M. (2005) . Rigby, R. A., & Stasinopoulos, D. M. (2005) . Lunetta, G. (1963). "Di una Generalizzazione dello Schema della Curva Normale". Rigby, R. A., Stasinopoulos, M. D., Heller, G. Z., & Bastiani, F. D. (2019) . Package: r-cran-nestedcategbayesimpute Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1071 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-dplyr, r-cran-rcpp, r-cran-rcppparallel Filename: pool/dists/noble/main/r-cran-nestedcategbayesimpute_1.2.1-1.ca2404.1_amd64.deb Size: 373830 MD5sum: c2cde9795eb9cb31bc0250a68bcb321a SHA1: a4645bfdd692aced0963db890439fd5155d1790a SHA256: 6d15ec46ab2752017aa3c4fbbd34dc230b83f498920ddd13ab60137f51bf0b3f SHA512: 2c808d96db9a4adbbac98422d067f751fa558b652c65848f227e9bc62683949fc94cdf8e7c735e9ab61bbd9610b2dffcd338b875c7da39f003482113379751d1 Homepage: https://cran.r-project.org/package=NestedCategBayesImpute Description: CRAN Package 'NestedCategBayesImpute' (Modeling, Imputing and Generating Synthetic Versions of NestedCategorical Data in the Presence of Impossible Combinations) This tool set provides a set of functions to fit the nested Dirichlet process mixture of products of multinomial distributions (NDPMPM) model for nested categorical household data in the presence of impossible combinations. 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Farine, D. R. (2017) ; Carsey, T., & Harden, J. (2014) . Primarily targeted at datasets of facial expressions coded with the Facial Action Coding System. Ekman, P., Friesen, W. V., & Hager, J. C. (2002). "Facial action coding system - investigator's guide" . 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Methods to sparsify adjacency matrices. Methods for graph pre-processing and for filtering edges of the graph. 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While the package includes the possibility to build more than 20 indices, its main focus lies on index-free assessment of centrality via partial rankings obtained by neighborhood-inclusion or positional dominance. These partial rankings can be analyzed with different methods, including probabilistic methods like computing expected node ranks and relative rank probabilities (how likely is it that a node is more central than another?). The methodology is described in depth in the vignettes and in Schoch (2018) . 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(2015) . Package: r-cran-nets Architecture: amd64 Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph Filename: pool/dists/noble/main/r-cran-nets_0.9.1-1.ca2404.1_amd64.deb Size: 44802 MD5sum: cb7173b5de1bfa972c52f922ebfd5c57 SHA1: 843da9291627bcb1ed964a8274d7c3bbd0fa9ff4 SHA256: 163197654225831318dd7f7e3aec4995bb49037a1b09e2790e2687efe9f4f332 SHA512: 883d1f2102516ce81a7baa4e2a0078be655f3cdb09ee84b325d873cad18ce21c21003369bac2762052712edf29fa3fdb5090d2d5c7c75f9453cb9c1d2d32f264 Homepage: https://cran.r-project.org/package=nets Description: CRAN Package 'nets' (Network Estimation for Time Series) Sparse VAR estimation based on LASSO. 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Provides multiple methods for fitting, model selection and goodness-of-fit testing in degree-corrected stochastic blocks models. Most of the computations are fast and scalable for sparse networks, esp. for Poisson versions of the models. Implements the following: Amini, Chen, Bickel and Levina (2013) Bickel and Sarkar (2015) Lei (2016) Wang and Bickel (2017) Zhang and Amini (2020) Le and Levina (2022) . Package: r-cran-netutils Architecture: amd64 Version: 0.8.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 493 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-igraph, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-ga, r-cran-testthat Filename: pool/dists/noble/main/r-cran-netutils_0.8.5-1.ca2404.1_amd64.deb Size: 249070 MD5sum: 38f337f1fcd533b8f47b7b975e069a92 SHA1: 28f9eae8de5730aa88322e42f8078a38a4c8ba7c SHA256: fd5277d8d836ddaf04694d7234a616f7f26e20375faa8651768e53315da5b1de SHA512: cb47bcba3c27a1cc5ca1de6b84e77f256a68b7227da98f6310f2118322456931e46913f0f1f4fc03dc9ba44ca2859205f318a00588ced6f5db6812a78046219b Homepage: https://cran.r-project.org/package=netUtils Description: CRAN Package 'netUtils' (A Collection of Tools for Network Analysis) Provides a collection of network analytic (convenience) functions which are missing in other standard packages. This includes triad census with attributes , core-periphery models , and several graph generators. Most functions are build upon 'igraph'. Package: r-cran-netvar Architecture: amd64 Version: 0.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields, r-cran-fgarch Filename: pool/dists/noble/main/r-cran-netvar_0.1-2-1.ca2404.1_amd64.deb Size: 115706 MD5sum: f03bbfe815aa75de355daaaf8498a550 SHA1: 6a43db2849a124690a016e5823a3e13887a75fdd SHA256: 1b8b3303d644121db6087fb1b3976cad421c08d191aaf284eeecc78cb46b72ca SHA512: 925e14377333973bca48116ed52ef8a33103d8a0bfd7a551efb27b5b05cc7a8537c793d487ff1c5ce524ee05e5130e9bbbc9b9ec014460dc3798210a82496813 Homepage: https://cran.r-project.org/package=NetVAR Description: CRAN Package 'NetVAR' (Network Structures in VAR Models) Vector AutoRegressive (VAR) type models with tailored regularisation structures are provided to uncover network type structures in the data, such as influential time series (influencers). Currently the package implements the LISAR model from Zhang and Trimborn (2023) . The package automatically derives the required regularisation sequences and refines it during the estimation to provide the optimal model. The package allows for model optimisation under various loss functions such as Mean Squared Forecasting Error (MSFE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). It provides a dedicated class, allowing for summary prints of the optimal model and a plotting function to conveniently analyse the optimal model via heatmaps. Package: r-cran-network Architecture: amd64 Version: 1.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1013 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-magrittr, r-cran-statnet.common Suggests: r-cran-sna, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-network_1.20.0-1.ca2404.1_amd64.deb Size: 820028 MD5sum: 19f68899eba42a6e4fb03ec3ac8b9082 SHA1: c7ae83d0ee89a1145b7907849366d2ae4dbaca76 SHA256: 859f2742c686980328c4ec89af5c6ee97a87a9ced01cde8ef48aaddd26cf9335 SHA512: 6a3f2902662647b0307c09b47b47f250fb9967c382ca799d1c78613e6127a76256fe22672654d2b8fef9b3476b9a6d4e81f629cd6e1dd3246df8ab2a6c9b3512 Homepage: https://cran.r-project.org/package=network Description: CRAN Package 'network' (Classes for Relational Data) Tools to create and modify network objects. The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes. Package: r-cran-networkabc Architecture: amd64 Version: 0.9-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 544 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-network, r-cran-sna Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-networkabc_0.9-1-1.ca2404.1_amd64.deb Size: 310008 MD5sum: e65773e63da52507102fad575c3650b2 SHA1: 93ee5cfb044040e8cb8a8f9c66a0f788694af581 SHA256: 6708d67b4b2a716fbf7b61a72d6963260a85900627f9c5a534cf227e8c37f12e SHA512: faebd55dd059c006757b85f950500a56cccadc417a6a9ea88eec138b47bf933ee9771cf5849a8c2edee5d36cb3bfc74418d5dea1421bc835331917b619f99443 Homepage: https://cran.r-project.org/package=networkABC Description: CRAN Package 'networkABC' (Network Reverse Engineering with Approximate BayesianComputation) We developed an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of the inferred links between network's actors. It is a new multi-level approximate Bayesian computation (ABC) approach. At the first level, the method captures the global properties of the network, such as a scale-free structure and clustering coefficients, whereas the second level is targeted to capture local properties, including the probability of each couple of genes being linked. Up to now, Approximate Bayesian Computation (ABC) algorithms have been scarcely used in that setting and, due to the computational overhead, their application was limited to a small number of genes. On the contrary, our algorithm was made to cope with that issue and has low computational cost. It can be used, for instance, for elucidating gene regulatory network, which is an important step towards understanding the normal cell physiology and complex pathological phenotype. Reverse-engineering consists in using gene expressions over time or over different experimental conditions to discover the structure of the gene network in a targeted cellular process. The fact that gene expression data are usually noisy, highly correlated, and have high dimensionality explains the need for specific statistical methods to reverse engineer the underlying network. Package: r-cran-networkchange Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1562 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-ggplot2, r-cran-rcpp, r-cran-rmpfr, r-cran-abind, r-cran-mvtnorm, r-cran-tidyr, r-cran-igraph, r-cran-qgraph, r-cran-network, r-cran-mass, r-cran-rcolorbrewer, r-cran-ggrepel, r-cran-rlang, r-cran-ggally, r-cran-patchwork, r-cran-viridis, r-cran-rcpparmadillo Suggests: r-cran-sna, r-cran-lifecycle Filename: pool/dists/noble/main/r-cran-networkchange_1.1.0-1.ca2404.1_amd64.deb Size: 1285896 MD5sum: b4a77d0e9b5e06c8f4e9f83a46364759 SHA1: e8c1e07e4a95c47b85a4d3fb3db3be27e1f0f628 SHA256: 04dcebc02dab37da1c02ea3b1ac2c211a2d8961ee1cc1ced3d47a34841c1c7b8 SHA512: 5fdb8f6c22f990351551ec1af51f8f4f76f71d38bfb946834957a8db123f649c0fab1578f675e670ca5524c8f63c1a22b66adaad13a142bfcce290e02576c67e Homepage: https://cran.r-project.org/package=NetworkChange Description: CRAN Package 'NetworkChange' (Bayesian Package for Network Changepoint Analysis) Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided. Version 1.1.0 includes high-performance C++ implementations via 'Rcpp'/'RcppArmadillo' for 5-15x faster MCMC sampling, along with modern 'ggplot2'-based visualizations with colorblind-friendly palettes. 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As an object of analysis, many distance or metric measures have been proposed to define the concept of similarity between two networks. We provide a number of distance measures for networks. See Jurman et al (2011) for an overview on spectral class of inter-graph distance measures. Package: r-cran-networkdynamic Architecture: amd64 Version: 0.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1321 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-network, r-cran-statnet.common, r-cran-networklite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-networkdynamic_0.12.0-1.ca2404.1_amd64.deb Size: 1067514 MD5sum: 2177a1463352da8a38b1ebf810da70f9 SHA1: bbb0c13836b4bcd0856e917686c33d548c29b8df SHA256: 87b21ff7bbcad763d6bb0a9ee41244ab734b33bcc86ed033dc1cea7cc109edcd SHA512: d791cababd1a91157c51b21217f6aeb8cc20c4901abfb72642e97075d323659946ac65777d4c6bb38cf0f93cd4a0ff3ccf3032a961476d4ea9927f71c2cbf116 Homepage: https://cran.r-project.org/package=networkDynamic Description: CRAN Package 'networkDynamic' (Dynamic Extensions for Network Objects) Simple interface routines to facilitate the handling of network objects with complex intertemporal data. This is a part of the "statnet" suite of packages for network analysis. 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Given a set of events that spread between a set of nodes the algorithm infers the most likely stable diffusion network that is underlying the diffusion process. Package: r-cran-networkr Architecture: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-fastmatch, r-cran-matrix, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-networkr_0.1.5-1.ca2404.1_amd64.deb Size: 74938 MD5sum: 2de08f6f0e6f87611c70afe15c70b3f8 SHA1: d45a7dc9bbe82384ff1eb14c60b3036fb76e8c67 SHA256: dd853a2b2ea4fc48aac9d74c93efbdb1ca93fb57868f522a6da3c7b9f6dce05f SHA512: 7e07eb40db76878647dc440384ad65fd02dc0ac94b489f83b5a1333a03058c1b213b8c87794a1c277825689ec6927661c9ec7799c1242abf097c5d102e5ab155 Homepage: https://cran.r-project.org/package=networkR Description: CRAN Package 'networkR' (Network Analysis and Visualization) Collection of functions for fast manipulation, handling, and analysis of large-scale networks based on family and social data. Functions are utility functions used to manipulate data in three "formats": sparse adjacency matrices, pedigree trio family data, and pedigree family data. When possible, the functions should be able to handle millions of data points quickly for use in combination with data from large public national registers and databases. Kenneth Lange (2003, ISBN:978-8181281135). Package: r-cran-networkscaleup Architecture: amd64 Version: 0.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15242 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-laplacesdemon, r-cran-dplyr, r-cran-ggplot2, r-cran-scales, r-cran-readr, r-cran-tibble, r-cran-tidyr, r-cran-rlang, r-cran-glmmtmb, r-cran-gridextra, r-cran-purrr, r-cran-stringr, r-cran-trialr, r-cran-tidyselect, r-cran-rmtstat, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-networkscaleup_0.2-1-1.ca2404.1_amd64.deb Size: 2919810 MD5sum: 1b504910f0fce977a095ce1952196c68 SHA1: 1ba5b3b1e98e1f2a8b10d550fc337528ed115ded SHA256: 60e5e7bc002e3a2f37a913f1f12bfac40e1cb9222cfab8062d88d15301bf46ff SHA512: af5f632e2bf671123b7e3731821e7f85488474617bea47c7a6e27436bb067ab6875e7c36e02f10164c58a5d161fc42399ee45f304307cf0dccc620efa0b67b56 Homepage: https://cran.r-project.org/package=networkscaleup Description: CRAN Package 'networkscaleup' (Network Scale-Up Models for Aggregated Relational Data) Provides a variety of Network Scale-up Models for researchers to analyze Aggregated Relational Data, through the use of Stan and 'glmmTMB'. Also provides tools for model checking In this version, the package implements models from Laga, I., Bao, L., and Niu, X (2023) , Zheng, T., Salganik, M. J., and Gelman, A. (2006) , Killworth, P. D., Johnsen, E. C., McCarty, C., Shelley, G. A., and Bernard, H. R. (1998) , and Killworth, P. D., McCarty, C., Bernard, H. R., Shelley, G. A., and Johnsen, E. C. (1998) . Package: r-cran-neuroim2 Architecture: amd64 Version: 0.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5807 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-purrr, r-cran-mmap, r-cran-rcpp, r-cran-rcppparallel, r-cran-rnifti, r-cran-dbscan, r-cran-stringr, r-cran-bigstatsr, r-cran-rniftyreg, r-cran-future, r-cran-future.apply, r-cran-deflist, r-cran-cli, r-cran-ggplot2, r-cran-magrittr, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-pkgdown, r-cran-roxygen2, r-cran-rmarkdown, r-cran-albersdown, r-cran-gmedian, r-cran-r.utils, r-cran-spelling, r-cran-vdiffr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-neuroim2_0.13.0-1.ca2404.1_amd64.deb Size: 2513124 MD5sum: 49355cc4655d9234446b14606b2210c5 SHA1: 27222c132e4d032b46f18c48ac35dde073245a99 SHA256: b61b7e57cf9414ae4d4a64a55fc9adeb5643eadc00db5f815dea9dffd04cab85 SHA512: fd2f46955956afa442a4ca96063424adccc24e5a09bd7f0b9e9f3eec9617b8e10a52e0c6dfc52154d8ece959e158133910acef7d73b9f63f02d09177785b7f66 Homepage: https://cran.r-project.org/package=neuroim2 Description: CRAN Package 'neuroim2' (Data Structures for Brain Imaging Data) A collection of data structures and methods for handling volumetric brain imaging data, with a focus on functional magnetic resonance imaging (fMRI). Provides efficient representations for three-dimensional and four-dimensional neuroimaging data through sparse and dense array implementations, memory-mapped file access for large datasets, and spatial transformation capabilities. Implements methods for image resampling, spatial filtering, region of interest analysis, and connected component labeling. General introduction to fMRI analysis can be found in Poldrack et al. (2024, "Handbook of functional MRI data analysis", ). Package: r-cran-neuroim Architecture: amd64 Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1672 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-hash, r-cran-matrix, r-cran-yaimpute, r-cran-rcpp, r-cran-iterators, r-cran-abind, r-cran-assertthat, r-cran-readr, r-cran-rgl Suggests: r-cran-foreach, r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-neuroim_0.0.6-1.ca2404.1_amd64.deb Size: 1017546 MD5sum: 94e298758793fdb8d560273e0c22e9f8 SHA1: 2a5178838889d072c7a1c4703e25cb8cd6f25771 SHA256: 9b928c7b958f06881d0b43274587f2fb5a8ad83baa2cba7f0fb4253b7fcd157f SHA512: 357dee32ded3b9cedace09485490d34a3d788e3e2c723a0c55424ab1365bcdbf15ee63973a858a77b2f23ef43950d1540d3f00017c65cff35642fe2b983843b3 Homepage: https://cran.r-project.org/package=neuroim Description: CRAN Package 'neuroim' (Data Structures and Handling for Neuroimaging Data) A collection of data structures that represent volumetric brain imaging data. The focus is on basic data handling for 3D and 4D neuroimaging data. In addition, there are function to read and write NIFTI files and limited support for reading AFNI files. Package: r-cran-neurosim Architecture: amd64 Version: 0.2-14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve Filename: pool/dists/noble/main/r-cran-neurosim_0.2-14-1.ca2404.1_amd64.deb Size: 129624 MD5sum: f165c646d5bff07f05a351c29787e161 SHA1: d26e5b2dab1d248969dc73d5c8ceef53d3e2d385 SHA256: f135bb0c2ab16ff247fad441cd81a9c96e5271cca2386d3f17879141caa55476 SHA512: 93ad1d191d46de9db693f876fdb7e3d4e16b2e7ba5d3f7e4ab481da6a5d784801d8b2efb7da724f7c06cfd0c766dc71ce65bc79b3a8d51ba10677393bd3da924 Homepage: https://cran.r-project.org/package=neuRosim Description: CRAN Package 'neuRosim' (Simulate fMRI Data) Generates functional Magnetic Resonance Imaging (fMRI) time series or 4D data. Some high-level functions are created for fast data generation with only a few arguments and a diversity of functions to define activation and noise. For more advanced users it is possible to use the low-level functions and manipulate the arguments. See Welvaert et al. (2011) . Package: r-cran-nevada Architecture: amd64 Version: 0.2.0-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-rcpp, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-forcats, r-cran-ggplot2, r-cran-rlang, r-cran-magrittr, r-cran-flipr, r-cran-cli, r-cran-withr, r-cran-tsne, r-cran-umap, r-cran-furrr, r-cran-rgeomstats, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-nevada_0.2.0-1.ca2404.1_amd64.deb Size: 319442 MD5sum: 698a52add3851714674f61f6e79be019 SHA1: a390e9d8d84f6012cde6753f23336f3d33fd2d30 SHA256: 99fb6efe030faa601ec7f1ffbac07b31011b72562070529ab3d265e6d60e44fb SHA512: 7a87cb109fcc9d2c60a4759f6232230f853c73ba2de338d70a2b9e6cbec59b0680d06c311e8f4f0e99b307b86582305d6c3981557ab76de3d04421d827b6250b Homepage: https://cran.r-project.org/package=nevada Description: CRAN Package 'nevada' (Network-Valued Data Analysis) A flexible statistical framework for network-valued data analysis. It leverages the complexity of the space of distributions on graphs by using the permutation framework for inference as implemented in the 'flipr' package. Currently, only the two-sample testing problem is covered and generalization to k samples and regression will be added in the future as well. It is a 4-step procedure where the user chooses a suitable representation of the networks, a suitable metric to embed the representation into a metric space, one or more test statistics to target specific aspects of the distributions to be compared and a formula to compute the permutation p-value. Two types of inference are provided: a global test answering whether there is a difference between the distributions that generated the two samples and a local test for localizing differences on the network structure. The latter is assumed to be shared by all networks of both samples. References: Lovato, I., Pini, A., Stamm, A., Vantini, S. (2020) "Model-free two-sample test for network-valued data" ; Lovato, I., Pini, A., Stamm, A., Taquet, M., Vantini, S. (2021) "Multiscale null hypothesis testing for network-valued data: Analysis of brain networks of patients with autism" . Package: r-cran-nfer Architecture: amd64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nfer_1.1.3-1.ca2404.1_amd64.deb Size: 110016 MD5sum: fbadfe8180f26ecaa97f01ee9f882c39 SHA1: ce831821b06ac3caf2d5f5e88b772bd77ee45084 SHA256: bda6c4c22083bc0dd0e05beda228587dd9491e07c5f408b2e08a97ad0d2a0eb2 SHA512: e0ef199f48729d23797587b1605e2030cc17839927ee2cfaa112648483c7ff0f00b0e54ead3fbbf6615d4d86d854321bcb5683dcaf089d14fbd12b1527d0ce2d Homepage: https://cran.r-project.org/package=nfer Description: CRAN Package 'nfer' (Event Stream Abstraction using Interval Logic) This is the R API for the 'nfer' formalism (). 'nfer' was developed to specify event stream abstractions for spacecraft telemetry such as the Mars Science Laboratory. Users write rules using a syntax that borrows heavily from Allen's Temporal Logic that, when applied to an event stream, construct a hierarchy of temporal intervals with data. The R API supports loading rules from a file or mining them from historical data. Traces of events or pools of intervals are provided as data frames. Package: r-cran-nftbart Architecture: amd64 Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-nnet, r-cran-lattice, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-nftbart_2.3-1.ca2404.1_amd64.deb Size: 353722 MD5sum: 6bd2e910fbc4c6e15ed09e5f18604b13 SHA1: 364b5b6682f9b0955f32d832dcff922dd97a8cf5 SHA256: 49b6b40033de930a829de8c40d335b02f5539bdf88daf70e2f06022377cde618 SHA512: 6a07d6b78ce13efbe51dabc84e08a8ba75df22e1a195e078da39b3002b63de7a0404ca730896ae39117418065430f70bcccfc49d3f11eeacf7a9ea119d65a60e Homepage: https://cran.r-project.org/package=nftbart Description: CRAN Package 'nftbart' (Nonparametric Failure Time Bayesian Additive Regression Trees) Nonparametric Failure Time (NFT) Bayesian Additive Regression Trees (BART): Time-to-event Machine Learning with Heteroskedastic Bayesian Additive Regression Trees (HBART) and Low Information Omnibus (LIO) Dirichlet Process Mixtures (DPM). An NFT BART model is of the form Y = mu + f(x) + sd(x) E where functions f and sd have BART and HBART priors, respectively, while E is a nonparametric error distribution due to a DPM LIO prior hierarchy. See the following for a description of the model at . Package: r-cran-ngme2 Architecture: amd64 Version: 0.9.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2931 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rlang, r-cran-ggplot2, r-cran-fmesher, r-cran-gridextra, r-cran-withr, r-cran-rcppeigen Suggests: r-cran-r.rsp, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mass, r-cran-dplyr, r-cran-fields, r-cran-inlabru, r-cran-metricgraph, r-cran-rspde, r-cran-sf, r-cran-zoo, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-ngme2_0.9.8-1.ca2404.1_amd64.deb Size: 1735104 MD5sum: 3be3f6759c5df96f6854d191dbd5b890 SHA1: 824f747d5e4d8c945e6952adebad5a0d242ed760 SHA256: 8996b4a683ff0f5c3c40c4f59ea1370b6f57d39c4594accffae502dcbdd9cff7 SHA512: fde12c7db0c1fce7ce07c6f0192e76ae0987693e658f8c1e296e6833d2f64d3bcbdce95664b4ff01b45e2085cfdd158c29cbef79096330b4bd014f0664a77352 Homepage: https://cran.r-project.org/package=ngme2 Description: CRAN Package 'ngme2' (Linear Latent Non-Gaussian Models with Flexible Distributions) Fits and analyzes linear latent non-Gaussian models for temporal, spatial, and space-time data. The package provides model components for autoregressive and Ornstein-Uhlenbeck processes, random walks, Matern fields based on stochastic partial differential equations, separable and non-separable space-time models, graph-based Matern models, bivariate type-G fields, and user-defined sparse operators. Latent fields and observation models can use Gaussian and non-Gaussian noise distributions, including normal inverse Gaussian, generalized asymmetric Laplace, and skew-t distributions. Functions are included for simulation, likelihood-based estimation, prediction, cross-validation, convergence diagnostics, stochastic gradient optimization, batch-means confidence intervals, and posterior-like sampling. The modeling framework is described in Bolin, Jin, Simas and Wallin (2026) "A Unified and Computationally Efficient Non-Gaussian Statistical Modeling Framework" . Package: r-cran-ngram Architecture: amd64 Version: 3.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ngram_3.2.3-1.ca2404.1_amd64.deb Size: 345802 MD5sum: e43b4989fa9a4e8cc8c05313575d9058 SHA1: 8ecf71e36a4812d42a4d58a3137a0ff11d5b24af SHA256: bf04412d8b047fdfee4a95120e3356a2ed94964b3141ea0ea36b5996d48629ef SHA512: 5da23c719424c1e60040e09dd9ab640fc5c9815e661c6d6eacd99f4c6046ac258d31a342f2e0e55b6aaff315e2e7190080de5f0870640caf0c8261c249761d2a Homepage: https://cran.r-project.org/package=ngram Description: CRAN Package 'ngram' (Fast n-Gram 'Tokenization') An n-gram is a sequence of n "words" taken, in order, from a body of text. This is a collection of utilities for creating, displaying, summarizing, and "babbling" n-grams. 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Package: r-cran-ngspatial Architecture: amd64 Version: 1.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-batchmeans, r-cran-rcpparmadillo Suggests: r-cran-pbapply Filename: pool/dists/noble/main/r-cran-ngspatial_1.2-2-1.ca2404.1_amd64.deb Size: 391502 MD5sum: 9b9dd0bd3077c607e9e5a070f56dc18d SHA1: bc82387ef9fd03b223cd1920b4f8338e8f2bfe5c SHA256: 05a4af24f399337fcf9b306e390f166f6ac1d38634c92f05f9effb6a59b20f48 SHA512: d2519deced53decef6300300521efa11ee0ca118a1228f1a20ed4daa78b127b90db6e6ec3a95e1ffaaad874aa43eed671b5e79b9d23ea6be880565a7a32c8c4b Homepage: https://cran.r-project.org/package=ngspatial Description: CRAN Package 'ngspatial' (Fitting the Centered Autologistic and Sparse Spatial GeneralizedLinear Mixed Models for Areal Data) Provides tools for analyzing spatial data, especially non- Gaussian areal data. The current version supports the sparse restricted spatial regression model of Hughes and Haran (2013) , the centered autologistic model of Caragea and Kaiser (2009) , and the Bayesian spatial filtering model of Hughes (2017) . Package: r-cran-nhlscraper Architecture: amd64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2279 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-xml2, r-cran-arrow Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nhlscraper_0.6.0-1.ca2404.1_amd64.deb Size: 1640906 MD5sum: 70a7c32b60b56c90512c7e538b8f48b5 SHA1: 09b99e5e7b3ba20a692c916d94ad3de05c50a9b3 SHA256: 6f15be72a4df16a22a4df5889f8ae7865aa00c9ab2453918825d5a92814aa67d SHA512: a35a2b1b4c6d26d5cb83887d1f0eb16961132801d6daef4c171e5740eab800f1776b285a4cf899625b8178ac9b628ebc892edd21db1118d72db4dbe757398819 Homepage: https://cran.r-project.org/package=nhlscraper Description: CRAN Package 'nhlscraper' (Scraper for National Hockey League Data) Scrapes and cleans data from the 'NHL' and 'ESPN' APIs into data.frames and lists. Wraps 125+ endpoints documented in from high-level multi-season summaries and award winners to low-level decisecond replays and bookmakers' odds, making them more accessible. Features cleaning and visualization tools, primarily for play-by-plays. Package: r-cran-nhm Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 780 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-maxlik, r-cran-mvtnorm Suggests: r-cran-msm, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-nhm_0.1.2-1.ca2404.1_amd64.deb Size: 658296 MD5sum: b4816bc78fcc360a18c46b74274f6a57 SHA1: debf46a1824d221f1e5b9f1a2dfa021e70c13b3f SHA256: 5d8cd58e798de2cf401638fdca31a5bd6ae461e3f5c23844dcce1ff176e39499 SHA512: b5a667ff2c0695b3c5675807de39a4cbe4212f46f5851edfac24729649012c8a489ec135276be0e864e610ffc98407b55584c46d3a9fc7e1f78a0c5ea1a26efb Homepage: https://cran.r-project.org/package=nhm Description: CRAN Package 'nhm' (Non-Homogeneous Markov and Hidden Markov Multistate Models) Fits non-homogeneous Markov multistate models and misclassification-type hidden Markov models in continuous time to intermittently observed data. Implements the methods in Titman (2011) . Uses direct numerical solution of the Kolmogorov forward equations to calculate the transition probabilities. Package: r-cran-nhppp Architecture: amd64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 973 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lifecycle, r-cran-rstream, r-cran-rcpp Suggests: r-cran-data.table, r-cran-ggplot2, r-cran-knitr, r-cran-rlecuyer, r-cran-rmarkdown, r-cran-testthat, r-cran-tictoc, r-cran-truncnorm, r-cran-withr Filename: pool/dists/noble/main/r-cran-nhppp_1.0.5-1.ca2404.1_amd64.deb Size: 511330 MD5sum: 460ad1b05ac0cc38f889bcef1777dd40 SHA1: c4f98d739b13d3ec5d38fb84a90f6d6b24013830 SHA256: 838c6c11740f273ce301b939a8d27150adafa1c24a6d3fa74b8c5267e1f0b14a SHA512: 4f93a097e09776cc7c211ac9f8ee0dcd19ac7bba85a4e71bb351497d511a402719e6aba4dab37eb1c066f12b4c79cfc57858eed99a4b5169aa74817aae6c175c 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: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 844 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-niaidmi_1.1.0-1.ca2404.1_amd64.deb Size: 290676 MD5sum: 7c9506b16aab57897406f8a76dae00bc SHA1: d396df4acdeed69ab82a04bcf34387a9303d8a8e SHA256: 510d7da13c45664b40794398fb8335ce09bd3bbb8ee2697f5394de5b642c23a0 SHA512: 77aacc4cf23bc584e0e573a3b99c19e1afa71b17aff0d6917d5bf48466902c5e49431c3d5d7f40deb3a71b25e8d869ad8a0d2d9f0766ef96b6dc6953e9e0ed6c 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. 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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) . 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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 . 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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-nltm Architecture: amd64 Version: 1.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-nltm_1.4.6-1.ca2404.1_amd64.deb Size: 126980 MD5sum: 39a61b2aa6718b78d66c68cd21e32176 SHA1: f5cacdd0342221fab03f7754f6e877fac38667e4 SHA256: 5fe9d6d76f167986cf95b8dc2024acaad94c39aa31aec2ee4900a94112f9ba2a SHA512: 737762b4ff2b9ce71aaa057dff6cbc2df9c3ce5ffd897dd497b0d52d5b12dc78faa3250918a2d02626dd29f12ac40abdf7c3487b194b1c336212093503d3e3d6 Homepage: https://cran.r-project.org/package=nltm Description: CRAN Package 'nltm' (Non-Linear Transformation Models) Fits a non-linear transformation model ('nltm') for analyzing survival data, see Tsodikov (2003) . The class of 'nltm' includes the following currently supported models: Cox proportional hazard, proportional hazard cure, proportional odds, proportional hazard - proportional hazard cure, proportional hazard - proportional odds cure, Gamma frailty, and proportional hazard - proportional odds. Package: r-cran-nmf Architecture: amd64 Version: 0.28-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3696 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-registry, r-cran-rngtools, r-cran-cluster, r-cran-stringr, r-cran-digest, r-cran-gridbase, r-cran-colorspace, r-cran-rcolorbrewer, r-cran-foreach, r-cran-doparallel, r-cran-ggplot2, r-cran-reshape2, r-bioc-biobase, r-cran-codetools, r-cran-biocmanager Suggests: r-cran-fastica, r-cran-dompi, r-cran-bigmemory, r-cran-synchronicity, r-cran-corpcor, r-cran-xtable, r-cran-devtools, r-cran-knitr, r-cran-runit Filename: pool/dists/noble/main/r-cran-nmf_0.28-1.ca2404.1_amd64.deb Size: 3013048 MD5sum: 83c0ca55000d9ef2b52d0999ce167bad SHA1: 8a081e47a19e1a73d48dd44a5e08c8b79c41740a SHA256: cee2a11590a5a1ebd61eb5d2189e4cb4f0398d40d1167143b166c544c8889aed SHA512: aec5011221ab61cb0db098a43a6bf1a9ef366a67155445c44e5a9ea90b960a65202d16a55516f9a563b978ea605f0a51b7e799c4e9f547396c9a2342aea90876 Homepage: https://cran.r-project.org/package=NMF Description: CRAN Package 'NMF' (Algorithms and Framework for Nonnegative Matrix Factorization(NMF)) Provides a framework to perform Non-negative Matrix Factorization (NMF). The package implements a set of already published algorithms and seeding methods, and provides a framework to test, develop and plug new/custom algorithms. Most of the built-in algorithms have been optimized in C++, and the main interface function provides an easy way of performing parallel computations on multicore machines. Package: r-cran-nmix Architecture: amd64 Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: libc6 (>= 2.27), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nmix_2.0.5-1.ca2404.1_amd64.deb Size: 93190 MD5sum: 9a1249fab4cbc12e0052d0a7268d4578 SHA1: 5a19c6e0762fd9891ea616c57c3e1908d8a6905e SHA256: ea5d8946683f56d64315bac44deff9486f4809a9227c588bd528f02b9602e60c SHA512: 2dcff9948623957d6bca76c36a6843805a1c70990aa79a617b36a38e23940fc0ada7911f26d749dd1aeceb25de8239161ba23a5b883acf32cc5b9e9316e320dc Homepage: https://cran.r-project.org/package=Nmix Description: CRAN Package 'Nmix' (Bayesian Inference on Univariate Normal Mixtures) A program for Bayesian analysis of univariate normal mixtures with an unknown number of components, following the approach of Richardson and Green (1997) . This makes use of reversible jump Markov chain Monte Carlo methods that are capable of jumping between the parameter sub-spaces corresponding to different numbers of components in the mixture. A sample from the full joint distribution of all unknown variables is thereby generated, and this can be used as a basis for a thorough presentation of many aspects of the posterior distribution. Package: r-cran-nmixgof Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-unmarked Filename: pool/dists/noble/main/r-cran-nmixgof_0.1.1-1.ca2404.1_amd64.deb Size: 80654 MD5sum: 7365eaaebe48f37f80a2493277ddfcee SHA1: 311b51cd0ec39e603d4e499aaed79b3a76c64a79 SHA256: a029581a2ed7c1a6ec8cfd505d5e6d22b7b7a93766b02417a14624996434b015 SHA512: 227ea73e16c956bea07a1758d213d5fbf4d64bb5fa142d4687867abfdb72cf4ff90f7b20f1b74ae1fc4879040809a6a17d0fdcbbe073c0201da9d8ce6cce6844 Homepage: https://cran.r-project.org/package=nmixgof Description: CRAN Package 'nmixgof' (Goodness of Fit Checks for Binomial N-Mixture Models) Provides residuals and overdispersion metrics to assess the fit of N-mixture models obtained using the package 'unmarked'. Details on the methods are given in Knape et al. (2017) . Package: r-cran-nmslibr Architecture: amd64 Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3228 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-reticulate, r-cran-r6, r-cran-matrix, r-cran-kernelknn, r-cran-lifecycle, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nmslibr_1.0.7-1.ca2404.1_amd64.deb Size: 2868286 MD5sum: 243f7038f1eea270c843fd2f89374ce0 SHA1: d3323e140a984a3ddcc1b758dde203bfa365c560 SHA256: 13aa3344307da95d95a483b6f7484104d6e77a31ee20bd39569eb235c1f6ddb0 SHA512: dde1b9fa9d15f21ac95823ba59ae62f45bcb9e76a979f7381bf122572ec76b6f828b29c2ef26528d8a1eba3960f29aa27a361082cae2ec40dc19c7b3f5eff8e6 Homepage: https://cran.r-project.org/package=nmslibR Description: CRAN Package 'nmslibR' (Non Metric Space (Approximate) Library) A Non-Metric Space Library ('NMSLIB' ) wrapper, which according to the authors "is an efficient cross-platform similarity search library and a toolkit for evaluation of similarity search methods. The goal of the 'NMSLIB' Library is to create an effective and comprehensive toolkit for searching in generic non-metric spaces. Being comprehensive is important, because no single method is likely to be sufficient in all cases. Also note that exact solutions are hardly efficient in high dimensions and/or non-metric spaces. Hence, the main focus is on approximate methods". The wrapper also includes Approximate Kernel k-Nearest-Neighbor functions based on the 'NMSLIB' 'Python' Library. Package: r-cran-nn2poly Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 912 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-generics, r-cran-matrixstats, r-cran-pracma, r-cran-rcpparmadillo Suggests: r-cran-keras, r-cran-tensorflow, r-cran-reticulate, r-cran-luz, r-cran-torch, r-cran-cowplot, r-cran-ggplot2, r-cran-patchwork, r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nn2poly_0.1.3-1.ca2404.1_amd64.deb Size: 483258 MD5sum: 6100c2c6f346fd10c746b9d7bf3b51bb SHA1: 8c8d6fda6a786c2840cc73599f6f39a76aec1f1e SHA256: b75fecfa1421af73e616e7329c6b57b96be9ba0823ca0f69d1844c68f9842505 SHA512: cbc59934c515fc17d3dc94dee7a893184e4582d1f9709380513e9db3e58884067be771f160aecf567147d7e4ce847ab5192eb6f4d4e52188e14cdee9142dc43f Homepage: https://cran.r-project.org/package=nn2poly Description: CRAN Package 'nn2poly' (Neural Network Weights Transformation into PolynomialCoefficients) Implements a method that builds the coefficients of a polynomial model that performs almost equivalently as a given neural network (densely connected). This is achieved using Taylor expansion at the activation functions. The obtained polynomial coefficients can be used to explain features (and their interactions) importance in the neural network, therefore working as a tool for interpretability or eXplainable Artificial Intelligence (XAI). See Morala et al. 2021 , and 2023 . Package: r-cran-nnet Architecture: amd64 Version: 7.3-20-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-nnet_7.3-20-1.ca2404.2_amd64.deb Size: 110632 MD5sum: ba3a62c78914aa642599152c1cddbcfb SHA1: 88578c26bbd0eb8430649242177f6470010252f6 SHA256: bd7ff5a639fc0af9a11e699b810cfef67e461ccea8aabf831323c6645e8f378f SHA512: 59f11def2ace8adc5e725b9df51f9dd4fdd7aa4100c786b17017542ad78640cfb570e9b82af714cc4e9db633f0cfd46d4dd7335b2a5133034a1e020005e8f828 Homepage: https://cran.r-project.org/package=nnet Description: CRAN Package 'nnet' (Feed-Forward Neural Networks and Multinomial Log-Linear Models) Software for feed-forward neural networks with a single hidden layer, and for multinomial log-linear models. Package: r-cran-nngeo Architecture: amd64 Version: 0.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 604 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-nabor, r-cran-units, r-cran-data.table Suggests: r-cran-dbi, r-cran-rpostgresql, r-cran-stars, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-nngeo_0.4.8-1.ca2404.1_amd64.deb Size: 508146 MD5sum: 7396748886d2052dee355dc699128eeb SHA1: f0a468c48aa57c37399e5f6c867638c20c2156cd SHA256: 59c10ac2485a256bcecd5639aa31cc40db2f6a0a0bb076251f4595f9ea9f3fe3 SHA512: a97d965868d6daeace9fac384536a588e6ea9187be7f2da9a96981784f50038844607de3622b4e6415f606021111368c038a6e991344475dbf7721371ac815c4 Homepage: https://cran.r-project.org/package=nngeo Description: CRAN Package 'nngeo' (k-Nearest Neighbor Join for Spatial Data) K-nearest neighbor search for projected and non-projected 'sf' spatial layers. Nearest neighbor search uses (1) C code from 'GeographicLib' for lon-lat point layers, (2) function knn() from package 'nabor' for projected point layers, or (3) function st_distance() from package 'sf' for line or polygon layers. The package also includes several other utility functions for spatial analysis. Package: r-cran-nnlib2rcpp Architecture: amd64 Version: 0.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1816 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-class Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-nnlib2rcpp_0.2.9-1.ca2404.1_amd64.deb Size: 771362 MD5sum: a85583631627860834e6fec875dc54e5 SHA1: be6eb677c8ffe4c32e585c65126891313df7d0df SHA256: 8aa26cc72746986b09522be882e4cf9b2303c5d9ab70b1171ce60028ae6e53cb SHA512: 545a411198d8528dd812ddf913bf730a298b8dd87f4a6f800c65af56bba9dd9157d1d07ee78ab2dfedd73e8c3ad55e0b3f9c9e92ea1773e69cb0ed272e968dee 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: amd64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-bvls, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-nnls_1.6-1.ca2404.1_amd64.deb Size: 37898 MD5sum: d2fd5b72422233163e81045b2aa088e8 SHA1: e88bc79793e5bc0b797463d1ab8e8e87c8282b68 SHA256: 6da006f7221447924d2f48d0b85655de5859d40bae1a9cde9816d52fe9995c96 SHA512: 032912c367930118afc0eea6b8d6af89a008bad91ee855d6ebcbe2cf98736b41ba8c55011ce4d42d2b7d7656d016bb5bf8c2e354390facc2bacf2d722f087526 Homepage: https://cran.r-project.org/package=nnls Description: CRAN Package 'nnls' (The Lawson-Hanson Algorithm for Non-Negative Least Squares(NNLS)) An R interface to the Lawson-Hanson implementation of an algorithm for non-negative least squares (NNLS). Also allows the combination of non-negative and non-positive constraints. Package: r-cran-nnmf Architecture: amd64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-clusterr, r-cran-compositional, r-cran-matrix, r-cran-osqp, r-cran-quadprog, r-cran-rangen, r-cran-rfast, r-cran-rglpk, r-cran-sparcl, r-cran-rcppeigen Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-nnmf_1.4-1.ca2404.1_amd64.deb Size: 149842 MD5sum: ac9fb2328e81001aaec42e02b5188bcf SHA1: 954186d19b8ef95e6b2116f7f5d4052eddf0f904 SHA256: 7e0450fe381fe94681e28669677df08401be116f329d9f4cbc9d4f859f86877c SHA512: 82b17cc7c5aabebe0df03e99c146c55aeadfc01bf812a61180258ea82937302c6b517a7054efa668deb625024a37e307c6cf6f3b79f1d47ba92d0b247c82ca92 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: amd64 Version: 12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2921 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-doparallel, r-cran-foreach, r-cran-quantmod, r-cran-rcpp, r-cran-rcppparallel, r-cran-rfast, r-cran-rgl, r-cran-xts, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nns_12.0-1.ca2404.1_amd64.deb Size: 1667808 MD5sum: b73f6afbb5450e120e59ae451cf7cd75 SHA1: 67ac269526b1925730fa5458145f75ac64aaa014 SHA256: cfd6c04d403b64cec9da983abdfb858f1653dc791d0beccb3e8d74edc6f14230 SHA512: ec62ea7cd3babc90b78725ba8b60339d7aa9d83182c1aadfac54d0e7b6adfdc539b41bf6ec772b823f65fdec2e65660defb422a7e0128d4d2a81569ad28f55d4 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: ). 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Package: r-cran-nntmvn Architecture: amd64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-nntmvn_1.3.0-1.ca2404.1_amd64.deb Size: 82750 MD5sum: a275a89532dfdade8fa7fd73446666e5 SHA1: 5762242ca7542c472ea1343f952dc38cbadd3da5 SHA256: 114950a6a78ce4fbf25573c5bec3520230c6b8d6a475d263961160ef97aa37fe SHA512: 2f41cf23d508cf03a96616950eaf0c5d596c6da7e3b4ad220779d081283af1722d1119377070d41484acee46526181ac434f113117a3a5fdf5c2e7e1f7b34f7e 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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Moreover, a graph inference procedure to recover Gaussian Graphical Model (GGM) from real data. This procedure comes with a control of the false discovery rate. The method is described in the article "Enhancing the Power of Gaussian Graphical Model Inference by Modeling the Graph Structure" by Kilian, Rebafka, and Villers (2024) . Package: r-cran-nomclust Architecture: amd64 Version: 2.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-clvalid, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-nomclust_2.8.1-1.ca2404.1_amd64.deb Size: 210150 MD5sum: f2b33c06c52385c808068d525e67f4bd SHA1: a4800a719b28d86f47fe47307690e106f72ad7e5 SHA256: 7de97eaf6b5de7b9b8e388bb580055268ba6055dc6477a370d1caf61c457e0e8 SHA512: 9c40d07091a9d16520c732b404177921f2f154cb2d8feb7156846dc9c94ce0319875ab98494d6bd26c7a04111ca3b3c93757988826376dace93c5fc8a2180cbd Homepage: https://cran.r-project.org/package=nomclust Description: CRAN Package 'nomclust' (Hierarchical Cluster Analysis of Nominal Data) Similarity measures for hierarchical clustering of objects characterized by nominal (categorical) variables. Evaluation criteria for nominal data clustering. Package: r-cran-noncompliance Architecture: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-noncompliance_0.2.2-1.ca2404.1_amd64.deb Size: 104390 MD5sum: 31fc3f9f9f1a278e0eb510e5799c9e8d SHA1: 6a928ed620d9c12bd562f172a7c132ec50c38c98 SHA256: d1974c1e2bb113e8cbc9154c81da60c22d104feec10825a47819457cca1c3383 SHA512: d9dc21cba07ef79aa202d618e06bf458a64605d68853aa3d7100ce5c15c1f8b62af02681ad4af637c789d29bfe3c15ab131f45935308bf72a5ce1fb5f8ace7f2 Homepage: https://cran.r-project.org/package=noncompliance Description: CRAN Package 'noncompliance' (Causal Inference in the Presence of Treatment NoncomplianceUnder the Binary Instrumental Variable Model) A finite-population significance test of the 'sharp' causal null hypothesis that treatment exposure X has no effect on final outcome Y, within the principal stratum of Compliers. 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Package: r-cran-nonlineardid Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-sandwich, r-cran-lmtest, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-did, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-nonlineardid_0.1.0-1.ca2404.1_amd64.deb Size: 221834 MD5sum: 0f189dad5200ced003c58d16de48762a SHA1: 769c521eb88f1111b00b8ba24fd3a628fa96caab SHA256: 6a345952e2cbd36897b7d4135e819c6fe2327c06565995a4e58caa3285a698f6 SHA512: aabc614fe9a435f7a4435f0e5b59bcf622d680f28458f506b435ad9b55bc764f545997b41c56bdca663640a7145e7e99c77107d5b85ecbf8cdb15cfbbe610a1d Homepage: https://cran.r-project.org/package=NonlinearDiD Description: CRAN Package 'NonlinearDiD' (Staggered Difference-in-Differences with Nonlinear Outcomes) Implements difference-in-differences estimators for staggered treatment adoption with binary, count, and other nonlinear outcomes. Extends Callaway and Sant'Anna (2021) to handle the fundamental identification challenges that arise with nonlinear outcome models (logit, probit, Poisson) in heterogeneous treatment timing designs. Provides group-time average treatment effects on the treated (ATT), aggregation schemes, and pre-treatment parallel trends tests appropriate for nonlinear settings. Methods include doubly-robust semiparametric estimators, nonparametric bounds, and an odds-ratio DiD approach for binary outcomes. Methods extend Callaway and Sant'Anna (2021) , Roth and Sant'Anna (2023) , and Wooldridge (2023) . Package: r-cran-nonlineartseries Architecture: amd64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 956 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-tseries, r-cran-zoo, r-cran-rcpp, r-cran-lifecycle, r-cran-rcpparmadillo Suggests: r-cran-rgl, r-cran-plot3d, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nonlineartseries_0.3.2-1.ca2404.1_amd64.deb Size: 611882 MD5sum: 54bbe5b801c0ce9e323905707d0bb672 SHA1: 527a96794bd266a6186479166bce68b9bfc3b986 SHA256: 53344f0978488996e6bce8489c106ba987643707f69eb81c232e3d4be1c59fef SHA512: ab59cca2e2266d625c62a8d16ef4e59c9931259a17d92ae0803ef5bff0c893bdbeec27068ba690ffd60272a60e11f85d123cd7e5eb4834604a734945001d2ad4 Homepage: https://cran.r-project.org/package=nonlinearTseries Description: CRAN Package 'nonlinearTseries' (Nonlinear Time Series Analysis) Functions for nonlinear time series analysis. This package permits the computation of the most-used nonlinear statistics/algorithms including generalized correlation dimension, information dimension, largest Lyapunov exponent, sample entropy and Recurrence Quantification Analysis (RQA), among others. Basic routines for surrogate data testing are also included. Part of this work was based on the book "Nonlinear time series analysis" by Holger Kantz and Thomas Schreiber (ISBN: 9780521529020). Package: r-cran-nonmem2rx Architecture: amd64 Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6667 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-digest, r-cran-dparser, r-cran-lotri, r-cran-rcpp, r-cran-rxode2, r-cran-magrittr, r-cran-cli, r-cran-data.table, r-cran-qs2, r-cran-xml2, r-cran-ggplot2, r-cran-ggforce, r-cran-crayon Suggests: r-cran-devtools, r-cran-testthat, r-cran-nonmemica, r-cran-nmdata, r-cran-nonmem2r, r-cran-withr, r-cran-nlme, r-cran-dplyr, r-cran-xgxr, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-nonmem2rx_0.1.9-1.ca2404.1_amd64.deb Size: 987836 MD5sum: 3b479c0c16ce27c3ce8820e738214ab3 SHA1: 9a1744b71addd2e8cec9dc7abc3ea3eb64baf730 SHA256: e08716180167816b74cbb5f07528747402df497cbc6cb2daaac4d8b9f9bd0e63 SHA512: 7f2ba001c6b7a01f5886f7d9396e9538b6c62f5760986a7f139d1601b210a0e2d7a7d369b8210e302f4506a281689c92e3db8541e527ce46ea9cc6b615c5a4d4 Homepage: https://cran.r-project.org/package=nonmem2rx Description: CRAN Package 'nonmem2rx' (Converts 'NONMEM' Models to 'rxode2') 'NONMEM' has been a tool for running nonlinear mixed effects models since the 80s and is still used today (Bauer 2019 ). This tool allows you to convert 'NONMEM' models to 'rxode2' (Wang, Hallow and James (2016) ) and with simple models 'nlmixr2' syntax (Fidler et al (2019) ). The 'nlmixr2' syntax requires the residual specification to be included and it is not always translated. If available, the 'rxode2' model will read in the 'NONMEM' data and compare the simulation for the population model ('PRED') individual model ('IPRED') and residual model ('IWRES') to immediately show how well the translation is performing. This saves the model development time for people who are creating an 'rxode2' model manually. Additionally, this package reads in all the information to allow simulation with uncertainty (that is the number of observations, the number of subjects, and the covariance matrix) with a 'rxode2' model. This is complementary to the 'babelmixr2' package that translates 'nlmixr2' models to 'NONMEM' and can convert the objects converted from 'nonmem2rx' to a full 'nlmixr2' fit. Package: r-cran-nonneg.cg Architecture: amd64 Version: 0.1.6-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-nonneg.cg_0.1.6-1-1.ca2404.1_amd64.deb Size: 44564 MD5sum: 2e10ada3ca50941c5dec72b2bd1d3703 SHA1: 70fd25f6b2f6f010388734e78c7226b50354a518 SHA256: c9ef87c09e137fa9d9c3da9567b2890e0f563da119f2d5ae1e312509b64c3306 SHA512: 2f1b1cd48b032136c5d813bf4ee566825e10d75020ec4b69a38d6311f39c4af13e1f5b0c5bdf4e59020cef3d51277ffc69248bdfb296d15328b6817b21a8c2b6 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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Package: r-cran-nopaco Architecture: amd64 Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 573 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-nopaco_1.0.9-1.ca2404.1_amd64.deb Size: 435324 MD5sum: 611ed3b20fe9f2e03829aeefe76e59ca SHA1: d3e02caa41bc43a20845b4f14feda7e3c0ef3d06 SHA256: 7f5427e3d466d316e79d785e737a5dbf351069b78873471c782213f5715c1c60 SHA512: 34e75a3ad16a52c1b2f96509f5d5727405b7e9dca374e16076f00b6987c6c50a069e2ee648f3e19c80fdca56825b6b094f2bea1a7e4e2a71a29a4b3a4f15afc0 Homepage: https://cran.r-project.org/package=nopaco Description: CRAN Package 'nopaco' (Non-Parametric Concordance Coefficient) A non-parametric test for multi-observer concordance and differences between concordances in (un)balanced data. 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Functions to compute and plot predictions in the natural scale of the psychometric test from the estimates of a linear mixed model estimated on the normalized scores are also provided. See Philipps et al (2014) for details. 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We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, ), the Social Sciences and Humanities Research Council of Canada (SSHRC, ), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, ). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints. 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Retrospective tests sensitive to changes in the expectation, the variance, the covariance, the autocovariance, the distribution function, Spearman's rho, Kendall's tau, Gini's mean difference, and the copula are provided, as well as a test for detecting changes in the distribution of independent block maxima (with environmental studies in mind). The package also contains a test sensitive to changes in the autocopula and a combined test of stationarity sensitive to changes in the distribution function and the autocopula. The latest additions are an open-end sequential test based on the retrospective CUSUM statistic that can be used for monitoring changes in the mean of possibly serially dependent univariate observations, as well as closed-end and open-end sequential tests based on empirical distribution functions that can be used for monitoring changes in the contemporary distribution of possibly serially dependent univariate or low-dimensional multivariate observations. 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Additionally, a parametric model (allometric model) can be estimated. 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This package is a parallel implementation of the 'np' package based on the 'MPI' specification that incorporates the 'Rmpi' package (Hao Yu ) with minor modifications and we are extremely grateful to Hao Yu for his contributions to the 'R' community. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, ), the Social Sciences and Humanities Research Council of Canada (SSHRC, ), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, ). We would also like to acknowledge the contributions of the 'GNU GSL' authors. In particular, we adapt the 'GNU GSL' B-spline routine 'gsl_bspline.c' adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints. 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The main methodological and numerical features of this package are described in Calonico, Cattaneo and Farrell (2019, ). 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Package: r-cran-nvmix Architecture: amd64 Version: 0.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2686 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-qrng, r-cran-matrix, r-cran-copula, r-cran-pcapp, r-cran-adgoftest, r-cran-mnormt, r-cran-pracma Suggests: r-cran-rcolorbrewer, r-cran-lattice, r-cran-qrmdata, r-cran-xts, r-cran-knitr, r-cran-rmarkdown, r-cran-qrmtools, r-cran-rugarch, r-cran-quadprog, r-cran-mvtnorm, r-cran-sensitivity, r-cran-microbenchmark, r-cran-qrm, r-cran-zoo Filename: pool/dists/noble/main/r-cran-nvmix_0.1-2-1.ca2404.1_amd64.deb Size: 2274756 MD5sum: 70c5f352d3332d7c97555b50f033eead SHA1: c325eeeb8963da647e9f8f2df2db7ac44061a186 SHA256: 868a7c8652838e16e07887ccf51c74e287a9125bdca1b724f5f076481f43a196 SHA512: f0f7f4e68eedf28d5bdb45e997ff7dc9452e69f643729038ed7389b7c687b677ed2b45154ad12340a15e5808e5b4eb4534a6346c7761b01671df2271d92f1f46 Homepage: https://cran.r-project.org/package=nvmix Description: CRAN Package 'nvmix' (Multivariate Normal Variance Mixtures) Functions for working with (grouped) multivariate normal variance mixture distributions (evaluation of distribution functions and densities, random number generation and parameter estimation), including Student's t distribution for non-integer degrees-of-freedom as well as the grouped t distribution and copula with multiple degrees-of-freedom parameters. See for a high-level description of select functionality. Package: r-cran-o2geosocial Architecture: amd64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1071 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-geosphere, r-cran-ggplot2, r-cran-visnetwork, r-cran-data.table, r-cran-outbreaker2 Suggests: r-cran-testthat, r-cran-tigris, r-cran-sf, r-cran-knitr, r-cran-socialmixr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-o2geosocial_1.1.3-1.ca2404.1_amd64.deb Size: 540732 MD5sum: 78a7179ff02e103b30ef7135cb1f62d7 SHA1: 83b056b419d942900879fdc436464cec9d7bd738 SHA256: 9f094ba5dcf589bba9c6e9670e91148447026ed5441b83edde5c027650e61eea SHA512: 1dcc54ec3d4c85aced615e1e7c8adfde8ec808fc51df662466c9b5c119759311e0c43329f42246d3fd80983ada86ce7e75c91e697dfe17b87370efb4401559be Homepage: https://cran.r-project.org/package=o2geosocial Description: CRAN Package 'o2geosocial' (Reconstruction of Transmission Chains from Surveillance Data) Bayesian reconstruction of who infected whom during past outbreaks using routinely-collected surveillance data. Inference of transmission trees using genotype, age specific social contacts, distance between cases and onset dates of the reported cases. (Robert A, Kucharski AJ, Gastanaduy PA, Paul P, Funk S. (2020) ). Package: r-cran-o2plsda Architecture: amd64 Version: 0.0.26-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 709 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-magrittr, r-cran-ggplot2, r-cran-ggrepel, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-mass Filename: pool/dists/noble/main/r-cran-o2plsda_0.0.26-1.ca2404.1_amd64.deb Size: 425566 MD5sum: 596c5b9077199eae838278ef0244b3bd SHA1: 5b124aaf1ffaae0fa8a8a844dc96809f7457eee2 SHA256: 9476418c610d0a9f1370413aa7056a719ac4334ec8f59a3abd1856bc197345a1 SHA512: c2f5f45c31d00d36e527d3395517b3aaca21dedce00d4096cfde3c5765f6016f112da92f50a3f2d9c025d022e30454192acc8a96a993bb938562b0e84697daa9 Homepage: https://cran.r-project.org/package=o2plsda Description: CRAN Package 'o2plsda' (Multiomics Data Integration) Provides functions to do 'O2PLS-DA' analysis for multiple omics data integration. The algorithm came from "O2-PLS, a two-block (X±Y) latent variable regression (LVR) method with an integral OSC filter" which published by Johan Trygg and Svante Wold at 2003 . 'O2PLS' is a bidirectional multivariate regression method that aims to separate the covariance between two data sets (it was recently extended to multiple data sets) (Löfstedt and Trygg, 2011 ; Löfstedt et al., 2012 ) from the systematic sources of variance being specific for each data set separately. 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Package: r-cran-oc Architecture: amd64 Version: 1.2.1-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-pscl Filename: pool/dists/noble/main/r-cran-oc_1.2.1-1.ca2404.2_amd64.deb Size: 436006 MD5sum: a0a54b7deda3b13da4cbe7ad0425e59b SHA1: f4a32aba1e863324b6e23bd6da29a5787573e5e0 SHA256: bb350135628e2e785a2e9f8abb42570823cd218831a7ea36f8d3aacf9385318d SHA512: 8d6025a0093f26e41411502c8c969662fe02b3161fbddc05b8d190b9d21f511d51cf7323610c45d19fc431714646b50ff0f70629439682696254b8340dee2a13 Homepage: https://cran.r-project.org/package=oc Description: CRAN Package 'oc' (Optimal Classification Roll Call Analysis Software) Estimates optimal classification (Poole 2000) scores from roll call votes supplied though a 'rollcall' object from package 'pscl'. 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Produces graphical displays that conform to the conventions of the Oceanographic literature. This package is discussed extensively by Kelley (2018) "Oceanographic Analysis with R" . Package: r-cran-oceanview Architecture: amd64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3517 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plot3d, r-cran-plot3drgl, r-cran-rgl, r-cran-shape Filename: pool/dists/noble/main/r-cran-oceanview_1.0.8-1.ca2404.1_amd64.deb Size: 3431464 MD5sum: 7828df089bf71f0aae43fde3dd0db81c SHA1: 58dc9bac7d542e931fd5cef5480eaf9afe0be6f8 SHA256: 0ce2582ad1a728040e8005cf93a315c09bd81aa9f24238fad1fff96de99e9c03 SHA512: 75997d37a93acfbd0063a5e1622a2cb30f8dd8e945fe21fdc59d06a0c2f9cb0a18cd375d424dcf549552ef059b88753167289edd75632f39da33592e0315e44a Homepage: https://cran.r-project.org/package=OceanView Description: CRAN Package 'OceanView' (Visualisation of Oceanographic Data and Model Output) Functions for transforming and viewing 2-D and 3-D (oceanographic) data and model output. 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A. and Balamuta, J. J. (2021) . Package: r-cran-olctools Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-olctools_0.3.0-1.ca2404.1_amd64.deb Size: 79504 MD5sum: 4e90c28e3aca9cda4bc90693a10bcac1 SHA1: 5bfa9fff51ae64a6d3a636ae4280f83616dfbc3a SHA256: 3edbfde895f049e6dd3fe4607d8c6378d3f54b375eace222e680bdf390c89249 SHA512: 703701e0cefe1cba74ab4fd58d73632c50491c5629525f1c9f7d1deffc63e6f15c54d0332fc6e3d1a26f5d5b5ddb51b3d7ab34f7a9896657e957879b758dbb2a 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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Package: r-cran-oncobayes2 Architecture: amd64 Version: 0.9-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3343 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rbest, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-posterior, r-cran-assertthat, r-cran-checkmate, r-cran-formula, r-cran-bayesplot, r-cran-ggplot2, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-abind, r-cran-scales, r-cran-rlang, r-cran-tidyselect, r-cran-matrixstats, r-cran-brms, r-cran-lifecycle, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-withr, r-cran-mvtnorm, r-cran-vdiffr, r-cran-ragg Filename: pool/dists/noble/main/r-cran-oncobayes2_0.9-4-1.ca2404.1_amd64.deb Size: 1496568 MD5sum: 7f4447f5adfa615cd98b51b67c3ebac9 SHA1: f4e7c52a4c89ea295e80c3d3bdfc8b40a841c5fd SHA256: b69b24bb07b495b76684e9981ad9c7afedd796e95765b6ea8308d12520272b84 SHA512: 22e448f6e6715349c3f6c6e51644feb1dc50c12b8b90f5d66be40c8c7e1f2d76ac358ef2826a779a74ef200b95fc11825fecf78733bde17d1246aab6ef3d6467 Homepage: https://cran.r-project.org/package=OncoBayes2 Description: CRAN Package 'OncoBayes2' (Bayesian Logistic Regression for Oncology Dose-Escalation Trials) Bayesian logistic regression model with optional EXchangeability-NonEXchangeability parameter modelling for flexible borrowing from historical or concurrent data-sources. 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: amd64 Version: 1.5-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2844 Depends: r-base-core (>= 4.4.0), 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/noble/main/r-cran-onion_1.5-3-1.ca2404.1_amd64.deb Size: 2171678 MD5sum: 34b7560d056bc08599bd554bb18d4508 SHA1: a47b7782a19b03febd0a03f54713463a980f0329 SHA256: 0c2f96b58d5db117747a5489f5b9d36a6ede0e8b1623f90a9d41500e499e9c26 SHA512: 0506e6fca820bebe2851403f691010dc60c196c1d1392ede582cd4bf3ec1558ce12dace4513b0170b3b0534aa28e3413558c71fc870c2f23c0181e4f4a122302 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: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-onlinecov_1.3-1.ca2404.1_amd64.deb Size: 29088 MD5sum: 387bce0ab9b63904eaacecb9cf90a250 SHA1: 95e12a723583b67b78332d5329a8bf6143d6261b SHA256: b4ba71832b8d27e62339cdaf9993553cec25c7900ae6273218d30cae35c6e4a0 SHA512: 46c44cb63925d8719d0d8f97320d6569eac5555b07883c38bba149ffd7f42cfc4770776810d7cbc4b66ec0cf38b47a4c010a6908b5f64929c22a778ceef9dfd8 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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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) . 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The noise function is comparable to classic Perlin noise, but with less directional artefacts and lower computational overhead. It can have applications in procedural generation or (flow fields) simulations. Package: r-cran-openssl Architecture: amd64 Version: 2.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3271 Depends: libc6 (>= 2.15), libssl3t64 (>= 3.0.0), r-base-core (>= 4.6.0), r-api-4.0, r-cran-askpass Suggests: r-cran-curl, r-cran-testthat, r-cran-digest, r-cran-knitr, r-cran-rmarkdown, r-cran-jsonlite, r-cran-jose, r-cran-sodium Filename: pool/dists/noble/main/r-cran-openssl_2.4.1-1.ca2404.1_amd64.deb Size: 624858 MD5sum: a4f75915eda80fb77f41826b3538c841 SHA1: 695e7f3931549d9ff71f453041385844988da5ea SHA256: 2caf9840a2f4d1d438d0d585c828ff9448e017ca61acd7715739d8207963507f SHA512: 59e47e13fef49c12170b408574f7b5e45a1d615939c62d7b36600d7507a16334b53c3f57edd4020d81c9cc5818701942bd5d6588ffb5e88acda377fbff7e54a6 Homepage: https://cran.r-project.org/package=openssl Description: CRAN Package 'openssl' (Toolkit for Encryption, Signatures and Certificates Based onOpenSSL) Bindings to OpenSSL libssl and libcrypto, plus custom SSH key parsers. Supports RSA, DSA and EC curves P-256, P-384, P-521, and curve25519. Cryptographic signatures can either be created and verified manually or via x509 certificates. AES can be used in cbc, ctr or gcm mode for symmetric encryption; RSA for asymmetric (public key) encryption or EC for Diffie Hellman. High-level envelope functions combine RSA and AES for encrypting arbitrary sized data. Other utilities include key generators, hash functions (md5, sha1, sha256, etc), base64 encoder, a secure random number generator, and 'bignum' math methods for manually performing crypto calculations on large multibyte integers. Package: r-cran-openxlsx2 Architecture: amd64 Version: 1.26-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4472 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-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/noble/main/r-cran-openxlsx2_1.26-1.ca2404.1_amd64.deb Size: 2658720 MD5sum: fbba626911036f3d31abe9523183d977 SHA1: 971a49c7c18bf17e40b444b5600a6191cd3587a1 SHA256: ad5c8682f940bce326113ebe8a381b8c972e8d44821be8993a705c7931aeab9b SHA512: 18d5fd48f2d6ab3e5c65357c94c8a6211fec75171be636bf4cdbd3956654976de5d581293c138bf40eae4c7c5702030a635e72f116952c76b4b8f80a4ccb3378 Homepage: https://cran.r-project.org/package=openxlsx2 Description: CRAN Package 'openxlsx2' (Read, Write and Edit 'xlsx' Files) Simplifies the creation of 'xlsx' files by providing a high level interface to writing, styling and editing worksheets. Package: r-cran-openxlsx Architecture: amd64 Version: 4.2.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2821 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-stringi, r-cran-zip Suggests: r-cran-curl, r-cran-formula.tools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-openxlsx_4.2.8.1-1.ca2404.1_amd64.deb Size: 2039350 MD5sum: 529d8b6e33532d5074e6e1a26b943ef9 SHA1: 9743ff577ed260c47ca44060e004c1428527f118 SHA256: 83707f7fb29f00f7ff5869e6615a5f4614f2bb6071e12424f85fb3fa8d03fd4b SHA512: 2c4f295148a4229f5132d47a83437c9b6604c0f1151ce0c606434486365af96f47bf331747cdd37430de290c4b8bd17c08a95331ca90cf32a7ecfa74d777a28a 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-opera Architecture: amd64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4190 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-htmltools, r-cran-ramcharts, r-cran-htmlwidgets, r-cran-piper, r-cran-alabama, r-cran-rdpack, r-cran-rcppeigen Suggests: r-cran-quantreg, r-cran-quadprog, r-cran-rcolorbrewer, r-cran-testthat, r-cran-caret, r-cran-mgcv, r-cran-survival, r-cran-knitr, r-cran-gbm, r-cran-rmarkdown, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-opera_1.2.0-1.ca2404.1_amd64.deb Size: 3173144 MD5sum: 78ffb002995ef215ccb3c2d907df54c5 SHA1: a415e14df4d1afda0abfc78a083ed523a61e0e37 SHA256: 5f1ed49d360bd588a6e540b1a6b8965519c49099f6822c3d330725a4e454bbfd SHA512: 812663ca89f809451448793baba76e777c687cf55ddff9c0f596f6bbba26b484adcbfeeabe2990677d0bf3cab61778ac31c47760fc82f882da20f2f527618d8f Homepage: https://cran.r-project.org/package=opera Description: CRAN Package 'opera' (Online Prediction by Expert Aggregation) Misc methods to form online predictions, for regression-oriented time-series, by combining a finite set of forecasts provided by the user. See Cesa-Bianchi and Lugosi (2006) for an overview. Package: r-cran-oppr Architecture: amd64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1917 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-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/noble/main/r-cran-oppr_1.0.5-1.ca2404.1_amd64.deb Size: 1140852 MD5sum: 70de38d0efa1975c5f13129569439c79 SHA1: 3f0aee01f727e4b0335dc88ef0111aee2003d433 SHA256: b39d3cb559b43d51308edc60f9e9698294b8ffa1f341427e46c044496cbaa1d0 SHA512: 261c975540cb3d05e0cedafa762fcf6f078738e66c8eb47f1fac6cc271be94dd8b5e71dcaf72b4be309ef49fa2de2d1d44082e39eb1236a6b6782c1c98dec848 Homepage: https://cran.r-project.org/package=oppr Description: CRAN Package 'oppr' (Optimal Project Prioritization) A decision support tool for prioritizing conservation projects. Prioritizations can be developed by maximizing expected feature richness, expected phylogenetic diversity, the number of features that meet persistence targets, or identifying a set of projects that meet persistence targets for minimal cost. Constraints (e.g. lock in specific actions) and feature weights can also be specified to further customize prioritizations. After defining a project prioritization problem, solutions can be obtained using exact algorithms, heuristic algorithms, or random processes. In particular, it is recommended to install the 'Gurobi' optimizer (available from ) because it can identify optimal solutions very quickly. Finally, methods are provided for comparing different prioritizations and evaluating their benefits. For more information, see Hanson et al. (2019) . 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Different model specifications are allowed for each treatment/regime. For more details on the method, see Wang & Mokhtarian (2024) or Chiburis & Lokshin (2007) . Package: r-cran-opt5pl Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrixcalc Filename: pool/dists/noble/main/r-cran-opt5pl_0.1.1-1.ca2404.1_amd64.deb Size: 183626 MD5sum: d2a41e0d06ae8ef3a96c6eea66cf0cae SHA1: 33485c77922ea8912e2d8d7c8208655e857e680d SHA256: 10d9ef0b411a59164a5d3f3cf159a2f36b2abadb7eac2bca532cdfdaea10bd05 SHA512: c9b6e3fa63da88cdafbcecc10b8d244fc11b49f776ba005305473a259a613be6100ba759a37c94eb25242370dee86814677068f8cd01c710dc8d5c9a9959161d Homepage: https://cran.r-project.org/package=Opt5PL Description: CRAN Package 'Opt5PL' (Optimal Designs for the 5-Parameter Logistic Model) Obtain and evaluate various optimal designs for the 3, 4, and 5-parameter logistic models. The optimal designs are obtained based on the numerical algorithm in Hyun, Wong, Yang (2018) . Package: r-cran-optbin Architecture: amd64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-optbin_1.4-1.ca2404.1_amd64.deb Size: 37606 MD5sum: 8c0962c4bfd0708a8c824223c16c1d86 SHA1: c01b809c86c12719da7da8ab315bf90dc9862037 SHA256: 902ea423a2124bf5562f4cf7c324abc57414bd729048348f0a69fa14c4c62268 SHA512: 422cd973058f5a3d2cdfe16edd11ab781cd09814e3aac26a5773650e9e4d3cc600c24ed9006cf05c8245d0168804260811ace244481ad5b339f19ac721fcc4e9 Homepage: https://cran.r-project.org/package=optbin Description: CRAN Package 'optbin' (Optimal Binning of Data) Defines thresholds for breaking data into a number of discrete levels, minimizing the (mean) squared error within all bins. Package: r-cran-optcirclust Architecture: amd64 Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1003 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ckmeans.1d.dp, r-cran-plotrix, r-cran-rcpp, r-cran-rdpack, r-cran-reshape2 Suggests: r-cran-ape, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-optcirclust_0.0.4-1.ca2404.1_amd64.deb Size: 552396 MD5sum: 9ed66229f90a4ea24f17b79417d47426 SHA1: aba28e4853b1c5ae87005b394866a510d45add0d SHA256: b2d7ec1c9e6c54dfa878b36c1816c930ee7312a1854617f28c0b88e573d6ba5a SHA512: a455ab1aa5ce119227ee10946e0b0a9c50d0427487599c32d9e16d929c214306518977c956da5f27f61c32d4a8fe1258bc36715de4f70d3d5ff3a62644cd2413 Homepage: https://cran.r-project.org/package=OptCirClust Description: CRAN Package 'OptCirClust' (Circular, Periodic, or Framed Data Clustering: Fast, Optimal,and Reproducible) Fast, optimal, and reproducible clustering algorithms for circular, periodic, or framed data. The algorithms introduced here are based on a core algorithm for optimal framed clustering the authors have developed (Debnath & Song 2021) . The runtime of these algorithms is O(K N log^2 N), where K is the number of clusters and N is the number of circular data points. On a desktop computer using a single processor core, millions of data points can be grouped into a few clusters within seconds. One can apply the algorithms to characterize events along circular DNA molecules, circular RNA molecules, and circular genomes of bacteria, chloroplast, and mitochondria. One can also cluster climate data along any given longitude or latitude. Periodic data clustering can be formulated as circular clustering. The algorithms offer a general high-performance solution to circular, periodic, or framed data clustering. Package: r-cran-optgs Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-optgs_1.2-1.ca2404.1_amd64.deb Size: 63826 MD5sum: 102bcf113921e93f006967b8e4bb363b SHA1: c9fc065d1e0ee22db8de1ae4fb94db08df229007 SHA256: 4f406287b45d0bcf903912e7c5c603f08cbbc9c7583dc7071251086561b959be SHA512: 6dcb059bb16722f350c91939ec3b1a08f8871f376bd7092b771fc31be9e934fa068d2b247b139d3e5eb50a53180fe8aaeed22e33e02a4d64158d261386cd2beb Homepage: https://cran.r-project.org/package=OptGS Description: CRAN Package 'OptGS' (Near-Optimal Group-Sequential Designs for Continuous Outcomes) Optimal group-sequential designs minimise some function of the expected and maximum sample size whilst controlling the type I error rate and power at a specified level. 'OptGS' provides functions to quickly search for near-optimal group-sequential designs for normally distributed outcomes. The methods used are described in Wason, JMS (2015) . Package: r-cran-opthedging Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-opthedging_1.0-1.ca2404.1_amd64.deb Size: 18912 MD5sum: 27a42066c56a200f90cb2155e093a84c SHA1: 637ebaba54041bf89874ea02d09a3e2c01c1c6e2 SHA256: 92c575697c9ca73d93c0c32f4a72b0bb5b766381c2e7aed002e1eec384b8213e SHA512: cfaaa9e4e4cc717c9d1c75cd2aa425afe0317372c52c2d21d2a755d91dd812d68f4e19aeea3bf54eecd759af2ae7ac645181b0aad374f6a62226cba25ef6d430 Homepage: https://cran.r-project.org/package=OptHedging Description: CRAN Package 'OptHedging' (Estimation of value and hedging strategy of call and putoptions) Estimation of value and hedging strategy of call and put options, based on optimal hedging and Monte Carlo method, from Chapter 3 of 'Statistical Methods for Financial Engineering', by Bruno Remillard, CRC Press, (2013). Package: r-cran-optimalbinningwoe Architecture: amd64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3483 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-recipes, r-cran-rlang, r-cran-tibble, r-cran-dials, r-cran-rcppeigen, r-cran-rcppnumerical Suggests: r-cran-testthat, r-cran-dplyr, r-cran-generics, r-cran-knitr, r-cran-rmarkdown, r-cran-tidymodels, r-cran-workflows, r-cran-parsnip, r-cran-proc, r-cran-scorecard Filename: pool/dists/noble/main/r-cran-optimalbinningwoe_1.0.8-1.ca2404.1_amd64.deb Size: 1648916 MD5sum: 155949007f72510252b9d7f92fb2096a SHA1: 4364ed934249455eb139d980963751db1639decd SHA256: ad8409dd5d12000e2f12dd1b5586dc205094fb7106b6c686da8b42467f1882a0 SHA512: 87297980161ec2d531b2c608c7eab5e53640f3b07aed61204da9390fb759b83a4571c11b8374d03cb5cdbb77ed7ac11663d3cd20a4dffc0617a6ddf8cb9ea0dd Homepage: https://cran.r-project.org/package=OptimalBinningWoE Description: CRAN Package 'OptimalBinningWoE' (Optimal Binning and Weight of Evidence Framework for Modeling) High-performance implementation of 36 optimal binning algorithms (16 categorical, 20 numerical) for Weight of Evidence ('WoE') transformation, credit scoring, and risk modeling. Includes advanced methods such as Mixed Integer Linear Programming ('MILP'), Genetic Algorithms, Simulated Annealing, and Monotonic Regression. Features automatic method selection based on Information Value ('IV') maximization, strict monotonicity enforcement, and efficient handling of large datasets via 'Rcpp'. Fully integrated with the 'tidymodels' ecosystem for building robust machine learning pipelines. Based on methods described in Siddiqi (2006) and Navas-Palencia (2020) . Package: r-cran-optimization Architecture: amd64 Version: 1.0-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1002 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-colorspace Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-optimization_1.0-9-1.ca2404.1_amd64.deb Size: 876608 MD5sum: 1b630b7916aa45fa3d05012bfc772267 SHA1: a7aeed68f99b9384987ca59a3d38267e710a8fee SHA256: 272e0a1bdbe05668b9eb4517d064df3300f2f619779a9062b839fa72ff61569f SHA512: 6270f95c34482b20dad704c3db53b4435b05cdd90c94b391cbdef6bf591988a51ca91529916f16eb08833e5bab63c07d263c49c2bdfe2509278abc11387efd2b Homepage: https://cran.r-project.org/package=optimization Description: CRAN Package 'optimization' (Flexible Optimization of Complex Loss Functions with State andParameter Space Constraints) Flexible optimizer with numerous input specifications for detailed parameterisation. Designed for complex loss functions with state and parameter space constraints. Visualization tools for validation and analysis of the convergence are included. Package: r-cran-optisel Architecture: amd64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3088 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-plyr, r-cran-kinship2, r-cran-nadiv, r-cran-pedigree, r-cran-pspline, r-cran-stringr, r-cran-mass, r-cran-purrr, r-cran-quadprog, r-cran-data.table, r-cran-magic, r-cran-doparallel, r-cran-foreach, r-cran-ecosolver, r-cran-reshape2, r-cran-optisolve, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown, r-cran-alabama, r-cran-cccp, r-cran-nloptr, r-cran-rsymphony, r-cran-smacof Filename: pool/dists/noble/main/r-cran-optisel_2.1.0-1.ca2404.1_amd64.deb Size: 950088 MD5sum: 536b604628de11769dc67e1e8957eb05 SHA1: b7be538abf258afa4618f8dbcca667def53ad643 SHA256: d12ae316692c8bdc0dccc8120a9285526183faa42af5c2b0f2b6cd373df03eb4 SHA512: 283d038e215a4abff01ce483407f87e6302c7c7831d3848f6bc417aea22f5ea1aee8823f7f396e9ff1c3d9e0b2fe43756ec8c54e0dd302b402fb2b48f50919f0 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: amd64 Version: 0.10.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3141 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-tibble, r-cran-rlemon Suggests: r-cran-ritools, r-cran-boot, r-cran-biglm, r-cran-survey, r-cran-testthat, r-cran-roxygen2, r-cran-brglm, r-cran-arm, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-pander, r-cran-xtable, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-optmatch_0.10.8-1.ca2404.1_amd64.deb Size: 1044940 MD5sum: ee3ab1ac01b2b188ce377c099dedab7f SHA1: 24d34f376284e59ece8948baf0303b49fd01a833 SHA256: 0e898a3c2ec25d7e39073b6aab71c66b2bdc5197f6425aeee2a56ff808dbb9a2 SHA512: 34c6cce53de5b05acfb7054acf20b71f5a82db2e73c295f362a0f5bdf08d6de4f83f3289c7aac55ab09a119d6e480943c099411552614e7421953e7babf07b0c 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-optpart Architecture: amd64 Version: 3.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-labdsv, r-cran-mass, r-cran-plotrix Suggests: r-cran-tree Filename: pool/dists/noble/main/r-cran-optpart_3.0-3-1.ca2404.1_amd64.deb Size: 282936 MD5sum: 37d687f36d53a964e688d5b61fadd57c SHA1: f9e7f9ad09d6a21e0bd1f608a2626779941f9080 SHA256: 97b522714cad7929ea3ac19c9af0dca92434a491519c5e0f1cecbd9fbec8f136 SHA512: 55dcfbb41bbdba5a24b9800628b52ecab6e9deee1f6eec9092ac2283e2e9a583d0c005536b4693d2846b872a12d5b403eb5dd58a118781d52b45fb1627e39b11 Homepage: https://cran.r-project.org/package=optpart Description: CRAN Package 'optpart' (Optimal Partitioning of Similarity Relations) Contains a set of algorithms for creating partitions and coverings of objects largely based on operations on (dis)similarity relations (or matrices). There are several iterative re-assignment algorithms optimizing different goodness-of-clustering criteria. In addition, there are covering algorithms 'clique' which derives maximal cliques, and 'maxpact' which creates a covering of maximally compact sets. Graphical analyses and conversion routines are also included. Package: r-cran-opusminer Architecture: amd64 Version: 0.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-arules, r-cran-matrix Filename: pool/dists/noble/main/r-cran-opusminer_0.1-1-1.ca2404.1_amd64.deb Size: 88808 MD5sum: 9cbcf0d2342b8da1c87d115e21694458 SHA1: fa0a0667a6c78145b8e338e909e3ae283904dc4c SHA256: 798b7fe2d186de8cb07cf60c34dc2ad5d69324c9f4cb3315e804101476744bde SHA512: a5abc6f7e382024cd6be68f9e09fdff995a2dd6753326f8b2b259c090241152dd7a21f65fdb4dd21ad034f3d292db27f9a963296adedd755b486bbe74be80ea1 Homepage: https://cran.r-project.org/package=opusminer Description: CRAN Package 'opusminer' (OPUS Miner Algorithm for Filtered Top-k Association Discovery) Provides a simple R interface to the OPUS Miner algorithm (implemented in C++) for finding the top-k productive, non-redundant itemsets from transaction data. The OPUS Miner algorithm uses the OPUS search algorithm to efficiently discover the key associations in transaction data, in the form of self-sufficient itemsets, using either leverage or lift. See for more information in relation to the OPUS Miner algorithm. Package: r-cran-orbitr Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14726 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rcpp, r-cran-tibble Suggests: r-cran-plotly, r-cran-gganimate, r-cran-gifski, r-cran-magick, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-orbitr_0.3.0-1.ca2404.1_amd64.deb Size: 2372584 MD5sum: 3e38bf12f249c31199a907bfdff863b3 SHA1: 4a94eabf604b199188b03a37f0c3193740abac08 SHA256: 6ce8a648535c7deb5ee1d435cc9b28a4591228c049cc6527d4985008b85dda0b SHA512: 1fbd9e3cd1b18d18d95082a0aaa747f990de90a0ef3f174893fda2e1988a8efdd68cf1d7a293188739873be62be55b0f3bf7b69eec57e8bb595ea47bd0b5dc75 Homepage: https://cran.r-project.org/package=orbitr Description: CRAN Package 'orbitr' (A Tidy Physics Engine for Building and Visualizing OrbitalSimulations) A lightweight, fully vectorized N-body physics engine built for the R ecosystem. 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Package: r-cran-orbweaver Architecture: amd64 Version: 0.18.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2891 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-orbweaver_0.18.2-1.ca2404.1_amd64.deb Size: 896678 MD5sum: 407babffe9f65a37aa6e28d55ac8c8b0 SHA1: 33ccc72aeeea6cfa47addb489c1e206d75e2655f SHA256: 7fa82acd3604de0d308f03a068812448862e5234cd2dcd15e604ed8117aed36f SHA512: aa4a1af7b9bf1193443cc39aa8fa8c8e33f55a0fadf32f2fc32b0e2db2242939821d138140c54bae20b4c36cece47fbb34ed6dfc26d07ccba1113c0f67128d9e 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: amd64 Version: 1.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-orca_1.1-3-1.ca2404.1_amd64.deb Size: 67578 MD5sum: c0df7dfa715131837c0dede788cf8780 SHA1: 3d75cc850d8562d2dcb7916bfbfcf7c477aacb61 SHA256: b014c3b70367b4e9ad7f2ed26945110d3243da7bf612cc71abc7ba64ae4e9ebf SHA512: 43cfab355a280ca2f49eb083e27ba00b4485816467f503b48c50c356b53d49b921fbe0768523c73f434bf6c07510b6afc3dec1d2d4c735d3b9c19ebf3732685c 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: amd64 Version: 2025.12-29-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1470 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ucminf, r-cran-mass, r-cran-matrix, r-cran-numderiv, r-cran-nlme Suggests: r-cran-lme4, r-cran-nnet, r-cran-xtable, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ordinal_2025.12-29-1.ca2404.1_amd64.deb Size: 1259554 MD5sum: 8c49cc22794b7ae319ea5d75717931bd SHA1: f9f8472d49fa3d414dceef87a731e50c146ec573 SHA256: 543eb7d9e84b40c80368a43ddda97215bc3d2355a984e04d98a24652d97c4ff5 SHA512: 11eaaf728c7c98110b38f0b08e75bae28a95f105cdea1f052cbb61443b1b0ee32e45e1a17f0342b3390dcdb694191cab3b387943405f5e3dfbf984528fb4c083 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: amd64 Version: 1.3.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 973 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppprogress, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-caret, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-ordinalclust_1.3.5.1-1.ca2404.1_amd64.deb Size: 398302 MD5sum: e80ceff2dabcad31774d89dc22d62ea5 SHA1: 37bdc41fe0701e155426ef1314d55f92449c04f6 SHA256: afdac55031d66e544fc5f167c9d8b5ea17f79c9f296aa002d87a43be2fa7a55d SHA512: 52eda859f14cf082b0d4c91f5d709b098be2bd8132aec7b747a11f6ee2793749a8a149909582dae5cf4e9f429a79991b0251fde490a65ec59c5b35e9144fd93e Homepage: https://cran.r-project.org/package=ordinalClust Description: CRAN Package 'ordinalClust' (Ordinal Data Clustering, Co-Clustering and Classification) Ordinal data classification, clustering and co-clustering using model-based approach with the BOS (Binary Ordinal Search) distribution for ordinal data (Christophe Biernacki and Julien Jacques (2016) ). Package: r-cran-ordinalforest Architecture: amd64 Version: 2.4-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 583 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-combinat, r-cran-nnet, r-cran-verification Filename: pool/dists/noble/main/r-cran-ordinalforest_2.4-4-1.ca2404.1_amd64.deb Size: 255664 MD5sum: 0b51f6519f021cada364b1fafb9f858c SHA1: 58d97f075b90336f13693344a3572a0704cd5365 SHA256: fd8bb1fab383c03d1a6caf4867b4436f06dcbd26be5d4e1e3e3f501c5a62b9ae SHA512: 3875031feef717f947e3f080e0cf35a01e4395d9fea6adf5b59a8511377390b8be107923476e271ca9840ac90d9f2a189959b33693fea8a6ee97c16951fbd976 Homepage: https://cran.r-project.org/package=ordinalForest Description: CRAN Package 'ordinalForest' (Ordinal Forests: Prediction and Variable Ranking with OrdinalTarget Variables) The ordinal forest (OF) method allows ordinal regression with high-dimensional and low-dimensional data. After having constructed an OF prediction rule using a training dataset, it can be used to predict the values of the ordinal target variable for new observations. Moreover, by means of the (permutation-based) variable importance measure of OF, it is also possible to rank the covariates with respect to their importance in the prediction of the values of the ordinal target variable. OF is presented in Hornung (2020). NOTE: Starting with package version 2.4, it is also possible to obtain class probability predictions in addition to the class point predictions. Moreover, the variable importance values can also be based on the class probability predictions. Preliminary results indicate that this might lead to a better discrimination between influential and non-influential covariates. The main functions of the package are: ordfor() (construction of OF) and predict.ordfor() (prediction of the target variable values of new observations). References: Hornung R. (2020) Ordinal Forests. Journal of Classification 37, 4–17. . Package: r-cran-ordinalgmifs Architecture: amd64 Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 614 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-ordinalgmifs_1.0.9-1.ca2404.1_amd64.deb Size: 522064 MD5sum: f547f05bc8a9c1c5cbd70ff02d3f990c SHA1: 24146425d1383f4ae407c09b5a0c448d82f3484b SHA256: 35418b9cbf1598c659b902d77d99d50f44a4b34f81de03b2f11684044c0becec SHA512: dc3951a36221379682e8937183f1add58659041894142db2b6c5fdeedf0aa8550dd604d54a8122172b447cd17fd60bf0de6bc74a98a69057cccae97c72d03c55 Homepage: https://cran.r-project.org/package=ordinalgmifs Description: CRAN Package 'ordinalgmifs' (Ordinal Regression for High-Dimensional Data) Provides a function for fitting cumulative link, adjacent category, forward and backward continuation ratio, and stereotype ordinal response models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method. Package: r-cran-ordinalnet Architecture: amd64 Version: 2.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-mass, r-cran-glmnet, r-cran-penalized, r-cran-vgam, r-cran-rms Filename: pool/dists/noble/main/r-cran-ordinalnet_2.12-1.ca2404.1_amd64.deb Size: 120080 MD5sum: c6a19868f9d58ddcfb2933f66027561a SHA1: 73cdc2c0afd0d2c94e34eab046a38cb939f38b25 SHA256: 9cbe0663edd567e7432be89c9cce5b33edf4d02d50b4cfad8f673135e38eafc0 SHA512: 1c10ea601e8410bc0461bb41873f2519f494f3782ae1ecfb44e3a8128db370802da73b8dce79ed74539817eb92f0a5bcec0c64f908dce297fe4357402502f6fb Homepage: https://cran.r-project.org/package=ordinalNet Description: CRAN Package 'ordinalNet' (Penalized Ordinal Regression) Fits ordinal regression models with elastic net penalty. Supported model families include cumulative probability, stopping ratio, continuation ratio, and adjacent category. These families are a subset of vector glm's which belong to a model class we call the elementwise link multinomial-ordinal (ELMO) class. Each family in this class links a vector of covariates to a vector of class probabilities. Each of these families has a parallel form, which is appropriate for ordinal response data, as well as a nonparallel form that is appropriate for an unordered categorical response, or as a more flexible model for ordinal data. The parallel model has a single set of coefficients, whereas the nonparallel model has a set of coefficients for each response category except the baseline category. It is also possible to fit a model with both parallel and nonparallel terms, which we call the semi-parallel model. The semi-parallel model has the flexibility of the nonparallel model, but the elastic net penalty shrinks it toward the parallel model. For details, refer to Wurm, Hanlon, and Rathouz (2021) . 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By comparing ordinal patterns of two times series, Schnurr (2014) defines a robust and non-parametric dependence measure: the ordinal pattern coefficient. Functions to calculate this and a method to detect a change in the pattern coefficient proposed in Schnurr and Dehling (2017) are provided. Furthermore, the package contains a function for calculating the ordinal pattern frequencies. Generalized ordinal patterns as proposed by Schnurr and Fischer (2022) are also considered. 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Package: r-cran-orsk Architecture: amd64 Version: 1.0-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bb Suggests: r-cran-setrng Filename: pool/dists/noble/main/r-cran-orsk_1.0-9-1.ca2404.1_amd64.deb Size: 156938 MD5sum: 90487c2b9c585526dced22286ed01fc6 SHA1: 0f78eeaa0697006104430be073fe6ad9662b81b9 SHA256: a4b8305e67f0e4fecbf8e113195b8099104e579efba0b747e72b327490a05043 SHA512: e9ca379f2bb59016198556fc4f08f509c75061097d624eb74560a897b7734272a9c677591f26b5b525c90399d015e60cc31684bd20ef50bb517c5794bd8438a1 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. 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Package: r-cran-osc Architecture: amd64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 797 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster Suggests: r-cran-testthat, r-cran-maps Filename: pool/dists/noble/main/r-cran-osc_1.0.5-1.ca2404.1_amd64.deb Size: 723672 MD5sum: 0f4cbee436a2409dda1cb83c1d0d0d8c SHA1: f87470e8d712e3a98cb56003a9b48beab8fa8df1 SHA256: 3d830e71416127335bc28a1091c83a4374ed28f4f70e45e59631ee5211b4a97f SHA512: 1d8c9c97ac41aa68a6d8382c55832d4545cbffd0ebd40c3db8bf0c1d3a010f66d3976f2b5ed15d3fc41047b2a237c5f400219bf67ccd28b025fa1344b18517c3 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. 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Package: r-cran-oscar Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 871 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-hamlet, r-cran-matrix, r-cran-survival, r-cran-proc Suggests: r-cran-epcr, r-cran-glmnet, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-oscar_1.2.1-1.ca2404.1_amd64.deb Size: 472346 MD5sum: 288fb7ba039c3f3a45930dc5c9b4e5a0 SHA1: 47c08250d09859543f2306c155238b8059df4307 SHA256: 166758252a07ddd5bc431d55f7e6a8be49720efc2c47361e8172036c07158c16 SHA512: 59939f6bc30c2591438f42106eb121f24a1fbf972bd5c8779f7103ed34a35dfa1bd0e62072d2cd0b78ea66523a22356fefad79ab1cdef2eae349b800ce33ed94 Homepage: https://cran.r-project.org/package=oscar Description: CRAN Package 'oscar' (Optimal Subset Cardinality Regression (OSCAR) Models Using theL0-Pseudonorm) Optimal Subset Cardinality Regression (OSCAR) models offer regularized linear regression using the L0-pseudonorm, conventionally known as the number of non-zero coefficients. The package estimates an optimal subset of features using the L0-penalization via cross-validation, bootstrapping and visual diagnostics. Effective Fortran implementations are offered along the package for finding optima for the DC-decomposition, which is used for transforming the discrete L0-regularized optimization problem into a continuous non-convex optimization task. These optimization modules include DBDC ('Double Bundle method for nonsmooth DC optimization' as described in Joki et al. (2018) ) and LMBM ('Limited Memory Bundle Method for large-scale nonsmooth optimization' as in Haarala et al. (2004) ). The OSCAR models are comprehensively exemplified in Halkola et al. (2023) ). Multiple regression model families are supported: Cox, logistic, and Gaussian. Package: r-cran-osfd Architecture: amd64 Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-lhs, r-cran-twinning, r-cran-dplyr, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-osfd_3.1-1.ca2404.1_amd64.deb Size: 155458 MD5sum: 430af20d7a5ec48b34150706c54651c6 SHA1: 30510f5875001b72a9d28f61bcb5e791b1ef87dd SHA256: 99fa1138f4be4c0648d8f6689ae51aad0461ed6a52ce6c289d0f3d3d8c84394f SHA512: a5616f798c886dd9dfce95b723c316d3476a9e4122c676224c12507146480bf203df18a4bbea5170141811c61f06ad1771e9e8eb912fdfdb1413010356354751 Homepage: https://cran.r-project.org/package=OSFD Description: CRAN Package 'OSFD' (Output Space-Filling Design) Methods to generate a design in the input space that sequentially fills the output space of a black-box function. The output space-filling designs are helpful in inverse design or feature-based modeling problems. See Wang, Shangkun, Adam P. Generale, Surya R. Kalidindi, and V. Roshan Joseph. (2024), Sequential designs for filling output spaces, Technometrics, 66, 65–76. for details. This work is supported by U.S. National Foundation grant CMMI-1921646. Package: r-cran-oskeyring Architecture: amd64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-askpass, r-cran-covr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-oskeyring_0.1.7-1.ca2404.1_amd64.deb Size: 66496 MD5sum: 4ac60d3b6d4a2ea2094fd50d97133061 SHA1: d4e94e92538a4dece75a078e1686c7da471bd6bc SHA256: 51e9d8dc816cf3d2bc9bd4332340d2891a9ecc2f89964c89e13eee99db998186 SHA512: fe8303ec655bb6d2a5cb215efe0d87c42ed6db7b6d448dc5e2ac58310bbacac7fe31312220a8ed9aa2be99ea24287e41612a73174dca6c86fe0cb0b641a1d807 Homepage: https://cran.r-project.org/package=oskeyring Description: CRAN Package 'oskeyring' (Raw System Credential Store Access from R) Aims to support all features of the system credential store, including non-portable ones. Supports 'Keychain' on 'macOS', and 'Credential Manager' on 'Windows'. 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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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This package contains the OpenTelemetry SDK, and exporters. Use this package to export traces, metrics, logs from instrumented R code. Use the otel package to instrument your R code for OpenTelemetry. 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Jombart T, Cori A, Didelot X, Cauchemez S, Fraser C and Ferguson N. 2014. . Campbell, F, Cori A, Ferguson N, Jombart T. 2019. . 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Thong Pham et al. (2015) . Thong Pham et al. (2016) . Thong Pham et al. (2020) . Thong Pham et al. (2021) . 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Disregarding group-level heterogeneity can introduce bias. Conversely, estimating individual coefficients for each cross-sectional unit is inefficient and may lead to high uncertainty. This package addresses the issue of unobservable group structures by implementing the pairwise adaptive group fused Lasso (PAGFL) by Mehrabani (2023) . PAGFL identifies latent group structures and group-specific coefficients in a single step. On top of that, we extend the PAGFL to time-varying coefficient functions (FUSE-TIME), following Haimerl et al. (2025) . Package: r-cran-pagoda2 Architecture: amd64 Version: 1.0.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2126 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-igraph, r-cran-dendsort, r-cran-drat, r-cran-fastcluster, r-cran-irlba, r-cran-magrittr, r-cran-mass, r-cran-mgcv, r-cran-n2r, r-cran-plyr, r-cran-r.utils, r-cran-rcpp, r-cran-rjson, r-cran-rlang, r-cran-r6, r-cran-rmtstat, r-cran-rook, r-cran-rtsne, r-cran-sccore, r-cran-urltools, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-rcppeigen Suggests: r-bioc-annotationdbi, r-cran-base64enc, r-bioc-biocgenerics, r-bioc-biocparallel, r-cran-colorramps, r-cran-data.table, r-cran-dbscan, r-cran-dplyr, r-cran-ggplot2, r-bioc-go.db, r-cran-gridextra, r-cran-kernsmooth, r-cran-knitr, r-bioc-org.dr.eg.db, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db, r-bioc-pcamethods, r-cran-pheatmap, r-cran-rgl, r-cran-rmarkdown, r-cran-robustbase, r-bioc-scde, r-cran-testthat, r-cran-uwot Filename: pool/dists/noble/main/r-cran-pagoda2_1.0.15-1.ca2404.1_amd64.deb Size: 1271234 MD5sum: 4e26e1e2d22e0f5ac0b718dc06abaf1a SHA1: e34d0f0399151c7de4b3754f4a4423f2d62d5c03 SHA256: 45d87d1e501797cef3ee9e69e6e3866a47f771ef0dc723257cbc293c9e301c92 SHA512: 640a964afd41a3c47d15d337326b22d15c7fd7c823e46b3528fbb4401741dbef58fc372f0dd009414efc5819410e3d9f5f1571fcc494b339da86223109dcb06f Homepage: https://cran.r-project.org/package=pagoda2 Description: CRAN Package 'pagoda2' (Single Cell Analysis and Differential Expression) Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos, . This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at . The size of the 'p2data' package is approximately 6 MB. Package: r-cran-pairscale Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pairscale_1.0-1.ca2404.1_amd64.deb Size: 155442 MD5sum: 1e24e7b89b211244f91a9ac78c56a040 SHA1: 624b7c27de071e0279b4998144d11c90b3988606 SHA256: 4d999f071999bca11d460ded84b7b63403a96c24e804548732acf5fdfc4be338 SHA512: 34df3de899caae15a949fad9dafa23e3474b8c238b7af282e5ac01b41c784a4bb5f585d54a83d44a36087616c4cb09a33d3e7925911773a3e363838884ceb2de Homepage: https://cran.r-project.org/package=pairscale Description: CRAN Package 'pairscale' (Pairwise Rescaling of Numeric Matrices) Normalization of numerical matrices by minimizing the mean/median/mode difference between all column pairs. Package: r-cran-pak Architecture: amd64 Version: 0.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9863 Depends: libc6 (>= 2.38), libcurl4t64 (>= 7.74.0), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-callr, r-cran-cli, r-cran-covr, r-cran-curl, r-cran-desc, r-cran-filelock, r-cran-gitcreds, r-cran-glue, r-cran-jsonlite, r-cran-keyring, r-cran-pingr, r-cran-pkgbuild, r-cran-pkgcache, r-cran-pkgdepends, r-cran-pkgload, r-cran-pkgsearch, r-cran-processx, r-cran-ps, r-cran-rstudioapi, r-cran-testthat, r-cran-webfakes, r-cran-withr, r-cran-yaml Filename: pool/dists/noble/main/r-cran-pak_0.9.5-1.ca2404.1_amd64.deb Size: 5772234 MD5sum: 62e000d016456eaf8b4ed8b0235f63c5 SHA1: 96eb710cd953ed8fa17f88fe4dc0b3f3a4663fc0 SHA256: afafd57dc6cb4ee7243f08bc5d0df4063d0a97c11d4df861c800b9688921e959 SHA512: 82a94fc33840ab7346fefed3e0148d184eba2aa163e2119e7a57e1f8b6a0d09fa554fb9a124ca5138434e6d30adce847259fcae9cb8d3e7d34ad5933e4fc3cf6 Homepage: https://cran.r-project.org/package=pak Description: CRAN Package 'pak' (Another Approach to Package Installation) The goal of 'pak' is to make package installation faster and more reliable. 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Package: r-cran-palm Architecture: amd64 Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-gsl, r-cran-minqa, r-cran-mvtnorm, r-cran-r6 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-palm_1.1.6-1.ca2404.1_amd64.deb Size: 218300 MD5sum: efc5062bac59882d085c09262a1cd54e SHA1: eeaa84db83d5a88f72ac3a0d215225210a29bcb0 SHA256: fd3033535e6fdb3c8417132f709115f782df14d33df7020ac5a1a5b5ab27106c SHA512: 857a2812552aa4b483e815edb29ecb23b3b124acf988f728cd2b2b382329d8a93d00362a67a0f0aad5e09a988484897d6b4c74ac6aa648eed0e85168fe7b2049 Homepage: https://cran.r-project.org/package=palm Description: CRAN Package 'palm' (Fitting Point Process Models via the Palm Likelihood) Functions to fit point process models using the Palm likelihood. First proposed by Tanaka, Ogata, and Stoyan (2008) , maximisation of the Palm likelihood can provide computationally efficient parameter estimation for point process models in situations where the full likelihood is intractable. This package is chiefly focused on Neyman-Scott point processes, but can also fit the void processes proposed by Jones-Todd et al. (2019) . The development of this package was motivated by the analysis of capture-recapture surveys on which individuals cannot be identified---the data from which can conceptually be seen as a clustered point process (Stevenson, Borchers, and Fewster, 2019 ). As such, some of the functions in this package are specifically for the estimation of cetacean density from two-camera aerial surveys. Package: r-cran-pammisc Architecture: amd64 Version: 1.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1014 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tuner, r-cran-seewave, r-cran-dplyr, r-cran-rcpproll, r-cran-pambinaries, r-cran-rsqlite, r-cran-lubridate, r-cran-rerddap, r-cran-ncdf4, r-cran-httr, r-cran-purrr, r-cran-xml2, r-cran-geosphere, r-cran-scales, r-cran-suncalc, r-cran-rjson, r-cran-fftw, r-cran-signal Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pammisc_1.13.0-1.ca2404.1_amd64.deb Size: 618840 MD5sum: 57583ce70b522292a5feb5232113a3cc SHA1: 3a651c0356b179b9a2becc5c8b339c4ebc5b45c1 SHA256: d9cf2524cb811d0eda1252adf2ce354ba8e180b2e75c5c347fc3a3c533da38a1 SHA512: f3d1b94d972edeb691313a99ef6d8fec86a11ed8765cae4f481842f8b937765afed42ec9433856aca6af7e05284b175d26f3f3afcb8d074b1451f9eb1f88aad5 Homepage: https://cran.r-project.org/package=PAMmisc Description: CRAN Package 'PAMmisc' (Miscellaneous Functions for Passive Acoustic Analysis) A collection of miscellaneous functions for passive acoustics. Much of the content here is adapted to R from code written by other people. If you have any ideas of functions to add, please contact Taiki Sakai. Package: r-cran-pan Architecture: amd64 Version: 1.9-1.ca2404.3 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 698 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mitools, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-pan_1.9-1.ca2404.3_amd64.deb Size: 542506 MD5sum: 74d1db154d238da8382c465ad01573bb SHA1: 830f6c4232514609b722cc1586222286c05c108b SHA256: f23b9db7c00160c5527413909886eb2c1b8979d1c9e074a74051733d2c9e51a0 SHA512: 4e0e1312a7ba8e7e3a6cc6e50c6088227d3be1104a8f364b6a1f918c38d466c89e360470cc8f8451c3c789b03785c8b13571ac51c9395a0ea19d8c861e1ef35b Homepage: https://cran.r-project.org/package=pan Description: CRAN Package 'pan' (Multiple Imputation for Multivariate Panel or Clustered Data) It provides functions and examples for maximum likelihood estimation for generalized linear mixed models and Gibbs sampler for multivariate linear mixed models with incomplete data, as described in Schafer JL (1997) "Imputation of missing covariates under a multivariate linear mixed model". Technical report 97-04, Dept. of Statistics, The Pennsylvania State University. Package: r-cran-panacea Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-igraph, r-cran-reshape2 Suggests: r-bioc-org.hs.eg.db, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-panacea_1.1.0-1.ca2404.1_amd64.deb Size: 2038888 MD5sum: ac157698bbca68b6cd61338d0b963c75 SHA1: d267f933647348c60be2c25e43fe427967194892 SHA256: 6dc3a1d67d68fb2ac83d81e4bd1676070df3c843a27d41040d2bf52869363d34 SHA512: c1cf79330d51311e3ab4b0d2620573860680233ffaeb556c0054dbf82e6ed28870b26fdba77ae298a74c915b8c5d7eaef38c555bf00bebbce15aa4260035df55 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 . 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Package: r-cran-panelcount Architecture: amd64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-statmod, r-cran-mass, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-panelcount_2.0.1-1.ca2404.1_amd64.deb Size: 254074 MD5sum: ede5051bf31b2de1587feb0f845080c2 SHA1: 39f2e534ddec3d68de0c7d57124b4241b1051e3e SHA256: cb36d7bcd3c29fcfa9c776eff8df8f8f7ac108f56b0ab8f1cff901ab1bfde2c5 SHA512: 6d372e3ddd987800eb95aeaf10de9b819f6ce72774f6e80ad38a80d4e8ecd7cadb927aadc69913edd183e9c72de61ed909cfac2577cc4b30fa568546c9cdff06 Homepage: https://cran.r-project.org/package=PanelCount Description: CRAN Package 'PanelCount' (Random Effects and/or Sample Selection Models for Panel CountData) A high performance package implementing random effects and/or sample selection models for panel count data. The details of the models are discussed in Peng and Van den Bulte (2023) . 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Imai, Kim, and Wang (2023) proposes a nonparametric generalization of the difference-in-differences estimator, which does not rely on the linearity assumption as often done in practice. Researchers first select a method of matching each treated observation for a given unit in a particular time period with control observations from other units in the same time period that have a similar treatment and covariate history. These methods include standard matching methods based on propensity score and Mahalanobis distance, as well as weighting methods. Once matching and refinement is done, treatment effects can be estimated with standard errors. The package also offers diagnostics for researchers to assess the quality of their results. 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To implement such models, simulate them and fit them to panel data, 'panelPomp' extends some of the facilities provided for time series data by the 'pomp' package. Implemented methods include filtering (panel particle filtering) and maximum likelihood estimation (Panel Iterated Filtering) as proposed in Breto, Ionides and King (2020) "Panel Data Analysis via Mechanistic Models" . Package: r-cran-panelselect Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-panelcount, r-cran-pbivnorm, r-cran-maxlik, r-cran-statmod, r-cran-mass, r-cran-data.table, r-cran-pbv, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-panelselect_1.0.0-1.ca2404.1_amd64.deb Size: 158668 MD5sum: e90c849475eba8beffe682fde6aa810f SHA1: 5535511cf8a752a992d3075be0ddff257c7f43a4 SHA256: 63bf5af60243998efdf563a2893972b6337c6d4f6b844f388f621c003944a8b8 SHA512: b47f45b0f41540b5436ab85d3f5a15d7ff3db291fd80e894e5a8706bfb46b52acdbc1f69993ff05305cbe192da8f1ca8583ac43315f63a8d6f014526754ab74f 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. 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Package: r-cran-panprsnext Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1934 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-panprsnext_1.2.1-1.ca2404.1_amd64.deb Size: 1796796 MD5sum: bed224468dbd554c9a35f9508f5a3bee SHA1: 5e40df9aeb7775b28939e8ea2e15eb3b7ab68ec4 SHA256: e44579cfdf46f27df381b08a506a5c4dd8648ebb466693682c49461bf9888c7b SHA512: b68dc56a57a746aa438b3d4f9c8bb7bd4a7d605095072dbb1f5ca568feb3fa46d7660595ca65764f23eb0b105e68cbc2d20aec0350119a9363fd3ce22f3a4303 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) . 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For ease of use, the 'parDist' function extends the signature of the 'dist' function and uses the same parameter naming conventions as distance methods of existing R packages. The package is mainly implemented in C++ and leverages the 'RcppParallel' package to parallelize the distance computations with the help of the 'TinyThread' library. Furthermore, the 'Armadillo' linear algebra library is used for optimized matrix operations during distance calculations. The curiously recurring template pattern (CRTP) technique is applied to avoid virtual functions, which improves the Dynamic Time Warping calculations while the implementation stays flexible enough to support different DTW step patterns and normalization methods. Package: r-cran-parallelly Architecture: amd64 Version: 1.47.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 982 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-commonmark, r-cran-base64enc Filename: pool/dists/noble/main/r-cran-parallelly_1.47.0-1.ca2404.1_amd64.deb Size: 605202 MD5sum: 1e820545a41c48543d5a477c000d8a9c SHA1: df17498c6832f625a5b23b0ab911e13eb3817e93 SHA256: dd2d37cd6d4395bf65d48b5fa9633650c7ede3ad518e8e1e528e00f8f6cf2ead SHA512: 6b6df582a6eff7c311ee4a536f3d9e4d809875bf3e427470287bc625ea68d95bd0b3cf29a69e1ab0bf9ff4d7243e6dd2f71bd37c3c861efcfa98f9a51d10b988 Homepage: https://cran.r-project.org/package=parallelly Description: CRAN Package 'parallelly' (Enhancing the 'parallel' Package) Utility functions that enhance the 'parallel' package and support the built-in parallel backends of the 'future' package. For example, availableCores() gives the number of CPU cores available to your R process as given by the operating system, 'cgroups' and Linux containers, R options, and environment variables, including those set by job schedulers on high-performance compute clusters. If none is set, it will fall back to parallel::detectCores(). Another example is makeClusterPSOCK(), which is backward compatible with parallel::makePSOCKcluster() while doing a better job in setting up remote cluster workers without the need for configuring the firewall to do port-forwarding to your local computer. Package: r-cran-parallelpam Architecture: amd64 Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2077 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-memuse Suggests: r-cran-knitr, r-cran-cluster Filename: pool/dists/noble/main/r-cran-parallelpam_1.4.3-1.ca2404.1_amd64.deb Size: 506198 MD5sum: 522784954e1209ba2f6722362666d5ae SHA1: b2c493ec6efddfd6d25f3655193fd234b1020747 SHA256: 9ba026ab8732ad530d02a21d55f7a9242a173008fc7e88db3518b4de19dc51cf SHA512: 0a2fd5f64f29f6fad8cabd5693a8d55ec4abc9938c02460a392f412f2e50a94652d2f1a8ed457997c7520434db136c9cd4a5781af64b85e760644e0035bb3569 Homepage: https://cran.r-project.org/package=parallelpam Description: CRAN Package 'parallelpam' (Parallel Partitioning-Around-Medoids (PAM) for Big Sets of Data) Application of the Partitioning-Around-Medoids (PAM) clustering algorithm described in Schubert, E. and Rousseeuw, P.J.: "Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms." Information Systems, vol. 101, p. 101804, (2021). . It uses a binary format for storing and retrieval of matrices developed for the 'jmatrix' package but the functionality of 'jmatrix' is included here, so you do not need to install it. Also, it is used by package 'scellpam', so if you have installed it, you do not need to install this package. PAM can be applied to sets of data whose dissimilarity matrix can be very big. It has been tested with up to 100.000 points. It does this with the help of the code developed for other package, 'jmatrix', which allows the matrix not to be loaded in 'R' memory (which would force it to be of double type) but it gets from disk, which allows using float (or even smaller data types). Moreover, the dissimilarity matrix is calculated in parallel if the computer has several cores so it can open many threads. The initial part of the PAM algorithm can be done with the BUILD or LAB algorithms; the BUILD algorithm has been implemented in parallel. The optimization phase implements the FastPAM1 algorithm, also in parallel. Finally, calculation of silhouette is available and also implemented in parallel. 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Therefore, using 'SUNDIALS' to solve the ODE-System (see Hindmarsh, Alan C., Peter N. Brown, Keith E. Grant, Steven L. Lee, Radu Serban, Dan E. Shumaker, and Carol S. Woodward. (2005) ). Furthermore, for optimization the particle swarm algorithm is used (see: Akman, Devin, Olcay Akman, and Elsa Schaefer. (2018) and Sengupta, Saptarshi, Sanchita Basak, and Richard Peters. (2018) ). 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Package: r-cran-partimeroc Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2706 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), 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/noble/main/r-cran-partimeroc_0.2.0-1.ca2404.1_amd64.deb Size: 960154 MD5sum: f6d449b7c8bb8ad64499e365ee363bf3 SHA1: b42c2205d42e2a2107252574f84a1c2b301ced2e SHA256: 7506826ea8b07998759ab83bf4e1fb9aca7dbed77adced9cb490711144f7abfd SHA512: 67bd9914036c1497718bcac3b88acfaa439ba2efb9c154db93eea5f8b4f13a67e4b3b676b2fe642370b557a6a00b8f4870edc8d20411cf432caf12abd1333660 Homepage: https://cran.r-project.org/package=parTimeROC Description: CRAN Package 'parTimeROC' (Parametric Time-Dependent Receiver Operating Characteristic) Producing the time-dependent receiver operating characteristic (ROC) curve through parametric approaches. Tools for generating random data, fitting, predicting and check goodness of fit are prepared. The methods are developed from the theoretical framework of proportional hazard model and copula functions. Using this package, users can now simulate parametric time-dependent ROC and run experiment to understand the behavior of the curve under different scenario. Package: r-cran-partition Architecture: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2337 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-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/noble/main/r-cran-partition_0.2.2-1.ca2404.1_amd64.deb Size: 1575616 MD5sum: eddc863f5c1a67c6345bb579abd86412 SHA1: 796ca58048e1f08e4f615b51d6cae112572046d5 SHA256: 757fff308a53f1919c391efdb8cd7fea053092dc3a77029f7e1efeb4f87331b1 SHA512: 00647c805629c4acbbb69fd22046c7e669381726ee501af3eb04e632ab3584597567b1f43008443b99d86864f15e2838101d8169b85d9ffb2d8a4b29f1e872b8 Homepage: https://cran.r-project.org/package=partition Description: CRAN Package 'partition' (Agglomerative Partitioning Framework for Dimension Reduction) A fast and flexible framework for agglomerative partitioning. 'partition' uses an approach called Direct-Measure-Reduce to create new variables that maintain the user-specified minimum level of information. Each reduced variable is also interpretable: the original variables map to one and only one variable in the reduced data set. 'partition' is flexible, as well: how variables are selected to reduce, how information loss is measured, and the way data is reduced can all be customized. 'partition' is based on the Partition framework discussed in Millstein et al. (2020) . Package: r-cran-partitions Architecture: amd64 Version: 1.10-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 647 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-gmp, r-cran-polynom, r-cran-sets, r-cran-rdpack Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-partitions_1.10-9-1.ca2404.1_amd64.deb Size: 511092 MD5sum: 01fe7a58a9f765615f8628a37e0b2e6d SHA1: 6aeb7094c5d9dc410cf3c26bf61dc8bccf568242 SHA256: df4838a9ce5ede263a8c438a82c926085b5d8cbd53289a4a6a1d020fc7b47866 SHA512: 3fc04f0e63d0a8ca122004f7803ce1b423e6f8971b7e84f0a4b01136d246cbb1d289536380db9820f2e3889652769b350e651d0cf60c3bc7f538455d0a14f758 Homepage: https://cran.r-project.org/package=partitions Description: CRAN Package 'partitions' (Additive Partitions of Integers) Additive partitions of integers. Enumerates the partitions, unequal partitions, and restricted partitions of an integer; the three corresponding partition functions are also given. Set partitions and now compositions and riffle shuffles are included. Package: r-cran-party Architecture: amd64 Version: 1.3-20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1193 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-modeltools, r-cran-strucchange, r-cran-survival, r-cran-coin, r-cran-zoo, r-cran-sandwich Suggests: r-cran-th.data, r-cran-mlbench, r-cran-colorspace, r-cran-mass, r-cran-vcd, r-cran-ipred, r-cran-varimp, r-cran-randomforest, r-cran-lattice, r-cran-aer, r-cran-bibtex Filename: pool/dists/noble/main/r-cran-party_1.3-20-1.ca2404.1_amd64.deb Size: 900180 MD5sum: b9e892fd95c66b215ceb65cf4d578213 SHA1: 1f9c86996dd8c3ae75c804d0bc3bf357c09fac01 SHA256: 6a93ac164dbb8c2b3e148f03c4d25eddb2dbfd0c7e246d8aa8192d92851d3eb1 SHA512: d45be0bd67320fc2786c57e947aa4f44442f425eef53aafc0d56455bab5491f4e17f3350dec8544fe2cfce8d769cce8cb05d9adebd00e33cb4a2725e5791133a Homepage: https://cran.r-project.org/package=party Description: CRAN Package 'party' (A Laboratory for Recursive Partytioning) A computational toolbox for recursive partitioning. The core of the package is ctree(), an implementation of conditional inference trees which embed tree-structured regression models into a well defined theory of conditional inference procedures. This non-parametric class of regression trees is applicable to all kinds of regression problems, including nominal, ordinal, numeric, censored as well as multivariate response variables and arbitrary measurement scales of the covariates. Based on conditional inference trees, cforest() provides an implementation of Breiman's random forests. The function mob() implements an algorithm for recursive partitioning based on parametric models (e.g. linear models, GLMs or survival regression) employing parameter instability tests for split selection. Extensible functionality for visualizing tree-structured regression models is available. The methods are described in Hothorn et al. (2006) , Zeileis et al. (2008) and Strobl et al. (2007) . Package: r-cran-partykit Architecture: amd64 Version: 1.2-27-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3182 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-libcoin, r-cran-mvtnorm, r-cran-survival, r-cran-formula, r-cran-inum, r-cran-rpart Suggests: r-cran-xml, r-cran-rjava, r-cran-sandwich, r-cran-strucchange, r-cran-vcd, r-cran-th.data, r-cran-mlbench, r-cran-aer, r-cran-coin, r-cran-party, r-cran-rweka, r-cran-psychotools, r-cran-psychotree, r-cran-randomforest, r-cran-knitr, r-cran-bibtex Filename: pool/dists/noble/main/r-cran-partykit_1.2-27-1.ca2404.1_amd64.deb Size: 2344732 MD5sum: 256cbca5f789de25b9c8ca86845648e9 SHA1: 0b0c3887d5e52b19de00e892791cce2380d58ede SHA256: bb0d08901a03471f91437fcc192c47c73e1ce46ddf2208e9dcfad33b77c1d076 SHA512: 9f5a6c3fbbe7eb63db3b4b60e832f77d1a739fe17bc2b9e2b30fb8fe0fbb583c27d1141c77d7207a85ff34cbb9368457a21d69b26de152976bc858ca89ce74a7 Homepage: https://cran.r-project.org/package=partykit Description: CRAN Package 'partykit' (A Toolkit for Recursive Partytioning) A toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models. This unified infrastructure can be used for reading/coercing tree models from different sources ('rpart', 'RWeka', 'PMML') yielding objects that share functionality for print()/plot()/predict() methods. Furthermore, new and improved reimplementations of conditional inference trees (ctree()) and model-based recursive partitioning (mob()) from the 'party' package are provided based on the new infrastructure. A description of this package was published by Hothorn and Zeileis (2015) . Package: r-cran-parzer Architecture: amd64 Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 918 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-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/noble/main/r-cran-parzer_0.4.4-1.ca2404.1_amd64.deb Size: 279068 MD5sum: 7a2ef1b68e5b26b978922340112d1f8c SHA1: dee02e056f15c516c7600ca6df9a7b3eae4a5b62 SHA256: 3e2e4ba99450cf73b4e59a330f07b8e25a8621eacac716f8a082bdb45afeb488 SHA512: f0150fa8fe88d6981844d959aaf8b9d5d8420f5cc365225625e78a3edee260b51ef7406ab4b5586554d140e8a38f89ba95497a261f3b0ced01b66b719af27919 Homepage: https://cran.r-project.org/package=parzer Description: CRAN Package 'parzer' (Parse Messy Geographic Coordinates) Parse messy geographic coordinates from various character formats to decimal degree numeric values. Parse coordinates into their parts (degree, minutes, seconds); calculate hemisphere from coordinates; pull out individually degrees, minutes, or seconds; add and subtract degrees, minutes, and seconds. C++ code herein originally inspired from code written by Jeffrey D. Bogan, but then completely re-written. 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This package provides a set of tools to quantify, visualize, and test partial associations between multiple ordinal variables. It can produce a number of $phi$ measures, partial regression plots, 3-D plots, and $p$-values for testing $H_0: phi=0$ or $H_0: phi <= delta$. Package: r-cran-pastboon Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-boolnet Filename: pool/dists/noble/main/r-cran-pastboon_0.1.4-1.ca2404.1_amd64.deb Size: 93480 MD5sum: 321484df834f41b735db64a4f87d6a81 SHA1: 9f555d8530c4cb5ee386b2f0c2df549fcbc10c04 SHA256: d64bfdb5051305769df9df115862e3db511e335ca1eb477882d0a1fec9c7ac35 SHA512: dbc26415073f60e10e3aeda8a01875c51175efbe27bfc683bbf428e8c774b22189c3f69ee863acac5fa3bf1f2d89937129f6fa6da5c10ae653c77869569de461 Homepage: https://cran.r-project.org/package=pastboon Description: CRAN Package 'pastboon' (Simulation of Parameterized Stochastic Boolean Networks) A Boolean network is a particular kind of discrete dynamical system where the variables are simple binary switches. Despite its simplicity, Boolean network modeling has been a successful method to describe the behavioral pattern of various phenomena. Applying stochastic noise to Boolean networks is a useful approach for representing the effects of various perturbing stimuli on complex systems. A number of methods have been developed to control noise effects on Boolean networks using parameters integrated into the update rules. This package provides functions to examine three such methods: Boolean network with perturbations (BNp), described by Trairatphisan et al. (2013) , stochastic discrete dynamical systems (SDDS), proposed by Murrugarra et al. (2012) , and Boolean network with probabilistic edge weights (PEW), presented by Deritei et al. (2022) . This package includes source code derived from the 'BoolNet' package, which is licensed under the Artistic License 2.0. Package: r-cran-patchdvi Architecture: amd64 Version: 1.11.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1058 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmdconcord Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-patchdvi_1.11.3-1.ca2404.1_amd64.deb Size: 487240 MD5sum: f5bc2c9c4e2f6906d8b902bda98c18a7 SHA1: 6c29d6e9ed4d00081d102bef1789c88621554e43 SHA256: 9e1af595e63a11289e82b73274ce1b1d1f55de6e9481bc60cb8fa01c2bd378b2 SHA512: a94a828983889ccfb922437c0e8ac76f7e04a54dd4fdcd8203dbf482f1de410a627d52753915ec1f7db07e2d1d693842e63ff8992965dad651be77fdcb7a6568 Homepage: https://cran.r-project.org/package=patchDVI Description: CRAN Package 'patchDVI' (Package to Patch '.dvi' or '.synctex' Files) Functions to patch specials in '.dvi' files, or entries in '.synctex' files. Works with concordance=TRUE in Sweave, knitr or R Markdown to link sources to previews. 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Allows conversion of bulk data after downloading directly from the USPTO bulk data website, eliminating need for users to wrangle multiple data formats to get large patent databases in tidy, rectangular format. Data details can be found on the USPTO website . Currently, all 3 formats: 1. TXT data (1976-2001); 2. XML format 1 data (2002-2004); and 3. XML format 2 data (2005-current) can be converted to rectangular, CSV format. Relevant literature that uses data from USPTO includes Wada (2020) and Plaza & Albert (2008) . Package: r-cran-pathling Architecture: amd64 Version: 9.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1461 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-sparklyr, r-cran-jsonlite, r-cran-purrr Suggests: r-cran-testthat, r-cran-lintr, r-cran-styler Filename: pool/dists/noble/main/r-cran-pathling_9.6.0-1.ca2404.1_amd64.deb Size: 243966 MD5sum: 030782d2b0ee471a046424edec18bf03 SHA1: 129a6d9f1ad79c9e24e6adab54b0461604440ecc SHA256: 7cf7a44974e84ed078b7aa7c1e2d444fbfa8629ed811ef203b9db2836dddf306 SHA512: d3a580f8f895db34a6a02f3079c3ea72e5747274882f96014c7ccc469f56b35050a90d26d675ca422e165f15275a77b21f05361be7105af1f90d93e6a43cc074 Homepage: https://cran.r-project.org/package=pathling Description: CRAN Package 'pathling' (A Library for using 'Pathling') R API for 'Pathling', a tool for querying and transforming electronic health record data that is represented using the 'Fast Healthcare Interoperability Resources' (FHIR) standard - see . 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Package: r-cran-pbdmpi Architecture: amd64 Version: 0.5-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1075 Depends: libc6 (>= 2.34), libopenmpi3t64 (>= 4.1.6), r-base-core (>= 4.5.0), r-api-4.0, r-cran-float Filename: pool/dists/noble/main/r-cran-pbdmpi_0.5-5-1.ca2404.1_amd64.deb Size: 729132 MD5sum: 1198327fe3312e83d175a61779e60531 SHA1: bbc554a56b627e2a929f91421e9925e218f4daa7 SHA256: ad7346992995817fc41ae31b720e8dafa8fb331b64d988c4db766c93296cfd2b SHA512: 248c04beef8798c716b82ce81366c4ca7b23756f9ff98227888297ed9f48072447eecae583cf3ed32b86303262731b28cadaf3086cc815e2bc3d1111f07ca92f Homepage: https://cran.r-project.org/package=pbdMPI Description: CRAN Package 'pbdMPI' (R Interface to MPI for HPC Clusters (Programming with Big DataProject)) A simplified, efficient, interface to MPI for HPC clusters. It is a derivation and rethinking of the Rmpi package. pbdMPI embraces the prevalent parallel programming style on HPC clusters. 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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: amd64 Version: 2.7-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5264 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-bioc-graph, r-bioc-rbgl, r-cran-igraph, r-cran-ggm, r-cran-corpcor, r-cran-robustbase, r-cran-vcd, r-cran-rcpp, r-cran-bdsmatrix, r-cran-sfsmisc, r-cran-fastica, r-cran-clue, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-mass, r-cran-matrix, r-bioc-rgraphviz, r-cran-mvtnorm, r-cran-huge, r-cran-ggplot2, r-cran-dagitty Filename: pool/dists/noble/main/r-cran-pcalg_2.7-12-1.ca2404.1_amd64.deb Size: 4818282 MD5sum: 26bfe3c8b47abfde091b3aa9fe53f388 SHA1: d276521733b1733c995021b15b7f590e79b2226d SHA256: bcc00b0f72a7af1b0963fb187ac4659dfd608ee9c0824a4e76f26af4c9110b38 SHA512: da5ca2cdf327c9e1e8c24c3cac80348ee96fcda03d62f27d457cf03eb3cc419bfc20903c3b81a75222c11fd58df1704eca2c06b0bb5f2e133ef2295d8e300e03 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: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pcaone_1.1.0-1.ca2404.1_amd64.deb Size: 123596 MD5sum: e76fbe364fd2a17bae027ecb5352f467 SHA1: 39ab5c8b9d3cb5f2e69a641d151c1fe0bf3c1f85 SHA256: ff53a07fe9f3d82d016a98d860b3d49c0aa70f7d614e69a59e1b0f2a01431a4a SHA512: 7500ed6b1997d77138d64537deee8eeae43138c178f6c8456af636601e44e92e5bc27d3bad648552cb67783d2e47563a19a0f0d5f76034dc4e31e3035f852bba 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: amd64 Version: 2.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 502 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-robustbase Filename: pool/dists/noble/main/r-cran-pcapp_2.0-5-1.ca2404.1_amd64.deb Size: 363730 MD5sum: 0d42802765bb88250f0b39782d5dd285 SHA1: 2d91e0ca8be546ba08aab8bb825236c9c55f0f20 SHA256: 19e89a36df178053c72b4393543f5f5c280978d99708a4a442cf4d6665f0a28a SHA512: e8b1f6aa2d80a1ae4cc4612491c947d8514ee5651992b3d2476ffea4c4b79b09265a0488b9e2317d65f45ac853a6eb54aee00ed3b00f9f250de170f09f82530c 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) . 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Package: r-cran-pcirt Architecture: amd64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-combinat Suggests: r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-pcirt_0.2.4-1.ca2404.1_amd64.deb Size: 194304 MD5sum: 83d129001181c19e065e168b6774a630 SHA1: 38a448313f04392db01a9146721bd79e51e215a7 SHA256: a050930634c56178a0e1b5de9065a3cbd626193e700b49c4b7603e2c0d3b16ff SHA512: 9e640021189656d71dd7723df80fb2b48a8ca28217c15116b780d6f96b7133f61bc8bffefd1c3671ecb05003e15d4dac904185b1220b5a71369adcbbba99ec60 Homepage: https://cran.r-project.org/package=pcIRT Description: CRAN Package 'pcIRT' (IRT Models for Polytomous and Continuous Item Responses) Estimates the multidimensional polytomous Rasch model (Rasch, 1961) with conditional maximum likelihood estimation. Package: r-cran-pclasso Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-svd Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pclasso_1.2-1.ca2404.1_amd64.deb Size: 144848 MD5sum: fe761189140134a13f2126d7a9a97eb7 SHA1: 0cb4f8dbaad6e84be65447f48df9b2c131ba7bbf SHA256: 611edc161fc414f3a96c01b8b1286cd3485d0a1a6bb828b4d2a9a2bd2028191c SHA512: 37e5bfac9b00d650481237447b3d404ef7308608a369c47bb6b10ddeeca6585106d66ed3e9253367eeca414f4e48fcd0cb1e06c5e9449f342c5fdfc47d4a1173 Homepage: https://cran.r-project.org/package=pcLasso Description: CRAN Package 'pcLasso' (Principal Components Lasso) A method for fitting the entire regularization path of the principal components lasso for linear and logistic regression models. The algorithm uses cyclic coordinate descent in a path-wise fashion. See URL below for more information on the algorithm. See Tay, K., Friedman, J. ,Tibshirani, R., (2014) 'Principal component-guided sparse regression' . Package: r-cran-pcmbasecpp Architecture: amd64 Version: 0.1.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3715 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-pcmbase, r-cran-data.table, r-cran-abind, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-pcmbasecpp_0.1.12-1.ca2404.1_amd64.deb Size: 1448522 MD5sum: 461d01326767ccc8b16a171fe68c355c SHA1: 86bc68dece7333b6538f52b592ba823a05af30a0 SHA256: 766c847ab092d97efe4fa304fc7f6554a9cd3f1f7ea7099df609b1f4837dc68c SHA512: 3bc1f093575e3e4cffe87ed43848b81deab292166a77935298e74f04a32138182e956bf7f4fca097d14247ab9ccdb0a06339f29fdda95318ae3106b48df58691 Homepage: https://cran.r-project.org/package=PCMBaseCpp Description: CRAN Package 'PCMBaseCpp' (Fast Likelihood Calculation for Phylogenetic Comparative Models) Provides a C++ backend for multivariate phylogenetic comparative models implemented in the R-package 'PCMBase'. Can be used in combination with 'PCMBase' to enable fast and parallel likelihood calculation. Implements the pruning likelihood calculation algorithm described in Mitov et al. (2020) . Uses the 'SPLITT' C++ library for parallel tree traversal described in Mitov and Stadler (2018) . Package: r-cran-pcmrs Architecture: amd64 Version: 0.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ltm, r-cran-statmod, r-cran-cubature, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-pcmrs_0.1-5-1.ca2404.1_amd64.deb Size: 139068 MD5sum: f16f82aa17398294239405d8b7726505 SHA1: d9a3ef8a8e553e9caa078ff82175ed3ec96fe020 SHA256: b36a95da5d3bc50f0dd4cdfe6609bde0aca7ab83d6184da910afb1b32e65f2e9 SHA512: d7edfcdf818d8cf73dc56f2065ea9024bff76abc22c826b0f96579fa00716db6d6cc56718ea0114d659bc725de119a3338e8c528fb924e25d07cb112f0fb2c73 Homepage: https://cran.r-project.org/package=PCMRS Description: CRAN Package 'PCMRS' (Model Response Styles in Partial Credit Models) Implementation of PCMRS (Partial Credit Model with Response Styles) as proposed in by Tutz, Schauberger and Berger (2018) . PCMRS is an extension of the regular partial credit model. PCMRS allows for an additional person parameter that characterizes the response style of the person. By taking the response style into account, the estimates of the item parameters are less biased than in partial credit models. Package: r-cran-pcobw Architecture: amd64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 565 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-pcobw_0.0.1-1.ca2404.1_amd64.deb Size: 208550 MD5sum: fa70128e569c5a051b898ef1b7f44f8a SHA1: b0101cfaf5c1991a8bde92a30b93c9e858de101c SHA256: edb2b2efc0c82f9acbacd05daf0625b439ed4a3398d27a5856e853042c0e028b SHA512: 2e22d0b0bb8ba962ea61ba9b1acb70a9e1b2d6f8113a141e2d26fec77716725aacca7af797d58eae285ae756eee78fc7f5df6c72a265184361a96d9e7ad1f0ff Homepage: https://cran.r-project.org/package=PCObw Description: CRAN Package 'PCObw' (Bandwidth Selector with Penalized Comparison to OverfittingCriterion) Bandwidth selector according to the Penalised Comparison to Overfitting (P.C.O.) criterion as described in Varet, S., Lacour, C., Massart, P., Rivoirard, V., (2019) . It can be used with univariate and multivariate data. Package: r-cran-pcplus Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 364 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-changepoint, r-cran-matrix, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-pcplus_1.0.1-1.ca2404.1_amd64.deb Size: 151348 MD5sum: 1632eb7e32b83c61ba62e37f92061845 SHA1: 5fce4d90cd3325fcd4d3d53c9c3225572c4d3ab0 SHA256: 8073fe3b300c968c87f9f3c5a5ba09f77c178eef91b657adc30c3a94ea0dfd11 SHA512: 2bf4266f28e29f3f3b0e0b9770289a6c9ecffa8fa5ba5866c23f664401a54f6016278c7f10d642dd26fc10a841979ad1d4bdc4c5efd02c0233078f12ffbc9943 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: amd64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1169 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-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/noble/main/r-cran-pda_1.3.0-1.ca2404.1_amd64.deb Size: 881548 MD5sum: 3d918d8fbce723d35e24995fefe1ec7e SHA1: 8c0fbdf9ca2f0a1a7dbcbfdb1a884ad4890580f5 SHA256: 4508a3047980ec0cc2d37b231e943fb5a04576ab7d4d946bbf6dfeefe07b7476 SHA512: 0a35ea835d1f09bbab73ea08839ff789dd3705ce00e5ab82be1e14d25cabf92d1cfcf5fcadd91544c00137ad8584ec10dfd79b9e1005486e22662e9c42fe6401 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 . 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Stier, Q., Hoffmann, J., and Thrun, M.C.: "Classifying with the Fine Structure of Distributions: Leveraging Distributional Information for Robust and Plausible Naive Bayes" (2026), Machine Learning and Knowledge Extraction (MAKE), . 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Package: r-cran-pecv Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-irlba, r-cran-rcpparmadillo Suggests: r-cran-mirtjml, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pecv_1.0.1-1.ca2404.1_amd64.deb Size: 136308 MD5sum: 52bce79d013563c823538dc31bdb1be7 SHA1: c17776ba839f174559a684227278d6da6271c229 SHA256: 1f73b6fe9c1056b4d33c63480705a9247d88191339b66732300e0a6182552f7d SHA512: 2ad65c996a64c1a76094d36d27d69aeabf9acd8c0497f8d3575fa9fa5e5b796ac5549cc755221944040694c0c27dfc26b48acef5d2dceabd3fbb5a858034add9 Homepage: https://cran.r-project.org/package=pECV Description: CRAN Package 'pECV' (Entrywise Splitting Cross-Validation for Factor Models) Implements entrywise splitting cross-validation (ECV) and its penalized variant (pECV) for selecting the number of factors in generalized factor models. Package: r-cran-pedbp Architecture: amd64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3507 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp, r-cran-scales, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-data.table, r-cran-dt, r-cran-digest, r-cran-ggpubr, r-cran-gridextra, r-cran-knitr, r-cran-markdown, r-cran-png, r-cran-qwraps2, r-cran-rmarkdown, r-cran-shiny, r-cran-shinybs, r-cran-shinydashboard Filename: pool/dists/noble/main/r-cran-pedbp_2.1.0-1.ca2404.1_amd64.deb Size: 1864112 MD5sum: 35bf668d1371fc634124b46963ed7ac0 SHA1: f16e52d2929f93e58b65953dbfcddc53276c704d SHA256: 64a3f6bcfc6deece1ff897bbb9aa1109806728c8b10d8f5ab5844b4c7d3d8338 SHA512: 579a7fb82da0213616e881f05832c862c929935e4f04245ba7ceea6fee5ed064b317a2b91a9cd932ecca998605bdb9b6f3616c49fca015818b65350928f21cf7 Homepage: https://cran.r-project.org/package=pedbp Description: CRAN Package 'pedbp' (Pediatric Blood Pressure) Data and utilities for estimating pediatric blood pressure percentiles by sex, age, and optionally height (stature) as described in Martin et al. (2022) . Blood pressure percentiles for children under one year of age come from Gemelli et al. (1990) . Estimates of blood pressure percentiles for children at least one year of age are informed by data from the National Heart, Lung, and Blood Institute (NHLBI) and the Centers for Disease Control and Prevention (CDC) or from Lo et al. (2013) . The source-selection flowchart comes from Martin et al. (2022) . Package: r-cran-pedigree Architecture: amd64 Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-haplosim, r-cran-reshape Filename: pool/dists/noble/main/r-cran-pedigree_1.4.2-1.ca2404.1_amd64.deb Size: 66204 MD5sum: 4d20248a4f06acb4b29905207049bb44 SHA1: bcf322b8f6cba8492a8c74e43dcf33b04a0a9445 SHA256: a192e5d174857651afe70fd042d0f6fcb44bf814d56fdf8787f80e25711efdf2 SHA512: 76557c0aa59d33e9aca6650475b17f5b45adb00c0114239abafd472e558ca21de67880d525751c26640031351a0d9dfa7868f6621db2a2e875ad425a819ab359 Homepage: https://cran.r-project.org/package=pedigree Description: CRAN Package 'pedigree' (Pedigree Functions) Pedigree related functions. Package: r-cran-pedigreemm Architecture: amd64 Version: 0.3-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1550 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-matrix Filename: pool/dists/noble/main/r-cran-pedigreemm_0.3-5-1.ca2404.1_amd64.deb Size: 1390470 MD5sum: 681afa17c05801292915369641ef8664 SHA1: dd7bfedf81c056f948443dde88d8ea6fd2b0473f SHA256: 4ad1d5b61644f8c964599c2335ecd7c4171b32606880b2399e85b030c4a2fc49 SHA512: adc34e1bb20c2089c34e0514519d857b5d9da6d3de06ec3d6a2df6b0a9ef7e74dd7296b2787d87ac6d74542921d113571938cab7129cc38019c84a12899854d4 Homepage: https://cran.r-project.org/package=pedigreemm Description: CRAN Package 'pedigreemm' (Pedigree-Based Mixed-Effects Models) Fit pedigree-based mixed-effects models. Package: r-cran-pedigreetools Architecture: amd64 Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-pedigreetools_0.3-1.ca2404.1_amd64.deb Size: 305320 MD5sum: 25343da8c97d37cee2cee917ba5cad5f SHA1: 8091e17f304e29d4c5c453842c4a69eaae24b0cb SHA256: 7d3c9d1defb213d28a2b0f4ca9f19c23a39167df8f05bf47bd013a206edd89c3 SHA512: a95c450b23b751977fa00715fafadf88281ca45ba1c4d929f7ab6381d6d32ad92019c4e9a902ecb3674268d9fda1ab7f5e0b4befa1072514c57433c680d27c31 Homepage: https://cran.r-project.org/package=pedigreeTools Description: CRAN Package 'pedigreeTools' (Versatile Functions for Working with Pedigrees) Tools to sort, edit and prune pedigrees and to extract the inbreeding coefficients and the relationship matrix (includes code for pedigrees from self-pollinated species). The use of pedigree data is central to genetics research within the animal and plant breeding communities to predict breeding values. The relationship matrix between the individuals can be derived from pedigree structure ('Vazquez et al., 2010') . Package: r-cran-pedmod Architecture: amd64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5628 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-alabama, r-cran-rcpparmadillo, r-cran-bh, r-cran-testthat, r-cran-psqn Suggests: r-cran-mvtnorm, r-cran-xml2, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-abind, r-cran-kinship2, r-cran-igraph, r-cran-truncatednormal, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-pedmod_0.2.4-1.ca2404.1_amd64.deb Size: 2709678 MD5sum: f7861aa36f0e8c7782f59cabd2cbfc42 SHA1: 000f8fcf37c9bea03b56777cb343f601fb90a4af SHA256: fe8ad81cbda20eec2147c02863cfd666cc5dd1334826a88709982b64cb9a12e0 SHA512: d20fdb6ebf5ef1739e73a4b5c412537cc6579eccab1d6a585f027b0437596817774586ccd94d98680c47039c14020118b837db9267b4c289ced39871e366c1e4 Homepage: https://cran.r-project.org/package=pedmod Description: CRAN Package 'pedmod' (Pedigree Models) Provides functions to estimate mixed probit models using, for instance, pedigree data like in . The models are also commonly called liability threshold models. The approximation is based on direct log marginal likelihood approximations like the randomized Quasi-Monte Carlo suggested by with a similar procedure to approximate the derivatives. The minimax tilting method suggested by is also supported. Graph-based methods are also provided that can be used to simplify pedigrees. Package: r-cran-pedometrics Architecture: amd64 Version: 0.12.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 364 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-latticeextra, r-cran-rcpp Suggests: r-cran-car, r-cran-fields, r-cran-georob, r-cran-gstat, r-cran-knitr, r-cran-mass, r-cran-sp, r-cran-spatialtools Filename: pool/dists/noble/main/r-cran-pedometrics_0.12.1-1.ca2404.1_amd64.deb Size: 242226 MD5sum: 20383930536dc71d25f468748b12ee20 SHA1: cb57bfd96686c77d1c45f3f163bfb432f348aa15 SHA256: 4f851b9a6504327ccb3e05c8111ee38327c3879543f3384c6e2cd80de8066f81 SHA512: 79dd598cd460d2f2e4089060d6260fbbf1cac975d911255df321365bdf0ab89e5966deecb59b6dbcbca0093a3fe1ba9059c8e2a44a7791a6b8dabb3e78e626a3 Homepage: https://cran.r-project.org/package=pedometrics Description: CRAN Package 'pedometrics' (Miscellaneous Pedometric Tools) An R implementation of methods employed in the field of pedometrics, soil science discipline dedicated to studying the spatial, temporal, and spatio-temporal variation of soil using statistical and computational methods. The methods found here include the calibration of linear regression models using covariate selection strategies, computation of summary validation statistics for predictions, generation of summary plots, evaluation of the local quality of a geostatistical model of uncertainty, and so on. Other functions simply extend the functionalities of or facilitate the usage of functions from other packages that are commonly used for the analysis of soil data. Formerly available versions of suggested packages no longer available from CRAN can be obtained from the CRAN archive . Package: r-cran-pegas Architecture: amd64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 954 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape Suggests: r-cran-rgl, r-bioc-snpstats, r-cran-adegenet Filename: pool/dists/noble/main/r-cran-pegas_1.4-1.ca2404.1_amd64.deb Size: 828748 MD5sum: 10350f18d3e66ca7b3e791fdf5730793 SHA1: a07ecda543ae9cd06606e0e614245d0c1ccccd91 SHA256: 9e0a1bf14831e20cef68f09d987e6ef52760357861c198f44cf6276c318d5f7a SHA512: 1b98556083b35f7413480cf22c61ae2c8dcd1e838e590855b6c42e8fbfddf26a36f2a773fd4d3a38cd9b435aa9b88350313dacce6dfb1bdb22f1d58753514162 Homepage: https://cran.r-project.org/package=pegas Description: CRAN Package 'pegas' (Population and Evolutionary Genetics Analysis System) Functions for reading, writing, plotting, analysing, and manipulating allelic and haplotypic data, including from VCF files, and for the analysis of population nucleotide sequences and micro-satellites including coalescent analyses, linkage disequilibrium, population structure (Fst, Amova) and equilibrium (HWE), haplotype networks, minimum spanning tree and network, and median-joining networks. Package: r-cran-pegs Architecture: amd64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pegs_0.2-1.ca2404.1_amd64.deb Size: 129814 MD5sum: c90ce332e3545e3c2456a75cd9a30569 SHA1: f6598f1427077094c3ae7937721dafba794b1a68 SHA256: c358fd950856c6ad256843147264ff507ff8e87a3e0323613c27f44756f1504a SHA512: ee78ddb0459884fec9495a4d69fac9bbf663c6005c8a759c028fa0a05c24ce829faf14830301fd98626b45a8778ed0058b9b120b57c6dd98b227dd5ffd2d0800 Homepage: https://cran.r-project.org/package=pegs Description: CRAN Package 'pegs' (Pseudo-Expectation Gauss-Seidel) A lightweight, dependency-free, and simplified implementation of the Pseudo-Expectation Gauss-Seidel (PEGS) algorithm. It fits the multivariate ridge regression model for genomic prediction Xavier and Habier (2022) and Xavier et al. (2025) , providing heritability estimates, genetic correlations, breeding values, and regression coefficient estimates for prediction. This package provides an alternative to the 'bWGR' package by Xavier et al. (2019) by using 'LAPACK' for its algebraic operations. Package: r-cran-pema Architecture: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9782 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-rcpp, r-cran-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/noble/main/r-cran-pema_0.1.5-1.ca2404.1_amd64.deb Size: 2147316 MD5sum: 8d3b47d95ae4ae4e33811747ff8f5b62 SHA1: 754741477152ebf0503384970bb48d3fee45726e SHA256: 5d4b9009f7863c7fa676a610b13cdf0c4f9a8d6a39f5c35b8e029555cdabc4f4 SHA512: cebb9aa828012b6a9017ab0f5f9240bb5af1b94a44660182be3aa3741e3c1bb29e681e92c21292c6fc4cac1c9b89a2c677564e75613cc1b6a21cf578deb2b343 Homepage: https://cran.r-project.org/package=pema Description: CRAN Package 'pema' (Penalized Meta-Analysis) Conduct penalized meta-analysis, see Van Lissa, Van Erp, & Clapper (2023) . In meta-analysis, there are often between-study differences. These can be coded as moderator variables, and controlled for using meta-regression. However, if the number of moderators is large relative to the number of studies, such an analysis may be overfit. Penalized meta-regression is useful in these cases, because it shrinks the regression slopes of irrelevant moderators towards zero. Package: r-cran-pemultinom Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-rcpp, r-cran-nnet, r-cran-magrittr, r-cran-lpsolve Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pemultinom_0.1.1-1.ca2404.1_amd64.deb Size: 135928 MD5sum: 54917bff77a185eb9eddf768057f9def SHA1: 2c279647e7cecf58b93863e597b1720dbc227810 SHA256: 5c34e58bfe853d6eecaf75d4468286cd37105cb44e7e68e163e50997a5c6b61e SHA512: 3b09811ea55947fa7dc7ff9cba771c812c23964c3f624f25594a7a685eea733497e639fa8d2cf9cefae187c820933cf8bceede5cda814daab406766d386d05ef Homepage: https://cran.r-project.org/package=pemultinom Description: CRAN Package 'pemultinom' (L1-Penalized Multinomial Regression with Statistical Inference) We aim for fitting a multinomial regression model with Lasso penalty and doing statistical inference (calculating confidence intervals of coefficients and p-values for individual variables). It implements 1) the coordinate descent algorithm to fit an l1-penalized multinomial regression model (parameterized with a reference level); 2) the debiasing approach to obtain the inference results, which is described in "Tian, Y., Rusinek, H., Masurkar, A. V., & Feng, Y. (2024). L1‐Penalized Multinomial Regression: Estimation, Inference, and Prediction, With an Application to Risk Factor Identification for Different Dementia Subtypes. Statistics in Medicine, 43(30), 5711-5747." Package: r-cran-penaft Architecture: amd64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-ggplot2, r-cran-irlba, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-penaft_0.3.2-1.ca2404.1_amd64.deb Size: 173758 MD5sum: 2e20796d7b98e1898c1a87b58513622a SHA1: ef9d96cab4307a139e5125773a4a3008bb80e19f SHA256: d25d980ab52efa1b5171069b10b29a041d0389caf822d06bf88e42138e37d0cf SHA512: 49e713752ef43d74e9a7b4ac89b611b0f98a5bfbc3aa52f10052e0c388bc7560eacf546101de38f99ffc1cbe5bd7c1e6fe019f7e0d3cab827db7295c5ae67959 Homepage: https://cran.r-project.org/package=penAFT Description: CRAN Package 'penAFT' (Fit the Semiparametric Accelerated Failure Time Model withElastic Net and Sparse Group Lasso Penalties) The semiparametric accelerated failure time (AFT) model is an attractive alternative to the Cox proportional hazards model. This package provides a suite of functions for fitting one popular rank-based estimator of the semiparametric AFT model, the regularized Gehan estimator. Specifically, we provide functions for cross-validation, prediction, coefficient extraction, and visualizing both trace plots and cross-validation curves. For further details, please see Suder, P. M. and Molstad, A. J., (2022) Scalable algorithms for semiparametric accelerated failure time models in high dimensions, Statistics in Medicine . Package: r-cran-penalized Architecture: amd64 Version: 0.9-53-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1239 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-rcpp, r-cran-rcpparmadillo Suggests: r-bioc-globaltest Filename: pool/dists/noble/main/r-cran-penalized_0.9-53-1.ca2404.1_amd64.deb Size: 823770 MD5sum: 5c34602c9e74ea0f8b200fa918812c25 SHA1: c91926cdb49b4a65322210bd43d8d9a41b8cd26c SHA256: cc85cb711b2dbfc683ef65cb236c1b0046430b75adee5a5e3bc31d5fdfaa9bba SHA512: 9644da5a29211307cf76fb6da4dd8deec971146584baba0c44bb453574fee4993d5c9965a6fff04dbca7da6670a926c74a13e1afc58adec62f072bebe9b5222f Homepage: https://cran.r-project.org/package=penalized Description: CRAN Package 'penalized' (L1 (Lasso and Fused Lasso) and L2 (Ridge) Penalized Estimationin GLMs and in the Cox Model) Fitting possibly high dimensional penalized regression models. The penalty structure can be any combination of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a positivity constraint on the regression coefficients. The supported regression models are linear, logistic and Poisson regression and the Cox Proportional Hazards model. Cross-validation routines allow optimization of the tuning parameters. 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Package: r-cran-pencoxfrail Architecture: amd64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-matrix, r-cran-coxme, r-cran-rcpparmadillo Suggests: r-cran-mgcv Filename: pool/dists/noble/main/r-cran-pencoxfrail_2.0.1-1.ca2404.1_amd64.deb Size: 326172 MD5sum: e29b781fd99692f3b54f9b7213baf3e5 SHA1: 519d063ae35733e5218efda35020207ec9df3427 SHA256: aca9fc37112df86516fc48ab985ba46457ea56a85b4728732d88adeadfa39879 SHA512: 6f459cf07f6c8b42a326505cd0844dd7a80c630884d416f56081e5b77f687db59ca01ec5b11e79380a13a2b72c5c868fd529d9057458ea2e067aad904bf1028f 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. 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Package: r-cran-penphcure Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 587 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-mass, r-cran-rdpack, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-penphcure_1.0.2-1.ca2404.1_amd64.deb Size: 280426 MD5sum: 13d94c6eb554d545140796ba9e26a859 SHA1: 37df163ecb0b22eeac19acdd8af5d50bcd027433 SHA256: 32fbd49a499dbe9b653abb6769309182e91623cc5cc3007dfadf0f476375b35f SHA512: 879cc275152d8fe8b598cae7a65172c456df30699624381e1b84c69d12221c3b5f2e88aea9f05546e257c7d0ecc21724001f418fb4c25f8bd0d9931b3bfb774b Homepage: https://cran.r-project.org/package=penPHcure Description: CRAN Package 'penPHcure' (Variable Selection in PH Cure Model with Time-Varying Covariates) Implementation of the semi-parametric proportional-hazards (PH) of Sy and Taylor (2000) extended to time-varying covariates. Estimation and variable selection are based on the methodology described in Beretta and Heuchenne (2019) ; confidence intervals of the parameter estimates may be computed using a bootstrap approach. Moreover, data following the PH cure model may be simulated using a method similar to Hendry (2014) , where the event-times are generated on a continuous scale from a piecewise exponential distribution conditional on time-varying covariates. 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The methodology is based on Breinlich, Corradi, Rocha, Ruta, Santos Silva, and Zylkin (2021) and takes advantage of the method of alternating projections of Gaure (2013) for dealing with HDFE, as well as the coordinate descent algorithm of Friedman, Hastie and Tibshirani (2010) for fitting lasso regressions. The package is also able to carry out cross-validation and to implement the plugin lasso of Belloni, Chernozhukov, Hansen and Kozbur (2016) . 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The adaptive methods are proposed in Kepplinger, D. (2023) and the non-adaptive methods in Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E. (2019) . The package implements robust hyper-parameter selection with robust information sharing cross-validation according to Kepplinger & Wei (2025) . Package: r-cran-pepa Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3288 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-pepa_1.2-1.ca2404.1_amd64.deb Size: 3241276 MD5sum: 192cd7d8b56d7b969a6a128055979eb7 SHA1: 58822c02372266fdd65adf3248540db1dc0f572f SHA256: 93727688ba1eff8386800ab56a79af7db2c1b289beb7fd28741427b2f2453c35 SHA512: 1cc922f4f9195edfce5c2f005b50830fbbfd811c0363682ed40fe7cdd0d9b6bf2924010522acc7d7c36352726bed26ea7b0ad2b5d0e5b7e3e03299887c695cd2 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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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) . 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The considered distances are Kendall's-tau, Cayley, Hamming and Ulam and it includes functions for making inference, sampling and learning such distributions, some of which are novel in the literature. As a by-product, PerMallows also includes operations for permutations, paying special attention to those related with the Kendall's-tau, Cayley, Ulam and Hamming distances. It is also possible to generate random permutations at a given distance, or with a given number of inversions, or cycles, or fixed points or even with a given length on LIS (longest increasing subsequence). 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This work was supported by a National Institute of Allergy and Infectious Disease/National Institutes of Health contract (No. HHSN272200900059C). 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Regression, ANOVA and ANCOVA, omnibus F-tests, marginal unilateral and bilateral t-tests are available. Several methods to handle nuisance variables are implemented (Kherad-Pajouh, S., & Renaud, O. (2010) ; Kherad-Pajouh, S., & Renaud, O. (2014) ; Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014) ). An extension for the comparison of signals issued from experimental conditions (e.g. EEG/ERP signals) is provided. Several corrections for multiple testing are possible, including the cluster-mass statistic (Maris, E., & Oostenveld, R. (2007) ) and the threshold-free cluster enhancement (Smith, S. M., & Nichols, T. E. (2009) ). 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Package: r-cran-ph2bayes Architecture: amd64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-ph2bayes_0.0.2-1.ca2404.1_amd64.deb Size: 46052 MD5sum: 61516af494d7f69ee420a5c77a4b6a22 SHA1: cb9907ecfb7d56ab68e949713378ce1f185f58a1 SHA256: 9a061d925c8e3c13e86370b23e1b85f1dfd043a64c67912868093ae04c920d29 SHA512: 361f536b5f6cc584d131f1f0479964ba82d603a9bdfb35ba29786fcb0cb874f8f979cc6d8d0f19bd7db58fb4495a1143b739ba40d271378ff26a77bac406a08f Homepage: https://cran.r-project.org/package=ph2bayes Description: CRAN Package 'ph2bayes' (Bayesian Single-Arm Phase II Designs) An implementation of Bayesian single-arm phase II design methods for binary outcome based on posterior probability (Thall and Simon (1994) ) and predictive probability (Lee and Liu (2008) ). Package: r-cran-ph2bye Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-animation, r-cran-nleqslv, r-cran-rcpp, r-cran-vgam Suggests: r-cran-clinfun, r-cran-gsdesign, r-cran-survival Filename: pool/dists/noble/main/r-cran-ph2bye_0.1.4-1.ca2404.1_amd64.deb Size: 76282 MD5sum: 468dbb5e7fda434c1c696c6467feece6 SHA1: e3e0aa1d706ba33b2386463def11d3dd1c572dd4 SHA256: 0872e2a1cce9c10788e958836815fbe7cc213978e0e64aab9cf1d1f1f76dcd26 SHA512: 3b16c282322d7ce12d5b686c88bcea36a15f9c54079ab7fc158a27961dd3797dd384436de68b8dcaac7362e7fb38206b9c16616eb4fe404927aa9714265aee66 Homepage: https://cran.r-project.org/package=ph2bye Description: CRAN Package 'ph2bye' (Phase II Clinical Trial Design Using Bayesian Methods) Calculate the Bayesian posterior/predictive probability and determine the sample size and stopping boundaries for single-arm Phase II design. Package: r-cran-phacking Architecture: amd64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1498 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-metabias, r-cran-metafor, r-cran-purrr, r-cran-rlang, r-cran-truncnorm, r-cran-rcpp, r-cran-rdpack, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-phacking_0.2.1-1.ca2404.1_amd64.deb Size: 567518 MD5sum: 74119d194c9bbeb480f06bb7b3e23cdd SHA1: 37d692473d4e6ffd1929bdf0c7ab071a1bf7b037 SHA256: 08b751ca65b7d23c29f9b3980cab598028dde6744c976489217476fe87e54372 SHA512: 5317c0de01ad58b7146ce0f0d74915315506dca2d3302af2c768d66fa424db640fa6aab301f023bb475ad514da4a434650a6162cdd8491d05f99ed7f1b52cc72 Homepage: https://cran.r-project.org/package=phacking Description: CRAN Package 'phacking' (Sensitivity Analysis for p-Hacking in Meta-Analyses) Fits right-truncated meta-analysis (RTMA), a bias correction for the joint effects of p-hacking (i.e., manipulation of results within studies to obtain significant, positive estimates) and traditional publication bias (i.e., the selective publication of studies with significant, positive results) in meta-analyses [see Mathur MB (2022). "Sensitivity analysis for p-hacking in meta-analyses." .]. Unlike publication bias alone, p-hacking that favors significant, positive results (termed "affirmative") can distort the distribution of affirmative results. To bias-correct results from affirmative studies would require strong assumptions on the exact nature of p-hacking. In contrast, joint p-hacking and publication bias do not distort the distribution of published nonaffirmative results when there is stringent p-hacking (e.g., investigators who hack always eventually obtain an affirmative result) or when there is stringent publication bias (e.g., nonaffirmative results from hacked studies are never published). This means that any published nonaffirmative results are from unhacked studies. Under these assumptions, RTMA involves analyzing only the published nonaffirmative results to essentially impute the full underlying distribution of all results prior to selection due to p-hacking and/or publication bias. The package also provides diagnostic plots described in Mathur (2022). 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Offers methods for tree comparison, model selection and visualization of phylogenetic networks as described in Schliep et al. (2017). Package: r-cran-phase123 Architecture: amd64 Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 321 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-phase123_2.1-1.ca2404.1_amd64.deb Size: 153168 MD5sum: e8ea4c00ae161f79401cf0d87bd5f976 SHA1: 3beb8f0d4bb71a6c2ddce0be469a0bb4d05d6e65 SHA256: 1ac46b859266cb3d3bd428c7acfb22485c98da7b0edab581f8ae631d90c32f91 SHA512: c8e5fbb78b322a8b3459f64751ffc3ba00760b3e2cd0ffca7d964c852f0e310583d3c37a40886939b61f2bff032faf938c0c4fc50e40a55f899f2b7b6629fe50 Homepage: https://cran.r-project.org/package=Phase123 Description: CRAN Package 'Phase123' (Simulating and Conducting Phase 123 Trials) Contains three simulation functions for implementing the entire Phase 123 trial and the separate Eff-Tox and Phase 3 portions of the trial, which may be beneficial for use on clusters. The functions AssignEffTox() and RandomizeEffTox() assign doses to patient cohorts during phase 12 and Reoptimize() determines the optimal dose to continue with during Phase 3. The functions ReturnMeansAgent() and ReturnMeanControl() gives the true mean survival for the agent doses and control and ReturnOCS() gives the operating characteristics of the design. Package: r-cran-phase12compare Architecture: amd64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-phase12compare_1.5-1.ca2404.1_amd64.deb Size: 196322 MD5sum: e7f2acd5582a0c0726bc8ed234cc4f03 SHA1: f3ba0de4e1f02fc5636cd405323592b8553b8eff SHA256: a913c3c0d505e95399b7c9dd89948da097f6529d0f66bc48bfe5de66d0915e60 SHA512: a3854beb449e372a1c899386fdc9f9330eaec97ceb3597bd698abcb80cfd7dbbddf3306fb3cb0499d03ab2c2a94847e8f3bf2b700e39b2273a57f7bc093a9586 Homepage: https://cran.r-project.org/package=Phase12Compare Description: CRAN Package 'Phase12Compare' (Simulates SPSO and Efftox Phase 12 Trials with CorrelatedOutcomes) Simulating and conducting four phase 12 clinical trials with correlated binary bivariate outcomes described. Uses the 'Efftox' (efficacy and toxicity tradeoff, ) and SPSO (Semi-Parametric Stochastic Ordering) models with Utility and Desirability based objective functions for dose finding. Package: r-cran-phasetype Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-ggplot2, r-cran-reshape Suggests: r-cran-actuar Filename: pool/dists/noble/main/r-cran-phasetype_0.3.0-1.ca2404.1_amd64.deb Size: 66882 MD5sum: 23932fdd7ffa1032a0f9d7ec09d35630 SHA1: 30f1732efccd2f0dfe1ae860fbf8a8fa52996167 SHA256: 0ea414a827848a19d892ee9ddf35d280c2ca8c86dabb376212eeb454ebb95310 SHA512: f5405acf000b750f9b253e757f852dbfaf7cf095707e7ee94f366c5665fac0e9e490ab805b83cbd5c746f129360fc8abe3da1e8674ef6cbcb0ac28ce7844a2de Homepage: https://cran.r-project.org/package=PhaseType Description: CRAN Package 'PhaseType' (Inference for Phase-Type Distributions) Functions to perform Bayesian inference on absorption time data for Phase-type distributions. The methods of Bladt et al (2003) and Aslett (2012) are provided. Package: r-cran-phenex Architecture: amd64 Version: 1.4-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-deoptim Filename: pool/dists/noble/main/r-cran-phenex_1.4-5-1.ca2404.1_amd64.deb Size: 152966 MD5sum: 6ac49b73c4f5ac7305bc68ed0d48de24 SHA1: 4ff71080ce42af2b54e8659b5f5577fdd2313e9b SHA256: ec5ee6f955514b5571262b15c218f83b6a1efd801f237d80f3aeaf847fc94997 SHA512: 82b5d978e9ee12cb5e03c40d847d1f946db9639215c85aec012486b540caf13bbb27700be3f37683a4c3c9ace322cd001bf5b25796a9b0e5dd60d95499c2f7e0 Homepage: https://cran.r-project.org/package=phenex Description: CRAN Package 'phenex' (Auxiliary Functions for Phenological Data Analysis) Provides some easy-to-use functions for spatial analyses of (plant-) phenological data sets and satellite observations of vegetation. Package: r-cran-phenmod Architecture: amd64 Version: 1.2-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-gstat, r-cran-rcolorbrewer, r-cran-lattice, r-cran-pheno Filename: pool/dists/noble/main/r-cran-phenmod_1.2-7-1.ca2404.1_amd64.deb Size: 272380 MD5sum: 4ac17ff4fdf5beab23543d7f0d9c23e5 SHA1: bcf887888a4f4d32d3da39a196435fcabcee83da SHA256: 9f9652d9d7eb22bfd6977e258d392ed721e9d5cc3efd466853cee4c551a834c3 SHA512: 300e50d44e3b2c72ea9899955eafb9cf3100bb74a99ca34a6c7f324b5da30dd22f27b9e7ca6766aadab03b8f73b21ab2c0223ecb21e6a1c7246c2e9735cb9f74 Homepage: https://cran.r-project.org/package=phenmod Description: CRAN Package 'phenmod' (Auxiliary Functions for Phenological Data Processing, Modellingand Result Handling) Provides functions for phenological data preprocessing, modelling and result handling. For more information, please refer to Lange et al. (2016) . Package: r-cran-pheno Architecture: amd64 Version: 1.7-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-sparsem, r-cran-quantreg Filename: pool/dists/noble/main/r-cran-pheno_1.7-1-1.ca2404.1_amd64.deb Size: 96310 MD5sum: 55365dd0b931995ce4704a6686dbaca6 SHA1: ffee68907da711200c13fd81307465a51fb864aa SHA256: cb48e91ad9eaea480a181565cd831062f80dda533d6aa126a594d5eb7cb6a0ba SHA512: e56eca752cbcfb4b72627d504bab1ae6120f183e016ec23119b2a00f64b63dca2969edb207c5176a2668d80062779f9a1688d1d48ad9cdb2455a97fd6b25ba12 Homepage: https://cran.r-project.org/package=pheno Description: CRAN Package 'pheno' (Auxiliary Functions for Phenological Data Analysis) Provides some easy-to-use functions for time series analyses of (plant-) phenological data sets. These functions mainly deal with the estimation of combined phenological time series and are usually wrappers for functions that are already implemented in other R packages adapted to the special structure of phenological data and the needs of phenologists. Some date conversion functions to handle Julian dates are also provided. Package: r-cran-phenofit Architecture: amd64 Version: 0.3.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1354 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-purrr, r-cran-dplyr, r-cran-stringr, r-cran-magrittr, r-cran-lubridate, r-cran-data.table, r-cran-zoo, r-cran-gridextra, r-cran-ggplot2, r-cran-optimx, r-cran-ucminf, r-cran-numderiv, r-cran-zeallot, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phenofit_0.3.11-1.ca2404.1_amd64.deb Size: 943932 MD5sum: 0af00b92095d77d1fe5bdd947b4d0f82 SHA1: 9b07671e511d7a1d4e468dc0aa9fdf150767b791 SHA256: 3b42acbe76988135492b3c74f610753ba58cd929cb98fcff60dcb2fb245f843c SHA512: 688e0fc9b21b1d9ebdcb0b3c6e9be3f8336fd4378c53f1a6cfe4ce11b5d17edd3c90ff670d178b9315d12aceeb419f5f27c6968d52e4430662d1e86335cdd5ec Homepage: https://cran.r-project.org/package=phenofit Description: CRAN Package 'phenofit' (Extract Remote Sensing Vegetation Phenology) The merits of 'TIMESAT' and 'phenopix' are adopted. Besides, a simple and growing season dividing method and a practical snow elimination method based on Whittaker were proposed. 7 curve fitting methods and 4 phenology extraction methods were provided. Parameters boundary are considered for every curve fitting methods according to their ecological meaning. And 'optimx' is used to select best optimization method for different curve fitting methods. Reference: Kong, D., (2020). R package: A state-of-the-art Vegetation Phenology extraction package, phenofit version 0.3.1, ; Kong, D., Zhang, Y., Wang, D., Chen, J., & Gu, X. (2020). Photoperiod Explains the Asynchronization Between Vegetation Carbon Phenology and Vegetation Greenness Phenology. Journal of Geophysical Research: Biogeosciences, 125(8), e2020JG005636. ; Kong, D., Zhang, Y., Gu, X., & Wang, D. (2019). A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 155, 13–24; Zhang, Q., Kong, D., Shi, P., Singh, V.P., Sun, P., 2018. Vegetation phenology on the Qinghai-Tibetan Plateau and its response to climate change (1982–2013). Agric. For. Meteorol. 248, 408–417. . Package: r-cran-phenotypesimulator Architecture: amd64 Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5022 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-optparse, r-cran-hmisc, r-cran-r.utils, r-cran-mvtnorm, r-bioc-snpstats, r-cran-zoo, r-cran-data.table, r-cran-rcpp, r-cran-cowplot, r-cran-ggplot2, r-cran-reshape2, r-cran-dplyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-formatr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-phenotypesimulator_0.3.4-1.ca2404.1_amd64.deb Size: 2695710 MD5sum: a49e82cc5182786e98820b395a5161de SHA1: b7a849d38a347f1119c823a120202181dfe971a4 SHA256: d9bc58026fca012652fb014020b3936fe80ce44b69f62788dc5e4d9003096e92 SHA512: 86d524dfdbedc92f24544fb53f3c1e4440f0e4e83b20a9d4379345c76d2f4760b8373ca9b1093dad173e0feeddb19deb849b8a2f749f77dafc4a36356227ae0e Homepage: https://cran.r-project.org/package=PhenotypeSimulator Description: CRAN Package 'PhenotypeSimulator' (Flexible Phenotype Simulation from Different Genetic and NoiseModels) Simulation is a critical part of method development and assessment in quantitative genetics. 'PhenotypeSimulator' allows for the flexible simulation of phenotypes under different models, including genetic variant and infinitesimal genetic effects (reflecting population structure) as well as non-genetic covariate effects, observational noise and additional correlation effects. The different phenotype components are combined into a final phenotype while controlling for the proportion of variance explained by each of the components. For each effect component, the number of variables, their distribution and the design of their effect across traits can be customised. For the simulation of the genetic effects, external genotype data from a number of standard software ('plink', 'hapgen2'/ 'impute2', 'genome', 'bimbam', simple text files) can be imported. The final simulated phenotypes and its components can be automatically saved into .rds or .csv files. In addition, they can be saved in formats compatible with commonly used genetic association software ('gemma', 'bimbam', 'plink', 'snptest', 'LiMMBo'). Package: r-cran-phevis Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 576 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glmnet, r-cran-knitr, r-cran-lme4, r-cran-purrr, r-cran-randomforest, r-cran-rcpp, r-cran-tidyr, r-cran-viridis, r-cran-zoo Suggests: r-cran-prroc, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phevis_1.0.4-1.ca2404.1_amd64.deb Size: 433640 MD5sum: 02fc439995ce1977e39aa5e71189f797 SHA1: 4e3d5ca4e6cb1b94b50f10c86bbcb5777d8a0f5a SHA256: c4971af206d124d0f90d201cce150384e7fadd25a3bf697cd4288d9388c438d8 SHA512: 52391a4ff3d06c0f77091c02530c9627c6fdb9bc8ee69b8a7492d2178232eb3a6fd4f9a1f46d519a57a7c0e3105a0f2987f957f00ac456e8a86f41205e7e0428 Homepage: https://cran.r-project.org/package=PheVis Description: CRAN Package 'PheVis' (Automatic Phenotyping of Electronic Health Record at VisitResolution) Using Electronic Health Record (EHR) is difficult because most of the time the true characteristic of the patient is not available. Instead we can retrieve the International Classification of Disease code related to the disease of interest or we can count the occurrence of the Unified Medical Language System. None of them is the true phenotype which needs chart review to identify. However chart review is time consuming and costly. 'PheVis' is an algorithm which is phenotyping (i.e identify a characteristic) at the visit level in an unsupervised fashion. It can be used for chronic or acute diseases. An example of how to use 'PheVis' is available in the vignette. Basically there are two functions that are to be used: `train_phevis()` which trains the algorithm and `test_phevis()` which get the predicted probabilities. The detailed method is described in preprint by Ferté et al. (2020) . Package: r-cran-philentropy Architecture: amd64 Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1490 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-kernsmooth, r-cran-poorman Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-philentropy_0.10.0-1.ca2404.1_amd64.deb Size: 300054 MD5sum: 481ef49ba18d597183177d847ea81449 SHA1: 99d0bc0b323d81708b667ab086e4f44728c72c65 SHA256: e8a6912e8e6cda9920b4b8e952a9fee95f7f862b11b182c48619a896c2873af1 SHA512: 17f219e2d20f44eb210213ad8af205b24f5143c0ac5e03835cdd8df4dc6c129d9b84a349f7026ceea7b4c353165ceba8cdbe36a870b031d9503c913f7d112ad8 Homepage: https://cran.r-project.org/package=philentropy Description: CRAN Package 'philentropy' (Similarity and Distance Quantification Between ProbabilityFunctions) Computes 46 optimized distance and similarity measures for comparing probability functions (Drost (2018) ). These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions. Package: r-cran-phinterval Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 757 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-pillar, r-cran-rcpp, r-cran-rlang, r-cran-tibble, r-cran-tzdb, r-cran-vctrs Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-withr Filename: pool/dists/noble/main/r-cran-phinterval_1.0.0-1.ca2404.1_amd64.deb Size: 352650 MD5sum: 07103a8d117eec4f56d6a3a0bcbadaec SHA1: f351364b2bdf0c31bb7e89395b52c0863e89d519 SHA256: a6eec3fad810dc8fd03420e316fcd162c9edeab13c97b1e8bc2652766824c6f8 SHA512: 321cd17a915afdf682cc17ad6e4ff5547acee7f20507847491cdecdfa18ad9a437ba7ccbee75ce9e8baf0cbb3e495fabd8b01a6bc0b92d2f138717c9fe8726ef Homepage: https://cran.r-project.org/package=phinterval Description: CRAN Package 'phinterval' (Set Operations on Time Intervals) Implements the phinterval vector class for representing time spans that may contain gaps (disjoint intervals) or be empty. 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Package: r-cran-phm Architecture: amd64 Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 489 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-tm, r-cran-matrix, r-cran-smallstuff, r-cran-nlp, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-phm_2.1.2-1.ca2404.1_amd64.deb Size: 216172 MD5sum: 08a164181272f172665c197c08e8153f SHA1: 4ba2387e2170b3ef151bb0043f5e9ee5a5d6d720 SHA256: efd6481a1d235621852671b44c523c82a1fbf4bd85df522b71674a60e96e7df2 SHA512: e70d1cc9d6f863589cde07b2598ec468ae807124c6bf5ee647f3de1141ecd10be2c872694a6112685484a2e6a0d49a3ed1575d34dfe3facdd71e0aa11a11911b Homepage: https://cran.r-project.org/package=phm Description: CRAN Package 'phm' (Phrase Mining) Functions to extract and handle commonly occurring principal phrases obtained from collections of texts. Major speed improvements - core functions rewritten in C++ for faster phrase-document parsing, clustering, and text distance computations. Based on, Small, E., & Cabrera, J. (2025). Principal phrase mining, an automated method for extracting meaningful phrases from text. International Journal of Computers and Applications, 47(1), 84–92. 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Package: r-cran-phsmm Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-phsmm_1.0-1.ca2404.1_amd64.deb Size: 239730 MD5sum: 14f2f5a27cb2dccbe0aea09ae561521c SHA1: 5a0ea50f72a7fdbb4495b6d09e25c5755e142e89 SHA256: 1698a2e4591929f856e532c4e90386af2065cce5a9fc8467f264e0391870c58e SHA512: c25d49b8ffb40451b7c586bfaee18a42aa9673e2b5d37f8f05040eae151c0fc4979f902c83150320d976a18ecf2670f237c4293f5c5f7bf0a6bdbf63e5167efc 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. . 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It is part of the 'TDAverse' suite of packages, which is designed to provide a collection of packages for enabling machine learning and data science tasks using persistent homology. Implements a class for hosting persistence data, a number of coercers from and to already existing and used data structures from other packages and functions to compute distances between persistence diagrams. A formal definition and study of bottleneck and Wasserstein distances can be found in Bubenik, Scott and Stanley (2023) . Their implementation in 'phutil' relies on the 'C++' Hera library developed by Kerber, Morozov and Nigmetov (2017) . Package: r-cran-phyclust Architecture: amd64 Version: 0.1-34-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1565 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape Filename: pool/dists/noble/main/r-cran-phyclust_0.1-34-1.ca2404.1_amd64.deb Size: 973982 MD5sum: d0b9f876ac3e102a76ed34ef788d15b8 SHA1: 0fdbc74d7d3d52f3c9e528fd3a72af6d8eb5a1f7 SHA256: d4a272882a7542ac5bba69eddef7e67f3edc6479a8120bb7bc6f3bd75d626257 SHA512: 4c637a34e6a7d2ff220f720fc584036ff3a59a81c4f703038be223733742d9c1e3a86acbb8caa65c2dcfa033a39cf0271e17e16e9b9ca53e1c966bb8b5eeb742 Homepage: https://cran.r-project.org/package=phyclust Description: CRAN Package 'phyclust' (Phylogenetic Clustering (Phyloclustering)) Phylogenetic clustering (phyloclustering) is an evolutionary Continuous Time Markov Chain model-based approach to identify population structure from molecular data without assuming linkage equilibrium. The package phyclust (Chen 2011) provides a convenient implementation of phyloclustering for DNA and SNP data, capable of clustering individuals into subpopulations and identifying molecular sequences representative of those subpopulations. It is designed in C for performance, interfaced with R for visualization, and incorporates other popular open source programs including ms (Hudson 2002) , seq-gen (Rambaut and Grassly 1997) , Hap-Clustering (Tzeng 2005) and PAML baseml (Yang 1997, 2007) , , for simulating data, additional analyses, and searching the best tree. See the phyclust website for more information, documentations and examples. Package: r-cran-phylobase Architecture: amd64 Version: 0.8.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1263 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-phylobase_0.8.12-1.ca2404.1_amd64.deb Size: 677668 MD5sum: febf6e6766e112ac16f847dc32553977 SHA1: dc81f2a41fd1093b7bf727e84a09940c9c3879f1 SHA256: 998a276d866bf470c5db31b7885d3d80cc2cad8f35fc4ae788d24c90a26d99d0 SHA512: d139a4fe3f6b0a487e769e4d114f9c0d7fefd1c86063631ef4ca8fea48ef96c39ea1775704c7d0e1bc4a660f96abe8c68b95f833b20a1325917f6713cc8adb51 Homepage: https://cran.r-project.org/package=phylobase Description: CRAN Package 'phylobase' (Base Package for Phylogenetic Structures and Comparative Data) Provides a base S4 class for comparative methods, incorporating one or more trees and trait data. Package: r-cran-phylocomr Architecture: amd64 Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1826 Depends: libc6 (>= 2.34), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-sys Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ape Filename: pool/dists/noble/main/r-cran-phylocomr_0.3.4-1.ca2404.1_amd64.deb Size: 721154 MD5sum: c1134a2367e8b16fdcee116fded28eb1 SHA1: 29e56416a890ca092fff69114118b40703335dc6 SHA256: e674eb46c6a4d95a5cce68b18a079367cf9ab1698f86358d9709837b2cee10ed SHA512: fdc5b800bda9d3f03bc0fa9ceaf1b78051d4646915a12676f41b12752845466371d5412e3dc088f9ef8e4557eb2ec0c2c24e8698ab0c7fb9eea5f7e6e5592347 Homepage: https://cran.r-project.org/package=phylocomr Description: CRAN Package 'phylocomr' (Interface to 'Phylocom') Interface to 'Phylocom' (), a library for analysis of 'phylogenetic' community structure and character evolution. 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Some tools to handle equivalent shifts configurations are also available. See Bastide et al. (2017) and Bastide et al. (2018) . Package: r-cran-phylolm Architecture: amd64 Version: 2.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 558 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-future.apply Suggests: r-cran-testthat, r-cran-nlme Filename: pool/dists/noble/main/r-cran-phylolm_2.6.5-1.ca2404.1_amd64.deb Size: 495358 MD5sum: 570b835ee67d2debbeb047af7bd37fc0 SHA1: 9850a330d45d975a5aed1c724017f2512f2e4abe SHA256: 213fd2fcb4462b4bc2016bcbde3a1b4d52cbc02b762ae335f854ea138dcb2d3f SHA512: 5a84357e5b9f1180aaa0de94c8704e315b10e3b9dd75f997fac0fb7a9d53cfb381fd3d19915daa0c92e1f062cf1280964a1756f71aac0ab73749a9e492c09176 Homepage: https://cran.r-project.org/package=phylolm Description: CRAN Package 'phylolm' (Phylogenetic Linear Regression) Provides functions for fitting phylogenetic linear models and phylogenetic generalized linear models. The computation uses an algorithm that is linear in the number of tips in the tree. The package also provides functions for simulating continuous or binary traits along the tree. Other tools include functions to test the adequacy of a population tree. Package: r-cran-phylopairs Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3192 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-loo, r-cran-phytools, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/noble/main/r-cran-phylopairs_0.1.1-1.ca2404.1_amd64.deb Size: 932260 MD5sum: e28560fedbf46f2f01cab67c74fc61eb SHA1: eacbba60e048057a4ecd5d89656c2ae73dc67351 SHA256: d104439e90188cdac164bcefd11cfba631a6673c5af1e73f6307f0c2cb385d82 SHA512: cc86e9c52db48d47e21e2ac2fb653fd65806c4487dece9daee56928677022e38689632255ccc09dd30ddad6dc254af8468194c0399c40dc6e86ced8bb8386e46 Homepage: https://cran.r-project.org/package=phylopairs Description: CRAN Package 'phylopairs' (Comparative Analyses of Lineage-Pair Traits) Facilitates the testing of causal relationships among lineage-pair traits in a phylogenetically informed context. Lineage-pair traits are characters that are defined for pairs of lineages instead of individual taxa. Examples include the strength of reproductive isolation, range overlap, competition coefficient, diet niche similarity, and relative hybrid fitness. Users supply a lineage-pair dataset and a phylogeny. 'phylopairs' calculates a covariance matrix for the pairwise-defined data and provides built-in models to test for relationships among variables while taking this covariance into account. Bayesian sampling is run through built-in 'Stan' programs via the 'rstan' package. The various models and methods that this package makes available are described in Anderson et al. (In Review), Coyne and Orr (1989) , Fitzpatrick (2002) , and Castillo (2007) . Package: r-cran-phylosem Architecture: amd64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3560 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tmb, r-cran-sem, r-cran-ape, r-cran-phylobase, r-cran-phylopath, r-cran-rcppeigen Suggests: r-cran-semplot, r-cran-treetools, r-cran-rphylopars, r-cran-phylolm, r-cran-fishtree, r-cran-phyr, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat, r-cran-phylosignal, r-cran-adephylo Filename: pool/dists/noble/main/r-cran-phylosem_1.1.4-1.ca2404.1_amd64.deb Size: 1051266 MD5sum: aca74609e815ab44d8fee0e5cea98133 SHA1: bd04912cb5fe065ef0994a102cea0d4f239e68a7 SHA256: 0f59403d490c46f7edc60ebdb227346e072d9d0719d0653e259d667f0c25e7c8 SHA512: f0cf7b4bb3d1b65282e02178fb1a76fa88b7342f87457086f4911cbf79f300aa0f9573b8ede46124813c34310362337654722d47ed951303c9e1dc94f02f0599 Homepage: https://cran.r-project.org/package=phylosem Description: CRAN Package 'phylosem' (Phylogenetic Structural Equation Model) Applies phylogenetic comparative methods (PCM) and phylogenetic trait imputation using structural equation models (SEM), extending methods from Thorson et al. (2023) . This implementation includes a minimal set of features, to allow users to easily read all of the documentation and source code. PCM using SEM includes phylogenetic linear models and structural equation models as nested submodels, but also allows imputation of missing values. Features and comparison with other packages are described in Thorson and van der Bijl (2023) . Package: r-cran-phylosignal Architecture: amd64 Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2933 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-adephylo, r-cran-igraph, r-cran-ape, r-cran-phylobase, r-cran-boot, r-cran-dbi, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-phylosignal_1.3.1-1.ca2404.1_amd64.deb Size: 1216792 MD5sum: 48831ef9a39f66883b833de8eef5db9c SHA1: 0fc47a807f62e6766afbb537aab385336924a9fd SHA256: 5ee98cc0569f7a3f65abed9fabc6d82d858144a5f7286a2271fc53b5742ca6ab SHA512: 6e41587a2193dff6dc48516db521d0b56f5b829f6f727fcbf19ac377237185502dd78fec6b9d9e7adeef1d16a5d13a7f9efd22056b8d7b3d23d6f228a450b6f0 Homepage: https://cran.r-project.org/package=phylosignal Description: CRAN Package 'phylosignal' (Exploring the Phylogenetic Signal in Continuous Traits) A collection of tools to explore the phylogenetic signal in univariate and multivariate data. The package provides functions to plot traits data against a phylogenetic tree, different measures and tests for the phylogenetic signal, methods to describe where the signal is located and a phylogenetic clustering method. Package: r-cran-phylotypr Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4730 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-readr, r-cran-rfast, r-cran-stringi Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-purrr, r-cran-dplyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phylotypr_0.1.1-1.ca2404.1_amd64.deb Size: 1984430 MD5sum: c88de15b486f0101579df029dc3f29e0 SHA1: 77eec5754ed56275716e44a6bf1067c75806a6c0 SHA256: 94ec81bf4a7f0ee272d90cf31fa60750ed146928b1cd04f3460aab1c7ab78857 SHA512: eb16ea27a7f287a9ed64b32d7af2c8c933fd5e4ae2497418784bc0e6cb3f8f05b23c73642391622d1e6293e6db946f098342c8348f9046a076232282cf3b3a23 Homepage: https://cran.r-project.org/package=phylotypr Description: CRAN Package 'phylotypr' (Classifying DNA Sequences to Taxonomic Groupings) Classification based analysis of DNA sequences to taxonomic groupings. 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It takes an input either a collection of gene trees (then transformed to matrices) or directly a collection of gene matrices and performs an iterative process to identify what species in what genes are outliers, and whose elimination significantly improves the concordance between the input matrices. The methods builds upon the Distatis approach (Abdi et al. (2005) ), a generalization of classical multidimensional scaling to multiple distance matrices. Package: r-cran-phyr Architecture: amd64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3390 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-rcpp, r-cran-matrix, r-cran-dplyr, r-cran-lme4, r-cran-nloptr, r-cran-gridextra, r-cran-mvtnorm, r-cran-latticeextra, r-cran-tidyr, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-pez, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-picante, r-cran-rbenchmark, r-cran-mcmcglmm, r-cran-logistf, r-cran-phylolm, r-cran-ggplot2, r-cran-ggridges, r-cran-dharma, r-cran-rr2, r-cran-future.apply Filename: pool/dists/noble/main/r-cran-phyr_1.1.3-1.ca2404.1_amd64.deb Size: 1814232 MD5sum: 48e458b3f0d556573eb25459528114bd SHA1: 95adce4b5edf40010e8b04f042db181ad97d7e5e SHA256: b3649bf31240fcd36a17a8341b57b501379d2e463565b56a33a4e558a6733cec SHA512: cc40dbbc62bbd60fbdd4e2ee60d0d3d1bda88ac5297575ec8855eb793cc8bb58d85e09e998fe2972e29bcc834be4571cfa96b78bf575146914ef5444cb7a4d0b Homepage: https://cran.r-project.org/package=phyr Description: CRAN Package 'phyr' (Model Based Phylogenetic Analysis) A collection of functions to do model-based phylogenetic analysis. 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Inference of allele similarity clusters, an alternative naming scheme and genotype inference for immunoglobulin heavy chain repertoires. The main tools are allele similarity clusters, and allele based genotype. The first tool is designed to reduce the ambiguity within the immunoglobulin heavy chain V alleles. The ambiguity is caused by duplicated or similar alleles which are shared among different genes. The second tool is an allele based genotype, that determined the presence of an allele based on a threshold derived from a naive population. See Peres et al. (2023) . Package: r-cran-pijavski Architecture: amd64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-pijavski_1.0.5-1.ca2404.1_amd64.deb Size: 74446 MD5sum: 21340f865e20604510089ba555bc90bf SHA1: cacc7b13c840fa8ead402bb1d74429252e049247 SHA256: a507179273ef0de95e817ce33da7ccf7e6aeafcd33d763c1cd99ee23dd5556b0 SHA512: 6c1f592c91a1656f6a7cd54bb4041f4b80ece9b890ace862301817a3a93ea674b2d376b6ceb4243a0c05d54d0a3fed3dfa9d9cf31c1e33972680ebfe787c738b 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) . 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(2009) , Partlett and Riley (2017) , and Nagashima et al. (2019) , . Package: r-cran-pingr Architecture: amd64 Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: libc6 (>= 2.34), r-base-core (>= 4.4.0), r-api-4.0, r-cran-processx Suggests: r-cran-covr, r-cran-ps, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pingr_2.0.5-1.ca2404.1_amd64.deb Size: 42298 MD5sum: 545de44ad6b25369faed4ab6d6d953be SHA1: ae7ddbdecfa5e2f8437f165a6fd87fa31fc21416 SHA256: 60b15b41112f8e69697c7dfb404bebdfa02f2a2c1c5de3e1ecb67e1987d33f89 SHA512: a8644286b4616a19754b9cd7670d5b63ea435cf82c86a2eb8608d6295fb4bea7a0430c8231b6219e89a31663f00abd27703ba17f623c9821f167bf800543e71c Homepage: https://cran.r-project.org/package=pingr Description: CRAN Package 'pingr' (Check if a Remote Computer is Up) Check if a remote computer is up. It can either just call the system ping command, or check a specified TCP port. Package: r-cran-pinsplus Architecture: amd64 Version: 2.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 965 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-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/noble/main/r-cran-pinsplus_2.0.9-1.ca2404.1_amd64.deb Size: 818942 MD5sum: 15944b03acda26442f3820e83823d976 SHA1: ecb597014cfe04d2c848b0897c5ac1aa078e0451 SHA256: 1aaa03c9dd829bdd781f9ffc846a45f689d42d65ae633ceac8e3b4c7b83e37db SHA512: 8f9696e358e2f5282abd43f43af1f4a4e56229e5fea890ec2e55cbf460a5a41d7b14f16ecf50570de42d1226cd4934ed942e307d853fb873e71b007c103967b3 Homepage: https://cran.r-project.org/package=PINSPlus Description: CRAN Package 'PINSPlus' (Clustering Algorithm for Data Integration and Disease Subtyping) Provides a robust approach for omics data integration and disease subtyping. 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Package: r-cran-pintervals Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1989 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-foreach, r-cran-hmisc, r-cran-mass, r-cran-purrr, r-cran-rcpp, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-pintervals_1.1.1-1.ca2404.1_amd64.deb Size: 760516 MD5sum: 504856b8f6a3a6bee8e7804a034f8c70 SHA1: 0253efe91965bb5955e1f59644835e7bb5f4900b SHA256: 27b4aa8420bc8cae174f7bd9899f700a21f7523764a81222c7f2e7fc74c21046 SHA512: 1c7318c1eefd1c7c6a15d6cbebb2961df39089f2161e1065e37c8cf4b758d71d2679ddcf073d920e75bf788a5d1b032f221f04019691eb797b6ca6150476db4b Homepage: https://cran.r-project.org/package=pintervals Description: CRAN Package 'pintervals' (Model Agnostic Prediction Intervals) Provides tools for estimating model-agnostic prediction intervals using conformal prediction, bootstrapping, and parametric prediction intervals. The package is designed for ease of use, offering intuitive functions for both binned and full conformal prediction methods, as well as parametric interval estimation with diagnostic checks. Currently only working for continuous predictions. For details on the conformal and bin-conditional conformal prediction methods, see Randahl, Williams, and Hegre (2026) . Package: r-cran-piqp Architecture: amd64 Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 675 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-s7, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-slam, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-piqp_0.6.2-1.ca2404.1_amd64.deb Size: 287852 MD5sum: e21ade87413d49008ca0dffdec87f703 SHA1: d22aa5c7e74b77e64c7dbd37b2219191ad67269c SHA256: 5639fd862e43a1d46c4e51a31b46453a94d94047e36c1bf9a8f5bfe9b64a963c SHA512: 46a4d2856fdf8cb457a4771385ace380369e51082fe288e2de576103e76dbf278112dc8b8051bf7b2caa0fc9c5be9ce379d0d0ed8ea2d4e72afc9c37c0473140 Homepage: https://cran.r-project.org/package=piqp Description: CRAN Package 'piqp' (R Interface to Proximal Interior Point Quadratic ProgrammingSolver) An embedded proximal interior point quadratic programming solver, which can solve dense and sparse quadratic programs, described in Schwan, Jiang, Kuhn, and Jones (2023) . Combining an infeasible interior point method with the proximal method of multipliers, the algorithm can handle ill-conditioned convex quadratic programming problems without the need for linear independence of the constraints. The solver is written in header only 'C++ 14' leveraging the 'Eigen' library for vectorized linear algebra. For small dense problems, vectorized instructions and cache locality can be exploited more efficiently. Allocation free problem updates and re-solves are also provided. Package: r-cran-piton Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 637 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-piton_1.0.1-1.ca2404.1_amd64.deb Size: 83990 MD5sum: 70bc0bf51cd3ab97f8440def82c26381 SHA1: f15d217ea8c3093ba800b9f2273f7ae9aa3b6948 SHA256: 3beb5d433302e87a06500828104a516af6d9966c0e9db9041947a6ecd89de40e SHA512: 07577bed4a1d8dd009b873ab728cac90621825c267b1a5a2821272d1c638821987a547016f37168634da4e7134ace0fafdaa301a4e3c86db1d7c0fca346f8697 Homepage: https://cran.r-project.org/package=piton Description: CRAN Package 'piton' (Parsing Expression Grammars in Rcpp) A wrapper around the 'Parsing Expression Grammar Template Library', a C++11 library for generating Parsing Expression Grammars, that makes it accessible within Rcpp. With this, developers can implement their own grammars and easily expose them in R packages. Package: r-cran-pjfm Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1252 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-statmod, r-cran-pracma, r-cran-matrix, r-cran-rcpparmadillo, r-cran-rcppensmallen Filename: pool/dists/noble/main/r-cran-pjfm_0.1.0-1.ca2404.1_amd64.deb Size: 776362 MD5sum: 6c057d428a4aaf5a7e4ee9628a043556 SHA1: d100c9dd0b26d0dff96743bf80aeb487624ff90a SHA256: d1576508a6316663458dcfdc24c6c12e99e73c9d5b75a56a606a2442ec96efba SHA512: 8185dad264a3e3c078842f22a602fad4f4ecef2655b596f464f4eb2f9ab0faac02f1a5f5139224678f75abcf10f57fc49dffffbb0272dd961df758355dfcbf19 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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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. 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Package: r-cran-pki Architecture: amd64 Version: 0.1-15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: libc6 (>= 2.14), libssl3t64 (>= 3.0.0), r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc Filename: pool/dists/noble/main/r-cran-pki_0.1-15-1.ca2404.1_amd64.deb Size: 116582 MD5sum: 27effb2e0793be5cbf80e7d0cb9fa177 SHA1: 29d59d22dc1b64e55e35f15f32a945265494857e SHA256: 71d2cf0f3f69f4974c1846acaf2496fba927ad4af37ccb06c1abdcf50d3bfd9f SHA512: 691621ac7c62ea41c41c8de1c87b520e1427a26721c571fa1d4ee5cb59589afe8ceadf3cb387703079b861481cd9134d5e3283e19905d310f5e3d1704aa9b3f7 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. 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Contains pdf documentation of a reproducible analysis using approximately two million chess matches. Also contains an Elo based method for multi-player games where the result is a placing or a score. This includes zero-sum games such as poker and mahjong. Package: r-cran-pleiotest Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 711 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcolorbrewer, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-pleiotest_1.0.0-1.ca2404.1_amd64.deb Size: 551784 MD5sum: 594c28ef1d40c84863cf3e75aae88e92 SHA1: a271d69543bd29b4b51a802a05cffdecf007f873 SHA256: 88605566eb2c133d5ffcdd864eaba837b7e5e35eaf86dcf7c064d96a7dfa6efc SHA512: a066f86eb0fac2ab286a8403d092101cb22278acf7681607bc5393ea01cc731b647e1404d2ab07f585fd1c87aa0a6a2c9e8b2209c8eb3cc5498715bfef2d4ce8 Homepage: https://cran.r-project.org/package=pleiotest Description: CRAN Package 'pleiotest' (Fast Sequential Pleiotropy Test) It performs a fast multi-trait genome-wide association analysis based on seemingly unrelated regressions. It tests for pleiotropic effects based on a series of Intersection-Union Wald tests. The package can handle large and unbalanced data and plot results. Package: r-cran-plfd Architecture: amd64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mathjaxr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-plfd_0.2.1-1.ca2404.1_amd64.deb Size: 92696 MD5sum: f4d74f1b903c660a474c92b803c965c6 SHA1: dc6ba07a044eda73bf43eb30a32c24372d7ad2ab SHA256: 7142eda5c0e0f684018e0b531df25288ab3dd27dbfa3e4477eca89428bc32ef6 SHA512: d3ffea0e77d9dd434ecf84c67d64169e7059cade8c1c789bc6bbf2fa5ed31b43a43f83f01670f8118856575ead27f9ed2cc0381e916cd528b9940a0b06e01529 Homepage: https://cran.r-project.org/package=PLFD Description: CRAN Package 'PLFD' (Portmanteau Local Feature Discrimination for Matrix-Variate Data) The portmanteau local feature discriminant approach first identifies the local discriminant features and their differential structures, then constructs the discriminant rule by pooling the identified local features together. This method is applicable to high-dimensional matrix-variate data. See the paper by Xu, Luo and Chen (2023, ). Package: r-cran-plfm Architecture: amd64 Version: 2.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 579 Depends: libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sfsmisc, r-cran-abind Filename: pool/dists/noble/main/r-cran-plfm_2.2.6-1.ca2404.1_amd64.deb Size: 401646 MD5sum: 1d1159f3eb5a3a94fe67fb18a5003922 SHA1: 001dd17c66f21246112813882d13c4994a804770 SHA256: 6cf22598174b643533c804d06224854c631389b8dda28f5aa588f64b2e389681 SHA512: 8ea6de9e6857db528c252fc3d20344923ca2c56c12fae800fbfb441759c295cd905e436044c354c949ae020e29fc0fff0b0cbd40be9933a014e3a3ebe0779f03 Homepage: https://cran.r-project.org/package=plfm Description: CRAN Package 'plfm' (Probabilistic Latent Feature Analysis) Functions for estimating probabilistic latent feature models with a disjunctive, conjunctive or additive mapping rule on (aggregated) binary three-way data. Package: r-cran-plgp Architecture: amd64 Version: 1.1-13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-tgp Suggests: r-cran-ellipse, r-cran-splancs, r-cran-interp, r-cran-mass Filename: pool/dists/noble/main/r-cran-plgp_1.1-13-1.ca2404.1_amd64.deb Size: 211796 MD5sum: f1adb0280a8dca48bc260447f2f29624 SHA1: b90961edc286f92f4f168352f4fc2ec4726e3655 SHA256: 96527d4590631e04d31bdb18af91d822b4a5ee563b92727cfd157b75f9a680a8 SHA512: a3d1b1b3fb9ef475e792fa71f0cb3073f82fdd3573b2ce094369fee620afe029bc82c2b4be6016c52adc06d4ba8208b8674e4ec3555f1c25217d25d0edb690eb Homepage: https://cran.r-project.org/package=plgp Description: CRAN Package 'plgp' (Particle Learning of Gaussian Processes) Sequential Monte Carlo (SMC) inference for fully Bayesian Gaussian process (GP) regression and classification models by particle learning (PL) following Gramacy & Polson (2011) . The sequential nature of inference and the active learning (AL) hooks provided facilitate thrifty sequential design (by entropy) and optimization (by improvement) for classification and regression models, respectively. This package essentially provides a generic PL interface, and functions (arguments to the interface) which implement the GP models and AL heuristics. Functions for a special, linked, regression/classification GP model and an integrated expected conditional improvement (IECI) statistic provide for optimization in the presence of unknown constraints. Separable and isotropic Gaussian, and single-index correlation functions are supported. See the examples section of ?plgp and demo(package="plgp") for an index of demos. Package: r-cran-pliman Architecture: amd64 Version: 3.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3866 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-exactextractr, r-cran-mirai, r-cran-purrr, r-cran-rcpp, r-cran-sf, r-cran-terra, r-cran-rcpparmadillo Suggests: r-cran-biocmanager, r-cran-curl, r-bioc-ebimage, r-cran-fields, r-cran-knitr, r-cran-leafem, r-cran-leaflet, r-cran-mapedit, r-cran-mapview, r-cran-pak, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-pliman_3.1.1-1.ca2404.1_amd64.deb Size: 3468668 MD5sum: 2b1fd2afdd49b9fd9f74e502575cccce SHA1: 70b48eae73c5cce5bb32893b390208fce372f96b SHA256: 27bdffde1a57f9515515031f69f7a842ec422e2360e347e319227c4f926995ba SHA512: ab7a949cda288fe9defbcb00197963ba53ab262ee0ecdb17cae8c6505687e287ed5ddaf675491ff87aab06e89ff1e583c66998d7b40e1e01da61239b0cd1e3fd Homepage: https://cran.r-project.org/package=pliman Description: CRAN Package 'pliman' (Tools for Plant Image Analysis) Tools for both single and batch image manipulation and analysis (Olivoto, 2022 ) and phytopathometry (Olivoto et al., 2022 ). The tools can be used for the quantification of leaf area, object counting, extraction of image indexes, shape measurement, object landmark identification, and Elliptical Fourier Analysis of object outlines (Claude (2008) ). The package also provides a comprehensive pipeline for generating shapefiles with complex layouts and supports high-throughput phenotyping of RGB, multispectral, and hyperspectral orthomosaics. This functionality facilitates field phenotyping using UAV- or satellite-based imagery. Package: r-cran-plmix Architecture: amd64 Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 775 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-abind, r-cran-foreach, r-cran-ggplot2, r-cran-ggmcmc, r-cran-coda, r-cran-reshape2, r-cran-rcdd, r-cran-gridextra, r-cran-mcmcpack, r-cran-label.switching, r-cran-plackettluce, r-cran-radarchart Suggests: r-cran-doparallel, r-cran-pmr, r-cran-prefmod, r-cran-rankdist, r-cran-statrank, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-plmix_2.2.0-1.ca2404.1_amd64.deb Size: 519870 MD5sum: 1eb849382bdb3206910b484572c3effb SHA1: 6427958aa65a15adf98ed0936bfe604c484cd4a9 SHA256: 0d3f0aa8422445b602671b91df106fd498a6566f78d5d138913dba070660c303 SHA512: 00eccacd1875f7da461867c2b17b593ec73124aafa9bcd2298be2e9fda90e9a54c3c102f8fe01cfda7ed90e3dc6a91e2567e56c2b3202459f3c36a401ab7953f Homepage: https://cran.r-project.org/package=PLMIX Description: CRAN Package 'PLMIX' (Bayesian Analysis of Finite Mixture of Plackett-Luce Models) Fit finite mixtures of Plackett-Luce models for partial top rankings/orderings within the Bayesian framework. It provides MAP point estimates via EM algorithm and posterior MCMC simulations via Gibbs Sampling. It also fits MLE as a special case of the noninformative Bayesian analysis with vague priors. In addition to inferential techniques, the package assists other fundamental phases of a model-based analysis for partial rankings/orderings, by including functions for data manipulation, simulation, descriptive summary, model selection and goodness-of-fit evaluation. Main references on the methods are Mollica and Tardella (2017) and Mollica and Tardella (2014) . Package: r-cran-plmmr Architecture: amd64 Version: 4.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3926 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigalgebra, r-cran-bigmemory, r-cran-biglasso, r-cran-data.table, r-cran-glmnet, r-cran-matrix, r-cran-ncvreg, r-cran-bh, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-bigsnpr, r-cran-bigstatsr, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-plmmr_4.2.3-1.ca2404.1_amd64.deb Size: 2645952 MD5sum: 9354dde0d23ec8a8e0e680dc5fa23062 SHA1: d1d9780cd0a876016ea7cbc7b3b67254aac1339c SHA256: f1dc3f588e64c64f7e11d8e385f5c7e8024c299568db4fc2f251252a3fd17987 SHA512: 1e38fc524817e975c1a8937ff8d946e396992cd602d0026aa0cb0fd18b46d9146069f417fdecbb64a292cfdf61fae31e5a5e0ad8e6cc839c78141b5fbdbc7120 Homepage: https://cran.r-project.org/package=plmmr Description: CRAN Package 'plmmr' (Penalized Linear Mixed Models for Correlated Data) Fits penalized linear mixed models that correct for unobserved confounding factors. 'plmmr' infers and corrects for the presence of unobserved confounding effects such as population stratification and environmental heterogeneity. It then fits a linear model via penalized maximum likelihood. Originally designed for the multivariate analysis of single nucleotide polymorphisms (SNPs) measured in a genome-wide association study (GWAS), 'plmmr' eliminates the need for subpopulation-specific analyses and post-analysis p-value adjustments. Functions for the appropriate processing of 'PLINK' files are also supplied. For examples, see the package homepage. . Package: r-cran-pln Architecture: amd64 Version: 0.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pln_0.2-3-1.ca2404.1_amd64.deb Size: 86434 MD5sum: 12a2a71b187ab3668a8d91c2127604d4 SHA1: 874824f0290f0330d2492de58fa7f248931a8ee7 SHA256: fa5db367bfe87ccd3532594b8805596804cb4e5d3d773a0a6652c0d6db168745 SHA512: 52833d7fe26fc8538479b9c027e85d40b6acbc6153a4417745a46524323e521b0ffd9c8242bac8f72362bb7bb9518bb7ab1f3f998914f215a98a3a6c7f27798e Homepage: https://cran.r-project.org/package=pln Description: CRAN Package 'pln' (Polytomous Logit-Normit (Graded Logistic) Model Estimation) Performs bivariate composite likelihood and full information maximum likelihood estimation for polytomous logit-normit (graded logistic) item response theory (IRT) models. 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Implements variational algorithms to fit such models accompanied with a set of functions for visualization and diagnostic. Package: r-cran-plordprob Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mnormt Filename: pool/dists/noble/main/r-cran-plordprob_1.1-1.ca2404.1_amd64.deb Size: 49436 MD5sum: f87f006b927792a1d50a0313fa29bd63 SHA1: bde7e7d1b99ea306892ba2e4d52d29b7d99fad8c SHA256: a3c65f09edea5239f3816adb5e4e73ad0219708524446b01fbbbf4ad3adeac3b SHA512: 9ab17944fb901542b31d6fe24a22a9d50d4d51cb267dea6efb6714d90a17cd5bb4e94eff581013947dd349bebcd84d29b4742ff867d4347f790818e74ab6313d Homepage: https://cran.r-project.org/package=PLordprob Description: CRAN Package 'PLordprob' (Multivariate Ordered Probit Model via Pairwise Likelihood) Multivariate ordered probit model, i.e. the extension of the scalar ordered probit model where the observed variables have dimension greater than one. 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Package: r-cran-plotcli Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 790 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-ggplot2, r-cran-crayon, r-cran-stringr, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-plotcli_0.2.0-1.ca2404.1_amd64.deb Size: 477888 MD5sum: fdb7ad043e467bc4c82c503e7e4e6887 SHA1: d8c60109de0bf4889731171ffe6e1406ad35c523 SHA256: 266104e6d22aea6e66b5a6d7663c0fb483660cbac798f67e5f6fcdbc85d5ba08 SHA512: 2a2176a230c8b7c36a169b2f8fd3645d78484c96503d4bda0f2ca6463d0e64be6d13a8610eb161637c5cc394b62d62d1c469381144cc4e2c64f09533209759ba Homepage: https://cran.r-project.org/package=plotcli Description: CRAN Package 'plotcli' (Command Line Interface Plotting) The 'plotcli' package provides terminal-based plotting in R. 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This package provides several functions implemented in C++ for explaining the algorithms used for Hidden Markov Models (forward, backward, decoding, learning). 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Kok, & Losardo (2015; ) to investigate nonlinear bivariate relationships in latent regression models using structural equation mixture models (SEMMs). Package: r-cran-plpoisson Architecture: amd64 Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-plpoisson_0.3.1-1.ca2404.1_amd64.deb Size: 58436 MD5sum: eea241ac76e68af9498ba319726d59dd SHA1: 6bff371874203c31bfaee50d4b8b35b1e2949673 SHA256: 3983f2fc5720c5735856385c05cea69ff397d2bf32b5fc091567878f27a3da83 SHA512: 5f923a062139e57c76a16b0d8b0d1c36dab9dc2a91552809de275e74eaf4ccdfe86aadf37e7f8d590b1cc81ad33f2ebc9655ea094fc9191f2987f699d8a02d01 Homepage: https://cran.r-project.org/package=plpoisson Description: CRAN Package 'plpoisson' (Prediction Limits for Poisson Distribution) Prediction limits for the Poisson distribution are produced from both frequentist and Bayesian viewpoints. 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Please see H. (2010) at for more details. 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It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available. Package: r-cran-plugdensity Architecture: amd64 Version: 0.8-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-plugdensity_0.8-5-1.ca2404.1_amd64.deb Size: 19156 MD5sum: 74b76c61a90990db4bf47394d0bbce33 SHA1: f5c790dd97111c133a44c669d7a2a3224165f1e5 SHA256: 2d9a18798582f00c2917198b120aeb9f087603b28c9999e2327f47efcd764c22 SHA512: 83306ca245ee4ca635e09dae42ee8ea7d55b840a04c88f2b28e24b98218aa5c98291079b4ad9db0c259ad3bb966450d043956c7465927e08104132a4f14d85b9 Homepage: https://cran.r-project.org/package=plugdensity Description: CRAN Package 'plugdensity' (Plug-in Kernel Density Estimation) Kernel density estimation with global bandwidth selection via "plug-in". 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Package: r-cran-pma Architecture: amd64 Version: 1.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pma_1.2-4-1.ca2404.1_amd64.deb Size: 266832 MD5sum: f7505f411fe827a8eb18a5420774c638 SHA1: 84263ef1c0af06ac473ff6d705cd71c3fbdb8b1d SHA256: 8787134059bfb5bd203536629699e6579000f362ed3bdb885254b2c581eb7cc8 SHA512: d68623051117945514de10cf79b113eb72895944c6ba3fc9578c22da958866af16d300f562c7d33862ae675c23e6706ded34263efe3b8ee595ac991ee5ccff6c Homepage: https://cran.r-project.org/package=PMA Description: CRAN Package 'PMA' (Penalized Multivariate Analysis) Performs Penalized Multivariate Analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis, described in Witten, Tibshirani and Hastie (2009) and Witten and Tibshirani (2009) Extensions of sparse canonical correlation analysis, with applications to genomic data . Package: r-cran-pmartr Architecture: amd64 Version: 2.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2882 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-data.table, r-cran-doparallel, r-cran-dplyr, r-cran-ggplot2, r-cran-e1071, r-cran-foreach, r-cran-mvtnorm, r-bioc-pcamethods, r-cran-purrr, r-cran-rrcov, r-cran-stringr, r-cran-tidyr, r-cran-rcolorbrewer, r-cran-magrittr, r-cran-parallelly, r-cran-patchwork, r-cran-glmpca, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-bioc-limma, r-cran-rmarkdown, r-bioc-edger, r-bioc-deseq2, r-cran-plotly, r-cran-scales, r-bioc-s4vectors, r-cran-survival, r-cran-testthat, r-cran-trelliscopejs, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-pmartr_2.5.1-1.ca2404.1_amd64.deb Size: 2354880 MD5sum: ee3319f3855a0b86a4d6676b748a78f0 SHA1: c701c8461e40adf4be9fae1e9d37c7009607e279 SHA256: 3bc4a42ba0a72bc824d0c5936086239f73a21240a5873a0f5c906c8ac6f42507 SHA512: 392647447c81ea3c4871cbc275c49ec1d605f2eb16a55efc6f8b4036ef54c64cd4877675c7ac84e2e0574733da2e53a89abf4ea6929e140bae5e2d1314d7ba97 Homepage: https://cran.r-project.org/package=pmartR Description: CRAN Package 'pmartR' (Panomics Marketplace - Quality Control and Statistical Analysisfor Panomics Data) Provides functionality for quality control processing and statistical analysis of mass spectrometry (MS) omics data, in particular proteomic (either at the peptide or the protein level), lipidomic, and metabolomic data, as well as RNA-seq based count data and nuclear magnetic resonance (NMR) data. This includes data transformation, specification of groups that are to be compared against each other, filtering of features and/or samples, data normalization, data summarization (correlation, PCA), and statistical comparisons between defined groups. Implements methods described in: Webb-Robertson et al. (2014) . Webb-Robertson et al. (2011) . Matzke et al. (2011) . Matzke et al. (2013) . Polpitiya et al. (2008) . Webb-Robertson et al. (2010) . Package: r-cran-pmclust Architecture: amd64 Version: 0.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 764 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pbdmpi, r-cran-mass Filename: pool/dists/noble/main/r-cran-pmclust_0.2-1-1.ca2404.1_amd64.deb Size: 558420 MD5sum: 831e054bda06d4ecacd34231d0bc533b SHA1: 56f533625771ed08f83bd8acbf692cbee8c56094 SHA256: b738d4ed29ec9e2ea0e3ced4bb2848c57edaf34ca604fe1937cae71dbf8ddff1 SHA512: 288fd644e7ae6c38cfc093eb0e09e036c704c414a4e44127715a62d0089e6d14cc32b9ad0c62c5886988fced6052fa2412a7dde17837431b9709630ca1ac7f3d Homepage: https://cran.r-project.org/package=pmclust Description: CRAN Package 'pmclust' (Parallel Model-Based Clustering usingExpectation-Gathering-Maximization Algorithm for Finite MixtureGaussian Model) Aims to utilize model-based clustering (unsupervised) for high dimensional and ultra large data, especially in a distributed manner. 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Wiener, Ferdous Gheyas, Pavel Fiser, Justina Ivanauskaite, Frank Liu and Jeffrey R. Sachs (NPJ Vaccines, 2021), . Package: r-cran-poibin Architecture: amd64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-poibin_1.6-1.ca2404.1_amd64.deb Size: 28754 MD5sum: ee450e22f2c2761c1bbd8bbbab4c633c SHA1: 42a1b839fc93c51278463bc9140e432a5ecba739 SHA256: 788a9c19b230f3a96aea68c1312945455241dfdb3b7b5e4626ae17023a88f8c4 SHA512: 54b3d46c58d38cde64a1ed8341f2bc6081f1548fe5869bad3130c709196ee9feb9bdd001dd3446dbfe9d176209aa52f76143c4a3b9879777ec4ea7b4a4d3590e Homepage: https://cran.r-project.org/package=poibin Description: CRAN Package 'poibin' (The Poisson Binomial Distribution) Implementation of both the exact and approximation methods for computing the cdf of the Poisson binomial distribution as described in Hong (2013) . 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Package: r-cran-poisbinom Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-poisbinom_1.0.2-1.ca2404.1_amd64.deb Size: 53030 MD5sum: fdde77785ac8489ac8e0efa8e2ce75f6 SHA1: 3cc1af24ee834fe253ca278dbfdcca6414467e56 SHA256: f1e99f64b75de139bdb5241e24c3fa30af9e5aa3cb16c2bd0af6a02b9379b494 SHA512: 48db9d84d796eb67499b558cc7ae4068352a3ce87f558e203744deae199a36c2586c752ddb3efb15dcd583bea6c232c13445986a99061af42d146b80bc152735 Homepage: https://cran.r-project.org/package=poisbinom Description: CRAN Package 'poisbinom' (A Faster Implementation of the Poisson-Binomial Distribution) Provides the probability, distribution, and quantile functions and random number generator for the Poisson-Binomial distribution. This package relies on FFTW to implement the discrete Fourier transform, so that it is much faster than the existing implementation of the same algorithm in R. Package: r-cran-poisdoublesamp Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-poisdoublesamp_1.1.1-1.ca2404.1_amd64.deb Size: 87122 MD5sum: d933294dc6e33914f5ba5453fa607ea5 SHA1: de47d4c71ab3ebc02f0c395d6f88cab64ea639a7 SHA256: b47d0c56b59e4243b362e9663b5773335ec81b9f74d23573f2aa644246866a61 SHA512: e73a8bf6bc98babd8f38c4242da79d32476524b29c673f7201f36716cd25907b659fd8ae222e4a55fd957ffb1ad23ab8731672530baa76894c9ca1ecf923488e Homepage: https://cran.r-project.org/package=poisDoubleSamp Description: CRAN Package 'poisDoubleSamp' (Confidence Intervals with Poisson Double Sampling) Functions to create confidence intervals for ratios of Poisson rates under misclassification using double sampling. Implementations of the methods described in Kahle, D., P. Young, B. Greer, and D. Young (2016). "Confidence Intervals for the Ratio of Two Poisson Rates Under One-Way Differential Misclassification Using Double Sampling." Computational Statistics & Data Analysis, 95:122–132. Package: r-cran-poismf Architecture: amd64 Version: 0.4.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 6), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-poismf_0.4.0-4-1.ca2404.1_amd64.deb Size: 100918 MD5sum: bfee87ea7d8a527d5aadf639753f7aa5 SHA1: 41a62584231245f958a66221eddcc4fd8f00a219 SHA256: 5d5646bba26b2967cc429566799765b246b4f277bab3e1354333f15a70b4346b SHA512: 4d1403503c922c50e4449c0d4891475c452845617765106f478c765087da3fbcca4ee408ab1f3929e4078b0a4e4dfaa99ec314debadd43b62dcf53df99e3ee13 Homepage: https://cran.r-project.org/package=poismf Description: CRAN Package 'poismf' (Factorization of Sparse Counts Matrices Through PoissonLikelihood) Creates a non-negative low-rank approximate factorization of a sparse counts matrix by maximizing Poisson likelihood with L1/L2 regularization (e.g. for implicit-feedback recommender systems or bag-of-words-based topic modeling) (Cortes, (2018) ), which usually leads to very sparse user and item factors (over 90% zero-valued). Similar to hierarchical Poisson factorization (HPF), but follows an optimization-based approach with regularization instead of a hierarchical prior, and is fit through gradient-based methods instead of variational inference. Package: r-cran-poissonbinomial Architecture: amd64 Version: 1.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 799 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-poissonbinomial_1.2.8-1.ca2404.1_amd64.deb Size: 197912 MD5sum: 355a6050d1ad054bb1df9f58b741d8bb SHA1: 245e786095bf47879b3012009b0561aa2e6d5774 SHA256: a95c1037357ceb6ce7da9929cf8cdc1dd3c3198e6dc069eadd2f38b04903d300 SHA512: ab3d15bb480b9e2c21754972f1483fb22893f9f383c489f7792850270b9cd2996c87c65b725363082d20fa5d0cf2c737a3d4a20fb6e5d9dc723effe3c5ab1cf6 Homepage: https://cran.r-project.org/package=PoissonBinomial Description: CRAN Package 'PoissonBinomial' (Efficient Computation of Ordinary and Generalised PoissonBinomial Distributions) Efficient implementations of multiple exact and approximate methods as described in Hong (2013) , Biscarri, Zhao & Brunner (2018) and Zhang, Hong & Balakrishnan (2018) for computing the probability mass, cumulative distribution and quantile functions, as well as generating random numbers for both the ordinary and generalised Poisson binomial distribution. Package: r-cran-poissoned Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-poissoned_0.1.3-1.ca2404.1_amd64.deb Size: 23984 MD5sum: 8a6ffd448a5ea5cded4dce0293a97909 SHA1: fe79815b6d0f6b294c68d00225fc36492850df4c SHA256: 78af8fe62c3f62be07f59dfe9315fa8641b1d75d1c121be1e81fa028b9ec1d02 SHA512: b48bbd2dfc7cc33096025a3bcc2fe2a8ded63d57d7d21758e75d1de3b739751834e20663ee592743105bfedcdcf9383569b38a2519ae12058458dfe3883a7682 Homepage: https://cran.r-project.org/package=poissoned Description: CRAN Package 'poissoned' (Poisson Disk Sampling in 2D and 3D) Poisson disk sampling is a method of generating blue noise sample patterns where all samples are at least a specified distance apart. Poisson samples may be generated in two or three dimensions with this package. The algorithm used is an implementation of Bridson's "Fast Poisson disk sampling in arbitrary dimensions" . Package: r-cran-poissonmt Architecture: amd64 Version: 0.3-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-robustbase, r-cran-robcbi, r-cran-checkmate Filename: pool/dists/noble/main/r-cran-poissonmt_0.3-5-1.ca2404.1_amd64.deb Size: 109352 MD5sum: 38282f105de2b4fbdf6ab8512c3a6bc1 SHA1: 8742a66ec9888fe9e0de43f1006824d4c2db2942 SHA256: c2ae612902aa4a114ad883e0b41a0e22d8305d38283ab9b8bf15b995ff1ad16a SHA512: d62d3fa0f479e9edef69e5617d921a482acdc15063f66d85ca3fa87dc23000da6ef68966bf97ae835d7d5e149675085cf91b14f42221401bf853e26ee92b6ace Homepage: https://cran.r-project.org/package=poissonMT Description: CRAN Package 'poissonMT' (Robust M-Estimators Based on Transformations for Poisson Model) R functions for the computation of Least Square based on transformation (L2T) and robust M-estimators based on transformations (MT-estimators) for Poisson regression models. Package: r-cran-poissonmultinomial Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-poissonmultinomial_1.1-1.ca2404.1_amd64.deb Size: 73184 MD5sum: 39e06dc06abd618e15df75edc40898fc SHA1: 10140ec3f8936663727c6699bf215fa2c76af247 SHA256: 210a326a9766579609e4f5b09183daba8ffc35743441c118e41e5eaaf69dfdc0 SHA512: c0a2a1e2c4b0f559a9242a224c931c861ceedf09fb2d309cdb25f95ee58aaf6256c1ab24fe0b7cbd1e6b8369674829cc1f89161007ff3f560a949c6199a4a730 Homepage: https://cran.r-project.org/package=PoissonMultinomial Description: CRAN Package 'PoissonMultinomial' (The Poisson-Multinomial Distribution) Implementation of the exact, normal approximation, and simulation-based methods for computing the probability mass function (pmf) and cumulative distribution function (cdf) of the Poisson-Multinomial distribution, together with a random number generator for the distribution. The exact method is based on multi-dimensional fast Fourier transformation (FFT) of the characteristic function of the Poisson-Multinomial distribution. The normal approximation method uses a multivariate normal distribution to approximate the pmf of the distribution based on central limit theorem. The simulation method is based on the law of large numbers. Details about the methods are available in Lin, Wang, and Hong (2022) . Package: r-cran-poissonpca Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.3), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-poissonpca_1.0.3-1.ca2404.1_amd64.deb Size: 155314 MD5sum: b4a9c0c1edf3eb6fc10ae1dcb855564d SHA1: 72b3ebe78df1c2c695e629e8f11e38141d2828e8 SHA256: ae6a1e8a1eee778684c5323101efcc5bfa8921ac0a08a8c0b9a76eee658f4b08 SHA512: 7ed3606e6a1d779c328c9ac1091dc36df79d0621bdb9e0385815d526990760dd5f6c0f96676ba9d17a77dbb4473519538744e4296c261bc8eb118c0a01ffc115 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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The following cubature methods are implemented: product Gauss cubature (Sommariva and Vianello, 2007, ), the simple two-dimensional midpoint rule (wrapping 'spatstat.geom' functions), and adaptive cubature for radially symmetric functions via line integrate() along the polygon boundary (Meyer and Held, 2014, , Supplement B). For simple integration along the axes, the 'cubature' package is more appropriate. Package: r-cran-polykde Architecture: amd64 Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4360 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-dofuture, r-cran-foreach, r-cran-future, r-cran-gsl, r-cran-movmf, r-cran-progressr, r-cran-rcpp, r-cran-rcppprogress, r-cran-rotasym, r-cran-sphunif, r-cran-rcpparmadillo Suggests: r-cran-alphashape3d, r-cran-bessel, r-cran-dirstats, r-cran-fixedpoint, r-cran-ks, r-cran-manipulate, r-cran-numderiv, r-cran-optimparallel, r-cran-testthat, r-cran-viridis, r-cran-rgl, r-cran-scatterplot3d, r-cran-sdetorus, r-cran-smacof Filename: pool/dists/noble/main/r-cran-polykde_1.1.7-1.ca2404.1_amd64.deb Size: 4054322 MD5sum: 24083dc84814e4ee0f4ad50a6f6d8b61 SHA1: 44e850d7c95e31cba9116a0654e5739d16cb0a6d SHA256: cb57345580054982cddbae3bd4056c4917877ed3ede24867923318d1f954bed2 SHA512: ec8cb9bbb0791e5bb68ed5dcb2f798c68e5e922903229e925a95a246891f5c7f8b5574bfc46eb35d4d608a2ae1d153dfe5c8fe1d02076589ee12a6db31cd4f87 Homepage: https://cran.r-project.org/package=polykde Description: CRAN Package 'polykde' (Polyspherical Kernel Density Estimation) Kernel density estimation on the polysphere, (hyper)sphere, and circle. Includes functions for density estimation, regression estimation, ridge estimation, bandwidth selection, kernels, samplers, and homogeneity tests. Companion package to García-Portugués and Meilán-Vila (2025) and García-Portugués and Meilán-Vila (2023) . Package: r-cran-polylabelr Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-testthat, r-cran-spelling, r-cran-sf Filename: pool/dists/noble/main/r-cran-polylabelr_1.0.0-1.ca2404.1_amd64.deb Size: 69482 MD5sum: c360e22898337e8ffc3722be7d57e238 SHA1: c331e5ddd6fdd01a27211dacac511c91ae0c84ce SHA256: 2dec8c5bb146f6d1ad6bb4530b8ebdfe34a51716dc621a59fd47c405e8643aad SHA512: 46d06d4b984b96a663b1127e26873c48286576106246832c55c3c89996fa57c17eb3dbeaf2d9c6d512f943ace468737dd73843edea91a4b74f1a312ad7d2defe Homepage: https://cran.r-project.org/package=polylabelr Description: CRAN Package 'polylabelr' (Find the Pole of Inaccessibility (Visual Center) of a Polygon) A wrapper around the C++ library 'polylabel' from 'Mapbox', providing an efficient routine for finding the approximate pole of inaccessibility of a polygon, which usually serves as an excellent candidate for labeling of a polygon. Package: r-cran-polynomf Architecture: amd64 Version: 2.0-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 924 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-polynomf_2.0-8-1.ca2404.1_amd64.deb Size: 598290 MD5sum: b406ce4186473ca9baa3092e02065352 SHA1: c0f707e1f38a5b0cbe5eef65a88b76333bee20e5 SHA256: 5ff8a59c09e0e1bcc74cb081ca365d1ce15653d6c26d512243112592b1ccf70a SHA512: 8b74bcbefa4ad24cc60cd2a88314bb64ccb0a8b58fb0c2ed8604afe1260f84337bf77fd2d1fac8f59475521bebbfdc16e18d667938fefe711909abad6c6c4d4a Homepage: https://cran.r-project.org/package=PolynomF Description: CRAN Package 'PolynomF' (Polynomials in R) Implements univariate polynomial operations in R, including polynomial arithmetic, finding zeros, plotting, and some operations on lists of polynomials. Package: r-cran-polyqtlr Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5049 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-doparallel, r-cran-foreach, r-cran-hmisc, r-cran-knitr, r-cran-nlme, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-reshape2, r-cran-rcpparmadillo Suggests: r-cran-igraph, r-cran-mappoly, r-cran-polymapr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-polyqtlr_0.1.1-1.ca2404.1_amd64.deb Size: 3972382 MD5sum: 4a0e82d580dba44278a040289e10b13a SHA1: 07cd6960713fff04cee4011473300799204dc47d SHA256: 7f6f991cedd0495f46988da2b026e0a532308ac70b27b01ea65cedbbf07a2d55 SHA512: 6cd8b28160c1a10b3e9fc979606b1af83dc56a2cceb3d25a5c1cdb114bbb01d9d486aebea9c3297aaf6abab414da6a1dd686eef982d696abd7112d38711088da Homepage: https://cran.r-project.org/package=polyqtlR Description: CRAN Package 'polyqtlR' (QTL Analysis in Autopolyploid Bi-Parental F1 Populations) Quantitative trait loci (QTL) analysis and exploration of meiotic patterns in autopolyploid bi-parental F1 populations. For all ploidy levels, identity-by-descent (IBD) probabilities can be estimated. Significance thresholds, exploring QTL allele effects and visualising results are provided. For more background and to reference the package see . Package: r-cran-polyrad Architecture: amd64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4651 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastmatch, r-bioc-pcamethods, r-cran-rcpp, r-cran-stringi Suggests: r-cran-rrblup, r-bioc-rsamtools, r-bioc-genomeinfodb, r-bioc-biostrings, r-bioc-genomicranges, r-bioc-variantannotation, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-bioc-iranges, r-bioc-biocgenerics, r-cran-knitr, r-cran-rmarkdown, r-bioc-genomicfeatures, r-cran-ggplot2, r-cran-adegenet, r-bioc-txdbmaker, r-cran-polymapr, r-bioc-bsgenome Filename: pool/dists/noble/main/r-cran-polyrad_2.0.1-1.ca2404.1_amd64.deb Size: 2908144 MD5sum: 8c41a676cc04a29c85b93bec2df362c7 SHA1: f3497a07719a6b16094ebf304cc4f617473b200b SHA256: 5ef028accff12b34bcf291ea2e53d6a49b847260ef21f00bf2be275220976c13 SHA512: 38a74c598bb1908cb4a335366adf3a7af461ecbfe2e68fea87959898daa7da6ebc68d083b296511a485081ccf6efed1fa4daba2f4e3f05187eaffaac2a598001 Homepage: https://cran.r-project.org/package=polyRAD Description: CRAN Package 'polyRAD' (Genotype Calling with Uncertainty from Sequencing Data inPolyploids and Diploids) Read depth data from genotyping-by-sequencing (GBS) or restriction site-associated DNA sequencing (RAD-seq) are imported and used to make Bayesian probability estimates of genotypes in polyploids or diploids. The genotype probabilities, posterior mean genotypes, or most probable genotypes can then be exported for downstream analysis. 'polyRAD' is described by Clark et al. (2019) , and the Hind/He statistic for marker filtering is described by Clark et al. (2022) . A variant calling pipeline for highly duplicated genomes is also included and is described by Clark et al. (2020, Version 1) . Package: r-cran-polysat Architecture: amd64 Version: 1.7-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1833 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-ade4, r-cran-adegenet, r-cran-ape Filename: pool/dists/noble/main/r-cran-polysat_1.7-7-1.ca2404.1_amd64.deb Size: 1280178 MD5sum: 14448e682b81eaa9119ab932a6932b1c SHA1: 4356453dd3a3cf9750071b1d54a1a19fde5ee7e6 SHA256: bb88bc105a046362dce930c2542eb1c8a2e2e4896a6f80a29906a7b86cdeea97 SHA512: 192ba77b2749471f4b11f2fe64d197939c53302475b0334ca44ce8bb9216fa8f23f43e2a2f0568b17059d9cb883bce5317ab8caf1fd30469f2600a64091a316c Homepage: https://cran.r-project.org/package=polysat Description: CRAN Package 'polysat' (Tools for Polyploid Microsatellite Analysis) A collection of tools to handle microsatellite data of any ploidy (and samples of mixed ploidy) where allele copy number is not known in partially heterozygous genotypes. It can import and export data in ABI 'GeneMapper', 'Structure', 'ATetra', 'Tetrasat'/'Tetra', 'GenoDive', 'SPAGeDi', 'POPDIST', 'STRand', and binary presence/absence formats. It can calculate pairwise distances between individuals using a stepwise mutation model or infinite alleles model, with or without taking ploidies and allele frequencies into account. These distances can be used for the calculation of clonal diversity statistics or used for further analysis in R. Allelic diversity statistics and Polymorphic Information Content are also available. polysat can assist the user in estimating the ploidy of samples, and it can estimate allele frequencies in populations, calculate pairwise or global differentiation statistics based on those frequencies, and export allele frequencies to 'SPAGeDi' and 'adegenet'. Functions are also included for assigning alleles to isoloci in cases where one pair of microsatellite primers amplifies alleles from two or more independently segregating isoloci. polysat is described by Clark and Jasieniuk (2011) and Clark and Schreier (2017) . Package: r-cran-polywog Architecture: amd64 Version: 0.4-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-misctools, r-cran-foreach, r-cran-formula, r-cran-glmnet, r-cran-iterators, r-cran-matrix, r-cran-ncvreg, r-cran-rcpp, r-cran-stringr Suggests: r-cran-cardata, r-cran-lattice, r-cran-rgl Filename: pool/dists/noble/main/r-cran-polywog_0.4-2-1.ca2404.1_amd64.deb Size: 189380 MD5sum: f66ef815682c99b04e22a4358a934890 SHA1: 554bfab77e16fe01bf516ceaf443c9b07859e655 SHA256: f51ffb9d9de72765bcb35073f4e14e7f5e01d2080e69942f466ad6d701c374f5 SHA512: eeb8001b9ebcc703109af1d2463b41bb21a70ef65bed3365415ac73297724fb4e32ad212c48c75869c3b8b4712146c2fe09c589cb6255a251c26b18f357565da Homepage: https://cran.r-project.org/package=polywog Description: CRAN Package 'polywog' (Bootstrapped Basis Regression with Oracle Model Selection) Routines for flexible functional form estimation via basis regression, with model selection via the adaptive LASSO or SCAD to prevent overfitting. Package: r-cran-pomaspu Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrixstats, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-pomaspu_1.0.0-1.ca2404.1_amd64.deb Size: 72174 MD5sum: fc98f8d8f230c2caacec8b6998379d2c SHA1: f4b29fe1f8f0c8d5630dcc7106e44b70c71f9e3e SHA256: ea0d28271c20decd6b36a7e9abea418389b75249d98d525c5f82b1395a59e075 SHA512: 2a198e580e11ac691bd4317cb78979f03552593df72b96fdf0488338f3f092cd11970a8aa65812111e1d3675a979aef2a79f48cbb456c5e1f7352239b03627ab Homepage: https://cran.r-project.org/package=POMaSPU Description: CRAN Package 'POMaSPU' (Adaptive Association Tests for Multiple Phenotypes usingProportional Odds Model (POM-aSPU)) POM-aSPU test evaluates an association between an ordinal response and multiple phenotypes, for details see Kim and Pan (2017) . Package: r-cran-pomdp Architecture: amd64 Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1940 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pomdpsolve, r-cran-processx, r-cran-matrix, r-cran-rcpp, r-cran-foreach, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gifski, r-cran-testthat, r-cran-ternary, r-cran-visnetwork, r-cran-sarsop, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-pomdp_1.2.5-1.ca2404.1_amd64.deb Size: 1325304 MD5sum: f16902e0f71f32d2382eaab80a2f4a56 SHA1: 12ef7ec6b28e41dd46ff4ba11b38c31cbcf29269 SHA256: 46ebdfacb65ef1e29ed51facfe44b7612679fd71a3ce65a3258ecc4a9e87cd36 SHA512: bdc3daae95d716ef18b00db9d6fed4e9b7b9737c4c2c374f38d5be0d3f73c82691262bf61d96d6e4be7fa375833dfdba2d2d820f81e7eed800760587490cf1d6 Homepage: https://cran.r-project.org/package=pomdp Description: CRAN Package 'pomdp' (Infrastructure for Partially Observable Markov DecisionProcesses (POMDP)) Provides the infrastructure to define and analyze the solutions of Partially Observable Markov Decision Process (POMDP) models. Interfaces for various exact and approximate solution algorithms are available including value iteration, point-based value iteration and SARSOP. Hahsler and Cassandra . Package: r-cran-pomdpsolve Architecture: amd64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-pomdp Filename: pool/dists/noble/main/r-cran-pomdpsolve_1.0.6-1.ca2404.1_amd64.deb Size: 154220 MD5sum: cb41b7d826673b4929d9f39ddbb206a8 SHA1: 7740b58b62b51f062b8a577c56b2fd9eb529832f SHA256: 4d5b4f086931a24b444395db0f9f3bf4d5a252cd35ff9139268848fdb31c5f16 SHA512: 288fadacc655cccabbed76942b1d9c5a3f98bade6d595c886dc5562deaa183d64ecd1c58e560dc11f5c2468a755ef832d0977d2990c6427617185c9811ae5819 Homepage: https://cran.r-project.org/package=pomdpSolve Description: CRAN Package 'pomdpSolve' (Interface to 'pomdp-solve' for Partially Observable MarkovDecision Processes) Installs an updated version of 'pomdp-solve' and provides a low-level interface. Pomdp-solve is a program to solve Partially Observable Markov Decision Processes (POMDPs) using a variety of exact and approximate value iteration algorithms. A convenient R infrastructure is provided in the separate package pomdp. Hahsler and Cassandra . Package: r-cran-pomp Architecture: amd64 Version: 6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2016 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-mvtnorm, r-cran-desolve, r-cran-coda, r-cran-data.table Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-dplyr, r-cran-tidyr, r-cran-subplex, r-cran-nloptr Filename: pool/dists/noble/main/r-cran-pomp_6.4-1.ca2404.1_amd64.deb Size: 1446066 MD5sum: 188c71c627bfae19d536b13247cff0c3 SHA1: e6cc6b79fb7adda182e3f0a83641369f6c0d76e9 SHA256: baf0a7cccc72ad3214280e510a87b9930f7b5843632ffbedc32a43e51459f2e2 SHA512: d90c41fc893f9c27bd534c5f8b9305d3c40e86a9479da47f508f4aef3cb723247628481b3115c60c86a957eb6324a953f1649484c3a7a25b0f7292e8236efc8f Homepage: https://cran.r-project.org/package=pomp Description: CRAN Package 'pomp' (Statistical Inference for Partially Observed Markov Processes) Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models. Package: r-cran-pompp Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 720 Depends: libc6 (>= 2.35), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-coda, r-cran-geor, r-cran-rcppeigen, r-cran-rcppprogress Suggests: r-cran-bayesplot, r-cran-ggplot2, r-cran-mass Filename: pool/dists/noble/main/r-cran-pompp_0.1.3-1.ca2404.1_amd64.deb Size: 402218 MD5sum: a869695c399bb259edcdcf6a42f73e74 SHA1: a50972c5a4dbf54cbfe308c68d82a62619be54f2 SHA256: f33bbf50de3dee02f453b3cb10e1da1bda70048ea5be40ca92ddf53c8b9c2f2d SHA512: 110564789524083861b02630f5f059b562761f1918747b20daafc57ce1bae9974aa72ea2520467a909a886d627fcefa0570313447ffa675c2eba32725b1d174d Homepage: https://cran.r-project.org/package=pompp Description: CRAN Package 'pompp' (Presence-Only for Marked Point Process) Inspired by Moreira and Gamerman (2022) , this methodology expands the idea by including Marks in the point process. Using efficient 'C++' code, the estimation is possible and made faster with 'OpenMP' enabled computers. This package was developed under the project PTDC/MAT-STA/28243/2017, supported by Portuguese funds through the Portuguese Foundation for Science and Technology (FCT). Package: r-cran-pooh Architecture: amd64 Version: 0.3-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pooh_0.3-2-1.ca2404.1_amd64.deb Size: 19282 MD5sum: 585bc8c4ee045307e99779515fa14e20 SHA1: 6831796ffbd6f05841e563d2f64f4489539e7be2 SHA256: 76cbb5deee08a105e02d83b7f4964194593405b4cc252d160e47eb9a1e9eb340 SHA512: 8355dd28278a7812b6da27d1955ea4f7d4d9a380e37dfbe8858877532358c7153bf75c5adc7403a1bdbeeb483f881fabd22fc8a1e547c45026e2add0c8e56aaf Homepage: https://cran.r-project.org/package=pooh Description: CRAN Package 'pooh' (Partial Orders and Relations) Finds equivalence classes corresponding to a symmetric relation or undirected graph. Finds total order consistent with partial order or directed graph (so-called topological sort). Package: r-cran-poolfstat Architecture: amd64 Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3319 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-foreach, r-cran-doparallel, r-cran-diagrammer, r-cran-ape, r-cran-ryacas, r-cran-matrix, r-cran-rcppprogress, r-cran-progress, r-cran-nnls Filename: pool/dists/noble/main/r-cran-poolfstat_3.1.0-1.ca2404.1_amd64.deb Size: 2924526 MD5sum: 4e71e2eed2d7ada26675db153ff1ee13 SHA1: d58673bd0efd2739eddef240c58ab4fd64bd8124 SHA256: bab4761087bcb9f85365aeb77f3c4b961126f14605a18e794d2193f09ed6a9d8 SHA512: 09993ad20a09134720be0dbdc7f546851d19ac270f3b42b2867135a54e814f44a6b4fdca1872e704ba40dcffa97c20ba2b54ed5f69b7bb5d61c6c2be62c62f4d Homepage: https://cran.r-project.org/package=poolfstat Description: CRAN Package 'poolfstat' (Computing f-Statistics and Building Admixture Graphs Based onAllele Count or Pool-Seq Read Count Data) Functions for the computation of F-, f- and D-statistics (e.g., Fst, hierarchical F-statistics, Patterson's F2, F3, F3*, F4 and D parameters) in population genomics studies from allele count or Pool-Seq read count data and for the fitting, building and visualization of admixture graphs. The package also includes several utilities to manipulate Pool-Seq data stored in standard format (e.g., such as 'vcf' files or 'rsync' files generated by the the 'PoPoolation' software) and perform conversion to alternative format (as used in the 'BayPass' and 'SelEstim' software). As of version 2.0, the package also includes utilities to manipulate standard allele count data (e.g., stored in 'TreeMix', 'BayPass' and 'SelEstim' format, see the Package vignette for details). Package: r-cran-pooltestr Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3127 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-brms, r-cran-dplyr, r-cran-lme4, r-cran-progress, r-cran-rcpp, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-stringr, r-cran-tibble, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-covr Filename: pool/dists/noble/main/r-cran-pooltestr_0.2.0-1.ca2404.1_amd64.deb Size: 932918 MD5sum: 033953761b9b449afa8cb55e5a103dfd SHA1: 8fb537f27e96cefbb735d5810e642d41df7a72e3 SHA256: aa00af1c39403ea7d69e468a4b20bcd239743efcb81d4bc7e03621e66bf1a3d0 SHA512: fbd38c969c5822e3a072a1a438504469ab224042ba154c913aa28cfc85a9e3abbb5fb3a99b4084811cb8130408f263a528fbd932481d0506c03cd2b956f218f1 Homepage: https://cran.r-project.org/package=PoolTestR Description: CRAN Package 'PoolTestR' (Prevalence and Regression for Pool-Tested (Group-Tested) Data) An easy-to-use tool for working with presence/absence tests on 'pooled' or 'grouped' samples. The primary application is for estimating prevalence of a marker in a population based on the results of tests on pooled specimens. This sampling method is often employed in surveillance of rare conditions in humans or animals (e.g. molecular xenomonitoring). The package was initially conceived as an R-based alternative to the molecular xenomonitoring software, 'PoolScreen' . However, it goes further, allowing for estimates of prevalence to be adjusted for hierarchical sampling frames, and perform flexible mixed-effect regression analyses (McLure et al. Environmental Modelling and Software. ). The package is currently in early stages, however more features are planned or in the works: e.g. adjustments for imperfect test specificity/sensitivity, functions for helping with optimal experimental design, and functions for spatial modelling. Package: r-cran-pop.lion Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pop.lion_1.0.1-1.ca2404.1_amd64.deb Size: 42464 MD5sum: 0191a26bb72b4236db5dfe8ffbf6b2d7 SHA1: 363be927a4636128154c1f01a19bd6cdcf19ebc3 SHA256: d0560daf3f4f7fccbd0e9e8cc0acb752984de495a6a1b2aea8baf395e6e10696 SHA512: 8c1d7f4697edf21f45c4040760cea212ae3ede87f25e13c9de81fd1f6f74c93927fd7d2123b06485f0861c6f153515dcb0a636cbf509caa7400c54904ab5d706 Homepage: https://cran.r-project.org/package=pop.lion Description: CRAN Package 'pop.lion' (Models for Simulating Lion Populations) Simulate the dynamic of lion populations using a specific Individual-Based Model (IBM) compiled in C. Package: r-cran-pop.wolf Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind Filename: pool/dists/noble/main/r-cran-pop.wolf_1.0-1.ca2404.1_amd64.deb Size: 33412 MD5sum: 813881ec176394bcea97d4df13eda89a SHA1: d9ab81d8b861d8ae31831a51d24aa8865999518c SHA256: 90ee761c93ef410b1eeaf9bf374051c25c40d044fa583e209682821ca7d03f80 SHA512: 7bd42447f0b39373e84b21f8fd5cc12f49b8d7ebf2aa9e6d7c3a76c087edef6002a09bb0a2a61d30cd9034f43418641d7590d682c0a5945f0bc1654443ff4a97 Homepage: https://cran.r-project.org/package=pop.wolf Description: CRAN Package 'pop.wolf' (Models for Simulating Wolf Populations) Simulate the dynamic of wolf populations using a specific Individual-Based Model (IBM) compiled in C, see Chapron et al. (2016) . Package: r-cran-poppcr Architecture: amd64 Version: 0.1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-poppcr_0.1.1.1-1.ca2404.1_amd64.deb Size: 232548 MD5sum: bb87467cb1a7c974e91e92ecaba3b5ba SHA1: 644733a34fbefb33db7dc617a2ed9b8ba0c6ef3c SHA256: 4d60d59fb1fb022a7da8ffdf01328c60fedcf8425e1bff46da1fdc6958abe6c7 SHA512: 2a7c0f62edf6a3ee260c7a192f6363a5889384e6487bcfa85317f845e4152d200a06bfee7f19bd5a6cbb8427470883b0449f7934a7d72ad48b0c9f78c3b983b5 Homepage: https://cran.r-project.org/package=popPCR Description: CRAN Package 'popPCR' (Classify Digital PCR Droplets by Fitting FluorescencePopulations) Estimates DNA target concentration by classifying digital PCR (polymerase chain reaction) droplets as positive, negative, or rain, using Expectation-Maximization Clustering. The fitting is accomplished using the 'EMMIXskew' R package (v. 1.0.3) by Kui Wang, Angus Ng, and Geoff McLachlan (2018) as based on their paper "Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data" . 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Originally described in Kamvar, Tabima, and Grünwald (2014) with version 2.0 described in Kamvar, Brooks, and Grünwald (2015) . Package: r-cran-popsom7 Architecture: amd64 Version: 7.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: libc6 (>= 2.2.5), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields, r-cran-ggplot2, r-cran-hash, r-cran-som Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-popsom7_7.1.0-1.ca2404.1_amd64.deb Size: 97594 MD5sum: b9fd7eca92b19516b4ed6f6ea078ecfb SHA1: 14929db24176298d97680bae792f656f0e1fa657 SHA256: d476628e0744c16c188d50f1c8f772434cf98185079477d179afe9e8be43eddc SHA512: d6a935d6f16bc4c7804441ed28f8b39a891e1218e88b7b3c1488647e79937a8f5f250dcfb9a7f94bcb5abb3773053a8ebea53d5a039675cc2b3333873851910c Homepage: https://cran.r-project.org/package=popsom7 Description: CRAN Package 'popsom7' (A Fast, User-Friendly Implementation of Self-Organizing Maps(SOMs)) Methods for building self-organizing maps (SOMs) with a number of distinguishing features such automatic centroid detection and cluster visualization using starbursts. For more details see the paper "Improved Interpretability of the Unified Distance Matrix with Connected Components" by Hamel and Brown (2011) in . The package provides user-friendly access to two models we construct: (a) a SOM model and (b) a centroid based clustering model. The package also exposes a number of quality metrics for the quantitative evaluation of the map, Hamel (2016) . Finally, we reintroduced our fast, vectorized training algorithm for SOM with substantial improvements. It is about an order of magnitude faster than the canonical, stochastic C implementation . Package: r-cran-population Architecture: amd64 Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind Filename: pool/dists/noble/main/r-cran-population_0.3-1.ca2404.1_amd64.deb Size: 31444 MD5sum: f9ee7762942e60d1e650b2be94c49c9c SHA1: 4d78f94d0ab440cbff1a237959bc324e7d1bda44 SHA256: 1459357c31ad5a77d3af0622cac87ec19f6d9015fddcbffd1226eea7ba24b4b7 SHA512: f038953e41d8df6fa9b355a00b89ae2ab2301f9b2bae1da9e0ec3599cc201428c5280190d7d50ca9ceec3caf993562d2a37157871fa6a211fb7e70c8aebeda06 Homepage: https://cran.r-project.org/package=population Description: CRAN Package 'population' (Models for Simulating Populations) Run population simulations using an Individual-Based Model (IBM) compiled in C. Package: r-cran-poputils Architecture: amd64 Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1207 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/noble/main/r-cran-poputils_0.6.1-1.ca2404.1_amd64.deb Size: 1031990 MD5sum: adb83dedb948615114ef9d3d34ffb5d3 SHA1: 16c3f758961e3e9ea8189ee9c5b3009c80ef1f45 SHA256: 7c36183d27459f7dbc8f8140a49ab0862533589ecbecd654b793c1b6bff543c7 SHA512: afb2f8fe6a26a405694706943b7919d439bd0b344add748cd47714f159c5b49fac3e543eefd14e35c524af7a110f43508f77b07d3e79e52f6dffc9c614d1cf9b Homepage: https://cran.r-project.org/package=poputils Description: CRAN Package 'poputils' (Demographic Analysis and Data Manipulation) Perform tasks commonly encountered when preparing and analysing demographic data. Some functions are intended for end users, and others for developers. Includes functions for working with life tables. Package: r-cran-porridge Architecture: amd64 Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 687 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-mvtnorm, r-cran-rcpp, r-cran-pracma, r-cran-rcpparmadillo Suggests: r-cran-rags2ridges Filename: pool/dists/noble/main/r-cran-porridge_0.3.3-1.ca2404.1_amd64.deb Size: 392138 MD5sum: af541e24b33e7567eacc9c627ea78797 SHA1: 09885c5f700d20476bb535501cbe2a928c41ec18 SHA256: 60973cd2adc458f26ad9d873ee17f78c3feba9eeccf24d13a878c5eb60b84a56 SHA512: 7afe022e27d607491cd9ad85abd47b43f0cbd1af5ea1069afdb62d6bc6e4eee46946be963dd514b078e3453c7c91107d4ce917cd5ddd411a2662c10434440557 Homepage: https://cran.r-project.org/package=porridge Description: CRAN Package 'porridge' (Ridge-Type Penalized Estimation of a Potpourri of Models) The name of the package is derived from the French, 'pour' ridge, and provides functionality for ridge-type estimation of a potpourri of models. Currently, this estimation concerns that of various Gaussian graphical models from different study designs. Among others it considers the regular Gaussian graphical model and a mixture of such models. The porridge-package implements the estimation of the former either from i) data with replicated observations by penalized loglikelihood maximization using the regular ridge penalty on the parameters (van Wieringen, Chen, 2021) or ii) from non-replicated data by means of either a ridge estimator with multiple shrinkage targets (as presented in van Wieringen et al. 2020, ) or the generalized ridge estimator that allows for both the inclusion of quantitative and qualitative prior information on the precision matrix via element-wise penalization and shrinkage (van Wieringen, 2019, ). Additionally, the porridge-package facilitates the ridge penalized estimation of a mixture of Gaussian graphical models (Aflakparast et al., 2018). On another note, the package also includes functionality for ridge-type estimation of the generalized linear model (as presented in van Wieringen, Binder, 2022, ). Package: r-cran-port4me Architecture: amd64 Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-port4me_0.7.1-1.ca2404.1_amd64.deb Size: 55870 MD5sum: 0973518af0aa46329d3522b501b969aa SHA1: 0055d28dd4fa875cf3d248fb7127eba6d2e693c0 SHA256: e1e3fef4fffefeb89d91e973c31659ad4e4971113eb61506f36c6fda3d9ffe52 SHA512: 5435f6b8245ea1f05155b3f689f0e93ef4ba8de9e2c1e4635f94e3a8140cc7d8877392b39c1cc981e99c1fe34a46251afa43549cb054b1b23b3b2844d6c66ac6 Homepage: https://cran.r-project.org/package=port4me Description: CRAN Package 'port4me' (Get the Same, Personal, Free 'TCP' Port over and over) An R implementation of the cross-platform, language-independent "port4me" algorithm (), which (1) finds a free Transmission Control Protocol ('TCP') port in [1024,65535] that the user can open, (2) is designed to work in multi-user environments, (3), gives different users, different ports, (4) gives the user the same port over time with high probability, (5) gives different ports for different software tools, and (6) requires no configuration. Package: r-cran-portfolioanalytics Architecture: amd64 Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2527 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-xts, r-cran-foreach, r-cran-performanceanalytics, r-cran-gensa, r-cran-roi.plugin.symphony, r-cran-mco, r-cran-pso Suggests: r-cran-quantmod, r-cran-deoptim, r-cran-iterators, r-cran-doparallel, r-cran-domc, r-cran-fgarch, r-cran-rglpk, r-cran-quadprog, r-cran-roi, r-cran-roi.plugin.glpk, r-cran-roi.plugin.quadprog, r-cran-corpcor, r-cran-testthat, r-cran-nloptr, r-cran-mass, r-cran-robustbase, r-cran-osqp, r-cran-cvxr, r-cran-data.table, r-cran-knitr, r-cran-rmarkdown, r-cran-gse, r-cran-robstattm, r-cran-pcra, r-cran-r.rsp, r-cran-rpese, r-cran-ttr, r-cran-matrix Filename: pool/dists/noble/main/r-cran-portfolioanalytics_2.1.2-1.ca2404.1_amd64.deb Size: 1806982 MD5sum: 68b0edd01dcd9937b119fc256dbb510a SHA1: 6b12a82667ccfd4a487cd907fe054c08d5406754 SHA256: c033dfeb8163335531388cee871d72749403d1cfb15e8f014861bea2c4f0248d SHA512: 1def5179fc32ac5a5a499a40db0e5f83307508a7c806a808c14409b868f646669aeb0d55f5290e7da700ab81147eab4938fd817208555c36d782a824d7130175 Homepage: https://cran.r-project.org/package=PortfolioAnalytics Description: CRAN Package 'PortfolioAnalytics' (Portfolio Analysis, Including Numerical Methods for Optimizationof Portfolios) Portfolio optimization and analysis routines and graphics. Package: r-cran-portvine Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5354 Depends: libc6 (>= 2.35), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-dplyr, r-cran-dtplyr, r-cran-future.apply, r-cran-ppcor, r-cran-rcpp, r-cran-rlang, r-cran-rugarch, r-cran-rvinecopulib, r-cran-tidyr, r-cran-bh, r-cran-kde1d, r-cran-rcppeigen, r-cran-rcppthread, r-cran-wdm Suggests: r-cran-covr, r-cran-future, r-cran-ggplot2, r-cran-ggtext, r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-portvine_1.0.3-1.ca2404.1_amd64.deb Size: 1690796 MD5sum: ed5e349789401e5a62c7e66ead19fa61 SHA1: 3fae0a92284a5f4159d3e62d465dd2448d72ddbd SHA256: cea82b502b1b929af403e475e4406f51980285689849fce1a1e4f49655fcc3e2 SHA512: 340b27ad3483d4192f77437bfa1646628217ea390e7b50fbad8f841f5d85f492991f1ecb2629082f623e7bc7307bb40c6c58c56371eeb67d6955f0e102c0394d Homepage: https://cran.r-project.org/package=portvine Description: CRAN Package 'portvine' (Vine Based (Un)Conditional Portfolio Risk Measure Estimation) Following Sommer (2022) portfolio level risk estimates (e.g. Value at Risk, Expected Shortfall) are estimated by modeling each asset univariately by an ARMA-GARCH model and then their cross dependence via a Vine Copula model in a rolling window fashion. One can even condition on variables/time series at certain quantile levels to stress test the risk measure estimates. Package: r-cran-posetr Architecture: amd64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 845 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-igraph, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-posetr_1.1.4-1.ca2404.1_amd64.deb Size: 284634 MD5sum: 723471363149c37f6e51355f6b5ecf97 SHA1: b89cfd7860a8db4ed6e6055076bfe07216815a05 SHA256: ed7ef054e7717844219eaf0a6ed0363e7d2c3ecec8f5606842b3de7b78e02cd8 SHA512: ef4c99688d506c06849692e2b4fd01358abd1f26370235ad4dacbf02f7420bdd6ef8f4ba4c1ef85ce965ae9d6da82d53368d192ffdd1dc38545f93b79b486890 Homepage: https://cran.r-project.org/package=POSetR Description: CRAN Package 'POSetR' (Partially Ordered Sets in R) Provides a set of basic tools for generating, analyzing, summarizing and visualizing finite partially ordered sets. In particular, it implements flexible and very efficient algorithms for the extraction of linear extensions and for the computation of mutual ranking probabilities and other user-defined functionals, over them. The package is meant as a computationally efficient "engine", for the implementation of data analysis procedures, on systems of multidimensional ordinal indicators and partially ordered data, in the spirit of Fattore, M. (2016) "Partially ordered sets and the measurement of multidimensional ordinal deprivation", Social Indicators Research , and Fattore M. and Arcagni, A. (2018) "A reduced posetic approach to the measurement of multidimensional ordinal deprivation", Social Indicators Research . 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The package implements a familiar, formula-based regression interface that adjusts for linkage uncertainty, accommodating workflows where direct access to unlinked primary files is restricted. It consolidates diverse adjustment methodologies, all of which support generalized linear models (linear, logistic, Poisson, and Gamma). These methodologies include weighting approaches (Chambers (2009) ; Chambers et al. (2023) ), mixture modeling (Slawski et al. (2025) ), and Bayesian mixture modeling (Gutman et al. (2016) ). For time-to-event data, both the weighting (Vo et al. (2024) ) and mixture modeling approaches accommodate Cox proportional hazards models, while the Bayesian approaches extend to parametric survival analysis. Additionally, the package leverages mixture modeling for contingency table analyses and Bayesian methods to enable the multiple imputation of latent match status. Package: r-cran-pot Architecture: amd64 Version: 1.1-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1420 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pot_1.1-11-1.ca2404.1_amd64.deb Size: 1282152 MD5sum: cd1fbeab0394f0db7c87309a0b6dd7df SHA1: 03debc0eba551f5abdff2407e4106a7c22915d51 SHA256: 88404cc0dc3a0a3d8924a56dfd54db53529a98c9418834c175488c0a06c4b875 SHA512: 575bfa8b202d52e767d63457c7e5b1ca388df498b3ff898263fdb22fdca17642dbad984ece8810da51a5220da836e56b03df5459f3070cc71a960b7f99b038cc Homepage: https://cran.r-project.org/package=POT Description: CRAN Package 'POT' (Generalized Pareto Distribution and Peaks Over Threshold) Some functions useful to perform a Peak Over Threshold analysis in univariate and bivariate cases, see Beirlant et al. (2004) . A user guide is available in the vignette. Package: r-cran-potts Architecture: amd64 Version: 0.5-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-pooh Filename: pool/dists/noble/main/r-cran-potts_0.5-11-1.ca2404.1_amd64.deb Size: 227166 MD5sum: 9feed6810c7b23dff0572b7ba08c0c59 SHA1: 0d910a30d64bbb24703dcd0a7b77e76cabcfb01f SHA256: ac97c789d9ecec1d06bbe722f8e2114137523f263bc0cc370a76ef4ccb02a4fe SHA512: 7e585f8e77e487e6c31a22b0450db02172d6641fe0a757e720e3288d145f451f08b03a3ffc5b762531e7096e9f3c6d9bf02f22657f96a03d5604dcf34cd0fe54 Homepage: https://cran.r-project.org/package=potts Description: CRAN Package 'potts' (Markov Chain Monte Carlo for Potts Models) Do Markov chain Monte Carlo (MCMC) simulation of Potts models (Potts, 1952, ), which are the multi-color generalization of Ising models (so, as as special case, also simulates Ising models). Use the Swendsen-Wang algorithm (Swendsen and Wang, 1987, ) so MCMC is fast. Do maximum composite likelihood estimation of parameters (Besag, 1975, , Lindsay, 1988, ). Package: r-cran-pottsutils Architecture: amd64 Version: 0.3-3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-miscf Filename: pool/dists/noble/main/r-cran-pottsutils_0.3-3.1-1.ca2404.1_amd64.deb Size: 262876 MD5sum: 1b41741fb008a4e164b23596c42c4c71 SHA1: ed9cf8fbc19f74f5681d6d87246b102e739932d3 SHA256: a8087c90b33359fba3812d5ad85c51e898ac4d222d592a92a118c9e28a4dfc28 SHA512: 593892473bd7505f0460de58e799442552ec919e14f9a93c2696f15b157dbef5d6af1aa1005688372a2cbca325a2d9ab750edf46ed81862e37567a2587a2c815 Homepage: https://cran.r-project.org/package=PottsUtils Description: CRAN Package 'PottsUtils' (Utility Functions of the Potts Models) There are three sets of functions. 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Package: r-cran-poumm Architecture: amd64 Version: 2.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1767 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ape, r-cran-data.table, r-cran-coda, r-cran-foreach, r-cran-ggplot2, r-cran-lamw, r-cran-adaptmcmc Suggests: r-cran-testthat, r-cran-usethis, r-cran-rmpfr, r-cran-mvtnorm, r-cran-lmtest, r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-poumm_2.1.8-1.ca2404.1_amd64.deb Size: 1036996 MD5sum: 2cf6f7f917b3d1aad4bf20f71b1fe90b SHA1: 3dfd14f19dd60ca62dc305c479f0b1804fd3a79c SHA256: ae8e7656df861434947ad50e86d987db4e96d3cabf3cca44e01412262ffc3b34 SHA512: 3481fd1f3569b1c68944a2a55d05e2f248540b65e242d70fc5b10961eefc0586aea9f32b03ac4ea6ecc6556eee3d30f382e5586c41bb8521a2cd7310a458f49f Homepage: https://cran.r-project.org/package=POUMM Description: CRAN Package 'POUMM' (The Phylogenetic Ornstein-Uhlenbeck Mixed Model) The Phylogenetic Ornstein-Uhlenbeck Mixed Model (POUMM) allows to estimate the phylogenetic heritability of continuous traits, to test hypotheses of neutral evolution versus stabilizing selection, to quantify the strength of stabilizing selection, to estimate measurement error and to make predictions about the evolution of a phenotype and phenotypic variation in a population. The package implements combined maximum likelihood and Bayesian inference of the univariate Phylogenetic Ornstein-Uhlenbeck Mixed Model, fast parallel likelihood calculation, maximum likelihood inference of the genotypic values at the tips, functions for summarizing and plotting traces and posterior samples, functions for simulation of a univariate continuous trait evolution model along a phylogenetic tree. So far, the package has been used for estimating the heritability of quantitative traits in macroevolutionary and epidemiological studies, see e.g. Bertels et al. (2017) and Mitov and Stadler (2018) . The algorithm for parallel POUMM likelihood calculation has been published in Mitov and Stadler (2019) . Package: r-cran-pow.int Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pow.int_1.3-1.ca2404.1_amd64.deb Size: 13296 MD5sum: 905d90a828847bc9528c5961888a974e SHA1: 9beb90857b23e9cf02e25914573b1027b0930a7a SHA256: a35a7a6eedc0ccf0c5095f78f99a0da74023b492652124a2bda964fcbc395676 SHA512: 9a75aed6923bff62f2d4755ee48eb249df42c8e6204f73015d9de4e1965d215c0283fca07f00e8dd142d09e3dabf45cfeaf632011664767409b900207e518dbc Homepage: https://cran.r-project.org/package=pow.int Description: CRAN Package 'pow.int' (Binary Exponentiation) Fast exponentiation when the exponent is an integer. 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Package: r-cran-ppca Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rspectra, r-cran-matrix, r-cran-rcpp Suggests: r-cran-ggbiplot Filename: pool/dists/noble/main/r-cran-ppca_1.1-1.ca2404.1_amd64.deb Size: 54090 MD5sum: 8210232547119a37c012f1b2864ebb20 SHA1: df691a9de0d89110bd3d8fb7338059bc9e108d93 SHA256: c047996082b10b30f69efd6b8b94ee6c935e60aaf4a946cc52ef797efecdef03 SHA512: bb28ff12c15e41e5f70f89fd5daf281c7d879452a7deabe170e2e99a3fce1a3aec4b2cfc7c1b2c58f8a09a394e6bdfe8c72a9d650c86dd7683239e358255d034 Homepage: https://cran.r-project.org/package=pPCA Description: CRAN Package 'pPCA' (Partial Principal Component Analysis of Partitioned Large SparseMatrices) Performs partial principal component analysis of a large sparse matrix. 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The corresponding composite hypothesis test that was first introduced by Filliben (1975) can be performed to test whether the sample X is element of either the Normal, log-Normal, Exponential, Uniform, Cauchy, Logistic, Generalized Logistic, Gumbel (GEVI), Weibull, Generalized Extreme Value, Pearson III (Gamma 2), Mielke's Kappa, Rayleigh or Generalized Logistic Distribution. The PPCC test is performed with a fast Monte-Carlo simulation. 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For more details see Scrucca and Serafini (2019) . Package: r-cran-ppmiss Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-pracma, r-cran-zoo Filename: pool/dists/noble/main/r-cran-ppmiss_0.1.2-1.ca2404.1_amd64.deb Size: 45672 MD5sum: fda95ce8b74e84fcb457ebb965bd7c1b SHA1: 74096d74ddc57aeb5f1b8131f588578c12613fbe SHA256: 60dd0a0d3b0947765f3c7382e8bd216448d7ab775289c245e38591ad6ff88b12 SHA512: 63e66765fdb04142bb5ac38d0f2a177e77df4cb7209004063d3838a6836607e5fbb4c52b9f7674bf3e863ed1c9c58bdf891fb6520e324394e42a5c38ded79f25 Homepage: https://cran.r-project.org/package=PPMiss Description: CRAN Package 'PPMiss' (Copula-Based Estimator for Long-Range Dependent Processes underMissing Data) Implements the copula-based estimator for univariate long-range dependent processes, introduced in Pumi et al. (2023) . Notably, this estimator is capable of handling missing data and has been shown to perform exceptionally well, even when up to 70% of data is missing (as reported in ) and has been found to outperform several other commonly applied estimators. Package: r-cran-ppmr Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-ppmr_1.0.1-1.ca2404.1_amd64.deb Size: 175528 MD5sum: f8d7f698cb9969bf047ab4743fd4bd68 SHA1: c0f4d599915277d766d6287e1bee22952ea426eb SHA256: 3d975a235bd20da1339eafdb29a61f2c1b9fd705efe220f44ba7796942f30734 SHA512: 578eaffda6ba936359643fa0bf41794a52d2de3dd68673e38ee7ed0455e47af23b9d3185ccd6d7e869628fb47a611f821a8b294fb98bda291bd15eb7a8d86093 Homepage: https://cran.r-project.org/package=PPMR Description: CRAN Package 'PPMR' (Probabilistic Two Sample Mendelian Randomization) Efficient statistical inference of two-sample MR (Mendelian Randomization) analysis. It can account for the correlated instruments and the horizontal pleiotropy, and can provide the accurate estimates of both causal effect and horizontal pleiotropy effect as well as the two corresponding p-values. There are two main functions in the 'PPMR' package. One is PMR_individual() for individual level data, the other is PMR_summary() for summary data. Package: r-cran-ppmsuite Architecture: amd64 Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-cluster Filename: pool/dists/noble/main/r-cran-ppmsuite_0.3.4-1.ca2404.1_amd64.deb Size: 285690 MD5sum: ae2e57d2b71887577d7dfe90bc3bf78c SHA1: f2a0ddce13edc6fb3bdf2dc5ce1abf8222b555ee SHA256: b77300f676a9f6a4c2b4c285d9ac694fa6502fdb38e17550f70495ee18a39d8e SHA512: 6df21975abd9b93dfdc0c5d4b8ee79135503884cdb0ce96615f9fd9676149cba0498230064ee6b2889c60b2bd1a00ee7ced00b32784f9f71973ecb06cdb4607c Homepage: https://cran.r-project.org/package=ppmSuite Description: CRAN Package 'ppmSuite' (A Collection of Models that Employ Product PartitionDistributions as a Prior on Partitions) Provides a suite of functions that fit models that use PPM type priors for partitions. Models include hierarchical Gaussian and probit ordinal models with a (covariate dependent) PPM. If a covariate dependent product partition model is selected, then all the options detailed in Page, G.L.; Quintana, F.A. (2018) are available. If covariate values are missing, then the approach detailed in Page, G.L.; Quintana, F.A.; Mueller, P (2020) is employed. Also included in the package is a function that fits a Gaussian likelihood spatial product partition model that is detailed in Page, G.L.; Quintana, F.A. (2016) , and multivariate PPM change point models that are detailed in Quinlan, J.J.; Page, G.L.; Castro, L.M. (2023) . In addition, a function that fits a univariate or bivariate functional data model that employs a PPM or a PPMx to cluster curves based on B-spline coefficients is provided. Package: r-cran-pprl Architecture: amd64 Version: 0.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1639 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-settings Filename: pool/dists/noble/main/r-cran-pprl_0.3.9-1.ca2404.1_amd64.deb Size: 369614 MD5sum: 21185436c7f57997a4207f2586449c0b SHA1: bcfa1d0069e35029b387956c3f59719887a7b3ff SHA256: 1c3f549196375de76e44f4fe0ed2550d8e9f34d6d75a45ccec91ca49225d5be3 SHA512: bdd55145dbdd21992cc8f4f4b471ff3650da7212ad8402121777ab46ac155f28dfc49bbc7b757bd1b2ad0fd0306f93e916eaf9a3c02e85917bfa6c7588f5113f Homepage: https://cran.r-project.org/package=PPRL Description: CRAN Package 'PPRL' (Privacy Preserving Record Linkage) A toolbox for deterministic, probabilistic and privacy-preserving record linkage techniques. Combines the functionality of the 'Merge ToolBox' () with current privacy-preserving techniques. Package: r-cran-pprof Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1603 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-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/noble/main/r-cran-pprof_1.0.3-1.ca2404.1_amd64.deb Size: 1304490 MD5sum: 181fbeffbd25055d4d90b1fcafb2313b SHA1: 284f6da8316450c57884d1661f6c86629fceb600 SHA256: 3c3e01cd993fa9e508631021c732426e7b2386fa224a1e07bfd428da8cafa425 SHA512: 06258b81608596a469edafeb80af59d4ebbd9ea834b832b181e42f86b99e85738b1fec3ae2f3ef46ce0cf4e444c488bd2cdee56943cd9b607e10cd6535ef349b Homepage: https://cran.r-project.org/package=pprof Description: CRAN Package 'pprof' (Modeling, Standardization and Testing for Provider Profiling) Implements linear and generalized linear models for provider profiling, incorporating both fixed and random effects. For large-scale providers, the linear profiled-based method and the SerBIN method for binary data reduce the computational burden. Provides post-modeling features, such as indirect and direct standardization measures, hypothesis testing, confidence intervals, and post-estimation visualization. For more information, see Wu et al. (2022) . Package: r-cran-ppsfs Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-brglm2, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-ppsfs_0.1.3-1.ca2404.1_amd64.deb Size: 69630 MD5sum: 5e939c95e9092aac092c909f238e0b3d SHA1: 966f65c2be1163df1e0d95189cf4c084ba2b37ce SHA256: f277cd5f87efc23b00d6e5590bc72207f43e8c88c7538783396ea58244010e79 SHA512: 7a93dd472db7d4895d353e6021b1571751d8b90e351afdc2a8754390d31c0acbf28370022862541daba1b405cfd1c5deb63140d19398642601a1b075a0219185 Homepage: https://cran.r-project.org/package=PPSFS Description: CRAN Package 'PPSFS' (Partial Profile Score Feature Selection in High-DimensionalGeneralized Linear Interaction Models) This is an implementation of the partial profile score feature selection (PPSFS) approach to generalized linear (interaction) models. The PPSFS is highly scalable even for ultra-high-dimensional feature space. See the paper by Xu, Luo and Chen (2022) . Package: r-cran-pptreeext Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1014 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-shiny, r-cran-mass, r-cran-gridextra, r-cran-mixsim, r-cran-pptreeviz, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-randomforest, r-cran-rpart, r-cran-ggally, r-cran-rcolorbrewer, r-cran-roxygen2, r-cran-rmarkdown, r-cran-rsample Filename: pool/dists/noble/main/r-cran-pptreeext_0.1.0-1.ca2404.1_amd64.deb Size: 695612 MD5sum: 14ec649bd2e976d9051603fed20fe1c7 SHA1: 2eef702a386722936bedb1e2ba6cf44031d61855 SHA256: 0c6dff7b6766218eb7b42aa2dabfe6f464fa999ea29af8d31b9d6b02ebec0967 SHA512: 977c24efabf616094c0257e6405bf6c519529e1077200ca8ef57b4474e18dc93d0ec55573b6c3f9d5117ff3cae67d1ec1513426631bd46ea5706bff3247572f9 Homepage: https://cran.r-project.org/package=PPtreeExt Description: CRAN Package 'PPtreeExt' (Projection Pursuit Classification Tree Extensions) Implements extensions to the projection pursuit tree algorithm for supervised classification, see Lee, Y. (2013), and Lee, E-K. (2018) . The algorithm is changed in two ways: improving prediction boundaries by modifying the choice of split points-through class subsetting; and increasing flexibility by allowing multiple splits per group. Package: r-cran-pptreeregviz Architecture: amd64 Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1030 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-dalex, r-cran-shapr, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-pptreeviz, r-cran-reshape2, r-cran-magrittr, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-gridextra, r-cran-ggextra, r-cran-partykit, r-cran-ggparty, r-cran-progress, r-cran-tidyselect, r-cran-ggforce, r-cran-waterfalls, r-cran-forcats, r-cran-rcolorbrewer, r-cran-gtable, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-covr Filename: pool/dists/noble/main/r-cran-pptreeregviz_2.0.5-1.ca2404.1_amd64.deb Size: 797660 MD5sum: 9d7479fc118704181ca14f5b04fe8671 SHA1: 31a5f72f167a08c915cc8bd6594afe36141920c6 SHA256: fac1bd134528e97a76e35bc1a25024365788adf15852d9a80c4e8bff47d5ece1 SHA512: 1543853d72bc04826ed05b984771c0c5f1822a5a0bf9dc9714e0986ce659e3f4ad229474d5bf4ae51f586b7011c280cfe91f747225a5f809c337cdf0134a5c9c Homepage: https://cran.r-project.org/package=PPtreeregViz Description: CRAN Package 'PPtreeregViz' (Projection Pursuit Regression Tree Visualization) It was developed as a tool for exploring 'PPTreereg' (Projection Pursuit TREE of REGression). It uses various projection pursuit indexes and 'XAI' (eXplainable Artificial Intelligence) methods to help understand the model by finding connections between the input variables and prediction values of the model. The 'KernelSHAP' (Aas, Jullum and Løland (2019) ) algorithm was modified to fit ‘PPTreereg’, and some codes were modified from the 'shapr' package (Sellereite, Nikolai, and Martin Jullum (2020) ). The implemented methods help to explore the model at the single instance level as well as at the whole dataset level. Users can compare with other machine learning models by applying it to the 'DALEX' package of 'R'. Package: r-cran-pptreeviz Architecture: amd64 Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-gridextra, r-cran-ggplot2, r-cran-partykit, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-pptreeviz_2.0.4-1.ca2404.1_amd64.deb Size: 191334 MD5sum: d96e7409c03c8928e85e75f40817db89 SHA1: 934958a8ea0f3fe7cf0f21cbc1d8ca1a1c9ba643 SHA256: 2eb08dff1dff337ddde7ff90369c34d9d5cc5349864ad80fd00be20821b90a75 SHA512: a14f481a55684440ed7fb5d0a6d2c8dd97dca2995cf15d280f6401cac85a427396bcecc3c59d10afa686b124b51c59bc623a4409f7290d9cc2379f76aca55b17 Homepage: https://cran.r-project.org/package=PPtreeViz Description: CRAN Package 'PPtreeViz' (Projection Pursuit Classification Tree Visualization) Tools for exploring projection pursuit classification tree using various projection pursuit indexes. Package: r-cran-pqlseq Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-foreach, r-cran-doparallel, r-cran-matrix, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-pqlseq_1.2.1-1.ca2404.1_amd64.deb Size: 220122 MD5sum: 91161fad1d8499074d78aebe86bf6923 SHA1: 3e584a5ee2926c8f5e32000744f3b0081fd5717a SHA256: 803c299074ddd133d6d17d6f36db10686a01b6ff319fe9f31b3235b26d5f7b78 SHA512: 395070007d2fd569562c5366eaa37eefcf5ccc3499f8572a9e71a7770827f37a95fb994d1c5a52833dd0d2784c3f85f1ecfcad76ea8bf9bc3a75864c9b281063 Homepage: https://cran.r-project.org/package=PQLseq Description: CRAN Package 'PQLseq' (Efficient Mixed Model Analysis of Count Data in Large-ScaleGenomic Sequencing Studies) An efficient tool designed for differential analysis of large-scale RNA sequencing (RNAseq) data and Bisulfite sequencing (BSseq) data in the presence of individual relatedness and population structure. 'PQLseq' first fits a Generalized Linear Mixed Model (GLMM) with adjusted covariates, predictor of interest and random effects to account for population structure and individual relatedness, and then performs Wald tests for each gene in RNAseq or site in BSseq. Package: r-cran-pqrbayes Architecture: amd64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 910 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-pqrbayes_1.2.2-1.ca2404.1_amd64.deb Size: 351742 MD5sum: 6f73b6537f68eaf338b7df7bbe7bda0c SHA1: 81a430c9dd2145cfcab88fbe1896d075986be9cc SHA256: 2b331fd9b9f05e09d512c36cc7b23b2c18b7fcad12c1c991be9dc9bc76771622 SHA512: 17efe0c94c137b7a888d2234bd468e38626a1ce171b5904a02c0f4ea72205a11e4e2da2caf70c5bc0e38a0fe2b788c42f7b0c112914b519a2537d4c40535fbe6 Homepage: https://cran.r-project.org/package=pqrBayes Description: CRAN Package 'pqrBayes' (Bayesian Penalized Quantile Regression) Bayesian regularized quantile regression utilizing two major classes of shrinkage priors (the spike-and-slab priors and the horseshoe family of priors) leads to efficient Bayesian shrinkage estimation, variable selection and valid statistical inference. In this package, we have implemented robust Bayesian variable selection with spike-and-slab priors under high-dimensional linear regression models (Fan et al. (2024) and Ren et al. (2023) ), and regularized quantile varying coefficient models (Zhou et al.(2023) ). In particular, valid robust Bayesian inferences under both models in the presence of heavy-tailed errors can be validated on finite samples. Additional models with spike-and-slab priors include robust Bayesian group LASSO and robust binary Bayesian LASSO (Fan and Wu (2025) ). Besides, robust sparse Bayesian regression with the horseshoe family of (horseshoe, horseshoe+ and regularized horseshoe) priors has also been implemented and yielded valid inference results under heavy-tailed model errors (Fan et al.(2026) ). The Markov chain Monte Carlo (MCMC) algorithms of the proposed and alternative models are implemented in C++. Package: r-cran-pqrfe Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-pqrfe_1.3-1.ca2404.1_amd64.deb Size: 105770 MD5sum: e9333e5ed85927729ca2d11b6ed3cea6 SHA1: 5c458480b768bb59ea5877619945f48a1636f18b SHA256: 7febb6a5a9ec434541065c3a02d09c3942b00a981922fa58b21d443cb26ff4ad SHA512: 6ec0c941938494fe3b90cae03451822077b745fa6bc284608d3d0369168aa9b9762d5b5d563a266d1f90904a250928adce99abba2051df210ef1b44d17273712 Homepage: https://cran.r-project.org/package=pqrfe Description: CRAN Package 'pqrfe' (Penalized Quantile Regression with Fixed Effects) Quantile regression with fixed effects is a general model for longitudinal data. Here we proposed to solve it by several methods. The estimation methods include three loss functions as check, asymmetric least square and asymmetric Huber functions; and three structures as simple regression, fixed effects and fixed effects with penalized intercepts by LASSO. Package: r-cran-praznik Architecture: amd64 Version: 12.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 661 Depends: libc6 (>= 2.4), libgomp1 (>= 6), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-praznik_12.0.0-1.ca2404.1_amd64.deb Size: 544256 MD5sum: 73cfe58ee712ae2c68471665a90aa2bb SHA1: 9bca9eed1568aae88122d2374024846beb81a4b8 SHA256: 61f6d7624bb0282010ed84477075642293543457ba20c8d1a5dffa1819cb5f46 SHA512: 11840ef1f35b1697d5010b014c0e314fb34ba346feb704d1c64f32cf28d4850fffbb38a15fc2bc419b60c476209d927e40fa804ca85109bafe1e8626e12ecf2e 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: amd64 Version: 1.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1002 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-assertthat, r-cran-gridextra, r-cran-ggplot2, r-cran-memoise, r-cran-rocr, r-cran-prroc, r-cran-precrec Suggests: r-cran-microbenchmark, r-cran-rjava, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-vdiffr, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-prcbench_1.1.10-1.ca2404.1_amd64.deb Size: 731716 MD5sum: 31f8d33530ad34fe1c41dc66bd9ea04c SHA1: f322461505c6ac8084604a7eebe0e7c3b85d636e SHA256: cd836a33fff0cb14716fe1b4264f1ca991b37678815d0cd8da73071025a0fd98 SHA512: c6a23375f7981aef8c01454835f8eddcf2ec9f1141673801f78ecae1624398cd11762243a14d594566d32c61277f0ebec6449026dd9b816393028f3683b15acf 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: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-prclust_1.3-1.ca2404.1_amd64.deb Size: 95192 MD5sum: 2cd86d306d8a24133cbda36f3651b1b6 SHA1: 8b51bf665a7b898a73b2ea6581c5801a98b83d31 SHA256: 3f82cf58d0bc711c9fde7c88e7c62961b0d0d462ecc48901f0bc79cbf54bba69 SHA512: 24b6de63b926cf40d306d78470655abb36af5e41f20025eaa200715dff292efd96f1a91900badc966745cae895949d9d9e99311e65224088d006de4c636a2a4a Homepage: https://cran.r-project.org/package=prclust Description: CRAN Package 'prclust' (Penalized Regression-Based Clustering Method) Clustering is unsupervised and exploratory in nature. Yet, it can be performed through penalized regression with grouping pursuit. In this package, we provide two algorithms for fitting the penalized regression-based clustering (PRclust) with non-convex grouping penalties, such as group truncated lasso, MCP and SCAD. One algorithm is based on quadratic penalty and difference convex method. Another algorithm is based on difference convex and ADMM, called DC-ADD, which is more efficient. Generalized cross validation and stability based method were provided to select the tuning parameters. Rand index, adjusted Rand index and Jaccard index were provided to estimate the agreement between estimated cluster memberships and the truth. 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It combines the evaluation of Power-Analysis with other inferential-risks as Type-M error (i.e. Magnitude) and Type-S error (i.e. Sign). See also Altoè et al. (2020) and Bertoldo et al. (2020) . 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Additional functions include finding prime factors and Ruth-Aaron pairs, finding next and previous prime numbers in the series, finding or estimating the nth prime, estimating the number of primes less than or equal to an arbitrary number, computing primorials, prime k-tuples (e.g., twin primes), finding the greatest common divisor and smallest (least) common multiple, testing whether two numbers are coprime, and computing Euler's totient function. Most functions are vectorized for speed and convenience. Package: r-cran-primme Architecture: amd64 Version: 3.2-6-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2327 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Filename: pool/dists/noble/main/r-cran-primme_3.2-6-1.ca2404.2_amd64.deb Size: 544250 MD5sum: 15543000ca6bee7227ecb7f59a27c2a9 SHA1: 48eb4206677e0c1cf5e2ac4d6358d42f78a09039 SHA256: 9c081bb3a053ed6e6a6ccd0c8fedb788306c196d0dcacc4b0d463f7951a6427f SHA512: 09a15a753a7cfe5b1ff03c2ea18dd6eba0ad1b78a16dcdaf8472be8fabc2fbb136e5d7a5ce0ddf3ba17fc4c9d1e2b3a8839083ece071ac6653610da6b83434da Homepage: https://cran.r-project.org/package=PRIMME Description: CRAN Package 'PRIMME' (Eigenvalues and Singular Values and Vectors from Large Matrices) R interface to 'PRIMME' , a C library for computing a few eigenvalues and their corresponding eigenvectors of a real symmetric or complex Hermitian matrix, or generalized Hermitian eigenproblem. It can also compute singular values and vectors of a square or rectangular matrix. 'PRIMME' finds largest, smallest, or interior singular/eigenvalues and can use preconditioning to accelerate convergence. General description of the methods are provided in the papers Stathopoulos (2010, ) and Wu (2017, ). See 'citation("PRIMME")' for details. Package: r-cran-princurve Architecture: amd64 Version: 2.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-devtools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-princurve_2.1.6-1.ca2404.1_amd64.deb Size: 102404 MD5sum: aee4d8ffbbac8cd9eac13a191001e47b SHA1: a729a0823d75c370f89ac0000056246ed42f6cee SHA256: 43d47b6e2e06f3c601d116c8489c89768bdcb13f889bbfa7a48ece8484e81576 SHA512: 47782d3c5300dcc1fabe234f1cf185fed4e8d72be1c2952c5a516fa679b3a9fcce2ecc543dc660d43475d20962948e2a74aed55f634cdcb29435d13ec25231c3 Homepage: https://cran.r-project.org/package=princurve Description: CRAN Package 'princurve' (Fit a Principal Curve in Arbitrary Dimension) Fitting a principal curve to a data matrix in arbitrary dimensions. Hastie and Stuetzle (1989) . Package: r-cran-prioriactions Architecture: amd64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5189 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-matrix, r-cran-proto, r-cran-magrittr, r-cran-tidyr, r-cran-dplyr, r-cran-rcpp, r-cran-rlang, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rcplex, r-cran-roxygen2, r-cran-rsymphony, r-cran-rmarkdown, r-cran-testthat, r-cran-raster, r-cran-tmap, r-cran-sp, r-cran-viridis, r-cran-markdown, r-cran-data.table, r-cran-purrr, r-cran-readr, r-cran-slam, r-cran-tibble, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-prioriactions_0.5.0-1.ca2404.1_amd64.deb Size: 2614334 MD5sum: 32a1d6ff2bc4b4fc4f3d143ba3796356 SHA1: b552f8eca666ae39bf43f9fca1cbc86957dca926 SHA256: db0275ea62ba97962a7c1264216bbfb9201476698d1ec18b649ccccd894f3589 SHA512: 30e20ce79298ba44e130ca6d904ffa7505356aa9fd32c05345c657ed47d6a0c11dc707327639b308b0434e7b925f82c3a88d80327848fce32b7f92ffa9957fab Homepage: https://cran.r-project.org/package=prioriactions Description: CRAN Package 'prioriactions' (Multi-Action Conservation Planning) This uses a mixed integer mathematical programming (MIP) approach for building and solving multi-action planning problems, where the goal is to find an optimal combination of management actions that abate threats, in an efficient way while accounting for spatial aspects. Thus, optimizing the connectivity and conservation effectiveness of the prioritized units and of the deployed actions. The package is capable of handling different commercial (gurobi, CPLEX) and non-commercial (symphony, CBC) MIP solvers. Gurobi optimization solver can be installed using comprehensive instructions in the 'gurobi' installation vignette of the prioritizr package (available in ). Instead, 'CPLEX' optimization solver can be obtain from IBM CPLEX web page (available here ). Additionally, the 'rcbc' R package (available at ) can be used to obtain solutions using the CBC optimization software (). Methods used in the package refers to Salgado-Rojas et al. (2020) , Beyer et al. (2016) , Cattarino et al. (2015) and Watts et al. (2009) . See the prioriactions website for more information, documentations and examples. Package: r-cran-prioritizr Architecture: amd64 Version: 8.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9722 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rlang, r-cran-cli, r-cran-sf, r-cran-units, r-cran-terra, r-cran-raster, r-cran-matrix, r-cran-assertthat, r-cran-igraph, r-cran-ape, r-cran-magrittr, r-cran-exactextractr, r-cran-tibble, r-cran-withr, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-testthat, r-cran-knitr, r-bioc-lpsymphony, r-cran-slam, r-cran-rsymphony, r-cran-highs, r-cran-rmarkdown, r-cran-prioritizrdata, r-cran-fields, r-cran-vroom Filename: pool/dists/noble/main/r-cran-prioritizr_8.1.0-1.ca2404.1_amd64.deb Size: 5795176 MD5sum: 4e5e413c99d1c152f098fb23e1147fb6 SHA1: 534fcecab15dca8151d1c38509060c8741cd310f SHA256: 548b1864d8dec1a7b90499ac9118af3ba61b95c8091b303b062a5f9853c85dca SHA512: 6f9b96cb3087023dc7a0dd4f0f5218dcebff7840891b1bdeb35bee8eddafc56d36519e2e9f2b81b21b844b42f11caef18a21ccfec559e404a7c668c17d9a9c9e 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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Package: r-cran-proclhmm Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-statmod Filename: pool/dists/noble/main/r-cran-proclhmm_1.0.1-1.ca2404.1_amd64.deb Size: 156292 MD5sum: 77d16c73080095eae1e1c33960555e6f SHA1: d9622cbd754b0e164785ad1748131b3a404f0478 SHA256: b65b0856e2ab98f4978a7c5b443e109c10a8f7545275a8fa84305e57e741b095 SHA512: 4ea48bf317f30d0ed805b28de0718d086af29957c8630983e2d61df79d35bef6eba9ccf4671b30cbb7230e69c31fe94e6afa1d3f95f44be594fced155f887a89 Homepage: https://cran.r-project.org/package=proclhmm Description: CRAN Package 'proclhmm' (Latent Hidden Markov Models for Response Process Data) Provides functions for simulating from and fitting the latent hidden Markov models for response process data (Tang, 2024) . It also includes functions for simulating from and fitting ordinary hidden Markov models. Package: r-cran-procmaps Architecture: amd64 Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-procmaps_0.0.5-1.ca2404.1_amd64.deb Size: 21674 MD5sum: f0e1ec30456d427e0333e17136e5cb7f SHA1: fabf62da1bea756bde018903e9886e9a652ffd99 SHA256: d1b57d1b51df04c756d896130b36676f9fddef6966353a8a5d6e646949ee6fdf SHA512: cb7a9cfe19206ab5901ff46267a90bc9dd23f658b423392f01d4421205dceae7d728a121b30354c59f74266f578fc47be684c5047ca1ecd6bd2b584174fb89d9 Homepage: https://cran.r-project.org/package=procmaps Description: CRAN Package 'procmaps' (Portable Address Space Mapping) Portable '/proc/self/maps' as a data frame. Determine which library or other region is mapped to a specific address of a process. -- R packages can contain native code, compiled to shared libraries at build or installation time. When loaded, each shared library occupies a portion of the address space of the main process. When only a machine instruction pointer is available (e.g. from a backtrace during error inspection or profiling), the address space map determines which library this instruction pointer corresponds to. Package: r-cran-prodlim Architecture: amd64 Version: 2026.03.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 572 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, 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/noble/main/r-cran-prodlim_2026.03.11-1.ca2404.1_amd64.deb Size: 522910 MD5sum: c0225ea3ced1cebe7bccdb6c5da4b468 SHA1: f2a758230dcb070c9efc3e041bb6a5bfcf38ca9f SHA256: 33b3a25b8237705309a268143bcc394a7775d1cac44e920713a814983bd77158 SHA512: 4652100987b82ea8fbfc47aafebca7aba4b833c4db96462cea92f044161aa88d4c7053fea8a31998b006557440f35a72c2bf889359e8396e7e601e49476afa1f 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. 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More details can be referred to Wei Liu, et al. (2023) . Package: r-cran-profileglmm Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1390 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-laplacesdemon, r-cran-mcmcpack, r-cran-matrix, r-cran-spectrum, r-cran-mvtnorm, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-profileglmm_1.1.0-1.ca2404.1_amd64.deb Size: 1123744 MD5sum: 33f7dfda0b3cfbb3105a79eda9b8c284 SHA1: 759aba6528ddaa78b4bc805181ef84af6a545666 SHA256: 16637c56b35f765c65dff2bce5fbcdc683e0d5199864ebf3c8818fa14a4651f6 SHA512: accfb23b9acc7638bd2fc0697a09f67b640f41da99729829314f10dfd5db4e449ccee0b97bb4e44fbdc85ab73f0aad55841440e7d2d4ecc1a26507ef7d26f460 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. 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Package: r-cran-profileladder Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chainladder, r-cran-raw Filename: pool/dists/noble/main/r-cran-profileladder_0.1.1-1.ca2404.1_amd64.deb Size: 161358 MD5sum: 2ea9f4ecd153504e94298c29e5cb9d3d SHA1: 8d1e112284346af861f843fc4c1786e3f2caa137 SHA256: bd4980bb1ade297c2399db06da3e5f467c3cd37b0dfcc5fd9eecf098c502249e SHA512: af32125bf338ac923e1d2c74cb3901ba7d3cc8a31b2041cfde7c5ec9d2070e39ba75596128974058b33e050da97bc22a4c053b67fdb52c1a8b35023e93e7f21c Homepage: https://cran.r-project.org/package=ProfileLadder Description: CRAN Package 'ProfileLadder' (Functional Profile Chain Ladder for Claims Reserving) Functional claims reserving methods based on aggregated chain-ladder data, also known as the run-off triangle (functional) development profiles, implemented in three nonparametric algorithms (PARALLAX, REACT, and MACRAME) proposed in Maciak, Mizera, and Pešta (2022) . Package: r-cran-profoc Architecture: amd64 Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2939 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-abind, r-cran-lifecycle, r-cran-generics, r-cran-tibble, r-cran-ggplot2, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-splines2, r-cran-rcpptimer Suggests: r-cran-testthat, r-cran-gamlss.dist, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-profoc_1.3.4-1.ca2404.1_amd64.deb Size: 1554108 MD5sum: 2ed2d5a7bb95ee39c5238a247373e86c SHA1: 2ff190776fccc0705df4177d66aae5c2ba7f3289 SHA256: b272386f958b0be074493c8d130c67738eb2d06cf19af79c44d2afd5e0e656c2 SHA512: 2b4cc3a395d91998c2a68367d7cfbedb787429655d4a43781feef7d36a1d5a42848aeba20785b5d34bb46299e78e552f3f310d5d94198b70cf06bfe4172d2b81 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: amd64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 983 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-rlang, r-cran-vctrs Suggests: r-cran-htmltools, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-profvis_0.4.0-1.ca2404.1_amd64.deb Size: 209858 MD5sum: c2fb9567d24332e17567bc42d9c5383a SHA1: 6381732c31f33edc99201d74d3fac6eb42ae79e9 SHA256: d5af10c144558cc0fdfd6f12a0d9f0931100ea95228321573c09a41d8eedcb26 SHA512: 58788fea3f733ef9482355c0aa143672d9041eb1f92302c3edb5cebd2b5862199d7f3c1bbbc82ccf7b44660b4678cfa473881c2208f45bd7263ae7f8f707f0f6 Homepage: https://cran.r-project.org/package=profvis Description: CRAN Package 'profvis' (Interactive Visualizations for Profiling R Code) Interactive visualizations for profiling R code. Package: r-cran-proj4 Architecture: amd64 Version: 1.0-15-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: libc6 (>= 2.4), libproj25 (>= 6.1.0), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-proj4_1.0-15-1.ca2404.2_amd64.deb Size: 26348 MD5sum: 55ca20d9575d875c82ab99a9a837ce40 SHA1: ec0ac5a9692578db73bb3cb2828fa2043f1fc955 SHA256: 27d35badf088df83f20e5e06832c92f77fe6c03afb2d6f76aa180cdaf15e3615 SHA512: d612a11e8e632debf2f0a2b951e92e9f10dab35e6583148da545e77b2cd644674a73c696f3e1f90b77e3c73bd75d20a29c093477074f8cdbe290543c54f4ac58 Homepage: https://cran.r-project.org/package=proj4 Description: CRAN Package 'proj4' (A simple interface to the PROJ.4 cartographic projectionslibrary) A simple interface to lat/long projection and datum transformation of the PROJ.4 cartographic projections library. It allows transformation of geographic coordinates from one projection and/or datum to another. Package: r-cran-proj Architecture: amd64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: libc6 (>= 2.4), libproj25 (>= 8.0.0), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lifecycle, r-cran-wk Suggests: r-cran-testthat, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/noble/main/r-cran-proj_0.6.0-1.ca2404.1_amd64.deb Size: 134656 MD5sum: 698831eb0673ca38201b3f1836fbc024 SHA1: 3d0aa2416bec9454e555c1d0bb1a692b6bc0b166 SHA256: daa9f9530a272929ec0a48b767d2a38c20c6de0d7b406d450ec3c512a9b6438b SHA512: 6187024c8a83366b95137f8220c9d5c762749d2aaf45e9e7d02b513cd94c0da0f3455729497a0f94f14d839173f51e1997e87f18379df7c2f4943c94723b75aa Homepage: https://cran.r-project.org/package=PROJ Description: CRAN Package 'PROJ' (Generic Coordinate System Transformations Using 'PROJ') A wrapper around the generic coordinate transformation software 'PROJ' that transforms coordinates from one coordinate reference system ('CRS') to another. This includes cartographic projections as well as geodetic transformations. The intention is for this package to be used by user-packages such as 'reproj', and that the older 'PROJ.4' and version 5 pathways be provided by the 'proj4' package. Package: r-cran-projectionbasedclustering Architecture: amd64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 631 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-vegan, r-cran-deldir, r-cran-geometry, r-cran-generalizedumatrix, r-cran-shiny, r-cran-shinyjs, r-cran-shinythemes, r-cran-plotly Suggests: r-cran-datavisualizations, r-cran-fastica, r-cran-tsne, r-cran-fastknn, r-cran-mass, r-cran-pcapp, r-cran-spdep, r-cran-pracma, r-cran-mgcv, r-cran-fields, r-cran-png, r-cran-reshape2, r-cran-rtsne, r-cran-dendextend, r-cran-umap, r-cran-uwot, r-cran-databionicswarm, r-cran-paralleldist Filename: pool/dists/noble/main/r-cran-projectionbasedclustering_1.2.2-1.ca2404.1_amd64.deb Size: 392592 MD5sum: bfdffc52c9c4e174bf1cb48d1376b683 SHA1: e621cbe51f4f2e6e1cd63c78f16b47eb5f1764fc SHA256: c10ebebc995516f84c807f92b516097d87e1d3a5846ce03f81e5f26ffed71673 SHA512: dff437dca5887546b0b4f3d0972fac28399dc342de884ea3cb7c8c7f7313cead22da6642b8d7cf2975dad556687c22164e27f19a66b3e1f23ec605518bf9e707 Homepage: https://cran.r-project.org/package=ProjectionBasedClustering Description: CRAN Package 'ProjectionBasedClustering' (Projection Based Clustering) A clustering approach applicable to every projection method is proposed here. The two-dimensional scatter plot of any projection method can construct a topographic map which displays unapparent data structures by using distance and density information of the data. The generalized U*-matrix renders this visualization in the form of a topographic map, which can be used to automatically define the clusters of high-dimensional data. The whole system is based on Thrun and Ultsch, "Using Projection based Clustering to Find Distance and Density based Clusters in High-Dimensional Data" . Selecting the correct projection method will result in a visualization in which mountains surround each cluster. The number of clusters can be determined by counting valleys on the topographic map. Most projection methods are wrappers for already available methods in R. By contrast, the neighbor retrieval visualizer (NeRV) is based on C++ source code of the 'dredviz' software package, and the Curvilinear Component Analysis (CCA) is translated from 'MATLAB' ('SOM Toolbox' 2.0) to R. Package: r-cran-projpred Architecture: amd64 Version: 2.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1465 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-gtools, r-cran-ggplot2, r-cran-scales, r-cran-rstantools, r-cran-loo, r-cran-lme4, r-cran-mvtnorm, r-cran-mgcv, r-cran-gamm4, r-cran-abind, r-cran-mass, r-cran-ordinal, r-cran-nnet, r-cran-mclogit, r-cran-reformulas, r-cran-rcpparmadillo Suggests: r-cran-ggrepel, r-cran-ggfortify, r-cran-rstanarm, r-cran-brms, r-cran-nlme, r-cran-optimx, r-cran-ucminf, r-cran-foreach, r-cran-iterators, r-cran-dorng, r-cran-unix, r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-glmnet, r-cran-rlang, r-cran-bayesplot, r-cran-posterior, r-cran-doparallel, r-cran-future, r-cran-future.callr, r-cran-dofuture, r-cran-progressr Filename: pool/dists/noble/main/r-cran-projpred_2.10.0-1.ca2404.1_amd64.deb Size: 961906 MD5sum: d47f15dc672c26ba2401b629fc25e47f SHA1: 018ed9234ee99f83fe57215e6f4f350ed6d3198b SHA256: 4286b5611834a56f368f35c6ab000a247da6e3b9d0a4516491988d5c05e86931 SHA512: bf5d8703e2870b0eb1f19f0d40fef55eff6cb9674c3004e70f7ec6ba1faa9066557227ef6a9146772e558f1186ca2715d20ed4102e11cdb5ca7325b241851244 Homepage: https://cran.r-project.org/package=projpred Description: CRAN Package 'projpred' (Projection Predictive Feature Selection) Performs projection predictive feature selection for generalized linear models (Piironen, Paasiniemi, and Vehtari, 2020, ) with or without multilevel or additive terms (Catalina, Bürkner, and Vehtari, 2022, ), for some ordinal and nominal regression models (Weber, Glass, and Vehtari, 2025, ), and for many other regression models (using the latent projection by Catalina, Bürkner, and Vehtari, 2021, , which can also be applied to most of the former models). The package is compatible with the 'rstanarm' and 'brms' packages, but other reference models can also be used. See the vignettes and the documentation for more information and examples. Package: r-cran-prome Architecture: amd64 Version: 4.0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bi, r-cran-rstan, r-cran-bridgesampling, r-cran-rcpp, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-posterior Filename: pool/dists/noble/main/r-cran-prome_4.0.2.5-1.ca2404.1_amd64.deb Size: 104548 MD5sum: 2ec495f798bf71b59c301989c9c6cc3e SHA1: 7a9836a850f0cba9d27b802624d36c9d40a7736b SHA256: 23148701ff47f882c60863d758a75c6a82b25683a56a66f3774cbdd6f3d4ca48 SHA512: 6df90c86aee6d9c5899187a054da696e140690021935d98f6af597f7d90cf3b119338843e07f14c98cdeff04f0f2119c539f708f8e7e79158fce4375f4595c4e Homepage: https://cran.r-project.org/package=prome Description: CRAN Package 'prome' (Patient-Reported Outcome Data Analysis with Stan) Estimation for blinding bias in randomized controlled trials with a latent continuous outcome, a binary response depending on treatment and the latent outcome, and a noisy surrogate subject to possibly response-dependent measurement error. Implements EM estimators in R backed by compiled C routines for models with and without the restriction delta0 = 0, and Bayesian Stan wrappers for the same two models. Functions were added for latent outcome models with differential measurement error. 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Asynchronous programming is useful for allowing a single R process to orchestrate multiple tasks in the background while also attending to something else. Semantics are similar to 'JavaScript' promises, but with a syntax that is idiomatic R. 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Package: r-cran-protoclust Architecture: amd64 Version: 1.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-protoclust_1.6.4-1.ca2404.1_amd64.deb Size: 49782 MD5sum: 1ac83954e92b4b8019f8d60669bb765e SHA1: 6cd204fdc10236c89a0206e8f7abf9137d67df5b SHA256: bab9d00d759e67f73463b53e8c035eb3a198f4d43e152d164da6c7e80beb0e6c SHA512: aaa598b7944e823c9fa13381c58a41c27991c9d0b1ac18c3fdf2c681cf11dee83c57c185f9fe2e1a10c7231d6a9623b4936dee8baf4c0e3b91670facf354688e Homepage: https://cran.r-project.org/package=protoclust Description: CRAN Package 'protoclust' (Hierarchical Clustering with Prototypes) Performs minimax linkage hierarchical clustering. Every cluster has an associated prototype element that represents that cluster as described in Bien, J., and Tibshirani, R. 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Package: r-cran-protviz Architecture: amd64 Version: 0.7.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3555 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-lattice, r-cran-testthat, r-cran-xtable Filename: pool/dists/noble/main/r-cran-protviz_0.7.9-1.ca2404.1_amd64.deb Size: 3232228 MD5sum: 4435dc2ed75c440af2efe2d354d43c8c SHA1: 6a6271671b747441001b5b546bfbf3e2dc5287c7 SHA256: 3a314171fb90a8c6167134d62923d01b6d63b0ccb5442c3c36321d3269774696 SHA512: 489431300ecc153d17f804790e8bed4e3821944bc68bb28aa0f1f69a68816f0b311b31df7b6d7ffadbbdaefc457be325a69e61ae5fd37dbd886817b0baee377c Homepage: https://cran.r-project.org/package=protViz Description: CRAN Package 'protViz' (Visualizing and Analyzing Mass Spectrometry Related Data inProteomics) Helps with quality checks, visualizations and analysis of mass spectrometry data, coming from proteomics experiments. 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These characteristics include the distribution of daily streamflow values and their temporal correlation as expressed by short- and long-range dependence. The approach is based on the randomization of the phases of the Fourier transform or the phases of the wavelet transform. The function prsim() is applicable to single site simulation and uses the Fourier transform. The function prsim.wave() extends the approach to multiple sites and is based on the complex wavelet transform. The function prsim.weather() extends the approach to multiple variables for weather generation. We further use the flexible four-parameter Kappa distribution, which allows for the extrapolation to yet unobserved low and high flows. Alternatively, the empirical or any other distribution can be used. A detailed description of the simulation approach for single sites and an application example can be found in Brunner et al. (2019) . A detailed description and evaluation of the wavelet-based multi-site approach can be found in Brunner and Gilleland (2020) . A detailed description and evaluation of the multi-variable and multi-site weather generator can be found in Brunner et al. (2021) . A detailed description and evaluation of the non-stationary streamflow generator can be found in Brunner and Gilleland (2024) . Package: r-cran-prspgx Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 590 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gglasso, r-cran-sgl, r-cran-glmnet, r-cran-bigsnpr, r-cran-matrix, r-cran-gigrvg, r-cran-mcmcpack, r-cran-bdsmatrix, r-cran-bigsparser, r-cran-lmtest, r-cran-mvtnorm, r-cran-propagate, r-cran-bigparallelr, r-cran-bigstatsr, r-cran-rfast, r-cran-matrixcalc Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-prspgx_0.3.0-1.ca2404.1_amd64.deb Size: 426624 MD5sum: 66d7a1bf6ffdc9baee4dbc21f45ac5cf SHA1: f3f0b18798d2de3006fbd4c3afae754a609b3b89 SHA256: 2d27f45eb073e3711f0af3ff657f5ea502daee82cf501fb552f8f646b767f445 SHA512: 1f7b46d7607862be38b55e20e3942d36930a9fedd75ce6098f85bd35055cf02ccf2ce03a18d86429c5fb5e6190630f18b937f8918a18d155dcbbfce97104fe3e Homepage: https://cran.r-project.org/package=PRSPGx Description: CRAN Package 'PRSPGx' (Construct PGx PRS) Construct pharmacogenomics (PGx) polygenic risk score (PRS) with PRS-PGx-Unadj (unadjusted), PRS-PGx-CT (clumping and thresholding), PRS-PGx-L, -GL, -SGL (penalized regression), PRS-PGx-Bayes (Bayesian regression). Package is based on ''Pharmacogenomics Polyenic Risk Score for Drug Response Prediction Using PRS-PGx Methods'' by Zhai, S., Zhang, H., Mehrotra, D.V., and Shen, J., 2021 (submitted). Package: r-cran-prsr Architecture: amd64 Version: 3.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 750 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-prsr_3.1.1-1.ca2404.1_amd64.deb Size: 719650 MD5sum: c56e7f012e902079e5a8d529449ea7e7 SHA1: 578a7d19aad59a7ca3ec0541cd3ebec399954a9d SHA256: 8602d4e3469043b205d033677c44367f9d4ab52ef7d4da0b5dd1984752a1a9da SHA512: 9fc96fecd08e55c0f61493930b9f977734b6b3babf8961925393b506708c10af28eb9bb988352a8b32f25f140545b314d42561565227ae8c3b517e783823183b Homepage: https://cran.r-project.org/package=pRSR Description: CRAN Package 'pRSR' (Test of Periodicity using Response Surface Regression) Tests periodicity in short time series using response surface regression. 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The main computations are implemented in 'Fortran' for high efficiency. The package is based on the PRTree methodology described in Alkhoury et al. (2020), "Smooth and Consistent Probabilistic Regression Trees" . Details on the treatment of missing data and implementation aspects are presented in Prass, T.S.; Neimaier, A.S.; Pumi, G. (2025), "Handling Missing Data in Probabilistic Regression Trees: Methods and Implementation in R" . 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Package: r-cran-psbayesborrow Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9096 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-boot, r-cran-matchit, r-cran-optmatch, r-cran-survival, r-cran-e1071, r-cran-overlapping, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-psbayesborrow_1.1.0-1.ca2404.1_amd64.deb Size: 1599708 MD5sum: dd76dbf9b54ebbe04a0276ea0de30d91 SHA1: 931cf12c5d2cd537fe8016fee43f6cf7e74e7c3d SHA256: 9242533784f3455fbf08b56cdc36db28af039bd86069ffb202ffc74d21ad9871 SHA512: eabe8efc8f31023c4a3752f0c67241b38d150714825153a6b8e98294a5f5f3de8a9132319ee99bb6efea0e4937ed2a9ab86694b43f7c5ff0ef786118f6563fe2 Homepage: https://cran.r-project.org/package=psBayesborrow Description: CRAN Package 'psBayesborrow' (Bayesian Information Borrowing with Propensity Score Matching) Hybrid control design is a way to borrow information from external controls to augment concurrent controls in a randomized controlled trial and is expected to overcome the feasibility issue when adequate randomized controlled trials cannot be conducted. A major challenge in the hybrid control design is its inability to eliminate a prior-data conflict caused by systematic imbalances in measured or unmeasured confounding factors between patients in the concurrent treatment/control group and external controls. To prevent the prior-data conflict, a combined use of propensity score matching and Bayesian commensurate prior has been proposed in the context of hybrid control design. The propensity score matching is first performed to guarantee the balance in baseline characteristics, and then the Bayesian commensurate prior is constructed while discounting the information based on the similarity in outcomes between the concurrent and external controls. 'psBayesborrow' is a package to implement the propensity score matching and the Bayesian analysis with commensurate prior, as well as to conduct a simulation study to assess operating characteristics of the hybrid control design, where users can choose design parameters in flexible and straightforward ways depending on their own application. Package: r-cran-psbcgroup Architecture: amd64 Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: libc6 (>= 2.29), libgsl27 (>= 2.7.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-learnbayes, r-cran-suppdists, r-cran-mvtnorm, r-cran-survival Filename: pool/dists/noble/main/r-cran-psbcgroup_1.7-1.ca2404.1_amd64.deb Size: 239008 MD5sum: 48e45c9a76aac88aa86d53ff779e39f0 SHA1: ed223f6e1172fc771d89afcb8bb3fac909c49994 SHA256: fbdf4cd08c8e830685fba8d1ca2e16b7bfe7470d2dec05d2e3949f01089f87c2 SHA512: 309f2646fad78bb33ba9ab52947c3850af640c4acee9ef8ff6a5b42df3993d01d831e0ac69ae1512bbfe5541415d37a645c06b7d402dd2b95c99222c405da86e Homepage: https://cran.r-project.org/package=psbcGroup Description: CRAN Package 'psbcGroup' (Penalized Parametric and Semiparametric Bayesian Survival Modelswith Shrinkage and Grouping Priors) Algorithms to implement various Bayesian penalized survival regression models including: semiparametric proportional hazards models with lasso priors (Lee et al., Int J Biostat, 2011 ) and three other shrinkage and group priors (Lee et al., Stat Anal Data Min, 2015 ); parametric accelerated failure time models with group/ordinary lasso prior (Lee et al. 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This optimization procedure is based on the method of Riedel and Sidorenko (1995), which minimizes the Mean Square Error (sum of variance and bias) at each frequency, but modified for computational stability. The same procedure can now be used to calculate the cross spectrum (multivariate analyses). 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When used for inference mid-ranks may lead to paradoxical results. Pseudo-ranks are in general not affected by such a problem. See Happ et al. (2020, ) for details. 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More details on implementation ('Barillec et. al.', 2010, ). 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Package: r-cran-pspmanalysis Architecture: amd64 Version: 0.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4328 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstudioapi, r-cran-pkgbuild Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pspmanalysis_0.3.9-1.ca2404.1_amd64.deb Size: 3383092 MD5sum: 6de40af0c78ae8ae7983231b18dc2039 SHA1: 4c20a4c1d583a504f9254d17a6d68fad74ec106f SHA256: e37ed787bb29eda680d1bd197b48c85bde8f77c00d9ccfa7cd3c0de3a10a68af SHA512: 4fc0736d7d8af741aa3637057531a44ed056d2d92f20fe8d80865ab8b86f9aa930da2e17dfabe7dadab3b20272435e24c4a316d6b4c4fc495ae1e902f6cf22ad Homepage: https://cran.r-project.org/package=PSPManalysis Description: CRAN Package 'PSPManalysis' (Analysis of Physiologically Structured Population Models) Performs demographic, bifurcation and evolutionary analysis of physiologically structured population models, which is a class of models that consistently translates continuous-time models of individual life history to the population level. A model of individual life history has to be implemented specifying the individual-level functions that determine the life history, such as development and mortality rates and fecundity. M.A. Kirkilionis, O. Diekmann, B. Lisser, M. Nool, B. Sommeijer & A.M. de Roos (2001) . O.Diekmann, M.Gyllenberg & J.A.J.Metz (2003) . A.M. de Roos (2008) . Package: r-cran-psqn Architecture: amd64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1546 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen, r-cran-testthat Suggests: r-cran-r.rsp, r-cran-rmarkdown, r-cran-rcpparmadillo, r-cran-bench, r-cran-numderiv, r-cran-lbfgsb3c, r-cran-lbfgs, r-cran-alabama Filename: pool/dists/noble/main/r-cran-psqn_0.3.2-1.ca2404.1_amd64.deb Size: 443236 MD5sum: 5e21aca460b5a8ca68c04d57e9fedb43 SHA1: 31def2a2eb5a660c030832fdefd343e689b51127 SHA256: 4bd9c651eddb33320d3ee11eb4a6bedfd07ec08bbfebe02b8fd5cba4e606d47f SHA512: db52453bb83f19788125148a22cf204b6d940c160ce51e844a0a1a2df55f0f51fad85835956d114a4a46a013d1e82ce34eeab6d7cb3f5b3144ac4e956b3c5731 Homepage: https://cran.r-project.org/package=psqn Description: CRAN Package 'psqn' (Partially Separable Quasi-Newton) Provides quasi-Newton methods to minimize partially separable functions. 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Package: r-cran-psrwe Architecture: amd64 Version: 3.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4070 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-rcpp, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-randomforest, r-cran-survival, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-psrwe_3.2-1-1.ca2404.1_amd64.deb Size: 1238308 MD5sum: 823a58bb705d2e6840abe0f1bda5eef0 SHA1: 9a986ff4f2d7c78b3a69ca60aa0ecb5234a923ff SHA256: 7405da60b3dae1a22bd9329d259a5516445ac241edc2f4ee26efb4f6ddde099a SHA512: 4f4e05620024cacd0913f789bf8140e6bb1b47e0c68bd849f5b781f28877a3da28e0a9014d4b60afce3d6b8db7ccbe57e1d5fdf525b19cb1cc96489628cb330b Homepage: https://cran.r-project.org/package=psrwe Description: CRAN Package 'psrwe' (PS-Integrated Methods for Incorporating Real-World Evidence inClinical Studies) High-quality real-world data can be transformed into scientific real-world evidence for regulatory and healthcare decision-making using proven analytical methods and techniques. 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Package: r-cran-pssubpathway Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4726 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-bioc-gsva, r-cran-igraph, r-cran-mpmi, r-cran-pheatmap Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pssubpathway_0.1.3-1.ca2404.1_amd64.deb Size: 4620464 MD5sum: f7d761853d5ef6ca4ae2929aebe75fc7 SHA1: 1c4f9b06c3e0f123df0e8cbc053af31a377229eb SHA256: c94e8996acd44b49a6c51a5453be338479e0ed6a417adf5074846d4bb805de15 SHA512: 7007246e959c794edc3e57fb4c4a759b4bcb9299fc367595fd5ff2461111a12ec639bb311579a54ef5d81d852d663394f8201503820891125769c75ba0ca1ac8 Homepage: https://cran.r-project.org/package=psSubpathway Description: CRAN Package 'psSubpathway' (Flexible Identification of Phenotype-Specific Subpathways) A network-based systems biology tool for flexible identification of phenotype-specific subpathways in the cancer gene expression data with multiple categories (such as multiple subtype or developmental stages of cancer). Subtype Set Enrichment Analysis (SubSEA) and Dynamic Changed Subpathway Analysis (DCSA) are developed to flexible identify subtype specific and dynamic changed subpathways respectively. The operation modes include extraction of subpathways from biological pathways, inference of subpathway activities in the context of gene expression data, identification of subtype specific subpathways with SubSEA, identification of dynamic changed subpathways associated with the cancer developmental stage with DCSA, and visualization of the activities of resulting subpathways by using box plots and heat maps. Its capabilities render the tool could find the specific abnormal subpathways in the cancer dataset with multi-phenotype samples. Package: r-cran-psvd Architecture: amd64 Version: 1.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-psvd_1.1-0-1.ca2404.1_amd64.deb Size: 33520 MD5sum: cd1c9a45f1f4d5013297d5fe2a433f7d SHA1: d1cc9b4c2a029e802b4e32bd2ed333bfb323f607 SHA256: 3bbde9077a74f57b8cfc60926e76217196ee91cd68e406485e4ad6d63de45ecb SHA512: 65a7ba80dbe99ccb12753ea9d8618370751a4e914c504e77b4b704926fc4c2655337e1c831d8390b7a9806674035b0349672a0a3836df43b580d362296859991 Homepage: https://cran.r-project.org/package=psvd Description: CRAN Package 'psvd' (Eigendecomposition, Singular-Values and the Power Method) For a data matrix with m rows and n columns (m>=n), the power method is used to compute, simultaneously, the eigendecomposition of a square symmetric matrix. This result is used to obtain the singular value decomposition (SVD) and the principal component analysis (PCA) results. Compared to the classical SVD method, the first r singular values can be computed. 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Intended as a common lightweight and efficient toolbox for psychometric modeling and a common building block for fitting psychometric mixture models in package "psychomix" and trees based on psychometric models in package "psychotree". 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References: Meyer, D. and Thevenard, D (2019) . 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Package: r-cran-ptsr Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-extradistr, r-cran-suppdists, r-cran-actuar, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-ptsr_0.1.2-1.ca2404.1_amd64.deb Size: 72390 MD5sum: 5167bde8cdf266ca0433affbc664777b SHA1: 5ce3d7bd6afa26bc6e78e151bb91fe570148e87b SHA256: 1de9b62bbfd9cdd3f38ba2f593ad47dea70aea4431293b5f07486643aac4f1cd SHA512: 16af184006d190f7e9d86059427156038cc462489098a07a09aa021bb1d1c9736915e362519ae88693d7cd389b70ae30f5a16d588585ebd1cda6bb868989e351 Homepage: https://cran.r-project.org/package=PTSR Description: CRAN Package 'PTSR' (Positive Time Series Regression) A collection of functions to simulate, estimate and forecast a wide range of regression based dynamic models for positive time series. This package implements the results presented in Prass, T.S.; Carlos, J.H.; Taufemback, C.G. and Pumi, G. (2022). "Positive Time Series Regression" . Package: r-cran-ptw Architecture: amd64 Version: 1.9-17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4637 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcppde Filename: pool/dists/noble/main/r-cran-ptw_1.9-17-1.ca2404.1_amd64.deb Size: 4175136 MD5sum: 3c7a3634a1ca383cc22bd4aa9b20d361 SHA1: de7deba42a50796bb9463cfb35f84c730b363e4e SHA256: 4af80c566db13a03388fef61b931d0c3f5a907a6313061401c90fbe5ca549b84 SHA512: 6523d44377baed7aac47b80d763fcb25690a1284a82843aa14f14ded48003844bd4c3ce3b72c8b00771d4470d14b0f701bf8b49e33c3299b52c502e723058eaa Homepage: https://cran.r-project.org/package=ptw Description: CRAN Package 'ptw' (Parametric Time Warping) Parametric Time Warping aligns patterns, i.e., it aims to put corresponding features at the same locations. The algorithm searches for an optimal polynomial describing the warping. It is possible to align one sample to a reference, several samples to the same reference, or several samples to several references. One can choose between calculating individual warpings, or one global warping for a set of samples and one reference. Two optimization criteria are implemented: RMS (Root Mean Square error) and WCC (Weighted Cross Correlation). Both warping of peak profiles and of peak lists are supported. A vignette for the latter is contained in the inst/doc directory of the source package - the vignette source can be found on the package github site. See `citation("ptw")` for more details. Package: r-cran-publipha Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3166 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-loo, r-cran-truncnorm, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-metafor, r-cran-spelling, r-cran-metadat Filename: pool/dists/noble/main/r-cran-publipha_0.1.2-1.ca2404.1_amd64.deb Size: 949426 MD5sum: 040a1e3799105bdd3653281c3f7e2daf SHA1: 9e884c59823eff290dfe9105ed3bb1fb80aa5901 SHA256: a95dacd056ac5a8083c077449c75cbf0373680596a920d5c327ad634b080e1fe SHA512: 37bbfd7e3f7e063a97f425d5922c5d71b252cf8b0f76513aec85304c84fbda5fc70ced91c1d81ec5872a16bd9807821742fc7328c938292dada35db1fab0cdc1 Homepage: https://cran.r-project.org/package=publipha Description: CRAN Package 'publipha' (Bayesian Meta-Analysis with Publications Bias and P-Hacking) Tools for Bayesian estimation of meta-analysis models that account for publications bias or p-hacking. For publication bias, this package implements a variant of the p-value based selection model of Hedges (1992) with discrete selection probabilities. It also implements the mixture of truncated normals model for p-hacking described in Moss and De Bin (2019) . Package: r-cran-pugmm Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-pugmm_0.1.2-1.ca2404.1_amd64.deb Size: 283558 MD5sum: cae57468831bd3dbd39a327575ebbeaf SHA1: 4bbfd9a5837e7af395560834b93f3d1644b4bb1f SHA256: 4d4a4c4038caabbbca2f5f43f53fd90d427cd2de016ac6ca4c0bc3bfa95c9f30 SHA512: fd955681aafb4d2cf059274f1217f3adb35357fa56d0077dc23a1538749a8c5f87ce2f98ca9ea523649ab0fc624b5fa70b543cf41b0e7f0a36d73691917e0843 Homepage: https://cran.r-project.org/package=PUGMM Description: CRAN Package 'PUGMM' (Parsimonious Ultrametric Gaussian Mixture Models) Parsimonious Ultrametric Gaussian Mixture Models via grouped coordinate ascent (equivalent to EM) algorithm characterized by the inspection of hierarchical relationships among variables via parsimonious extended ultrametric covariance structures. The methodologies are described in Cavicchia, Vichi, Zaccaria (2024) , (2022) and (2020) . Package: r-cran-pulasso Architecture: amd64 Version: 3.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1226 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pulasso_3.2.6-1.ca2404.1_amd64.deb Size: 548554 MD5sum: d2af272ac4f08aff54aacd89295225b0 SHA1: 978801181b002cd09d56d9bb6f57ab9122f44960 SHA256: f518fde357008ee3c07ad673733fe07dc213285d35fe2a18b81867d393338972 SHA512: 718718dd1d5762816f9051af9fc397dd02ce5d760baa0b0468d6794bac0403aecead3d3af9667791189e171c939cd89b5e8192ad75cba1905b2ff36316554c4d Homepage: https://cran.r-project.org/package=PUlasso Description: CRAN Package 'PUlasso' (High-Dimensional Variable Selection with Presence-Only Data) Efficient algorithm for solving PU (Positive and Unlabeled) problem in low or high dimensional setting with lasso or group lasso penalty. The algorithm uses Maximization-Minorization and (block) coordinate descent. Sparse calculation and parallel computing are supported for the computational speed-up. See Hyebin Song, Garvesh Raskutti (2018) . Package: r-cran-pullword Architecture: amd64 Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcurl Filename: pool/dists/noble/main/r-cran-pullword_0.3-1.ca2404.1_amd64.deb Size: 28556 MD5sum: 4178dfbb0519d6ec174f9fe164a4bffe SHA1: 0943131e5d1abd3ddc716b8abed79d3fcae984ac SHA256: 90ac77a0e8acea2ff6b5e5cb4433cd65ce50e93a73f78ab7491c7e8255894b51 SHA512: 2a7df0b7991ed1e5c5dd279f6aca6e5755a00a007024d9880c614b7979d409814dfab54461e9b712a54f5edd97904b598cb6ab3354b0abfbd7bbef3a1eb250e5 Homepage: https://cran.r-project.org/package=pullword Description: CRAN Package 'pullword' (R Interface to Pullword Service) R Interface to Pullword Service for natural language processing in Chinese. It enables users to extract valuable words from text by deep learning models. For more details please visit the official site (in Chinese) . Package: r-cran-pumbayes Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 560 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppdist, r-cran-mvtnorm, r-cran-rcpptn Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pscl, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-pumbayes_1.0.2-1.ca2404.1_amd64.deb Size: 315850 MD5sum: 54ed9f7d208ebc59f19f6002bad32908 SHA1: a6650070c77dc2d25256b7da52076ff55f303733 SHA256: 5f1bb262c21352eaffd9e896ffd79a77f3124efc54da5dba4bc21a34c303b8bd SHA512: 1e919988bfa0f07d8cf0f6c7e99dc8e67c7679921c42e970454a4cd4ce6e9a3ac79ebc266a45ff4b83387c0ae529c0ad509fa1be92b07f425fb8313116305ee5 Homepage: https://cran.r-project.org/package=pumBayes Description: CRAN Package 'pumBayes' (Bayesian Estimation of Probit Unfolding Models for BinaryPreference Data) Bayesian estimation and analysis methods for Probit Unfolding Models (PUMs), a novel class of scaling models designed for binary preference data. These models allow for both monotonic and non-monotonic response functions. The package supports Bayesian inference for both static and dynamic PUMs using Markov chain Monte Carlo (MCMC) algorithms with minimal or no tuning. Key functionalities include posterior sampling, hyperparameter selection, data preprocessing, model fit evaluation, and visualization. The methods are particularly suited to analyzing voting data, such as from the U.S. Congress or Supreme Court, but can also be applied in other contexts where non-monotonic responses are expected. For methodological details, see Shi et al. (2025) . Package: r-cran-puniform Architecture: amd64 Version: 0.2.8-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-adgoftest, r-cran-metafor, r-cran-numderiv, r-cran-rcpparmadillo Suggests: r-cran-metadat Filename: pool/dists/noble/main/r-cran-puniform_0.2.8-1.ca2404.1_amd64.deb Size: 311824 MD5sum: dc8992fee0da0c35d0c2eb7f8f1ed6b0 SHA1: 4348fdbe45125acc7be97f5c3c0f0f6d4d7eab33 SHA256: ba30ca6077015fe63a2b73abb8264d8577f5e7cc0d67c1c3bc8c60d4b9e69779 SHA512: af48c2c79c16124b4fb760094a8eb21387a8e29463551b2cf85f2781de6020591fe6fb27270431863f71490f6e3810680b41956cd4b6e3d9b07caeff713ffbb1 Homepage: https://cran.r-project.org/package=puniform Description: CRAN Package 'puniform' (Meta-Analysis Methods Correcting for Publication Bias) Provides meta-analysis methods that correct for publication bias and outcome reporting bias. Four methods and a visual tool are currently included in the package. The p-uniform method as described in van Assen, van Aert, and Wicherts (2015) can be used for estimating the average effect size, testing the null hypothesis of no effect, and testing for publication bias using only the statistically significant effect sizes of primary studies. The second method in the package is the p-uniform* method as described in van Aert and van Assen (2023) . This method is an extension of the p-uniform method that allows for estimation of the average effect size and the between-study variance in a meta-analysis, and uses both the statistically significant and nonsignificant effect sizes. The third method in the package is the hybrid method as described in van Aert and van Assen (2018) . The hybrid method is a meta-analysis method for combining a conventional study and replication/preregistered study while taking into account statistical significance of the conventional study. This method was extended in van Aert (2025) such that it allows for the inclusion of multiple conventional and replication/preregistered studies. The p-uniform and hybrid method are based on the statistical theory that the distribution of p-values is uniform conditional on the population effect size. The fourth method in the package is the Snapshot Bayesian Hybrid Meta-Analysis Method as described in van Aert and van Assen (2018) . This method computes posterior probabilities for four true effect sizes (no, small, medium, and large) based on an original study and replication while taking into account publication bias in the original study. The method can also be used for computing the required sample size of the replication akin to power analysis in null-hypothesis significance testing. The meta-plot is a visual tool for meta-analysis that provides information on the primary studies in the meta-analysis, the results of the meta-analysis, and characteristics of the research on the effect under study (van Assen et al., 2023). Helper functions to apply the Correcting for Outcome Reporting Bias (CORB) method to correct for outcome reporting bias in a meta-analysis (van Aert & Wicherts, 2023). Package: r-cran-pureseqtmr Architecture: amd64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 578 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-devtools, r-cran-dplyr, r-cran-ggplot2, r-cran-peptides, r-cran-plyr, r-cran-rappdirs, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-profvis Filename: pool/dists/noble/main/r-cran-pureseqtmr_1.4-1.ca2404.1_amd64.deb Size: 397742 MD5sum: 295255077d0438256356e98532db98cc SHA1: 3b7b567efe4e5afd044237c9e0cb3ef8b8d1243c SHA256: 6e1ab9852c77959f00736a3792cb485deafd49f4a367cfeb3e473446842189ce SHA512: 5451a23346e0b77d7694cff919ee13412782602dfaaa532ea4e6a4badb5e55fc288af4eb9c95a27db0cf26139c3135d3aa4ccb9a7a03d18697ff7fc98dc17db8 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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Being that projection pursuit searches for low-dimensional linear projections in high-dimensional data structures, while grand tour is a technique used to explore multivariate statistical data through animation. 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'QCA' is a methodology that bridges the qualitative and quantitative divide in social science research. It uses a Boolean minimization algorithm, resulting in a minimal causal configuration associated with a given phenomenon. 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The package contains functions aimed at evaluating the exact distribution of quadratic forms (QFs) and ratios of QFs. In particular, we propose to evaluate density, quantile and distribution functions of positive definite QFs and ratio of independent positive QFs by means of an algorithm based on the numerical inversion of Mellin transforms. Package: r-cran-qfa Architecture: amd64 Version: 5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 674 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-rhpcblasctl, r-cran-doparallel, r-cran-fields, r-cran-foreach, r-cran-matrix, r-cran-sparsem, r-cran-mgcv, r-cran-nlme, r-cran-quantreg, r-cran-colorramps, r-cran-mass, r-cran-osqp, r-cran-piqp, r-cran-boot Filename: pool/dists/noble/main/r-cran-qfa_5.0-1.ca2404.1_amd64.deb Size: 627846 MD5sum: 5453d2852a418eb18f882129222fe1ec SHA1: a1adc5e707d186daae5d357b3804f4b4d76a4289 SHA256: 524e6e5edcf5ceecfdc1bc3aa7bc7fac3c1067a3e528881aadecc21226fb80ad SHA512: 9479feb88c99b94e925683793bfedaeeb76b75f93b7ef86aaf030dda05f00ce2509a78ccae05755c902957bcab11a55e6e0f0888af76a1bedc0e2df78df3a8fc Homepage: https://cran.r-project.org/package=qfa Description: CRAN Package 'qfa' (Quantile-Frequency Analysis (QFA) of Time Series and SplineQuantile Regression (SQR)) Implementation of quantile frequency analysis (QFA) for time series based on trigonometric quantile regression and of spline quantile regression (SQR) for estimating the coefficients in linear quantile regression models as smooth functions of the quantile level. References: [1] Li, T.-H. (2012). ''Quantile periodograms,'' J. of the American Statistical Association, 107, 765–776. [2] Li, T.-H. (2014). Time Series with Mixed Spectra, CRC Press. [3] Li, T.-H. (2025). ''Quantile Fourier transform, quantile series, and nonparametric estimation of quantile spectra,'' Communications in Statistics: Simulation and Computation, 1–22. [4] Li, T.-H. (2025). ''Quantile-crossing spectrum and spline autoregression estimation,'' Statistical Inference for Stochastic Processes, 28, 20. [5] Li, T.-H. (2025). ''Spline autoregression method for estimation of quantile spectrum,'' J. of Computational and Graphical Statistics, 1-15. [6] Li, T.-H., and Megiddo, N. (2026). ''Spline quantile regression,'' J. of Statistical Theory and Practice, 20, 30. [7] Li, T.-H. (2026). ''Spline quantile regression with cubic and linear smoothing splines,'' . 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The diversity of fatty acids and their patterns in organisms, coupled with the narrow limitations on their biosynthesis, properties of digestion in monogastric animals, and the prevalence of large storage reservoirs of lipid in many predators, led to the development of quantitative fatty acid signature analysis (QFASA) to study predator diets. 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Rohde et al. (2019) . 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See Epskamp et al. (2012) . 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The implementations are based on the Python library 'splines'. Quaternions splines allow to construct spherical curves. References: Barry and Goldman , Kochanek and Bartels . 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This package is strongly inspired by the 'spray' package. It provides a function to compute Gröbner bases (reference ). It also includes some features for symmetric polynomials, such as the Hall inner product. The header file of the C++ code can be used by other packages. It provides the templated class 'Qspray' that can be used to represent and to deal with multivariate polynomials with another type of coefficients. Package: r-cran-qsrutils Architecture: amd64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1829 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-qsrutils_0.2.1-1.ca2404.1_amd64.deb Size: 1646280 MD5sum: cdf91f3fc82aedada33759c7b3c219c1 SHA1: f28ede5297859a7d5aca627c5c3e0e383ed47e47 SHA256: c1a2a39810637c5f1f5c7fce614967ab4858277f7edf9326536d22d6e5870803 SHA512: 42c8b4e4a71cb75cd7e9444e4ba97d7f72d6ec7fba7227d212553e70a2660059ba9e3b12a27ab524e5f543657d0edbf81341844069fb0812d7f092067010ae17 Homepage: https://cran.r-project.org/package=QsRutils Description: CRAN Package 'QsRutils' (R Functions Useful for Community Ecology) A collection of utility functions for community ecology analyses, with emphasis on workflows using the 'phyloseq' and 'vegan' packages. 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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) . 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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) . 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It is a reimplementation of the 'R/qtl' package to better handle high-dimensional data and complex cross designs. Broman et al. (2019) . 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The test differs from that in Jiang & Zeng (1995) and that in Tian et al. (2016) in that our test accommodates multiparental populations. Package: r-cran-qtl Architecture: amd64 Version: 1.74-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10218 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-qtl_1.74-1.ca2404.1_amd64.deb Size: 5568334 MD5sum: f979a615f75ff8ce7f303b0756de9863 SHA1: c5425d3a5583c21b38484e2dc72891d5f4c5dfdc SHA256: a2aff5459e778f879ff71c23d4d4d86e89ca6003f2f1ab0a2afff4c4670d798c SHA512: 58fdd0d3d99300ec19e149e93744bcf09e362be5e1070264bee56514f91f23c0f1abe4e355a73f53f86faf8f33e0c9f6f13d53f50c33827f840538ceda134907 Homepage: https://cran.r-project.org/package=qtl Description: CRAN Package 'qtl' (Tools for Analyzing QTL Experiments) Analysis of experimental crosses to identify genes (called quantitative trait loci, QTLs) contributing to variation in quantitative traits. Broman et al. (2003) . Package: r-cran-qtlhot Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2011 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-qtl, r-cran-mnormt, r-cran-corpcor Filename: pool/dists/noble/main/r-cran-qtlhot_1.0.4-1.ca2404.1_amd64.deb Size: 1707620 MD5sum: 969813e620ced6a4f9966c9c1be2f299 SHA1: ac43c59c232b52e8ad1ebd78bda08fe6ae3d8e6e SHA256: a640e3639fc07425f287d00aef136ed8bbbcdb90d3f439e49cf7e5ad5932f501 SHA512: 764457c40ebcdc4bae8e5f28c8fdc89bdf7da3195776c40ea21aa9e37bb50bc1430ac31370e2cc97f4540f758132fb50ed934153f433a365cfd18a8cc2ca2d19 Homepage: https://cran.r-project.org/package=qtlhot Description: CRAN Package 'qtlhot' (Inference for QTL Hotspots) Functions to infer co-mapping trait hotspots and causal models. Chaibub Neto E, Keller MP, Broman AF, Attie AD, Jansen RC, Broman KW, Yandell BS (2012) Quantile-based permutation thresholds for QTL hotspots. Genetics 191 : 1355-1365. . Chaibub Neto E, Broman AT, Keller MP, Attie AD, Zhang B, Zhu J, Yandell BS (2013) Modeling causality for pairs of phenotypes in system genetics. Genetics 193 : 1003-1013. . Package: r-cran-qtlmt Architecture: amd64 Version: 0.1-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 299 Depends: libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qtlmt_0.1-6-1.ca2404.1_amd64.deb Size: 231976 MD5sum: d7aa2b30d651240cb3c2b61bebb15f64 SHA1: 32d25b6ed566a230f0fa9c5998ce8d39a2bcbb2f SHA256: aa154dc2ce7d1eedc72475d3c5532a1a9b1066ec7db106aba31a4f23c43a0cc1 SHA512: b8fc22d8748dfd05f97ef01600200908ed43018cd6a5aff2004d758f012503d9891c5f17718cb1775d32281340f18283890ceece207ac18c7071adde0bb88fbb Homepage: https://cran.r-project.org/package=qtlmt Description: CRAN Package 'qtlmt' (Tools for Mapping Multiple Complex Traits) Provides tools for joint analysis of multiple traits in a backcross (BC) or recombinant inbred lines (RIL) population. 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(2020) . 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It can estimate background genetic variance components, impute missing genotypes, simulate genotypes, perform a genome scan for putative quantitative trait loci (QTL), and plot mapping results. It also has functions to calculate identity coefficients from pedigrees, especially suitable for pedigrees that consist of a large number of generations, or estimate identity coefficients from genotypic data in certain circumstances. 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These include methods for transformation-based quantile regression, quantile-based measures of location, scale and shape, methods for quantiles of discrete variables, quantile-based multiple imputation, restricted quantile regression, directional quantile classification, and quantile ratio regression. A vignette is given in Geraci (2016, The R Journal) and included in the package. 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For more information see Saraceno G., Markatou M., Mukhopadhyay R. and Golzy M. (2024) Markatou, M. and Saraceno, G. (2024) , Ding, Y., Markatou, M. and Saraceno, G. (2023) , and Golzy, M. and Markatou, M. (2020) . 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(2021) , the stepwise Wald test method (the Wald method) by Ma and de la Torre (2020) , the multiple logistic regression‑based Q‑matrix validation method (the MLR-B method) by Tu et al. (2022) , the beta method based on signal detection theory by Li and Chen (2024) and Q-matrix validation based on relative fit index by Chen et al. (2013) . Different research methods and iterative procedures during Q-matrix validating are available . Package: r-cran-qvarsel Architecture: amd64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-lpsolveapi Suggests: r-cran-mclust Filename: pool/dists/noble/main/r-cran-qvarsel_1.2-1.ca2404.1_amd64.deb Size: 63530 MD5sum: 7d47f886cdc2980e27afbffdd1237c52 SHA1: 55d0002df2e64bb4b44199d3879165b431b42edf SHA256: b39e2ce18999c01a080149ddd6f35864cdbc3c473c914cc4bbe8b914b53fcfad SHA512: 5d8c0d9ff8b39915c649f09be905b6d20b4a58497832c10e850f7fe6011f28068df7f3b5e58c2717ae32d021c88f36f18d26cedca054558ca9c8efd512ca1ccc Homepage: https://cran.r-project.org/package=qVarSel Description: CRAN Package 'qVarSel' (Select Variables for Optimal Clustering) Finding hidden clusters in structured data can be hindered by the presence of masking variables. 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Package: r-cran-qwdap Architecture: amd64 Version: 1.1.20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2347 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-pls, r-cran-corelearn, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-qwdap_1.1.20-1.ca2404.1_amd64.deb Size: 2172114 MD5sum: 4c9001b7e92863a9e886adb37bd55275 SHA1: 8acc3ced274fdc1cf2cca2a1c6d9c909bdd8095c SHA256: af78f10582af229c94baa0b63d0da15211c5f52ded53e8afa43e46a2960d73d6 SHA512: a9b8ded780105541ef6da3ef1c56832945f9519d86a292aada2ebaf8d699fb02d36fdf7cf72473c9eb8ac571474885441103777f8c146d91dbcb07bb05f2b6ef Homepage: https://cran.r-project.org/package=QWDAP Description: CRAN Package 'QWDAP' (Quantum Walk-Based Data Analysis and Prediction) The modeling and prediction of graph-associated time series(GATS) based on continuous time quantum walk. 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Package: r-cran-qwraps2 Architecture: amd64 Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2280 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-knitr, r-cran-rcpp, r-cran-xfun, r-cran-rcpparmadillo Suggests: r-cran-dplyr, r-cran-survival, r-cran-covr, r-cran-glmnet, r-cran-rbenchmark, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-qwraps2_0.6.2-1.ca2404.1_amd64.deb Size: 1127320 MD5sum: be7951bc91f1b1d8f9d67f49afd15f77 SHA1: e4a6f84845ffe2c2e40d918f3ec1b42906281f0a SHA256: 6dc8e5a06345ec03e9bc179c1c2c5d91af1d7ae108cd9641451f90e5615839a0 SHA512: 57847e062c2e83712a8e10250af987d61c305675e241130041e0a10d25428019cf9cefe68433ae5e738ccdac29a6f3311f65f02097ea97eaee4e45ab006a73f2 Homepage: https://cran.r-project.org/package=qwraps2 Description: CRAN Package 'qwraps2' (Quick Wraps 2) A collection of (wrapper) functions the creator found useful for quickly placing data summaries and formatted regression results into '.Rnw' or '.Rmd' files. 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Package: r-cran-qz Architecture: amd64 Version: 0.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-fda Filename: pool/dists/noble/main/r-cran-qz_0.2-4-1.ca2404.1_amd64.deb Size: 273512 MD5sum: d466858bd24ca23b762724ec8deed983 SHA1: 309b1cfc3756ad6b3b4a182597603352e3eaa131 SHA256: c3934ae8746ffe362d0d13e17115caf61ffba9f3d0e7f4616f2f05ae8f275741 SHA512: 5f7e8c24d2c94d5098787d314efb043ccf32ad281aa8000bbcd8ec444e174dbe5f6936a0d2e919ddfb535028c5bf2698fe308f86126c2c65f6fcbeac23e48d34 Homepage: https://cran.r-project.org/package=QZ Description: CRAN Package 'QZ' (Generalized Eigenvalues and QZ Decomposition) Generalized eigenvalues and eigenvectors use QZ decomposition (generalized Schur decomposition). 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Package: r-cran-r2pmml Architecture: amd64 Version: 0.31.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4641 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-caret, r-cran-e1071, r-cran-earth, r-cran-evtree, r-cran-glmnet, r-cran-lightgbm, r-cran-mlbench, r-cran-mlr, r-cran-partykit, r-cran-randomforest, r-cran-ranger, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-r2pmml_0.31.0-1.ca2404.1_amd64.deb Size: 4160492 MD5sum: d943089a935f64595d264362bfc5238c SHA1: 0dd6ec30b84d65a4fa297b4f11f16577ad61bdb4 SHA256: 8370e5b0b2fb8040acfc6b8e5a43b5aaf7caf13f4969567c50df0545a6e0ce12 SHA512: 269168881136780508865c7a1aefac19f29e4e4bd6c96881bf7a8bf9e0dc407fcf53a3f6e8078d53d2a2ffb5c32fe4ce849e256e5b6e7ba21d0447811af33ec0 Homepage: https://cran.r-project.org/package=r2pmml Description: CRAN Package 'r2pmml' (Convert R Models to 'PMML') R wrapper for the 'JPMML-R' library , which converts R models to Predictive Model Markup Language ('PMML'). Package: r-cran-r2sample Architecture: amd64 Version: 4.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 748 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-shiny, r-cran-ggplot2, r-cran-microbenchmark Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-r2sample_4.1.0-1.ca2404.1_amd64.deb Size: 363250 MD5sum: 9ce5b81d551d4941441157a40406b074 SHA1: 5f2a69fd0083bd6e175cd5b29bfabb72a2eb9a86 SHA256: b3a92dd1b3788b83c2b5b3eb97ce5027f17ed9327a37ebe087da708349861999 SHA512: 2ee42ad8dd8ab2c85a6e6195c04ae24ae086874f42119672ebbb9272e169d4b1175a46a47677ece311212b185d79ed2d6254c0aaad2799efd1fbc5e4c35123e4 Homepage: https://cran.r-project.org/package=R2sample Description: CRAN Package 'R2sample' (Various Methods for the Two Sample Problem) The routine twosample_test() in this package runs the two sample test using various test statistic. 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Package: r-cran-r2swf Architecture: amd64 Version: 0.9-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: libc6 (>= 2.33), libfreetype6 (>= 2.2.1), libpng16-16t64 (>= 1.6.2), zlib1g (>= 1:1.1.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sysfonts Suggests: r-cran-xml, r-cran-cairo Filename: pool/dists/noble/main/r-cran-r2swf_0.9-9-1.ca2404.1_amd64.deb Size: 187260 MD5sum: 71fa55db04ff95a055fc9cda1e9afd3e SHA1: ac325c8556a536924bd03b479c44d393203f4605 SHA256: acada98e2d5546adabb64a05f7a1bf4d17cca524f15dc96992e157bfc9be0ab9 SHA512: f9e2488f69de6e31379785c4ef6f5d99fd37ac25dd742faf8e00ea11d54fcd3f6cd1a45384a781b70b262cdebbfe1d2b7c7a92ba508c8548c2095fe7d450b326 Homepage: https://cran.r-project.org/package=R2SWF Description: CRAN Package 'R2SWF' (Convert R Graphics to Flash Animations) Using the 'Ming' library to create Flash animations. 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The r3PG serves as a flexible and easy-to-use interface for the 3-PGpjs (monospecific, evenaged and evergreen forests) described in Landsberg & Waring (1997) and the 3-PGmix (deciduous, uneven-aged or mixed-species forests) described in Forrester & Tang (2016) . Package: r-cran-raceid Architecture: amd64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9730 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coop, r-cran-cluster, r-cran-fateid, r-cran-fnn, r-cran-fpc, r-cran-ggplot2, r-cran-harmony, r-cran-ica, r-cran-igraph, r-cran-irlba, r-cran-leiden, r-cran-locfit, r-cran-mass, r-cran-matrix, r-cran-matrixstats, r-cran-pheatmap, r-cran-princurve, r-cran-quadprog, r-cran-randomforest, r-cran-runner, r-cran-rcpp, r-cran-rcolorbrewer, r-cran-rtsne, r-cran-umap, r-cran-vegan Suggests: r-bioc-batchelor, r-bioc-deseq2, r-cran-knitr, r-cran-rmarkdown, r-bioc-singlecellexperiment, r-bioc-slingshot, r-bioc-summarizedexperiment Filename: pool/dists/noble/main/r-cran-raceid_0.4.0-1.ca2404.1_amd64.deb Size: 6192930 MD5sum: 4e999dd22fefeeb1bd200e8956e2d6f6 SHA1: 1aa87d184534c6562d785f5ace612620c0d765d7 SHA256: a939161e8888b415f36ccb47ad139cab8f590b824524a9bf43fde76514b44324 SHA512: d7d10c87421f1f08c3c386d57122057198c53f60bf3c370bc22a714026ab6c5129d045c8bbfa48b9025947b84d92107e6807da38adab79147d53137b9586e937 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. 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Package: r-cran-raceland Architecture: amd64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2183 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotwidgets, r-cran-terra, r-cran-sf, r-cran-rcpp, r-cran-comat, r-cran-rcpparmadillo Suggests: r-cran-dplyr, r-cran-pbapply, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-raster Filename: pool/dists/noble/main/r-cran-raceland_1.2.2-1.ca2404.1_amd64.deb Size: 1367474 MD5sum: 9dfd1b26ee765b81430a55e2ba8ddf64 SHA1: d9b48aa8f9d977ec270ee7f1965659666dff7dc3 SHA256: eeb1e2fbd8260a79b2b49a0918959d2a2e26f48cbeed3decdd79b91cd3392d1c SHA512: 3cd59a85ce240dd2a88575c7cadc5921d92d5b6ed1d28d8ce942cde483134f56167cf129f7f4ee69d23496f1f4fade4dd867e84a1bc5cf486dbbc82b664f4e9c Homepage: https://cran.r-project.org/package=raceland Description: CRAN Package 'raceland' (Pattern-Based Zoneless Method for Analysis and Visualization ofRacial Topography) Implements a computational framework for a pattern-based, zoneless analysis, and visualization of (ethno)racial topography (Dmowska, Stepinski, and Nowosad (2020) ). It is a reimagined approach for analyzing residential segregation and racial diversity based on the concept of 'landscape’ used in the domain of landscape ecology. Package: r-cran-racmacs Architecture: amd64 Version: 1.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10358 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-jsonlite, r-cran-ks, r-cran-brotli, r-cran-shiny, r-cran-shinyfiles, r-cran-shinyjs, r-cran-htmlwidgets, r-cran-ggplot2, r-cran-htmltools, r-cran-rmarchingcubes, r-cran-shape, r-cran-ellipsis, r-cran-mass, r-cran-magrittr, r-cran-igraph, r-cran-dplyr, r-cran-vctrs, r-cran-rlang, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-rcppensmallen, r-cran-rapidjsonr Suggests: r-cran-testthat, r-cran-r3js, r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-plotly, r-cran-geometry, r-cran-readxl, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-base64enc, r-cran-lifecycle, r-cran-mcmcpack, r-cran-spelling Filename: pool/dists/noble/main/r-cran-racmacs_1.2.9-1.ca2404.1_amd64.deb Size: 2404912 MD5sum: 4128b101f423158fbcdcc2d05049bd1a SHA1: f91406e6bdb147f7abe93a380f4d720f5c73ff0d SHA256: 2625805c83cb3485a6872c53e69d83e9a2c9db0f58d987c76b6c07f36dec603e SHA512: a10aba972e910c4c9b987639e61ca27467b3c613f661195fb9ff96c4ffbd48395d0b2e545657e7453823b1c7d119dc74f3acd75d2349b3825844672f7e331c18 Homepage: https://cran.r-project.org/package=Racmacs Description: CRAN Package 'Racmacs' (Antigenic Cartography Macros) A toolkit for making antigenic maps from immunological assay data, in order to quantify and visualize antigenic differences between different pathogen strains as described in Smith et al. (2004) and used in the World Health Organization influenza vaccine strain selection process. Additional functions allow for the diagnostic evaluation of antigenic maps and an interactive viewer is provided to explore antigenic relationships amongst several strains and incorporate the visualization of associated genetic information. Package: r-cran-radero Architecture: amd64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-roxygen2, r-cran-usethis, r-cran-jsonlite, r-cran-data.table, r-cran-devtools, r-cran-ggplot2, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-radero_1.0.8-1.ca2404.1_amd64.deb Size: 77122 MD5sum: 17b4dc2e7e5441c23456ad1b01481080 SHA1: c8bde3070bee033c287d9531f9b02f36f0485ff9 SHA256: c7691634d538475f3197867b016836cbf9918bc7ea16530cd5c76dfef783c127 SHA512: 07817532e1057cc18428cb17c8a3c87ac07071af9c901df725fb5ca14689744e73dad206efe8622dc5570b7d84718f5ed16e537c3cade78b81a2732f675fe061 Homepage: https://cran.r-project.org/package=RadEro Description: CRAN Package 'RadEro' (Cs-137 Conversion Model) A straightforward model to estimate soil migration rates across various soil contexts. Based on the compartmental, vertically-resolved, physically-based mass balance model of Soto and Navas (2004) and Soto and Navas (2008) . 'RadEro' provides a user-friendly interface in R, utilizing input data such as 137Cs inventories and parameters directly derived from soil samples (e.g., fine fraction density, effective volume) to accurately capture the 137Cs distribution within the soil profile. The model simulates annual 137Cs fallout, radioactive decay, and vertical diffusion, with the diffusion coefficient calculated from 137Cs reference inventory profiles. Additionally, it allows users to input custom parameters as calibration coefficients. The RadEro user manual and protocol, including detailed instructions on how to format input data and configuration files, can be found at the following link: . Package: r-cran-radviz Architecture: amd64 Version: 0.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4269 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-rlang, r-cran-igraph, r-cran-pracma, r-cran-hexbin, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bodenmiller, r-cran-tidyr, r-cran-rcolorbrewer, r-cran-cytofan, r-cran-scales, r-cran-mass Filename: pool/dists/noble/main/r-cran-radviz_0.9.5-1.ca2404.1_amd64.deb Size: 2927754 MD5sum: cec887b51981be82db4d92a4c11a5264 SHA1: c0ca5bcf34b73dc9d26f594a2b519e6048d71095 SHA256: 4a2030ec58b229d764da1aeb62c8ab70b6c94d10a640266d91be411aff2ef55b SHA512: 91207a65e7f591b86b032e057077669cc689d52cedc30d00afd40fb067c41eea5cb343941f867e0b754b20571f9a8cde1e40bf3a92c880b2b03e5166a4e8b1d2 Homepage: https://cran.r-project.org/package=Radviz Description: CRAN Package 'Radviz' (Project Multidimensional Data in 2D Space) An implementation of the radviz projection in R. It enables the visualization of multidimensional data while maintaining the relation to the original dimensions. This package provides functions to create and plot radviz projections, and a number of summary plots that enable comparison and analysis. For reference see Hoffman *et al.* (1999) () for original implementation, see Di Caro *et al* (2012) (), for the original method for dimensional anchor arrangements, see Demsar *et al.* (2007) () for the original Freeviz implementation. Package: r-cran-ragg Architecture: amd64 Version: 1.5.2-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2528 Depends: libc6 (>= 2.38), libfreetype6 (>= 2.2.1), libgcc-s1 (>= 3.0), libjpeg8 (>= 8c), libpng16-16t64 (>= 1.6.2), libstdc++6 (>= 13), libtiff6 (>= 4.0.3), libwebp7 (>= 1.3.2), libwebpmux3 (>= 1.3.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-systemfonts, r-cran-textshaping Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ragg_1.5.2-1.ca2404.2_amd64.deb Size: 562104 MD5sum: e32f97156a720ba0cd723e1be6ed8e86 SHA1: 41ac75c613d29f9960624e17a5584bdb4a2e7e34 SHA256: eab5335d2373eb98ee1017f96dc77f55c972911d485defc052e67f75ffdccfa3 SHA512: dbb726a348cc17b82b47c9337cb179185fa9299dc00fd6e8b5e01368a8c8fc66b834b2cf22faa96b507a2fff4bd879fc26054a135f2420ccc3756ded5b9549a6 Homepage: https://cran.r-project.org/package=ragg Description: CRAN Package 'ragg' (Graphic Devices Based on AGG) Anti-Grain Geometry (AGG) is a high-quality and high-performance 2D drawing library. The 'ragg' package provides a set of graphic devices based on AGG to use as alternative to the raster devices provided through the 'grDevices' package. Package: r-cran-ragnar Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3633 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blob, r-cran-cli, r-cran-commonmark, r-cran-curl, r-cran-dbi, r-cran-mirai, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-reticulate, r-cran-rlang, r-cran-rvest, r-cran-s7, r-cran-stringi, r-cran-tidyr, r-cran-vctrs, r-cran-withr, r-cran-xml2 Suggests: r-cran-connectcreds, r-cran-ellmer, r-cran-gargle, r-cran-knitr, r-cran-lifecycle, r-cran-mcptools, r-cran-pandoc, r-cran-paws.common, r-cran-rmarkdown, r-cran-shiny, r-cran-stringr, r-cran-testthat, r-cran-tibble, r-cran-jose, r-cran-openssl Filename: pool/dists/noble/main/r-cran-ragnar_0.3.0-1.ca2404.1_amd64.deb Size: 3163730 MD5sum: 4f3532a24b39953aedd80ce2b4e85568 SHA1: 53b41bfa6ac6d536203a741a00e51ea78fc222fa SHA256: ca37dccc73814785ce7cca2831578c9643ed0c64b150991bcdb3804474c71dbb SHA512: 74458b2b2bee68798b913fe3dbef67991a094ea9e3ba07d3c200b2e653e5aa643b1053e644bf9fc4fdd51399cf398512f1309626090eb820861c45f5bca36ced Homepage: https://cran.r-project.org/package=ragnar Description: CRAN Package 'ragnar' (Retrieval-Augmented Generation (RAG) Workflows) Provides tools for implementing Retrieval-Augmented Generation (RAG) workflows with Large Language Models (LLM). Includes functions for document processing, text chunking, embedding generation, storage management, and content retrieval. Supports various document types and embedding providers ('Ollama', 'OpenAI'), with 'DuckDB' as the default storage backend. Integrates with the 'ellmer' package to equip chat objects with retrieval capabilities. Designed to offer both sensible defaults and customization options with transparent access to intermediate outputs. For a review of retrieval-augmented generation methods, see Gao et al. (2023) "Retrieval-Augmented Generation for Large Language Models: A Survey" . Package: r-cran-rags2ridges Architecture: amd64 Version: 2.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1481 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-expm, r-cran-reshape, r-cran-ggplot2, r-cran-hmisc, r-cran-fdrtool, r-cran-snowfall, r-cran-sfsmisc, r-cran-grbase, r-bioc-rbgl, r-bioc-graph, r-cran-rcpp, r-cran-rspectra, r-cran-rcpparmadillo Suggests: r-bioc-kegggraph, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rags2ridges_2.2.9-1.ca2404.1_amd64.deb Size: 1193404 MD5sum: 94e28cfa0499cf9c4a438c19a21e8ae7 SHA1: 885aa3bf9e5ca39312c9dcd4af3c397e3a25c337 SHA256: 80e1aeaa955c736fe53e40e100e9d8fe55e599129349a85f44c7d1bc3086b0c5 SHA512: b01bbd9efc84308774b44661c4d5c70cf444dcc7f55f59b55e4cbc6ba4c7397da31452fbefe892ee7cb8d9725f8f15e0a80d6a106dbbd3a23a040a222116c3c1 Homepage: https://cran.r-project.org/package=rags2ridges Description: CRAN Package 'rags2ridges' (Ridge Estimation of Precision Matrices from High-DimensionalData) Proper L2-penalized maximum likelihood estimators for precision matrices and supporting functions to employ these estimators in a graphical modeling setting. For details, see Peeters, Bilgrau, & van Wieringen (2022) and associated publications. Package: r-cran-rainbowr Architecture: amd64 Version: 0.1.38-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1998 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-cluster, r-cran-mass, r-cran-pbmcapply, r-cran-optimx, r-cran-ape, r-cran-stringr, r-cran-pegas, r-cran-rrblup, r-cran-expm, r-cran-here, r-cran-htmlwidgets, r-cran-rfast, r-cran-gaston, r-cran-mm4lmm, r-cran-r.utils, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-plotly, r-cran-haplotypes, r-cran-adegenet, r-cran-ggplot2, r-bioc-ggtree, r-cran-scatterpie, r-cran-phylobase, r-cran-ggimage, r-cran-furrr, r-cran-future, r-cran-progressr, r-cran-foreach, r-cran-doparallel, r-cran-data.table Filename: pool/dists/noble/main/r-cran-rainbowr_0.1.38-1.ca2404.1_amd64.deb Size: 1495236 MD5sum: ad638aa908a032dad42dc69c3410ad9a SHA1: 298fb345e171725e2cf14a87cd97f8a860040bd7 SHA256: 7ce829bebfb5b520437dd31ccf07aa6ce6b3ff91ec86d61e15cec510ff638811 SHA512: 29b6b472296793f8632ca792d892d8dc43f325edd087f786eabf73def83cd526e70e761dd4bb65b71e00be33efa1fb9841b3e26d6fefdf5fc94a799932534634 Homepage: https://cran.r-project.org/package=RAINBOWR Description: CRAN Package 'RAINBOWR' (Genome-Wide Association Study with SNP-Set Methods) By using 'RAINBOWR' (Reliable Association INference By Optimizing Weights with R), users can test multiple SNPs (Single Nucleotide Polymorphisms) simultaneously by kernel-based (SNP-set) methods. This package can also be applied to haplotype-based GWAS (Genome-Wide Association Study). Users can test not only additive effects but also dominance and epistatic effects. In detail, please check our paper on PLOS Computational Biology: Kosuke Hamazaki and Hiroyoshi Iwata (2020) . Package: r-cran-rainette Architecture: amd64 Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2482 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-ggplot2, r-cran-stringr, r-cran-quanteda, r-cran-quanteda.textstats, r-cran-rspectra, r-cran-dendextend, r-cran-ggwordcloud, r-cran-gridextra, r-cran-rlang, r-cran-shiny, r-cran-miniui, r-cran-highr, r-cran-progressr, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tm, r-cran-fnn, r-cran-vdiffr, r-cran-quanteda.textmodels Filename: pool/dists/noble/main/r-cran-rainette_0.3.3-1.ca2404.1_amd64.deb Size: 1462122 MD5sum: fd89f366066c5617d07f77fbd170e9ed SHA1: db6bea3c4fc5bcf2d9f63ac609533ff63ae01d57 SHA256: 3b1ffa97e45c60f47eaf4dd905a4fda6769754cd7a21df5d01c3c7c0a3ecd631 SHA512: c8325a1642acdb5628dd0a8e6bd570dffdcfbedf31cea8a15d7306ae53863142c2fe942c0cd22451bba9c7defbe6b8225d555956bf29674628b5fcf8ed3f66b8 Homepage: https://cran.r-project.org/package=rainette Description: CRAN Package 'rainette' (The Reinert Method for Textual Data Clustering) An R implementation of the Reinert text clustering method. For more details about the algorithm see the included vignettes or Reinert (1990) . 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Many useful network modeling, estimation, and processing methods are included. The work to build and improve this package is partially supported by the NSF grants DMS-2015298 and DMS-2015134. 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Package: r-cran-rankdist Architecture: amd64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-hash, r-cran-optimx, r-cran-permute Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rankdist_1.1.4-1.ca2404.1_amd64.deb Size: 268832 MD5sum: 1f19785446493db2168e9f2fb1c456bf SHA1: 8f79b53181fd1779ef7375757f78d9b4552eacc5 SHA256: 6da787364cf0860a750a3c67eb48a967d549caf924d824e6770a20a46c2a4c5f SHA512: 059038b3fe98daee46bfb092590f2235166cf0f0edbb197d51be96a37caad210441ec2e714c656064a4d8458f1d816d9f231cff3a40978978dfda5ab27fbb706 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: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 586 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-bioc-graph, r-bioc-rbgl, r-bioc-limma, r-cran-netpreproc, r-cran-perfmeas Suggests: r-cran-bionetdata Filename: pool/dists/noble/main/r-cran-ranks_1.1-1.ca2404.1_amd64.deb Size: 475876 MD5sum: 1ed9a0d3826ac7138910398b9a6547cb SHA1: e9b0ad1beaf7a71d8bde771b8f7eb97441173505 SHA256: 57f84df386a883994818f3c8b7139e65edd99f15529e8f70d52b66c42f1b4ed4 SHA512: e297a28016192e4856e5917617148b17065ee8c745f0c402d652c57412d79094794604820f9dc5b894ed32f11b58a74740639e06f5d05ac7cf1a18f2e1809d19 Homepage: https://cran.r-project.org/package=RANKS Description: CRAN Package 'RANKS' (Ranking of Nodes with Kernelized Score Functions) Implementation of Kernelized score functions and other semi-supervised learning algorithms for node label ranking to analyze biomolecular networks. RANKS can be easily applied to a large set of different relevant problems in computational biology, ranging from automatic protein function prediction, to gene disease prioritization and drug repositioning, and more in general to any bioinformatics problem that can be formalized as a node label ranking problem in a graph. The modular nature of the implementation allows to experiment with different score functions and kernels and to easily compare the results with baseline network-based methods such as label propagation and random walk algorithms, as well as to enlarge the algorithmic scheme by adding novel user-defined score functions and kernels. Package: r-cran-ranktreeensemble Architecture: amd64 Version: 0.24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 791 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-randomforestsrc, r-cran-gbm, r-cran-data.tree Filename: pool/dists/noble/main/r-cran-ranktreeensemble_0.24-1.ca2404.1_amd64.deb Size: 693068 MD5sum: 3d799b4939a9818b62146e9f9c4def3e SHA1: 3e62a2a7c410c6b5dc6205b97af5eb0e88847a2c SHA256: c592db0bb52c78fa1bc5c69303ecf34137d0f090431d24b2a93cba8936ce3442 SHA512: 0b3feea95879c0153691c831b5ad0c8714798e4a563729da1076b24fdbc4b3541210c47f6d6aa2c28e805e94c3c2f6c06ad13facf564cf8d4d85213285a7b2f3 Homepage: https://cran.r-project.org/package=ranktreeEnsemble Description: CRAN Package 'ranktreeEnsemble' (Ensemble Models of Rank-Based Trees with Extracted DecisionRules) Fast computing an ensemble of rank-based trees via boosting or random forest on binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. Decision rules can be extracted from trees. Package: r-cran-rann Architecture: amd64 Version: 2.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rann_2.6.2-1.ca2404.1_amd64.deb Size: 42378 MD5sum: 274a50da3a9f9da43fdd369f7047cf6a SHA1: 314f70553f79816b2cfdae842f097dae9243bd4b SHA256: 3d3e9337e903bbb84aef382e8ca4c9c3f38e9851933b01e5b5a60a561ada492f SHA512: 8434b045f6af20d5d265a54934c2eea14143766baec0688ff0ca922f6d1f11539a4785defe2787a28890a111bb20ba521b877310b8dd1dd97b48d273df9c3e9d Homepage: https://cran.r-project.org/package=RANN Description: CRAN Package 'RANN' (Fast Nearest Neighbour Search (Wraps ANN Library) Using L2Metric) Finds the k nearest neighbours for every point in a given dataset in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is support for approximate as well as exact searches, fixed radius searches and 'bd' as well as 'kd' trees. The distance is computed using the L2 (Euclidean) metric. Please see package 'RANN.L1' for the same functionality using the L1 (Manhattan, taxicab) metric. Package: r-cran-rapi Architecture: amd64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-crayon, r-cran-digest, r-cran-dplyr, r-cran-httr, r-cran-httr2, r-cran-glue, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-rlist, r-cran-stringr, r-cran-tibble, r-cran-writexl Suggests: r-cran-devtools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rapi_1.0.6-1.ca2404.1_amd64.deb Size: 220492 MD5sum: 71e62c1293f1215be8d789d8b164c123 SHA1: b17fd04c958d7f7bb9df1f1d937584061ef6939f SHA256: e1f9c1be916ba9718a55fb33f6d257d1cff01d5f698ad2f9e1b3fe5d736d15ce SHA512: 38d7a718a7fe14ae3be621827a2060fde00fc489c7faf645b817a09f5fbc6b580387d3a57c5b042f51e9d98c4bf104379280571aafab62a94251e0e4b8ca3856 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. 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The R Core group is the original author of the code made available with slight modifications by this package. Package: r-cran-rapidfuzz Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 685 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.4), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rapidfuzz_1.1.0-1.ca2404.1_amd64.deb Size: 266800 MD5sum: 8fdcdd334169e38f9dcafc2e1ca66a0a SHA1: 2aa0fc88e99c937b2c88fa0cd1a4660cb271c67e SHA256: 231c67fd7428ade50608265edc3244c33a245eaaaf9a1017fff495bb5dbe19b1 SHA512: 331e5778fe58cc0f0fdfe3d808efa4480d12a669fceb2947355e07d4cf4d28378a85ef931e36c8f73200112a5311b78a6dc15120fd04988c0888bbc53a51343a Homepage: https://cran.r-project.org/package=RapidFuzz Description: CRAN Package 'RapidFuzz' (String Similarity Computation Using 'RapidFuzz') Provides a high-performance interface for calculating string similarities and distances, leveraging the efficient library 'RapidFuzz' . 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Package: r-cran-rapidsplithalf Architecture: amd64 Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1097 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rapidsplithalf_0.7-1.ca2404.1_amd64.deb Size: 774090 MD5sum: 306f792d74e8d689bb3a0578c625defa SHA1: ed1237455f70ba134ba4898ba846793e46fc2aad SHA256: 69355654ccfa61a07bb3098e5ef93b15387e705c2068a6493431fd6007dea250 SHA512: 599174f7e02dd62966afdc6cd6e8c31627128706b9dcf8c6dcdd4ccbb8cecdbad5b0cddc26c5f2f7dced3e9caf1ea1ad65943fd43c05bb719b3283bb61f07148 Homepage: https://cran.r-project.org/package=rapidsplithalf Description: CRAN Package 'rapidsplithalf' (A Fast Permutation-Based Split-Half Reliability Algorithm) Accurately estimates the reliability of cognitive tasks using a fast and flexible permutation-based split-half reliability algorithm that supports stratified splitting while maintaining equal split sizes. See Kahveci, Bathke, and Blechert (2025) for details. Package: r-cran-rapiserialize Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rapiserialize_0.1.4-1.ca2404.1_amd64.deb Size: 16828 MD5sum: 84572771f3ad03d9b14f3f73449af9f5 SHA1: baf7800d6cb5fc3460b084fd81500a83a797262f SHA256: aeed13035bc46880e9fa3a7d4b3534b9fc360c62e6388458dbc64dc3c57fc4a8 SHA512: 8e9933447d4110e0a24b7878800992671989b288c65b4d9e5c542fd0eef5f6e7c9cb99b886704619febdd5d8bdadefdc8b58861a5af44e2746ee209cd6f789f8 Homepage: https://cran.r-project.org/package=RApiSerialize Description: CRAN Package 'RApiSerialize' (R API Serialization) Access to the internal R serialization code is provided for use by other packages at the C function level by using the registration of native function mechanism. Client packages simply include a single header file RApiSerializeAPI.h provided by this package. This packages builds on the Rhpc package by Ei-ji Nakama and Junji Nakano which also includes a (partial) copy of the file src/main/serialize.c from R itself. The R Core group is the original author of the serialization code made available by this package. Package: r-cran-rapparmor Architecture: amd64 Version: 3.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 Depends: libapparmor1 (>= 2.7.0~beta1+bzr1772), libc6 (>= 2.34), r-base-core (>= 4.4.0), r-api-4.0, r-cran-unix Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-rapparmor_3.2.5-1.ca2404.1_amd64.deb Size: 400718 MD5sum: 151eb5918e5c2540a60c3540da5aa5b3 SHA1: e4df297115e882cfae2e1605daa6f843e67d848d SHA256: 92865a07b1ea5bfb4d881a38145d429d9346a077a8a5522d2fc307788d50d72b SHA512: d07edb8504ce744d7f724f6b3bd9e495038efd3fb776395a3ba4cfc3d3442effbe09fdd360de4784432df7a7629376c524e069758887d6c98a449c3cfb936296 Homepage: https://cran.r-project.org/package=RAppArmor Description: CRAN Package 'RAppArmor' (Bindings to AppArmor and Security Related Linux Tools) Bindings to kernel methods for enforcing security restrictions. 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Package: r-cran-rappdirs Architecture: amd64 Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 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/noble/main/r-cran-rappdirs_0.3.4-1.ca2404.1_amd64.deb Size: 46400 MD5sum: f7083292da880c0b853d3312fdfc9a8f SHA1: e592a8118da953959a64f796439630c24746a2b1 SHA256: 0cfb31cf1d6e2d4ed984c00bdd6f885733f24dcd4b9701d5f467275fb6a295ec SHA512: 55c57e829862eb087a2825570cdff3bae9a69a4e850c34bb67f3e8d2c833742c0d9dfeb848e57399bbd3b7b14153e369f80d6891f5a57e7a75b634cdbd05228c Homepage: https://cran.r-project.org/package=rappdirs Description: CRAN Package 'rappdirs' (Application Directories: Determine Where to Save Data, Caches,and Logs) An easy way to determine which directories on the users computer you should use to save data, caches and logs. A port of Python's 'Appdirs' () to R. Package: r-cran-raptr Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7268 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-terra, r-cran-sp, r-cran-matrix, r-cran-assertthat, r-cran-boot, r-cran-pbsmapping, r-cran-scales, r-cran-shape, r-cran-adehabitathr, r-cran-rcolorbrewer, r-cran-ggplot2, r-cran-hypervolume, r-cran-ks, r-cran-mvtnorm, r-cran-withr, r-cran-rcpp, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-knitr, r-cran-roxygen2, r-cran-rmarkdown, r-cran-testthat, r-cran-rgooglemaps, r-cran-dplyr, r-cran-vegan, r-cran-gridextra, r-cran-rgl Filename: pool/dists/noble/main/r-cran-raptr_1.0.1-1.ca2404.1_amd64.deb Size: 4845550 MD5sum: f3de77eb24afbb583cd9fec9bb6989c5 SHA1: f3daf1141e7e39dd305e5ada4b5ff7dc155cb4f4 SHA256: 625be2897548ddae226d53b44b1e55fd13d98aea2b578d23e018a11d0e8f7893 SHA512: 82b9ab0f4581d545d0b4d5c76f48e057af5a5d57a8a5a70cc9b895d0759459d52ba9156f019b481a23624298f487f22454871a65c9a1599732dc765d43dcc323 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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(2024) . Package: r-cran-rare Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 558 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-glmnet, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-dendextend, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rare_0.1.2-1.ca2404.1_amd64.deb Size: 282768 MD5sum: 543c16a30ffae93ff8a8d109cf86ae4c SHA1: 79b3f9b59b34928bd50d99f0316e4d9dc2c0b61e SHA256: 1d49834130338605ad226c9e66e22c14b414b7f253a59fe8fd718a9e6fdd8a55 SHA512: d17a5cb05e529c406fc15858f60a511494033658bd0da1ac4feda6dcfc8a5f8f684dde59e9fc9af5d74a4fdc59eb975e22283f91c5263180c69b4cde865f76d6 Homepage: https://cran.r-project.org/package=rare Description: CRAN Package 'rare' (Linear Model with Tree-Based Lasso Regularization for RareFeatures) Implementation of an alternating direction method of multipliers algorithm for fitting a linear model with tree-based lasso regularization, which is proposed in Algorithm 1 of Yan and Bien (2020) . The package allows efficient model fitting on the entire 2-dimensional regularization path for large datasets. The complete set of functions also makes the entire process of tuning regularization parameters and visualizing results hassle-free. Package: r-cran-raschsampler Architecture: amd64 Version: 0.8-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-raschsampler_0.8-10-1.ca2404.1_amd64.deb Size: 204586 MD5sum: 3dedd2510dc214023a166031fe155bc4 SHA1: fdbdf5a270a53ce54c2dad8541b16758a85815b3 SHA256: 823ff63bf700ca9764f52081efa917a0ada45083f01a9d644d9b396d825165e9 SHA512: 6f428af9b914a3053aa0b3f0fc5c004c8640ed9cbfb13b406a4541fcc1d4988be8b81ff77f81dca868441ef60f10ec054262e75770ad2e28b3fcd6c8eb272426 Homepage: https://cran.r-project.org/package=RaschSampler Description: CRAN Package 'RaschSampler' (Rasch Sampler) MCMC based sampling of binary matrices with fixed margins as used in exact Rasch model tests. 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This package has been superseded by the "terra" package . Package: r-cran-rasterkernelestimates Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster Filename: pool/dists/noble/main/r-cran-rasterkernelestimates_1.0.2-1.ca2404.1_amd64.deb Size: 29076 MD5sum: b550ab896574811be59215c2a8c181b7 SHA1: a0b4956e091533cd3281865ff725492a80486149 SHA256: 3312987e4168295644d455bb936df27bb594373c0393c35310d9e4e28e374a1d SHA512: 5ecd87610b9dd11cec1a6469d34eab3e8c133fd823425e078a8b1f29367c5fa15d7edb7aad97740485ea99792145c504cbb0a201f81c46202951401998dbe72e Homepage: https://cran.r-project.org/package=rasterKernelEstimates Description: CRAN Package 'rasterKernelEstimates' (Kernel Based Estimates on in-Memory Raster Images) Performs kernel based estimates on in-memory raster images from the raster package. 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Package: r-cran-rater Architecture: amd64 Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4384 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-loo, r-cran-rcpp, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-coda, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rater_1.3.2-1.ca2404.1_amd64.deb Size: 1296344 MD5sum: 14f82aa66d5ae6c7ee20143aaddd091b SHA1: a2effb4a3174623db8b6a05f12966ed06fd407cb SHA256: 9a03603e320b133da6097cc9f8b66981f268a94d2ef28aca875209f83191f7ab SHA512: 58b31101c0368a6ce2f21488d5d15089ccaa283628e2fd82948206c76f41547fc54d97f97f3462f5e67c89d4c3ad4280a0a102e679ee4bd3591b9d3748bee562 Homepage: https://cran.r-project.org/package=rater Description: CRAN Package 'rater' (Statistical Models of Repeated Categorical Rating Data) Fit statistical models based on the Dawid-Skene model - Dawid and Skene (1979) - to repeated categorical rating data. 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Package: r-cran-rationalmatrix Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-gmp, r-cran-rcpp, r-cran-bh, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-rationalmatrix_1.0.0-1.ca2404.1_amd64.deb Size: 124714 MD5sum: d730121c86e35b867110efef90a25886 SHA1: 25195f7508f151547979961620f1da1dc6f2c52f SHA256: aeecd76b6663f8af2b1144410d517fd4790b40df81bab715a6085e1cf008da7a SHA512: 880cbeb95e70cc3e6f8fa5b5da218d6f7cf56f8c4c918eb10a9fec77e115a90096cc2e7b5c9d453e11c03f2d501fe2aca0d1d51b73af6dac0d4c25742da930a5 Homepage: https://cran.r-project.org/package=RationalMatrix Description: CRAN Package 'RationalMatrix' (Exact Matrix Algebra for Rational Matrices) Provides functions to deal with matrix algebra for matrices with rational entries: determinant, rank, image and kernel, inverse, Cholesky decomposition. 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Package: r-cran-ratioofqsprays Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1671 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-qspray, r-cran-gmp, r-cran-rcpp, r-cran-ryacas, r-cran-bh, r-cran-rcppcgal Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ratioofqsprays_1.1.0-1.ca2404.1_amd64.deb Size: 647862 MD5sum: 990124cb119bdde1d33c76bd02d94507 SHA1: d48df621bacff82d978a52a79cd5c77f112e5e87 SHA256: a2e8651249b950619c416b7d554d46152c1645075e3202231024593d400aa058 SHA512: 2bd8b60eb5a8a496f48c4c5730c6a06a38c8a965571fec62c2f90b5b67621707327c47fac6744062956ebedfdab0520ad760598cb4de9296e8ca324e4292477d Homepage: https://cran.r-project.org/package=ratioOfQsprays Description: CRAN Package 'ratioOfQsprays' (Fractions of Multivariate Polynomials with Rational Coefficients) Based on the 'qspray' package, this package introduces the new type 'ratioOfQsprays'. An object of type 'qspray' represents a multivariate polynomial with rational coefficients while an object of type 'ratioOfQsprays', defined by two 'qspray' objects, represents a fraction of two multivariate polynomials with rational coefficients. Arithmetic operations for these objects are available, and they always return irreducible fractions. Other features include: differentiation, evaluation, conversion to a function, and fine control of the way to print a 'ratioOfQsprays' object. The 'C++' library 'CGAL' is used to make the fractions irreducible. Package: r-cran-ravages Architecture: amd64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5620 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-gaston, r-cran-mlogit, r-cran-formula, r-cran-dfidx, r-cran-bedr, r-cran-curl, r-cran-data.table, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ravages_1.2.0-1.ca2404.1_amd64.deb Size: 5007140 MD5sum: cf79ccc5e2c183c776ecb7e6bc62e85e SHA1: c7888bca15da75fe3c573e08843f54956a0eb77e SHA256: 3f76b82b283f5db356eafc17fa7d1ca24c49650b0dde3f6c25e965f250fb37c7 SHA512: 8457940a91a3a5253cca54e6b64700a9e172f2fa3be0dcf3684c6730329839b58032225fb36f370c175aef48eb95ed1390fb3148a740f42d4c7e2303f976d25d 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 ). 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Includes various plotting functions, model diagnostics, reading output files into extensible time series format, and support for writing Raven input files. The 'RavenR' package is also archived at Chlumsky et al. (2020) . The Raven Hydrologic Modelling Framework method can be referenced with Craig et al. (2020) . 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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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Build 3D scenes out of spheres, cubes, planes, disks, triangles, cones, curves, line segments, cylinders, ellipsoids, and 3D models in the 'Wavefront' OBJ file format or the PLY Polygon File Format. Supports several material types, textures, multicore rendering, and tone-mapping. Based on the "Ray Tracing in One Weekend" book series. Peter Shirley (2018) . Package: r-cran-rayshader Architecture: amd64 Version: 0.37.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4155 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-rcpp, r-cran-progress, r-cran-raster, r-cran-scales, r-cran-png, r-cran-jpeg, r-cran-magrittr, r-cran-rgl, r-cran-terrainmeshr, r-cran-rayimage, r-cran-rayvertex, r-cran-rayrender, r-cran-rcpparmadillo Suggests: r-cran-reshape2, r-cran-viridis, r-cran-av, r-cran-magick, r-cran-ggplot2, r-cran-sf, r-cran-isoband, r-cran-car, r-cran-geosphere, r-cran-gifski, r-cran-ambient, r-cran-terra, r-cran-lidr, r-cran-elevatr, r-cran-gridextra, r-cran-testthat, r-cran-osmdata Filename: pool/dists/noble/main/r-cran-rayshader_0.37.3-1.ca2404.1_amd64.deb Size: 3936614 MD5sum: 857715636b0db9e7f4d171da64b2bffb SHA1: 90e4595b0e96c8f658f9d2ae2aea4c4fb0796099 SHA256: ecb73b5c696d1fb6c35c4adccad609affbf4244f7ff4b70f68e8433c8117b0b0 SHA512: 5055e380a9fb0a47dd7048163840fa12a9e6871916a6be86076b0e47c1f5ab4be0a32fa4bbaa58854f847e442d08e51c6d163c1df69f735c293a06ef948a8b2d Homepage: https://cran.r-project.org/package=rayshader Description: CRAN Package 'rayshader' (Create Maps and Visualize Data in 2D and 3D) Uses a combination of raytracing and multiple hill shading methods to produce 2D and 3D data visualizations and maps. 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Package: r-cran-rbacon Architecture: amd64 Version: 3.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1698 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-rcpp, r-cran-data.table, r-cran-rintcal, r-cran-rice Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-utf8 Filename: pool/dists/noble/main/r-cran-rbacon_3.5.2-1.ca2404.1_amd64.deb Size: 1095854 MD5sum: d373d1d6572a1ac6500c4e405680c4c8 SHA1: 307b8e05059b4a8ce046af83cb682a0a5d04ba80 SHA256: 49dec9394dc15a748ab161d6f3782ec46a33d7077e91644c4c9aa0801fd741d7 SHA512: 3285d1c56abfe9d5ffc567b194139660bbe641b976602f6097f8c21bdcb2f173278ec4cf1e4d229459480fe62133696e42733351198f53336210fce1a9b4f1c1 Homepage: https://cran.r-project.org/package=rbacon Description: CRAN Package 'rbacon' (Age-Depth Modelling using Bayesian Statistics) An approach to age-depth modelling that uses Bayesian statistics to reconstruct accumulation histories for deposits, through combining radiocarbon and other dates with prior information on accumulation rates and their variability. See Blaauw & Christen (2011). 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Interpretation of time series depends on model choice; different models can yield contrasting or contradicting estimates of patterns, trends, and mechanisms. BEAST alleviates this by abandoning the single-best-model paradigm and instead using Bayesian model averaging over many competing decompositions. It detects and characterizes abrupt changes (changepoints, breakpoints, structural breaks, joinpoints), cyclic or seasonal variation, and nonlinear trends. BEAST not only detects when changes occur but also quantifies how likely the changes are true. It estimates not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is generically applicable to any real-valued time series, such as those from remote sensing, economics, climate science, ecology, hydrology, and other environmental and biological systems. 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Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about 'Rcpp' is provided by several vignettes included in this package, via the 'Rcpp Gallery' site at , the paper by Eddelbuettel and Francois (2011, ), the book by Eddelbuettel (2013, ) and the paper by Eddelbuettel and Balamuta (2018, ); see 'citation("Rcpp")' for details. Package: r-cran-rcppalgos Architecture: amd64 Version: 2.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4782 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gmp, r-cran-cpp11 Suggests: r-cran-testthat, r-cran-partitions, r-cran-microbenchmark, r-cran-knitr, r-cran-rcppbigintalgos, r-cran-rmarkdown, r-cran-prettydoc, r-cran-covr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-rcppalgos_2.10.0-1.ca2404.1_amd64.deb Size: 1406714 MD5sum: 5d1480aff636c5c4426ae7890be6174e SHA1: 5dcb5b6d33cc816f101bb0d2cafed44153d752d0 SHA256: db11cc91af071ff1c8ab85b3fadd39c8b7c085520d03e88820b71707847aa126 SHA512: a9be98b67f003b78521454b0cc493b155f5ee2bb228e74a501e68df1aa30b5abe5620937e62e9ed6222d1676d5554a5c46940de69b0af844aacb4e9fb1495877 Homepage: https://cran.r-project.org/package=RcppAlgos Description: CRAN Package 'RcppAlgos' (High Performance Tools for Combinatorics and ComputationalMathematics) Provides optimized functions and flexible iterators implemented in C++ for solving problems in combinatorics and computational mathematics. Handles various combinatorial objects including combinations, permutations, integer partitions and compositions, Cartesian products, unordered Cartesian products, and partition of groups. Utilizes the RMatrix class from 'RcppParallel' for thread safety. The combination and permutation functions contain constraint parameters that allow for generation of all results of a vector meeting specific criteria (e.g. finding all combinations such that the sum is between two bounds). Capable of ranking/unranking combinatorial objects efficiently (e.g. retrieve only the nth lexicographical result) which sets up nicely for parallelization as well as random sampling. Gmp support permits exploration where the total number of results is large (e.g. comboSample(10000, 500, n = 4)). Additionally, there are several high performance number theoretic functions that are useful for problems common in computational mathematics. Some of these functions make use of the fast integer division library 'libdivide'. The primeSieve function is based on the segmented sieve of Eratosthenes implementation by Kim Walisch. It is also efficient for large numbers by using the cache friendly improvements originally developed by Tomás Oliveira. Finally, there is a prime counting function that implements Legendre's formula based on the work of Kim Walisch. Package: r-cran-rcppannoy Architecture: amd64 Version: 0.0.23-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1000 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.4), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rcppannoy_0.0.23-1.ca2404.1_amd64.deb Size: 264140 MD5sum: 6ce780c83f44b4ad42668805d34dea21 SHA1: 11e0f5b7ffcf87724fa9f2b47b040d868d5866c8 SHA256: 1cc084d593611ac4ae8bece1ac57167dfa2e2c8c32c67f7122a7f135a90a6f56 SHA512: b64911197c899a9ad98c540d9876d96dc01c3f427a1a43a6b18fc291503d90dbe6394e4f70c94c51f29467bdcb5e9ea79caf3c255a049c1ecf469cc750e23b69 Homepage: https://cran.r-project.org/package=RcppAnnoy Description: CRAN Package 'RcppAnnoy' ('Rcpp' Bindings for 'Annoy', a Library for Approximate NearestNeighbors) 'Annoy' is a small C++ library for Approximate Nearest Neighbors written for efficient memory usage as well an ability to load from / save to disk. This package provides an R interface by relying on the 'Rcpp' package, exposing the same interface as the original Python wrapper to 'Annoy'. See for more on 'Annoy'. 'Annoy' is released under Version 2.0 of the Apache License. Also included is a small Windows port of 'mmap' which is released under the MIT license. Package: r-cran-rcppapt Architecture: amd64 Version: 0.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-simplermarkdown Filename: pool/dists/noble/main/r-cran-rcppapt_0.0.10-1.ca2404.1_amd64.deb Size: 93170 MD5sum: e94f0d6b4acbce7d4145b8399a18ae12 SHA1: ac8022c9d1e3571cd8c2e5af29907aff3edcc619 SHA256: ca40063e16af7daa7350ad8558fbb5084fbd7e2a189cff5689e508c112c4f13d SHA512: b69effdb51e551159f209333d56d687b6f3f0a4f372a351fb41672e7caca502d24a22e34888a40f7899893974e7e9f7e37fe7886535b02ad8af5279e3400dcea Homepage: https://cran.r-project.org/package=RcppAPT Description: CRAN Package 'RcppAPT' ('Rcpp' Interface to the APT Package Manager) The 'APT Package Management System' provides Debian and Debian-derived Linux systems with a powerful system to resolve package dependencies. This package offers access directly from R. This can only work on a system with a suitable 'libapt-pkg-dev' installation so functionality is curtailed if such a library is not found. Package: r-cran-rcpparmadillo Architecture: amd64 Version: 15.2.6-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6658 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest, r-cran-matrix, r-cran-pkgkitten, r-cran-reticulate, r-cran-slam Filename: pool/dists/noble/main/r-cran-rcpparmadillo_15.2.6-1-1.ca2404.1_amd64.deb Size: 813352 MD5sum: f1b9921c5ad71b6a124c6496374b3778 SHA1: b826e45976d37a5cd3f1bcabddd692e771f3819f SHA256: c6a9dcddadbe646648ed2f4f6446da3dc589c3094dee27a11a6a12665996225b SHA512: c9846b51426a1862cf33cfa8305ef1eb830b1870f53a413b24c72e6b41fef3f2e0320a32cfb6f7e47b03d10ad4d33391d9f896802cb055cd93283faedf9fdc12 Homepage: https://cran.r-project.org/package=RcppArmadillo Description: CRAN Package 'RcppArmadillo' ('Rcpp' Integration for the 'Armadillo' Templated Linear AlgebraLibrary) 'Armadillo' is a templated C++ linear algebra library aiming towards a good balance between speed and ease of use. It provides high-level syntax and functionality deliberately similar to Matlab. It is useful for algorithm development directly in C++, or quick conversion of research code into production environments. It provides efficient classes for vectors, matrices and cubes where dense and sparse matrices are supported. Integer, floating point and complex numbers are supported. A sophisticated expression evaluator (based on template meta-programming) automatically combines several operations to increase speed and efficiency. Dynamic evaluation automatically chooses optimal code paths based on detected matrix structures. Matrix decompositions are provided through integration with LAPACK, or one of its high performance drop-in replacements (such as 'MKL' or 'OpenBLAS'). It can automatically use 'OpenMP' multi-threading (parallelisation) to speed up computationally expensive operations. The 'RcppArmadillo' package includes the header files from the 'Armadillo' library; users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'. Starting from release 15.0.0, the minimum compilation standard is C++14. Since release 7.800.0, 'Armadillo' is licensed under Apache License 2; previous releases were under licensed as MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that; 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'. Package: r-cran-rcpparray Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rcpparray_0.3.0-1.ca2404.1_amd64.deb Size: 46260 MD5sum: ca2654bdaa82f7c3768cb58260ac153e SHA1: 1c49d1ebdc3e9dfe6860bb2f3239e50ddbf8d492 SHA256: e12008f2a0e35093813c9e6b0d6d271942aaf207e88e6f38eb2e5687116eb6bc SHA512: 0700a832f1a55a3c6a2e8752e902af1ff77601d2693ef607259f6d3af838a8dd5ac3ee279281dbde5f72d2fbe6d190b76b25354c9b10a33e2fb4ae99544a4242 Homepage: https://cran.r-project.org/package=RcppArray Description: CRAN Package 'RcppArray' ('Rcpp' Meets 'C++' Arrays) Interoperability between 'Rcpp' and the 'C++11' array and tuple types. Linking to this package allows fixed-length 'std::array' objects to be converted to and from equivalent R vectors, and 'std::tuple' objects converted to lists, via the as() and wrap() functions. There is also experimental support for 'std::span' from 'C++20'. Package: r-cran-rcppbdt Architecture: amd64 Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1034 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/noble/main/r-cran-rcppbdt_0.2.8-1.ca2404.1_amd64.deb Size: 290914 MD5sum: f4ffbe95a0fc937c328d553be0944120 SHA1: 2c27ef7258c1dda1e99d283595701df4943ea996 SHA256: 705ea36e92e1fc7496cc3f85506a9cd7188a8400d4dbfe2edbff1d4eb1dbae9b SHA512: 8b184e55d2813e46baf910a7f5c0cedf1e2e4bac2ec17a0842a3510b089c9d76b7267840f55f193b0cd28521ad7d602d3facfdd3daaf75a3faea10410392571e Homepage: https://cran.r-project.org/package=RcppBDT Description: CRAN Package 'RcppBDT' ('Rcpp' Bindings for the Boost Date_Time Library) Access to Boost Date_Time functionality for dates, durations (both for days and date time objects), time zones, and posix time ('ptime') is provided by using 'Rcpp modules'. The posix time implementation can support high-resolution of up to nano-second precision by using 96 bits (instead of 64 with R) to present a 'ptime' object (but this needs recompilation with a #define set). Package: r-cran-rcppbessel Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 706 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-bessel, r-cran-testthat, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-rcppbessel_1.0.1-1.ca2404.1_amd64.deb Size: 145978 MD5sum: a6cba4e12817c2c7b544df0f1c055b55 SHA1: 61fabcbfbbf635cd745cb02737b0a125d9938f1d SHA256: 1dc56d9cf89eda76db8588e080418f06d681a7955025aee8b4675f899348ac4b SHA512: 92046276c66313a4f022797d45e5972e6abb1db1e16a3af94229c05bfe2a1a7321751d2b75d3016a99f47799f9108e052e56bb508c11a7afda0dd3e54c6f4378 Homepage: https://cran.r-project.org/package=RcppBessel Description: CRAN Package 'RcppBessel' (Bessel Functions Rcpp Interface) Exports an 'Rcpp' interface for the Bessel functions in the 'Bessel' package, which can then be called from the 'C++' code of other packages. For the original 'Fortran' implementation of these functions see Amos (1995) . Package: r-cran-rcppbigintalgos Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-gmp, r-cran-cpp11 Suggests: r-cran-testthat, r-cran-numbers, r-cran-rcppalgos Filename: pool/dists/noble/main/r-cran-rcppbigintalgos_1.1.0-1.ca2404.1_amd64.deb Size: 136374 MD5sum: f86c0af8af186b775a0b0e65b569e4cd SHA1: 264403789751e7f88f0122ebad7e50441e8090fb SHA256: 1839fc857b313cfabdb91c6cf80c5950236065012246942677fa4e4916fde163 SHA512: 8a5844db82d631263ba544bc9cf0103dd035db25c2e8e7bff4020bef0cfadf84681994c928b7f3934ea1073ec9f8797731fa1d7b3bd8f9d010923dcb4cda6941 Homepage: https://cran.r-project.org/package=RcppBigIntAlgos Description: CRAN Package 'RcppBigIntAlgos' (Factor Big Integers with the Parallel Quadratic Sieve) Features the multiple polynomial quadratic sieve (MPQS) algorithm for factoring large integers and a vectorized factoring function that returns the complete factorization of an integer. The MPQS is based off of the seminal work of Carl Pomerance (1984) along with the modification of multiple polynomials introduced by Peter Montgomery and J. Davis as outlined by Robert D. Silverman (1987) . Utilizes the C library GMP (GNU Multiple Precision Arithmetic). For smaller integers, a simple Elliptic Curve algorithm is attempted followed by a constrained version of Pollard's rho algorithm. The Pollard's rho algorithm is the same algorithm used by the factorize function in the 'gmp' package. Package: r-cran-rcppblaze Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 36931 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Suggests: r-cran-matrixextra, r-cran-tinytest, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-rcppblaze_1.0.2-1.ca2404.1_amd64.deb Size: 1188070 MD5sum: e76dde2777f94b22db06593a0c418c17 SHA1: ec71ae941b4546bfd6e1a9ab347bdcba156960ed SHA256: eddb3ea7203dcfa20f3b9f4bd0869961abf051eb6e8bb374118b40787b3b32b7 SHA512: ce39ad6ac038646226634fab4df8fcd4277c80e99634e0857007869c46b67159f9dae8897bd596684201451f0d7701869f4ef9a2594df601cfa3d4f1906b5c1e Homepage: https://cran.r-project.org/package=RcppBlaze Description: CRAN Package 'RcppBlaze' ('Rcpp' Integration for the 'Blaze' High-Performance 'C++' MathLibrary) Blaze is an open-source, high-performance 'C++' math library for dense and sparse arithmetic. With its state-of-the-art Smart Expression Template implementation Blaze combines the elegance and ease of use of a domain-specific language with HPC-grade performance, making it one of the most intuitive and fastest 'C++' math libraries available. The 'RcppBlaze' package includes the header files from the 'Blaze' library with disabling some functionalities related to link to the thread and system libraries which make 'RcppBlaze' be a header-only library. Therefore, users do not need to install 'Blaze'. Package: r-cran-rcppcctz Architecture: amd64 Version: 0.2.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rcppcctz_0.2.14-1.ca2404.1_amd64.deb Size: 132034 MD5sum: 2e6058da46710a48cbc5ac0d065b07c0 SHA1: 9cc4b3bfa8e24a42a5aeb9cc526cde62559ffe83 SHA256: cd5b63637a681a3858c71f1b947c4a1d40570b39c613d7100587d86995697c69 SHA512: 2458f38abc5a6a40cb5ade855b7e9bdbaeebd0751b1c5c0a801e4ee25d52d14e398e854713851d2ad8edac54e501442513f4ce6114be2aa1dc76c59fe51bf652 Homepage: https://cran.r-project.org/package=RcppCCTZ Description: CRAN Package 'RcppCCTZ' ('Rcpp' Bindings for the 'CCTZ' Library) 'Rcpp' access to the 'CCTZ' timezone library is provided. 'CCTZ' is a C++ library for translating between absolute and civil times using the rules of a time zone. The 'CCTZ' source code, released under the Apache 2.0 License, is included in this package. See for more details. Package: r-cran-rcppcensspatial Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-momtrunc, r-cran-mvtnorm, r-cran-rcpp, r-cran-rdpack, r-cran-relliptical, r-cran-stempcens, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-roptim Filename: pool/dists/noble/main/r-cran-rcppcensspatial_1.0.0-1.ca2404.1_amd64.deb Size: 315260 MD5sum: 0ef70cdba64215463c74aa0cd3770f20 SHA1: fdb5830506531ff748d3ff2202f354d1747043dc SHA256: ce2a913e796394e129f497b724ef587b6fea604bb513a7cc832fe27c23f2969f SHA512: 02be0b77bff5715d1bba684817560b4f6a6fb3462c09223e522c43c66f863fa709f1777c1b5d750ceeb00d2837205c86628a1f1ebe0233e481c78a0e031d70f1 Homepage: https://cran.r-project.org/package=RcppCensSpatial Description: CRAN Package 'RcppCensSpatial' (Spatial Estimation and Prediction for Censored/Missing Responses) It provides functions for estimating parameters in linear spatial models with censored or missing responses using the Expectation-Maximization (EM), Stochastic Approximation EM (SAEM), and Monte Carlo EM (MCEM) algorithms. These methods are widely used to obtain maximum likelihood (ML) estimates in the presence of incomplete data. The EM algorithm computes ML estimates when a closed-form expression for the conditional expectation of the complete-data log-likelihood is available. The MCEM algorithm replaces this expectation with a Monte Carlo approximation based on independent simulations of the missing data. In contrast, the SAEM algorithm decomposes the E-step into simulation and stochastic approximation steps, improving computational efficiency in complex settings. In addition, the package provides standard error estimation based on the Louis method. It also includes functionality for spatial prediction at new locations. References used for this package: Galarza, C. E., Matos, L. A., Castro, L. M., & Lachos, V. H. (2022). Moments of the doubly truncated selection elliptical distributions with emphasis on the unified multivariate skew-t distribution. Journal of Multivariate Analysis, 189, 104944 ; Valeriano, K. A., Galarza, C. E., & Matos, L. A. (2023). Moments and random number generation for the truncated elliptical family of distributions. Statistics and Computing, 33(1), 32 . Package: r-cran-rcppclassic Architecture: amd64 Version: 0.9.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 996 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rcppclassic_0.9.14-1.ca2404.1_amd64.deb Size: 170236 MD5sum: bccae4ee48aca97473d6ac80fdbc274c SHA1: 63b731ed4c4cbf3aede9b4cb4fc2f3c008305a9d SHA256: 9dd512944d192863ab41ea03b817cc501c72a5cf09a237690a1786ee5d3cb88b SHA512: 145b3d8ee728351fbec63190c768f1d7606323c7846a4935499d1819da092a15b16e147dffdd82e7c3877c04b878a87834b5aaecf74dc9057994fe5d516f9622 Homepage: https://cran.r-project.org/package=RcppClassic Description: CRAN Package 'RcppClassic' (Deprecated 'classic' 'Rcpp' 'API') The 'RcppClassic' package provides a deprecated C++ library which facilitates the integration of R and C++. New projects should use the new 'Rcpp' 'API' in the 'Rcpp' package. Package: r-cran-rcppclassicexamples Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppclassic Suggests: r-cran-runit Filename: pool/dists/noble/main/r-cran-rcppclassicexamples_0.1.4-1.ca2404.1_amd64.deb Size: 114154 MD5sum: 52f611d0f7a6887e4fb513de23e00c16 SHA1: 710a39617892dee45b2112456a7f436907fc86ae SHA256: 70158042a0057a0393202d283d5f799b2f4df9892651dcd6a82aee9d60674a48 SHA512: 099fc9f6ad4e6931fa5d1ac2c2d99927f302e47be02b3fa42dda6d2d3976c639911ab17ce32bc024d454f8cb300a41bff07ad33d1938b31acaa55022249273fd Homepage: https://cran.r-project.org/package=RcppClassicExamples Description: CRAN Package 'RcppClassicExamples' (Examples using 'RcppClassic' to Interface R and C++) The 'Rcpp' package contains a C++ library that facilitates the integration of R and C++ in various ways via a rich API. This API was preceded by an earlier version which has been deprecated since 2010 (but is still supported to provide backwards compatibility in the package 'RcppClassic'). This package 'RcppClassicExamples' provides usage examples for the older, deprecated API. There is also a corresponding package 'RcppExamples' with examples for the newer, current API which we strongly recommend as the basis for all new development. Package: r-cran-rcppclock Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcppclock_1.1-1.ca2404.1_amd64.deb Size: 59696 MD5sum: f33df33d454607ee8de49393343f65e6 SHA1: f93703cd477264a97016c5ae0794930458931e93 SHA256: 033c47c3ff1d5a8dddd59c62f5fd830622f771a6a88bf1cd24e54c0755344534 SHA512: 43c08945b8ecd2e8c4e4bc63b612f0198fa0f217cab5354ef1c7b969b1a40c028acd087e0b66cd110304d42c22c8a6bcaa037c308e45ed0ceda8f33ea2ada004 Homepage: https://cran.r-project.org/package=RcppClock Description: CRAN Package 'RcppClock' (Seamless 'Rcpp' Benchmarking) Time the execution of overlapping or unique 'Rcpp' code chunks using convenient methods, seamlessly write timing results to an 'RcppClock' object in the R global environment, and summarize and/or plot the results in R. Package: r-cran-rcppcnpy Architecture: amd64 Version: 0.2.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-reticulate, r-cran-rbenchmark Filename: pool/dists/noble/main/r-cran-rcppcnpy_0.2.15-1.ca2404.1_amd64.deb Size: 171522 MD5sum: cc130494e915c2c54e2a2fbe8c1587b3 SHA1: 8a3e56731b18075562e31f8149af01dbd9e8c6de SHA256: 7f05fc728587356d2c682184496f0f692cfaffc78817e7a4c635b0e2a1eedc3c SHA512: 84500221af8ea0057734e56115ac2d69abf4f493a426678d8f187aacd477d222bafa777d794a4f555d812f812c88dc7578b0ad5c2d890563f151804c45f84db0 Homepage: https://cran.r-project.org/package=RcppCNPy Description: CRAN Package 'RcppCNPy' (Read-Write Support for 'NumPy' Files via 'Rcpp') The 'cnpy' library written by Carl Rogers provides read and write facilities for files created with (or for) the 'NumPy' extension for 'Python'. Vectors and matrices of numeric types can be read or written to and from files as well as compressed files. Support for integer files is available if the package has been built with as C++11 which should be the default on all platforms since the release of R 3.3.0. Package: r-cran-rcppcolmetric Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-catools, r-cran-infotheo, r-cran-magrittr, r-cran-mass, r-cran-microbenchmark, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcppcolmetric_0.1.0-1.ca2404.1_amd64.deb Size: 94022 MD5sum: 76226a7b449695d1502e9bfe5b9f4929 SHA1: 30560a28758872cf0ff00d56f80855b5d483d043 SHA256: 7029bdd2971e367220d239a2b14d2484d3b66bf882a6aa2d4a7faf68fd3473ff SHA512: d4a10241129e266928d771a75ec8d72ab22bc62150315303c9bf3bcad145ad57e48927afc19832546803c3e3418cd68e0240bd52b04b92ba80b67cf132e85a29 Homepage: https://cran.r-project.org/package=RcppColMetric Description: CRAN Package 'RcppColMetric' (Efficient Column-Wise Metric Computation Against Common Vector) In data science, it is a common practice to compute a series of columns (e.g. features) against a common response vector. Various metrics are provided with efficient computation implemented with 'Rcpp'. Package: r-cran-rcppcolors Architecture: amd64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 605 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rcppcolors_0.6.0-1.ca2404.1_amd64.deb Size: 405756 MD5sum: edbb326b7924e6b9c8f21f8a04804a7d SHA1: c3c8d2b15a385b4049b93159282909b7ddb8bcd4 SHA256: 0abbfa2944d1aa85612126dc7abda9d54f818543a8040da6d6a37a94e370fdca SHA512: 5134213083a230f235b7073bbcac16e43cc56f173a3ad57cb93b99a38cb088823654356b6733543e7008ac9caab671d9f3257c99b4650cdcef0329becc5e316b Homepage: https://cran.r-project.org/package=RcppColors Description: CRAN Package 'RcppColors' (Color Mappings and 'C++' Header Files for Color Conversion) Provides 'C++' header files to deal with color conversion from some color spaces to hexadecimal with 'Rcpp', and exports some color mapping functions for usage in R. Also exports functions to convert colors from the 'HSLuv' color space for usage in R. 'HSLuv' is a human-friendly alternative to HSL. Package: r-cran-rcppcwb Architecture: amd64 Version: 0.6.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2193 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libglib2.0-0t64 (>= 2.14.0), libpcre2-8-0 (>= 10.22), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-fs Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rcppcwb_0.6.10-1.ca2404.1_amd64.deb Size: 771756 MD5sum: 3ba37e665f389304ffa1be150463a6ab SHA1: 17868cba25aa08216b88c99040972e76a84f5a80 SHA256: 433126be974da120e2d18b4969c865ea287d19d9ef9cdd504e24d44a8d4e1f11 SHA512: 767489bb61144f08997bd713be7257bcaa346214a6826e9c0dedb9ffc1e756c212b594cf8c454d7aabb58b3a14e7c2e09fafeacc8e8f9647eb0534b5cae09e6d Homepage: https://cran.r-project.org/package=RcppCWB Description: CRAN Package 'RcppCWB' ('Rcpp' Bindings for the 'Corpus Workbench' ('CWB')) 'Rcpp' Bindings for the C code of the 'Corpus Workbench' ('CWB'), an indexing and query engine to efficiently analyze large corpora (). 'RcppCWB' is licensed under the GNU GPL-3, in line with the GPL-3 license of the 'CWB' (). The 'CWB' relies on 'pcre2' (BSD license, see ) and 'GLib' (LGPL license, see ). See the file LICENSE.note for further information. The package includes modified code of the 'rcqp' package (GPL-2, see ). The original work of the authors of the 'rcqp' package is acknowledged with great respect, and they are listed as authors of this package. To achieve cross-platform portability (including Windows), using 'Rcpp' for wrapper code is the approach used by 'RcppCWB'. Package: r-cran-rcppde Architecture: amd64 Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 555 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-inline, r-cran-deoptim, r-cran-lattice Filename: pool/dists/noble/main/r-cran-rcppde_0.1.9-1.ca2404.1_amd64.deb Size: 311918 MD5sum: 020e5130e3f6993333b6cb480f8e06bb SHA1: 22c9933238f4bb432b3ca7c8b6ba2e6110a520a8 SHA256: b28917eb0866bc4956d30a89a2f0fe1c8fb81e703b910990b7559957e01c7c51 SHA512: 6f69718e8a17106eb0de66c58ed564f57d07f34a88d9a6e16bc058004f6427604294b39cfd5d350fbd98b3dfe272a845b237dd8bf406dbb5180a8d3b93a56375 Homepage: https://cran.r-project.org/package=RcppDE Description: CRAN Package 'RcppDE' (Global Optimization by Differential Evolution in C++) An efficient C++ based implementation of the 'DEoptim' function which performs global optimization by differential evolution. Its creation was motivated by trying to see if the old approximation "easier, shorter, faster: pick any two" could in fact be extended to achieving all three goals while moving the code from plain old C to modern C++. The initial version did in fact do so, but a good part of the gain was due to an implicit code review which eliminated a few inefficiencies which have since been eliminated in 'DEoptim'. Package: r-cran-rcppdist Architecture: amd64 Version: 0.1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 508 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rcppdist_0.1.1.1-1.ca2404.1_amd64.deb Size: 209546 MD5sum: 9cb4ca8dfae863e7e86d7bc9a1ce6756 SHA1: 865b18a09e81ec8d0c2cf17033cc34df85f16d97 SHA256: 29d066a410282927da4639b0bd1cc4ffdfdb1ddc683f0472a9a2ed625997c9fa SHA512: f21d64df8697d93a3fc46ef2371f398d5badb140977cbd51c6e3c90fe66cbaca5028f909cc19385b71be988708ceaf6e4d5e2847b6d6182e11652f4f54c1e63e Homepage: https://cran.r-project.org/package=RcppDist Description: CRAN Package 'RcppDist' ('Rcpp' Integration of Additional Probability Distributions) The 'Rcpp' package provides a C++ library to make it easier to use C++ with R. R and 'Rcpp' provide functions for a variety of statistical distributions. Several R packages make functions available to R for additional statistical distributions. However, to access these functions from C++ code, a costly call to the R functions must be made. 'RcppDist' provides a header-only C++ library with functions for additional statistical distributions that can be called from C++ when writing code using 'Rcpp' or 'RcppArmadillo'. Functions are available that return a 'NumericVector' as well as doubles, and for multivariate or matrix distributions, 'Armadillo' vectors and matrices. 'RcppDist' provides functions for the following distributions: the four parameter beta distribution; the location- scale t distribution; the truncated normal distribution; the truncated t distribution; a truncated location-scale t distribution; the triangle distribution; the multivariate normal distribution*; the multivariate t distribution*; the Wishart distribution*; and the inverse Wishart distribution*. Distributions marked with an asterisk rely on 'RcppArmadillo'. Package: r-cran-rcppdpr Architecture: amd64 Version: 0.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3038 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppgsl Suggests: r-cran-testthat, r-bioc-snpstats Filename: pool/dists/noble/main/r-cran-rcppdpr_0.1.10-1.ca2404.1_amd64.deb Size: 2511562 MD5sum: 4688f3872879c0784bb8e43c7037a866 SHA1: 2e948098a7f62ce58bfc0a9436a7c08b449a0b93 SHA256: 091b2018271a2accac8e7cd9c2ee47ed7147e8de25ea96b423f5fd1d6e648fe7 SHA512: 98321257e694ba7d530c623ecd992e0fe06dbeec23d0a52979206de61c1cb94c3086e69b244a67afc36d391f566452d83f4cc22d1f5cdbef97c34cc29e7fc4ef Homepage: https://cran.r-project.org/package=RcppDPR Description: CRAN Package 'RcppDPR' ('Rcpp' Implementation of Dirichlet Process Regression) 'Rcpp' reimplementation of the the Bayesian non-parametric Dirichlet Process Regression model for penalized regression first published in Zeng and Zhou (2017) . A full Bayesian version is implemented with Gibbs sampling, as well as a faster but less accurate variational Bayes approximation. Package: r-cran-rcppdynprog Architecture: amd64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 898 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-rcppdynprog_0.2.1-1.ca2404.1_amd64.deb Size: 524134 MD5sum: db7e99b825341ee84b95e6b51194317d SHA1: 49784bd5ea93b2f597730182c0694743cee3fb32 SHA256: 2bbde8056836e39c1bbc49c692d8e2b867b124996283f05a68f81224c830620e SHA512: 9a1730e7257156128f0954269ad2f111b5d089fee3f4ea4f3273f4cc75721a34b0a9fede19ceee1a8d62e2f32da7ce6c3fa7ee4089171b9d4a5da5a7a2dfd364 Homepage: https://cran.r-project.org/package=RcppDynProg Description: CRAN Package 'RcppDynProg' ('Rcpp' Dynamic Programming) Dynamic Programming implemented in 'Rcpp'. Includes example partition and out of sample fitting applications. Also supplies additional custom coders for the 'vtreat' package. Package: r-cran-rcppeigen Architecture: amd64 Version: 0.3.4.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9666 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-matrix, r-cran-inline, r-cran-tinytest, r-cran-pkgkitten, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-rcppeigen_0.3.4.0.2-1.ca2404.1_amd64.deb Size: 1422818 MD5sum: 39ae0a91bf11a09e035dddcd6d3dd796 SHA1: 6d390563c3f40eb6820af44ab79450a7f34629e8 SHA256: cc8845ae1cd04152547fc5f862cc69625c3bd01e481cf6142f4580a1a65908f3 SHA512: 4cae74330a1e9471150f161860ae1af417725518ae8b2845c3d62fee926885f6900be73510d814098e8df40a08ef351ec2c5a9e0c2b9e3a131cbfa644c10a945 Homepage: https://cran.r-project.org/package=RcppEigen Description: CRAN Package 'RcppEigen' ('Rcpp' Integration for the 'Eigen' Templated Linear AlgebraLibrary) R and 'Eigen' integration using 'Rcpp'. 'Eigen' is a C++ template library for linear algebra: matrices, vectors, numerical solvers and related algorithms. It supports dense and sparse matrices on integer, floating point and complex numbers, decompositions of such matrices, and solutions of linear systems. Its performance on many algorithms is comparable with some of the best implementations based on 'Lapack' and level-3 'BLAS'. The 'RcppEigen' package includes the header files from the 'Eigen' C++ template library. Thus users do not need to install 'Eigen' itself in order to use 'RcppEigen'. Since version 3.1.1, 'Eigen' is licensed under the Mozilla Public License (version 2); earlier version were licensed under the GNU LGPL version 3 or later. 'RcppEigen' (the 'Rcpp' bindings/bridge to 'Eigen') is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'. Package: r-cran-rcppeigenad Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3970 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-functional, r-cran-memoise, r-cran-readr, r-cran-rdpack, r-cran-rcppeigen, r-cran-bh Filename: pool/dists/noble/main/r-cran-rcppeigenad_1.1.0-1.ca2404.1_amd64.deb Size: 507352 MD5sum: 5cf0d179b833a98628b0cecd291f9c16 SHA1: e050ed0aab52366f52f39a2bf243da8e5c4e8a7b SHA256: 0a91a1fe489e73e38ba5caaf6f0df0ba8abf1499edcf94d74ee507498c7da3c8 SHA512: 86b461f3c63e4e76b60e298e4c075d7d3260765080d4b81d40df792d2e1e53c6d28985edf2f243fc55597fa6bebb9c8e7768c3771bdbe3ebb67967abc795435a Homepage: https://cran.r-project.org/package=RcppEigenAD Description: CRAN Package 'RcppEigenAD' (Generate Partial Derivatives using 'Rcpp', 'Eigen' and 'CppAD') Compiles 'C++' code using 'Rcpp' , 'Eigen' and 'CppAD' to produce first and second order partial derivatives. Also provides an implementation of Faa' di Bruno's formula to combine the partial derivatives of composed functions. Package: r-cran-rcppensmallen Architecture: amd64 Version: 0.3.10.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2100 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rcppensmallen_0.3.10.0.1-1.ca2404.1_amd64.deb Size: 254862 MD5sum: 28c25e8d0ec0127365bfdd18f6542795 SHA1: cea3d5d68baf88acff52ba3ea10a63ac9d4d2ed9 SHA256: a7b8468eac8805000468cfc4fd5535288f9b721eb9ff17cf5a9b41cdf107246b SHA512: b3325b1a9f24595a04510139de490c5495c1c017ab77fc4d65b4cb1e2db1af5fc4cfaa32ba8ce0ad4913c6eb5caf16176521a5f1bc7f6622021b9b789392a674 Homepage: https://cran.r-project.org/package=RcppEnsmallen Description: CRAN Package 'RcppEnsmallen' (Header-Only C++ Mathematical Optimization Library for'Armadillo') 'Ensmallen' is a templated C++ mathematical optimization library (by the 'MLPACK' team) that provides a simple set of abstractions for writing an objective function to optimize. Provided within are various standard and cutting-edge optimizers that include full-batch gradient descent techniques, small-batch techniques, gradient-free optimizers, and constrained optimization. The 'RcppEnsmallen' package includes the header files from the 'Ensmallen' library and pairs the appropriate header files from 'armadillo' through the 'RcppArmadillo' package. Therefore, users do not need to install 'Ensmallen' nor 'Armadillo' to use 'RcppEnsmallen'. Note that 'Ensmallen' is licensed under 3-Clause BSD, 'Armadillo' starting from 7.800.0 is licensed under Apache License 2, 'RcppArmadillo' (the 'Rcpp' bindings/bridge to 'Armadillo') is licensed under the GNU GPL version 2 or later. Thus, 'RcppEnsmallen' is also licensed under similar terms. Note that 'Ensmallen' requires a compiler that supports 'C++14' and 'Armadillo' 10.8.2 or later. Package: r-cran-rcppexamples Architecture: amd64 Version: 0.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rcppexamples_0.1.10-1.ca2404.1_amd64.deb Size: 100716 MD5sum: ba84e9c41835eed412e9ecb174ccb49f SHA1: a63f1473f13859ce6c9bfc91544541178ca45bab SHA256: 5634436640e8f07073228119c6ced784087d3ca58f0171d89b9485c6ec7cbc12 SHA512: dfdfb2bac34d04a6ffca43541692f4468dca993ecd7fde8d532fbaa01f235c2b111b0638dfe5ee5c334f515a5d8707dac5aab37970f459aa2500fbde99e3c14d Homepage: https://cran.r-project.org/package=RcppExamples Description: CRAN Package 'RcppExamples' (Examples using 'Rcpp' to Interface R and C++) Examples for Seamless R and C++ integration The 'Rcpp' package contains a C++ library that facilitates the integration of R and C++ in various ways. This package provides some usage examples. Note that the documentation in this package currently does not cover all the features in the package. The site regroups a large number of examples for 'Rcpp'. Package: r-cran-rcppfarmhash Architecture: amd64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppint64 Suggests: r-cran-bit64 Filename: pool/dists/noble/main/r-cran-rcppfarmhash_0.0.3-1.ca2404.1_amd64.deb Size: 41326 MD5sum: 6676b00e6f168ba8da03571740c2a629 SHA1: 8840066af20bfc8bfaf7175ab9b30669a3c6caa5 SHA256: b1f06cf75884d6764cd1082094cc1ed42f8432e8238165002ac596c7bf55fc95 SHA512: 448c32a4be2131093135870880c9afec3b11e4bae43ee3fd0a6ad8d8257f49b40475bbb0132f097118eb4817e62e5a84c1858128b1f75eb2711f2174ed574413 Homepage: https://cran.r-project.org/package=RcppFarmHash Description: CRAN Package 'RcppFarmHash' (Interface to the Google 'FarmHash' Family of Hash Functions) The Google 'FarmHash' family of hash functions is used by the Google 'BigQuery' data warehouse via the 'FARM_FINGERPRINT' function. This package permits to calculate these hash digest fingerprints directly from R, and uses the included 'FarmHash' files written by G. Pike and copyrighted by Google, Inc. Package: r-cran-rcppfastad Architecture: amd64 Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 481 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rcppfastad_0.0.4-1.ca2404.1_amd64.deb Size: 114040 MD5sum: ec5976c028646d130ff01aef8ddbc797 SHA1: 8b10f86f5bcca72fe3b8f74d74c02d7258b44631 SHA256: 16a64a9f8dac497c9c9b1f4fd1874f0f14e3e71bf8d549f451c2b75c541b4147 SHA512: 5ff2583e67e9f09c2ae65c63ce69708c750467080be3ce90fe6c596b1320208b5ab9bdf42aa4d0483c7f02d1531bb899ef263573dd7976f1ce22d4557ecd746b 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. 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The 'fast_float' header-only C++ library by Daniel Lemire does it very well and very fast at up to or over to 1 gigabyte per second as described in more detail in . 'fast_float' is licensed under the Apache 2.0 license and provided here for use by other R packages via a simple 'LinkingTo:' statement. Package: r-cran-rcppgetconf Architecture: amd64 Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rcppgetconf_0.0.4-1.ca2404.1_amd64.deb Size: 50514 MD5sum: cbcb37038af2b440f8a4c7a0b3d87854 SHA1: ad2fdcc1c076bb6753adaf1f35c358e6257ca085 SHA256: a0dc9e54caa86fac47a13e9ee871ea0b765f364dfec690a2a042a6b1270ca2a3 SHA512: 9754a36af7d9746c71b00fee3a4a69edf30d28b913bc5f05b6d342a58d8577231278b43423884a14dd4bacc63a133d17bb14ca73b9b287df173b8c58832088e3 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. 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Package: r-cran-rcppgsl Architecture: amd64 Version: 0.3.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 646 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rcppgsl_0.3.14-1.ca2404.1_amd64.deb Size: 374242 MD5sum: 8c82a4c6186c0a16c9a3f8ca472698dd SHA1: fcd72c0b6f55807eff2a22fb46ff3d6b96dba7af SHA256: 034e2e882c84773cf09c9e302f5911e9283281ab973c5eb5dd3d6d5163cb20e0 SHA512: ec638ce7ec8096715b9e4a6b80bc55532b4a4b1a7eb17553e40ef3c5bbffa970322210cb4d6e90524c43e51bc9106527171b4674cb358a64044570cab6ec1b52 Homepage: https://cran.r-project.org/package=RcppGSL Description: CRAN Package 'RcppGSL' ('Rcpp' Integration for 'GNU GSL' Vectors and Matrices) 'Rcpp' integration for 'GNU GSL' vectors and matrices The 'GNU Scientific Library' (or 'GSL') is a collection of numerical routines for scientific computing. 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Package: r-cran-rcpphmm Architecture: amd64 Version: 1.2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-rcpphmm_1.2.2.1-1.ca2404.1_amd64.deb Size: 230630 MD5sum: b25dc6aac3b32ff90392448276059645 SHA1: 3d476fce84a0940fa7e5c257044cc8e3a508e4a6 SHA256: 42527dd8f6f59908763af07163a0d92f0ff214f3b037d725d3d33ada7ac0eeee SHA512: b004111f7eff1f642af8a0d55efeabd767d76104d36c2a286e6ad46ceba59388fc94275f1a71aa3d0e9ca74040d803115c0ab10609e1b4466c308ebee736d071 Homepage: https://cran.r-project.org/package=RcppHMM Description: CRAN Package 'RcppHMM' (Rcpp Hidden Markov Model) Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. 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Package: r-cran-rcpphnsw Architecture: amd64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 741 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcpphnsw_0.6.0-1.ca2404.1_amd64.deb Size: 183282 MD5sum: eebb1d13b6824aa4143cefb8c3253cba SHA1: 2711128788cbc21ea8e3952237177472eaac3ad4 SHA256: 93319ea4081a1f1e36e92f9edc20c5191ce9c7a429c4fbae7ad8537352e8e4ec SHA512: 17acbeaa752fbaf1193348ae5b3f699549257b526624bb870aa4132c62e996c51a3506903e1fb887a0e6a0cd0bd1a8bcb2489051a916a878d7f471bf78cd2978 Homepage: https://cran.r-project.org/package=RcppHNSW Description: CRAN Package 'RcppHNSW' ('Rcpp' Bindings for 'hnswlib', a Library for Approximate NearestNeighbors) 'Hnswlib' is a C++ library for Approximate Nearest Neighbors. 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Package: r-cran-rcpphungarian Architecture: amd64 Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-rcpphungarian_0.3-1.ca2404.1_amd64.deb Size: 142422 MD5sum: 41f0b6de5e5bcff688dd269065647703 SHA1: a58a7429f3bc8efc60c68f28353b54e5d91bd7d0 SHA256: 98987cdab57873afdebbf852fa9ddcb2832d26624bb3654505e9b5f95fd034a4 SHA512: 05ee9e8d438683c2f2823653b8a76968061429869f9d398551899297880dfb41533cad0af69881ea3b65986ca4f62de5d8a4e9a775a1e92f4ece091e3ca5fc37 Homepage: https://cran.r-project.org/package=RcppHungarian Description: CRAN Package 'RcppHungarian' (Solves Minimum Cost Bipartite Matching Problems) Header library and R functions to solve minimum cost bipartite matching problem using Huhn-Munkres algorithm (Hungarian algorithm; ; Kuhn (1955) ). This is a repackaging of code written by Cong Ma in the GitHub repo . Package: r-cran-rcppint64 Architecture: amd64 Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest, r-cran-bit64, r-cran-nanotime Filename: pool/dists/noble/main/r-cran-rcppint64_0.0.5-1.ca2404.1_amd64.deb Size: 48970 MD5sum: 4827228e8bc32896f6909c3664883997 SHA1: a834c9b07fcc358e26aedfbe9a17b52253701af1 SHA256: e5e0b69e19c33a2bfb7062f87b4d575fcf0b2caf98617245e04a191420753a8f SHA512: f2881f10b3dd71ba4cfa94a9713fcb7eeff45329437dc3f3ab2b746f5f396daaeada0f87881e9f85f5658dcf368dccc134635520054357edeb118c3d33f49a0f Homepage: https://cran.r-project.org/package=RcppInt64 Description: CRAN Package 'RcppInt64' ('Rcpp'-Based Helper Functions to Pass 'Int64' and 'nanotime'Values Between 'R' and 'C++') 'Int64' values can be created and accessed via the 'bit64' package and its 'integer64' class which package the 'int64' representation cleverly into a 'double'. 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Package: r-cran-rcppjagger Architecture: amd64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-purrr, r-cran-rcpp, r-cran-rlang Suggests: r-cran-dplyr, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-rcppjagger_0.0.2-1.ca2404.1_amd64.deb Size: 104504 MD5sum: 789b6a60a917de6730a9f2ed9299c348 SHA1: 4b4509dc869f05e9be8a60f821d1a13903e6b548 SHA256: 29d7a63e4d191d306af4df16eabf15609db2d0f5bb4c1ce8f4561c7ddab214ac SHA512: dbfb143b18025bad17d5946471abe133f53f6a339f0dea0f171af3f8eb78be334cb13ba72212d0b2ec0c7ffa49461e81718a9c9cc9bb6d473c375994f54668fd Homepage: https://cran.r-project.org/package=RcppJagger Description: CRAN Package 'RcppJagger' (An R Wrapper for Jagger) A wrapper for Jagger, a morphological analyzer proposed in Yoshinaga (2023) . 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Package: r-cran-rcpplbfgsblaze Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppblaze Suggests: r-cran-tinytest, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-rcpplbfgsblaze_0.1.0-1.ca2404.1_amd64.deb Size: 82398 MD5sum: dd79ba7680fd567e1acdb5687e16b6da SHA1: fd9ccb6706bfcffbb88579eafb7c0c0b457105e8 SHA256: a0149e5989894c419aedc7949ae68c56f500b602729803eb15e2ed9c1fbec27b SHA512: 68b83c3c1763faf30fb2bc5d3b0f0544dd526ef1b5c910b90e4dc4e35e642041fbf6db59400a37b4b5d84a96a173b49ee6f41b73b7740b1b43f77549585aaa5c Homepage: https://cran.r-project.org/package=RcppLbfgsBlaze Description: CRAN Package 'RcppLbfgsBlaze' ('L-BFGS' Algorithm Based on 'Blaze' for 'R' and 'Rcpp') The 'L-BFGS' algorithm is a popular optimization algorithm for unconstrained optimization problems. 'Blaze' is a high-performance 'C++' math library for dense and sparse arithmetic. This package provides a simple interface to the 'L-BFGS' algorithm and allows users to optimize their objective functions with 'Blaze' vectors and matrices in 'R' and 'Rcpp'. Package: r-cran-rcppmagicenum Architecture: amd64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rcppmagicenum_0.0.1-1.ca2404.1_amd64.deb Size: 49012 MD5sum: e530052c39d23f80353c7605c7fea377 SHA1: 96da90417ea54338961e99f92d1553226e60311b SHA256: 919307a98c57f54dfd5d210b745ad108e0df023be928aac3133a5c14561a1f5b SHA512: 610bfc65a1bd6c26587940f42281ddbc2230918a19bdf3a3a1e30dd50644e5975205f8f9db9c61a34732834ab021a1df40a33aae3515c5a94d87509eb25a4523 Homepage: https://cran.r-project.org/package=RcppMagicEnum Description: CRAN Package 'RcppMagicEnum' ('Rcpp' Bindings to 'Magic Enum' 'C++' 'Enum' Support) The header-only modern 'C++' template library 'Magic Enum' for static reflection of 'enums' (to string, from string, iteration) is provided by this package. 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Package: r-cran-rcppmecab Architecture: amd64 Version: 0.0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libmecab2 (>= 0.996), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcppmecab_0.0.1.5-1.ca2404.1_amd64.deb Size: 137008 MD5sum: 7659d964301d55f129d53fdf3474b16c SHA1: 5833a1885d913011f0f13721ca844e92dfae7903 SHA256: 3fe177c04f5613dfc99c4d63b9a7f4f631290b8b623acad9955637ad0cb9516a SHA512: 47a5f6dfe39f1b3e34aff35dc6c9c035aae26acc15dad45ab3addbf5041e7f552208cb982c6be8ac79fe26c868274dd652a8dead0b3c8d65094612b7e5ec0b7a Homepage: https://cran.r-project.org/package=RcppMeCab Description: CRAN Package 'RcppMeCab' ('rcpp' Wrapper for 'mecab' Library) R package based on 'Rcpp' for 'MeCab': Yet Another Part-of-Speech and Morphological Analyzer. The purpose of this package is providing a seamless developing and analyzing environment for CJK texts. This package utilizes parallel programming for providing highly efficient text preprocessing 'posParallel()' function. For installation, please refer to README.md file. Package: r-cran-rcppml Architecture: amd64 Version: 0.3.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcppml_0.3.7.1-1.ca2404.1_amd64.deb Size: 186564 MD5sum: cd893b68e60357199ec17998eefea10f SHA1: 0e4b61bf1967b984b34f3aa90f78b844bc738a70 SHA256: 9b18cd770d212b3a8191de5ae09089de1370b75c98fbd22ecf3bb558670e525d SHA512: 4f0140f33b68c45b105d13658b9dbd1737c70e8363136fc9a55c4cf89f1203b4a6ff954d89a395a9e5ede7660d238e42588813d33dbad04d7fce4aa19288a27d 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: amd64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1812 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppensmallen, r-cran-mlpack Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rcppmlpackexamples_0.0.1-1.ca2404.1_amd64.deb Size: 1607148 MD5sum: abd9cbf967f5e67aa95ea0b30fa20d55 SHA1: b35b00f45436ffd879b9c96776bfa086d01b683d SHA256: 0e1c78068f1eb3d19605e90600d628d69e710bddeb062ccf8820698b392c9d91 SHA512: 3ccf3d1719d96913430e7f5c36caa8a6e2c8c4b6a9923656026131028a7d84b1153bce5a9499757e9cf0271610bb21e788322be58a5c90244f2ba45e15e71242 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: amd64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6150 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-rcppmsgpack_0.2.4-1.ca2404.1_amd64.deb Size: 558684 MD5sum: a6724d642bc618a7bde610b4a3890b5b SHA1: 17c72de7d23f62618bb4165c1a2bf998f652c282 SHA256: 51f4bf2fffd2a62fc0e49da9ce61bee857815c2c1d06c63cef19b290c0650333 SHA512: d3e19370d14e7896e2fac2d1760eed2c72fed4751d0dbd5ef11c1bbf56c09e56dc874cb434de20429e6368670e3cea9fb4cd1a6075b39ae3c7837bd99905a925 Homepage: https://cran.r-project.org/package=RcppMsgPack Description: CRAN Package 'RcppMsgPack' ('MsgPack' C++ Header Files and Interface Functions for R) 'MsgPack' header files are provided for use by R packages, along with the ability to access, create and alter 'MsgPack' objects directly from R. 'MsgPack' is an efficient binary serialization format. It lets you exchange data among multiple languages like 'JSON' but it is faster and smaller. Small integers are encoded into a single byte, and typical short strings require only one extra byte in addition to the strings themselves. This package provides headers from the 'msgpack-c' implementation for C and C++(11) for use by R, particularly 'Rcpp'. The included 'msgpack-c' headers are licensed under the Boost Software License (Version 1.0); the code added by this package as well the R integration are licensed under the GPL (>= 2). See the files 'COPYRIGHTS' and 'AUTHORS' for a full list of copyright holders and contributors to 'msgpack-c'. Package: r-cran-rcppnloptexample Architecture: amd64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-nloptr Filename: pool/dists/noble/main/r-cran-rcppnloptexample_0.0.2-1.ca2404.1_amd64.deb Size: 36874 MD5sum: 5970328425b7a3d91ce9896c3ba6b3eb SHA1: da84af2dd9fb936b6c684df9de5d6478633818af SHA256: 73dfd0e355723827e8f7fdbe1edddcde6bd4650795cace2ecabea62e647278e4 SHA512: 9a4a53358956f33093948762581b1fc818b5b5ce46338621c68aece803a9b7667a055b92c9f62d17f86cade5558106cd73e61eb86e193e9a2e0a03915ca8136a Homepage: https://cran.r-project.org/package=RcppNLoptExample Description: CRAN Package 'RcppNLoptExample' ('Rcpp' Package Illustrating Header-Only Access to 'NLopt') An example package which shows use of 'NLopt' functionality from C++ via 'Rcpp' without requiring linking, and relying just on 'nloptr' thanks to the exporting API added there by Jelmer Ypma. This package is a fully functioning, updated, and expanded version of the initial example by Julien Chiquet at also containing a large earlier pull request of mine. 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Package: r-cran-rcppparallel Architecture: amd64 Version: 5.1.11-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2491 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbbmalloc2 (>= 2017~U7), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rcpp, r-cran-runit, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rcppparallel_5.1.11-2-1.ca2404.1_amd64.deb Size: 501926 MD5sum: 4af71565da6f05cf9afedf0ef20084ed SHA1: baff39bbe38a7f35c547945d9e1d3f48238e93cf SHA256: 889b06b0694838b83a610e88c79a56eacdd449aefdea86c5e889f7b81fa3d5c8 SHA512: b3c1993f1725436ad7c24ba8b2d9c0c632cae2fa4c4e44ae9b4065b8b8da8c0726e8360bd5c2bd0de9de461ca1168cb5ea3813d1e34fc0604392207a2d366f69 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: amd64 Version: 2.0.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3811 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.4), libgomp1 (>= 6), libhdf5-103-1t64, liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-hdf5r.extra, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-highfive Suggests: r-cran-knitr, r-cran-withr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcppplanc_2.0.15-1.ca2404.1_amd64.deb Size: 1997760 MD5sum: 7e8b96169a5344fe41e416a069c5133b SHA1: f105b98bcaf2cfff41c1e334e84e81afb1059b53 SHA256: d15a40673c22e4502a08d119ed438e538928982e30178ec55494b62b60b4b5a4 SHA512: 88699135716a5e5cd8db0e6632d626ebbbdd01ed6bc9355e899e4f6349bf4722a5957e5e145c995ad64a9e70d0107beecd8085b5003a4d8ff0bcad8f373c8d84 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: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1005 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/noble/main/r-cran-rcppquantuccia_0.1.4-1.ca2404.1_amd64.deb Size: 297972 MD5sum: 8b29e37302804bda65845106e6d2242c SHA1: c9bc2e8a35a1872323b40dcecb73c5195d104477 SHA256: 7dd4c1b647ace050ffbc0e92aa3edb9aa28e42d6ac64597e90f8636e775a7545 SHA512: 1d04138afff1b8d143bf302afff8947fbd3722d5bf0dc45141187986ce9f3091c0780042c15ae0935962ee857c8bd3f0dc394c1c5c6de24f91e4665dd564d090 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. 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It now also includes the pub/sub functions from the 'rredis' package. Package: r-cran-rcpproll Architecture: amd64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-zoo, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcpproll_0.3.2-1.ca2404.1_amd64.deb Size: 93992 MD5sum: e1d7ce4e4c54b04ca9d565de4a0687a8 SHA1: 59b9990adde5e1c324621431c30132ef1d9bb7d4 SHA256: 1c31a76a8a0b09085f481791408b04d0194eb6b8f9cda420a565defbce32d174 SHA512: ef349fb251ffccd55cdafda41e9451301a198a51a0cc1ba764957951969f4aa72c0a064e6a2fa0ad09fdb500f305dbc97cd907f0508fb69cd01903c31c38461f 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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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: amd64 Version: 0.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 825 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-fkf, r-cran-rcpparmadillo Suggests: r-cran-pkgkitten Filename: pool/dists/noble/main/r-cran-rcppsmc_0.2.9-1.ca2404.1_amd64.deb Size: 280368 MD5sum: 56b6708b20086b456493205d17af7158 SHA1: cedac653ea6cae8350ef67722b208d12e098b63c SHA256: ca4a9772e06a9f03089d07f88527491e37a5385fc2d05d79f9d4f55915c7fe51 SHA512: 9df526f113a5101e1ccad8ffddada7a776a0ccdf0c02c4ca1e0ef6df4f7420eba3e3e27618bb886e6a795a752abfd12d9cd8e15d4e4c4408066e8985a82c3511 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: amd64 Version: 0.0.29-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1733 Depends: libc6 (>= 2.33), libgcc-s1 (>= 4.3), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-simplermarkdown Filename: pool/dists/noble/main/r-cran-rcppspdlog_0.0.29-1.ca2404.1_amd64.deb Size: 372382 MD5sum: e34a06c93ff61e5a269a2a5768314f45 SHA1: 43059fdcd4afbfcc37ab79dadd2b5eab3e08e5e5 SHA256: 336941d74615d2964f75c59c2dee3e518bda332ac85913220a7e8e4cc185529c SHA512: 62d1b3998d578731c56728ceedb8d9d45d16e7a5ae3c631cc147f5a46eb5d8e9170d174b087e7f059d74060587c3c127dea4d117ab78f68260871f840f1051fc 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-rcppthread Architecture: amd64 Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 486 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 12), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-r.rsp, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rcppthread_2.3.0-1.ca2404.1_amd64.deb Size: 336430 MD5sum: dbdb20744bb97ae097cffc888cab14f7 SHA1: cbdea5480b7b4cea1ca1569a747a681c9bb46411 SHA256: 51c65deb22f95293e25fbb3c57a69e6302eecd0246b9a3e5c84d120961a18b1f SHA512: 82f3f69aa339cffdee8382ccda49f52163b1d3fc672f088f1392ef7ce6b2c66dac4d48c3c734a6a95447e1feaea45dfd286696690c6628c908f70bde90df8301 Homepage: https://cran.r-project.org/package=RcppThread Description: CRAN Package 'RcppThread' (R-Friendly Threading in C++) Provides a C++11-style thread class and thread pool that can safely be interrupted from R. See Nagler (2021) . 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It's not just simple, it's blazing fast! This sleek tic-toc timer class supports overlapping timers as well as 'OpenMP' parallelism . It boasts a nanosecond-level time resolution. We did not find any overhead of the timer itself at this resolution. Results (with summary statistics) are automatically passed back to 'R' as a data frame. 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Package: r-cran-rcpptskit Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1172 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rcpp, r-cran-reticulate Suggests: r-cran-covr, r-cran-knitr, r-cran-quarto, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcpptskit_0.2.0-1.ca2404.1_amd64.deb Size: 415644 MD5sum: bd24f8fddea8d5d85458bf0bc7175db1 SHA1: 949df8719f49d6416150a255d39482c358bbb02a SHA256: 8eb1d07da20899f631c2494b884db612395114dfedb1f55d06b2f19def4d9b67 SHA512: 6584a724b5585c2ec3b783cae48fa7669b33f65b29e0b455dc5e391708998db657b677148bc320722ba0e9f52c79742df054588ba277150a1b7d1f2c5960e740 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. 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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-rdsdp Architecture: amd64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 563 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rdsdp_1.0.6-1.ca2404.1_amd64.deb Size: 218680 MD5sum: cd7696f33c1f47f290abb27a4a35a926 SHA1: 998cf718a34ea8f874f4cc1652f65cd3a8a76f76 SHA256: 0475bab016dc4fc1a33a0a1bbf6f25c80adedcf8c0dafda89088818d5b3d1a67 SHA512: 1da681c1cc0ee66714c9910438a7bb0871d3a478292facf7c615b61f818caf7ea5622fcdd1078729433a078374b5be815856cfaf12e667a53b7d252b10a0a47e Homepage: https://cran.r-project.org/package=Rdsdp Description: CRAN Package 'Rdsdp' (R Interface to DSDP Semidefinite Programming Library) R interface to DSDP semidefinite programming library. The DSDP software is a free open source implementation of an interior-point method for semidefinite programming. 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Package: r-cran-read.gt3x Architecture: amd64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 694 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-r.utils Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-data.table, r-cran-zoo, r-cran-readr, r-cran-lubridate, r-cran-zip Filename: pool/dists/noble/main/r-cran-read.gt3x_1.2.0-1.ca2404.1_amd64.deb Size: 426484 MD5sum: b8350d4254e9f511db9616418affb900 SHA1: 55b5b32899e8df2167f9ed88618c5f6dbd19aaf0 SHA256: ec8a2cb77c93cef485f3968b6a7a4283352fac5d322bbae1a639fa930b29046d SHA512: 24e602c2394c783f5d4f3ea58949f6c683cfece048fc5d1ecca966765dad5d63360e3abe564ee54aad4854af290351f2872ac021caaefc05d20f080f3ae593d7 Homepage: https://cran.r-project.org/package=read.gt3x Description: CRAN Package 'read.gt3x' (Parse 'ActiGraph' 'GT3X'/'GT3X+' 'Accelerometer' Data) Implements a high performance C++ parser for 'ActiGraph' 'GT3X'/'GT3X+' data format (with extension '.gt3x') for 'accelerometer' samples. 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Package: r-cran-read Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-bioc-qvalue, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-read_1.0.1-1.ca2404.1_amd64.deb Size: 88128 MD5sum: 50449295351a4740b8c6600d9d9fb1b8 SHA1: a899ddc8a8fc86ab64bc2b96ae8e2b9e5c2b5991 SHA256: ebf197054f054855b23fd4e85b7a2115252873d474460574560bc4a9115b807d SHA512: 72ffcfd2b5e2995b733c6afc12b957163f71b7a3dc3018531a053150331972fed170a7689a7bdfdf89cc3b068ad992957ad1b7a55350e76b055d052b7ee2aa54 Homepage: https://cran.r-project.org/package=ReAD Description: CRAN Package 'ReAD' (Powerful Replicability Analysis of Genome-Wide AssociationStudies) A robust and powerful approach is developed for replicability analysis of two Genome-wide association studies (GWASs) accounting for the linkage disequilibrium (LD) among genetic variants. 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Supports labelled data for regression, classification (binary, multi-class, multi-label), and ranking (with 'qid' field), and can handle header metadata and comments in files. 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Package: r-cran-readtextgrid Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-dplyr, r-cran-rlang, r-cran-withr, r-cran-cpp11 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-readtextgrid_0.2.0-1.ca2404.1_amd64.deb Size: 101552 MD5sum: a9896ed1f5d1b45d0ea423a0e878cd64 SHA1: 654c180b9d1e73d23d937ead668c4d14cb296e3e SHA256: 9b800e4196a8f62fedc8fa6434337fd5eae4f7758def309b4be87ad6e4b77a00 SHA512: 3453fd8f150ebc3c53b59545c95b8f0dfcdaeef14155cc8478bd55468c58c83f62549df9b72515c4a73c672ad2665cf3b537a7ecfffd07a8a9150cf186cdbd01 Homepage: https://cran.r-project.org/package=readtextgrid Description: CRAN Package 'readtextgrid' (Read in a 'Praat' 'TextGrid' File) 'Praat' is a widely used tool for manipulating, annotating and analyzing speech and acoustic data. 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Supports '.xls' via the embedded 'libxls' C library and '.xlsx' via the embedded 'RapidXML' C++ library . Works on Windows, Mac and Linux without external dependencies. Package: r-cran-readxlsb Architecture: amd64 Version: 0.1.61-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 542 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-xml2, r-cran-cellranger Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-readxlsb_0.1.61-1.ca2404.1_amd64.deb Size: 182552 MD5sum: 6c8fc0bc265ac2c4ea298599ef7540f5 SHA1: 0b3a4e61550e5f5550706441b12cd79332ad9b7f SHA256: c5dec82dd5ae62d4de108d2931858b1726db47a47f1955df3e829e4546907b20 SHA512: 96afa5e4f0df746f5f93d9fc70ad2cc0d644dec44265f73011cc4ebd3799ea579cc7281bfa3c7d22104e3b624c24041df95b49dcf67eec0e9c5ba2c86c4bf1a6 Homepage: https://cran.r-project.org/package=readxlsb Description: CRAN Package 'readxlsb' (Read 'Excel' Binary (.xlsb) Workbooks) Import data from 'Excel' binary (.xlsb) workbooks into R. Package: r-cran-realvams Architecture: amd64 Version: 0.4-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-numderiv, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-realvams_0.4-6-1.ca2404.1_amd64.deb Size: 223948 MD5sum: af47a5c4b9388f9e2d24c31d37524339 SHA1: 9900d58ec51e9f41920e78e051f206366c5d8483 SHA256: 1a0f5c460fe135f824531b80ab4ef6f647c0074e306346d1d679d706b7fd8ae3 SHA512: 7280561601d0ff6b5be7f442772d155883cb9f170913d3f7118827b5904dd755eeb514f1b34b5447bb401762a3d35ca3fa7c49228ac3226c3998fd665bdd4cd5 Homepage: https://cran.r-project.org/package=RealVAMS Description: CRAN Package 'RealVAMS' (Multivariate VAM Fitting) Fits a multivariate value-added model (VAM), see Broatch, Green, and Karl (2018) and Broatch and Lohr (2012) , with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) , is used for the estimation of this joint generalized linear mixed model. The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) . This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265. Package: r-cran-ream Architecture: amd64 Version: 1.0-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 739 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ream_1.0-10-1.ca2404.1_amd64.deb Size: 334092 MD5sum: 4317e478c7253f8835327fea9c50365e SHA1: ca9e4c69f48598194ba0df6a433763966a5d220d SHA256: 7cf67df1251076df005b340418a1b733659af5bfb103622f786fbde36a57a7ec SHA512: 08403a2b2769e6621f3f0341be0404fd580ef0bac8e31138f796a512efa068c5d94b89f68a1d3b56b249c1cf6e55173551cdfc4ea292386b5d6cc3970fb98a8e 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. 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These functions are described in "Recursive Association Rule Mining" Abdelkader Mokkadem, Mariane Pelletier, Louis Raimbault (2020) . 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Package: r-cran-reclin2 Architecture: amd64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 710 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-stringdist, r-cran-lpsolve, r-cran-rcpp Suggests: r-cran-simplermarkdown Filename: pool/dists/noble/main/r-cran-reclin2_0.6.0-1.ca2404.1_amd64.deb Size: 277506 MD5sum: cba6efb3072dff6b8bf64f92beceadb4 SHA1: f3517c78792ae24793658f0db95af8794833d3af SHA256: 8fd1a340e382edcc6fbe5e8b0ecb1e3038765a0a29e77a1fc382998929e81b5f SHA512: 379449a2499f9f21350ffc1338ee086797559d6a8ef67813b85229e7d569473966660bda892437e9372d943f2e0b0c6155303b9e4ebac232cb4e413f5055a409 Homepage: https://cran.r-project.org/package=reclin2 Description: CRAN Package 'reclin2' (Record Linkage Toolkit) Functions to assist in performing probabilistic record linkage and deduplication: generating pairs, comparing records, em-algorithm for estimating m- and u-probabilities (I. Fellegi & A. Sunter (1969) , T.N. Herzog, F.J. Scheuren, & W.E. Winkler (2007), "Data Quality and Record Linkage Techniques", ISBN:978-0-387-69502-0), forcing one-to-one matching. Can also be used for pre- and post-processing for machine learning methods for record linkage. Focus is on memory, CPU performance and flexibility. Package: r-cran-recmap Architecture: amd64 Version: 1.0.20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2045 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ga, r-cran-rcpp, r-cran-sp Suggests: r-cran-doparallel, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat, r-cran-tufte Filename: pool/dists/noble/main/r-cran-recmap_1.0.20-1.ca2404.1_amd64.deb Size: 1471968 MD5sum: db201b69b92e21fbd0ed072328898510 SHA1: 74fd8d76f148b65a9835d8d203f4ecdc932a54df SHA256: 1d11a9ea3f5c39ba772769a29d633d02639d4b61f0b2347d0bcc2cd84e7ccbad SHA512: 5141d5a0a0592f5b6eb7c4f8e632a545fe027fad397fdc3064fe41a02fd1cb06d02d5f34c929942f0468a2b54db463cebd72f40b3f3d0ea0fa09f2af5a86fba8 Homepage: https://cran.r-project.org/package=recmap Description: CRAN Package 'recmap' (Compute the Rectangular Statistical Cartogram) Implements the RecMap MP2 construction heuristic . 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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. 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Package: r-cran-recor Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-recor_1.0.3-1.ca2404.1_amd64.deb Size: 56532 MD5sum: 648d7c682adf21e45b10ac98aafecf11 SHA1: 8f916b67f21fb7459d96cad32a7bb52db974ae53 SHA256: a11166e2133220f5cb81510645cf0572f3c111b70467a99f46c3e7566d761eed SHA512: 60281e1c47180b69ccfa77ee4cd80da32ca733b1bb7737fab8688436d79b39b8c84c1d53be820059a034aa3a3516806c606b39552d49a5040fb1c1ca47f144a5 Homepage: https://cran.r-project.org/package=recor Description: CRAN Package 'recor' (Rearrangement Correlation Coefficient) The Rearrangement Correlation Coefficient is an adjusted version of Pearson's correlation coefficient that accurately measures monotonic dependence relationships, including both linear and nonlinear associations. This method addresses the underestimation problem of classical correlation coefficients in nonlinear monotonic scenarios through improved statistical bounds derived from rearrangement inequalities. For more details, see Ai (2024) . Package: r-cran-recordlinkage Architecture: amd64 Version: 0.4-12.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3579 Depends: libc6 (>= 2.3), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-rsqlite, r-cran-ff, r-cran-e1071, r-cran-rpart, r-cran-ipred, r-cran-evd, r-cran-data.table, r-cran-nnet, r-cran-xtable Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-recordlinkage_0.4-12.6-1.ca2404.1_amd64.deb Size: 1027484 MD5sum: eae339a8dc9a911847f05e76bf95b8b4 SHA1: 2743e05191d399f21ccd678a8f3d4f2dd71c96af SHA256: 989c82cb18f6005719120e240f9a26d3f69a643f09ed9e451aa8a667fb74bb4c SHA512: c330242197c66ab90c6dcdd051497278e8e6b6ee2e87252429894a47b9c3e654a60031a76a83395f48cbcd84b05a8f8f216174e08b79f509a7e7131d981be97b Homepage: https://cran.r-project.org/package=RecordLinkage Description: CRAN Package 'RecordLinkage' (Record Linkage Functions for Linking and Deduplicating Data Sets) Provides functions for linking and deduplicating data sets. Methods based on a stochastic approach are implemented as well as classification algorithms from the machine learning domain. For details, see our paper "The RecordLinkage Package: Detecting Errors in Data" Sariyar M / Borg A (2010) . Package: r-cran-recosystem Architecture: amd64 Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 805 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-float, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc, r-cran-matrix Filename: pool/dists/noble/main/r-cran-recosystem_0.5.1-1.ca2404.1_amd64.deb Size: 414924 MD5sum: 653529e1ea1217c98488efc16e076414 SHA1: 77daef3dac0663b92839be9fd1bb2845cea418c9 SHA256: 7ce1db900c6a7b7df8c3957e02d3413e865ea5bdb79d423670db4241e1c67049 SHA512: 6e91d51dd3e9dcb21f530def623fd73fabab5a2626da73675a698062d80368222f25f47977a378e9659932c5af633f3525fabdfdb13106042d71981bfe6ac545 Homepage: https://cran.r-project.org/package=recosystem Description: CRAN Package 'recosystem' (Recommender System using Matrix Factorization) R wrapper of the 'libmf' library for recommender system using matrix factorization. It is typically used to approximate an incomplete matrix using the product of two matrices in a latent space. Other common names for this task include "collaborative filtering", "matrix completion", "matrix recovery", etc. High performance multi-core parallel computing is supported in this package. Package: r-cran-rectpacker Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rectpacker_1.0.0-1.ca2404.1_amd64.deb Size: 21470 MD5sum: f89e70b42c121d95440c5c24e3c79c57 SHA1: d602ee74eecab3bf037e328e25c85b67e9f01993 SHA256: 7f2e0e2e22f2c417b6ea9cf5a0c3e2d9e94a28be414d291476627c1264ba2a83 SHA512: 8c8150bca5bf9e02d31a5041e7ddf2676e86996dfe5d2f736b103ddc8ad5021b204f7ea8de164435fd3d79186fa1fa60bb638c960153183c67d2ead2681e4cea Homepage: https://cran.r-project.org/package=rectpacker Description: CRAN Package 'rectpacker' (Rectangle Packing) Rectangle packing is a packing problem where rectangles are placed into a larger rectangular region (without overlapping) in order to maximise the use space. Rectangles are packed using the skyline heuristic as discussed in Lijun et al (2011) 'A Skyline-Based Heuristic for the 2D Rectangular Strip Packing Problem' . A function is also included for determining a good small-sized box for containing a given set of rectangles. Package: r-cran-recurse Architecture: amd64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 619 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-circular, r-cran-prevr, r-cran-scales, r-cran-fields, r-cran-move, r-cran-move2, r-cran-knitr, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/noble/main/r-cran-recurse_1.4.0-1.ca2404.1_amd64.deb Size: 347388 MD5sum: 6f4938b0a0baf0249a8caae329df4310 SHA1: 1155e120cdb44062eabed5e499b74877e58f9666 SHA256: 2b21a063d08b2b1c5fcc85b574cb227eb118218a03a22ec899231e6b5ebdbb80 SHA512: 67a74c06d451f12e2d5fa0c17b08bdf96ac77826b5f84f1da3e67057d4990c60e4b01c5fa3ba8b60318ba96384083672ba5cd35b8ad369c54b1646404394309e Homepage: https://cran.r-project.org/package=recurse Description: CRAN Package 'recurse' (Computes Revisitation Metrics for Trajectory Data) Computes revisitation metrics for trajectory data, such as the number of revisitations for each location as well as the time spent for that visit and the time since the previous visit. Also includes functions to plot data. Package: r-cran-reda Architecture: amd64 Version: 0.5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4340 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-splines2, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-reda_0.5.6-1.ca2404.1_amd64.deb Size: 1294148 MD5sum: 64d0f1c0d0a9495b43739b249d14e012 SHA1: a9511adac82f969f03da5087117269d1f4a4fbec SHA256: 69930cb36c1b88bb0625b44fd84c662cfc717d88e6806e39be7ffeba54ea6d9c SHA512: d2edba9bfab58e2c36a9e75305493b090d922d8b85196f4ac9caac985a5110dd41b61eea7ba3440c7cf2afdca3cffe531b7d4a6a348f50594ec5e99245e6eee4 Homepage: https://cran.r-project.org/package=reda Description: CRAN Package 'reda' (Recurrent Event Data Analysis) Contains implementations of recurrent event data analysis routines including (1) survival and recurrent event data simulation from stochastic process point of view by the thinning method proposed by Lewis and Shedler (1979) and the inversion method introduced in Cinlar (1975, ISBN:978-0486497976), (2) the mean cumulative function (MCF) estimation by the Nelson-Aalen estimator of the cumulative hazard rate function, (3) two-sample recurrent event responses comparison with the pseudo-score tests proposed by Lawless and Nadeau (1995) , (4) gamma frailty model with spline rate function following Fu, et al. (2016) . Package: r-cran-redatam Architecture: amd64 Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1358 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13), r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-janitor, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-cpp4r Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-redatam_2.3.0-1.ca2404.1_amd64.deb Size: 389438 MD5sum: eab6070ed38d5b2ac6e655c42e6a9447 SHA1: 7ca2c35f8cc3a5abeaf0c66910e985a67d3e8225 SHA256: 809db60deb5751c2f7042f6d5a257851676d9ef2356d9a06099ad932560b8a68 SHA512: 1202fffbe5f7e1964614a5e325f1d8b3c038ed9a493e21a46865939ec96fe8611afe477a6161ce18d5f7bad76e955dfbbff9e710a5dab6bef5cce3ea3b5213a0 Homepage: https://cran.r-project.org/package=redatam Description: CRAN Package 'redatam' (Import 'REDATAM' Files) Import 'REDATAM' formats into R via the 'Open REDATAM' C++ library. The full context of this project and details about the implementation are available in (Open Access). Package: r-cran-redatamx Architecture: amd64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 34088 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.4), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cpp11 Filename: pool/dists/noble/main/r-cran-redatamx_1.3.0-1.ca2404.1_amd64.deb Size: 9795724 MD5sum: 716be22e81be39b1f89a8da371f3e56c SHA1: bdba5785225b040d138a8d969b640d11281c74ca SHA256: d137f31c1dcf82384acf2f725e54a5cd4f40c158b2d90b209bf993daac37fceb SHA512: 61790d3476e9e75ed6d94780c56a9e79418805bd16a3a8994b2e706646ed8d8778e82b3515977801b6d018e65a3e33dfa256b9aab5a6add697208d2b7a4e2410 Homepage: https://cran.r-project.org/package=redatamx Description: CRAN Package 'redatamx' (R Interface to 'Redatam' Library) Provides an API to work with 'Redatam' (see ) databases in both formats: 'RXDB' (new format) and 'DICX' (old format) and running 'Redatam' programs written in 'SPC' language. It's a wrapper around 'Redatam' core and provides functions to open/close a database (redatam_open()/redatam_close()), list entities and variables from the database (redatam_entities(), redatam_variables()) and execute a 'SPC' program and gets the results as data frames (redatam_query(), redatam_run()). Package: r-cran-reddyproc Architecture: amd64 Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2617 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-readr, r-cran-tibble, r-cran-magrittr, r-cran-solartime, r-cran-bigleaf, r-cran-mlegp Suggests: r-cran-testthat, r-cran-minpack.lm, r-cran-segmented, r-cran-knitr, r-cran-rmarkdown, r-cran-lognorm, r-cran-ggplot2, r-cran-tidyr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-reddyproc_1.3.4-1.ca2404.1_amd64.deb Size: 2105424 MD5sum: d1e4687dd70cd2ea5f326cd6f3c8e733 SHA1: 181ba0dc56590a621c00b2cf75f73c6cd4164a33 SHA256: d7da84eb78d66b96e22c21349932df432dbef9b12470852837aff0d03d25ea65 SHA512: dd149a0bbc5bfaf8a6d0b6f84a1eb8afe443b413d476e394c4ef376238e5f772624bd6e471a8cf60084facd1d17c363d17fce1df65508318d7e4391e2903791f Homepage: https://cran.r-project.org/package=REddyProc Description: CRAN Package 'REddyProc' (Post Processing of (Half-)Hourly Eddy-Covariance Measurements) Standard and extensible Eddy-Covariance data post-processing (Wutzler et al. (2018) ) includes uStar-filtering, gap-filling, and flux-partitioning. The Eddy-Covariance (EC) micrometeorological technique quantifies continuous exchange fluxes of gases, energy, and momentum between an ecosystem and the atmosphere. It is important for understanding ecosystem dynamics and upscaling exchange fluxes. (Aubinet et al. (2012) ). This package inputs pre-processed (half-)hourly data and supports further processing. First, a quality-check and filtering is performed based on the relationship between measured flux and friction velocity (uStar) to discard biased data (Papale et al. (2006) ). Second, gaps in the data are filled based on information from environmental conditions (Reichstein et al. (2005) ). Third, the net flux of carbon dioxide is partitioned into its gross fluxes in and out of the ecosystem by night-time based and day-time based approaches (Lasslop et al. (2010) ). Package: r-cran-redist Architecture: amd64 Version: 4.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5011 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-redistmetrics, r-cran-rcpp, r-cran-rlang, r-cran-cli, r-cran-vctrs, r-cran-tidyselect, r-cran-stringr, r-cran-dplyr, r-cran-sf, r-cran-doparallel, r-cran-foreach, r-cran-dorng, r-cran-servr, r-cran-sys, r-cran-ggplot2, r-cran-patchwork, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-coda, r-cran-matrixstats, r-cran-loo, r-cran-rmpi, r-cran-withr, r-cran-knitr, r-cran-rmarkdown, r-cran-rmapshaper, r-cran-ggpattern, r-cran-scales, r-cran-units, r-cran-rspectra, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-redist_4.3.2-1.ca2404.1_amd64.deb Size: 3232396 MD5sum: 78385489cf021ca558b69ae863e199ec SHA1: 41f29701b02ffe0071dc2888fd393bd9d4f1a58b SHA256: af562c61344e45954bdb6d718441430612eba2a7da7401668bcba3d3a31f3e0b SHA512: 1c612b494c942d173c7990df345e9dc6a78d88a279ceaf11aec8543c8d6c1337679c6903ea0f6f8a511253dc5b86b89a5fe888644ba571f3b5d119789b9d6fbb Homepage: https://cran.r-project.org/package=redist Description: CRAN Package 'redist' (Simulation Methods for Legislative Redistricting) Enables researchers to sample redistricting plans from a pre-specified target distribution using Sequential Monte Carlo and Markov Chain Monte Carlo algorithms. The package allows for the implementation of various constraints in the redistricting process such as geographic compactness and population parity requirements. Tools for analysis such as computation of various summary statistics and plotting functionality are also included. The package implements the SMC algorithm of McCartan and Imai (2023) , the enumeration algorithm of Fifield, Imai, Kawahara, and Kenny (2020) , the Flip MCMC algorithm of Fifield, Higgins, Imai and Tarr (2020) , the Merge-split/Recombination algorithms of Carter et al. (2019) and DeFord et al. (2021) , and the Short-burst optimization algorithm of Cannon et al. (2020) . Package: r-cran-redistmetrics Architecture: amd64 Version: 1.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1097 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-rcpp, r-cran-vctrs, r-cran-cli, r-cran-foreach, r-cran-doparallel, r-cran-magrittr, r-cran-dplyr, r-cran-rlang, r-cran-geos, r-cran-wk, r-cran-libgeos, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-redistmetrics_1.0.11-1.ca2404.1_amd64.deb Size: 539364 MD5sum: a3f02ea360a3b1268dd45e1572120a8e SHA1: f25e19d60f32b5240cbca3841b3bfe1fdc86382e SHA256: da27ca7c753eb3b1900eda8ab36323890430501efa93624368574ad46f646ae0 SHA512: c7455198e4b9bfb63cd5bf8fa28b1f8a9854c07dbee9d768f7c2d0f9ce64dcabf091ca6a79f0b52afb9b3d0ce7bc9c75c5cd4db922bc31641a9aa92e584139a2 Homepage: https://cran.r-project.org/package=redistmetrics Description: CRAN Package 'redistmetrics' (Redistricting Metrics) Reliable and flexible tools for scoring redistricting plans using common measures and metrics. These functions provide key direct access to tools useful for non-simulation analyses of redistricting plans, such as for measuring compactness or partisan fairness. Tools are designed to work with the 'redist' package seamlessly. Package: r-cran-redland Architecture: amd64 Version: 1.0.17-19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1147 Depends: libc6 (>= 2.4), librdf0t64 (>= 1.0.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-roxygen2 Suggests: r-cran-spelling, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-stringi Filename: pool/dists/noble/main/r-cran-redland_1.0.17-19-1.ca2404.1_amd64.deb Size: 739894 MD5sum: d6c2bfda8e673fc4054632f0d3236c24 SHA1: 32440b080a6b4c6d48d5874ca0e3bdcbc36bbfbb SHA256: 9d2481b3b411a563d0dd05873301f23d770e24de031323c2442d8f757d634de8 SHA512: 329798829b1e36cfa1fa98a12627f491e868a2bc02ca5684554948e18c71345446eb39ec514fee77e5ddd1fcfe0266713474b816a803137eba7a6c24c1b73928 Homepage: https://cran.r-project.org/package=redland Description: CRAN Package 'redland' (RDF Library Bindings in R) Provides methods to parse, query and serialize information stored in the Resource Description Framework (RDF). RDF is described at . This package supports RDF by implementing an R interface to the Redland RDF C library, described at . In brief, RDF provides a structured graph consisting of Statements composed of Subject, Predicate, and Object Nodes. Package: r-cran-redm Architecture: amd64 Version: 1.15.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1707 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppthread Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-formatr Filename: pool/dists/noble/main/r-cran-redm_1.15.4-1.ca2404.1_amd64.deb Size: 959568 MD5sum: 896c66603373feb9e72385dfb11bf62b SHA1: 660c53bbf5aaa04c1f8596a9abb842ed02a8cc2e SHA256: 5b7f5e0ebd860e7eab2150fdb674dd82daf004691516ccfb2b22b863a9ddbf22 SHA512: 059ed05d3c0a2197e15eda0076d686ed047324a35736d13beae24857e932d0fe542decd3426c5e257f874a37393bd4fca4dbdc02927ae5997f7b0a5aa2a70fb5 Homepage: https://cran.r-project.org/package=rEDM Description: CRAN Package 'rEDM' (Empirical Dynamic Modeling ('EDM')) An implementation of 'EDM' algorithms based on research software developed for internal use at the Sugihara Lab ('UCSD/SIO'). The package is implemented with 'Rcpp' wrappers around the 'cppEDM' library. It implements the 'simplex' projection method from Sugihara & May (1990) , the 'S-map' algorithm from Sugihara (1994) , convergent cross mapping described in Sugihara et al. (2012) , and, 'multiview embedding' described in Ye & Sugihara (2016) . Package: r-cran-redux Architecture: amd64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: libc6 (>= 2.14), libhiredis1.1.0 (>= 1.2.0), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-storr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sys, r-cran-testthat Filename: pool/dists/noble/main/r-cran-redux_1.1.5-1.ca2404.1_amd64.deb Size: 226086 MD5sum: 79c440caa7b83fb1a3dbf019eadee885 SHA1: 220729fe28516f31d6c18023013aa80811b322c7 SHA256: 4ec2431da02daef2028661edea8acbbd2f053129d60a7a6cb2bbfdb9f6500edd SHA512: b46757119a5df5054274bf38b3755ba426a9b469a67abc507d64a8a96de933a9d8e259a01afb9782d572d32394feddcd579b5e7fc7d5fd6e4ad25d304e588c98 Homepage: https://cran.r-project.org/package=redux Description: CRAN Package 'redux' (R Bindings to 'hiredis') A 'hiredis' wrapper that includes support for transactions, pipelining, blocking subscription, serialisation of all keys and values, 'Redis' error handling with R errors. Includes an automatically generated 'R6' interface to the full 'hiredis' API. Generated functions are faithful to the 'hiredis' documentation while attempting to match R's argument semantics. Serialisation must be explicitly done by the user, but both binary and text-mode serialisation is supported. Package: r-cran-reems Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2991 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-raster, r-cran-sp, r-cran-sf, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-future, r-cran-future.apply, r-cran-matrix, r-cran-rcolorbrewer, r-cran-deldir, r-cran-dichromat, r-cran-rworldmap, r-cran-rworldxtra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-reems_0.1.0-1.ca2404.1_amd64.deb Size: 857204 MD5sum: 30f81ebcd4c8f05ed04ca437304e7154 SHA1: b11fa610f82ea82357b844c62b044b2b50c28ba0 SHA256: 4a322e0da67999dd96138da6d5651a0d5393d439ff5bec37c0327d1c345b85b9 SHA512: 1f7eaae72896a45547a17a7df3507958a430ce836df668a3d657ccc8199fb2f3ffd7c7f3bf5123bf7287315260fae07f51800d0728ba01c0640866a30c1c2170 Homepage: https://cran.r-project.org/package=reems Description: CRAN Package 'reems' (Estimating Effective Migration Surfaces from Single NucleotidePolymorphism Data) Wrapper and plotting utilities for the spatial population genetics tool 'EEMS' (Estimated Effective Migration Surfaces) for SNP (Single Nucleotide Polymorphism) data, originally provided as a command-line tool written in 'C++' together with an accompanying 'R' package for plotting the output of the 'EEMS' tool itself (). There are four main motivations for offering this to 'R' users as a package. Firstly, to remove the installation and configuration burden for the 'EEMS' command-line tool, which relies on manually installed 'Boost' and 'Eigen' system libraries and configuring their location; secondly, to streamline the workflow by having a singe environment (the 'R' system) for the entire analysis rather than a file-based command-line executable whose output files are then to be imported and analysed by a separate 'R' script; thirdly, to make the input formats compatible with other, 'R'-based spatial population genetics tools such as the 'ConStruct' package; and lastly, to allow for easily running several chains in parallel and combining them for plotting and further analysis. The package also adds more intuitive, streamlined tooling around creating more complex habitats. The method of estimating effective migration surfaces was first described by Petkova, D., Novembre, J. & Stephens, M. (2016) . Package: r-cran-refbasedmi Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 867 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-hmisc, r-cran-mice, r-cran-pastecs, r-cran-assertthat Filename: pool/dists/noble/main/r-cran-refbasedmi_0.2.0-1.ca2404.1_amd64.deb Size: 441986 MD5sum: d4d111ce4dadd90cf122227e781da1b9 SHA1: 687899007d8a090c44a9341a6d3dca8ac65946c8 SHA256: eab37cc5d08bd7776b73f65a9f46b26b7c74e534b088eb9c667bcfb332178b59 SHA512: cbf7ee67714b50d7ccccfe0c1165f598d678cba1bd61b913a87a3fd97fcbbcd7f4ff6e41a98360830962ee6cf9688dcd6a13d4b4d8cb72b5959aedc43fde13a9 Homepage: https://cran.r-project.org/package=RefBasedMI Description: CRAN Package 'RefBasedMI' (Reference-Based Imputation for Longitudinal Clinical Trials withProtocol Deviation) Imputation of missing numerical outcomes for a longitudinal trial with protocol deviations. The package uses distinct treatment arm-based assumptions for the unobserved data, following the general algorithm of Carpenter, Roger, and Kenward (2013) , and the causal model of White, Royes and Best (2020) . Sensitivity analyses to departures from these assumptions can be done by the Delta method of Roger. The program uses the same algorithm as the 'mimix' 'Stata' package written by Suzie Cro, with additional coding for the causal model and delta method. The reference-based methods are jump to reference (J2R), copy increments in reference (CIR), copy reference (CR), and the causal model, all of which must specify the reference treatment arm. Other methods are missing at random (MAR) and the last mean carried forward (LMCF). Individual-specific imputation methods (and their reference groups) can be specified. Package: r-cran-refinr Architecture: amd64 Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-stringdist, r-cran-stringi Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-refinr_0.3.3-1.ca2404.1_amd64.deb Size: 131710 MD5sum: 4343d406350aef681721c706452db380 SHA1: 601eb0685a6daf44fed75c6212c0bc7c12d8ec70 SHA256: b1c05ccd9b2873b46568fd057865f02d975873c198d2d52fd5cff4a04b20482e SHA512: c29afdc9fbf1b6b730fe62753f708f6d0a4785e6f91f979dd22c1902e2e2d693c8333d73fef611bfff7969f3e01da4018fa50a306ad4d664df60824fd0fdb210 Homepage: https://cran.r-project.org/package=refinr Description: CRAN Package 'refinr' (Cluster and Merge Similar Values Within a Character Vector) These functions take a character vector as input, identify and cluster similar values, and then merge clusters together so their values become identical. The functions are an implementation of the key collision and ngram fingerprint algorithms from the open source tool Open Refine . More info on key collision and ngram fingerprint can be found here . Package: r-cran-registr Architecture: amd64 Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2369 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-magrittr, r-cran-dplyr, r-cran-pbs, r-cran-rcpp, r-cran-mass, r-cran-gamm4, r-cran-lme4, r-cran-mgcv, r-cran-purrr, r-cran-matrix, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-cowplot, r-cran-ggplot2, r-cran-pbapply, r-cran-fastglm Filename: pool/dists/noble/main/r-cran-registr_2.2.1-1.ca2404.1_amd64.deb Size: 1670042 MD5sum: d61e824876c0e663029d82b0dc7af928 SHA1: 0affd0b344c2a8efe01c71fe71b0b596f92bd445 SHA256: 48ce59f789d10814821fefd6287fee05c16ae88c1c8639c96f8f024917d3e6c9 SHA512: 3d7dab48230b56a60ad01cc1c2bfd5b73109ab25261ecf9d92bd92319a30ba256bf41cd65497b67cad1b782548908396da5c88d29fbb20a78630912c29a1462f Homepage: https://cran.r-project.org/package=registr Description: CRAN Package 'registr' (Curve Registration for Exponential Family Functional Data) A method for performing joint registration and functional principal component analysis for curves (functional data) that are generated from exponential family distributions. This mainly implements the algorithms described in 'Wrobel et al. (2019)' and further adapts them to potentially incomplete curves where (some) curves are not observed from the beginning and/or until the end of the common domain. Curve registration can be used to better understand patterns in functional data by separating curves into phase and amplitude variability. This software handles both binary and continuous functional data, and is especially applicable in accelerometry and wearable technology. Package: r-cran-reglogit Architecture: amd64 Version: 1.2-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: libc6 (>= 2.29), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-boot, r-cran-matrix Suggests: r-cran-plgp Filename: pool/dists/noble/main/r-cran-reglogit_1.2-8-1.ca2404.1_amd64.deb Size: 98572 MD5sum: be789d470d3d2b746065b5d6bc5098dc SHA1: c3d316e3fd6bf8fce17898b540c2b86b184609ea SHA256: 98073ecf3034550b08af935c54262609e148fae7fd22ac1ddc78f13beaf8d8d6 SHA512: 359fda7a4fffba1887bdd4b90fce5d8aa5c2567c09c4bd235fb44b578db9d1a9f33af99b1ae45460bf2f6a814c081e77d95c2dc801d15da515bedf6290a0cb21 Homepage: https://cran.r-project.org/package=reglogit Description: CRAN Package 'reglogit' (Simulation-Based Regularized Logistic Regression) Regularized (polychotomous) logistic regression by Gibbs sampling. The package implements subtly different MCMC schemes with varying efficiency depending on the data type (binary v. binomial, say) and the desired estimator (regularized maximum likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a unified interface. For details, see Gramacy & Polson (2012 ). Package: r-cran-regmed Architecture: amd64 Version: 2.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 972 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-glasso, r-cran-igraph, r-cran-knitr, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-lavaan, r-cran-gtools Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-regmed_2.1.5-1.ca2404.1_amd64.deb Size: 613288 MD5sum: 45d2f90dd41a47b84445dfec8bd45184 SHA1: 7c433b26a71aded238e7bf7e46af13564617d667 SHA256: 86916f15848a29112bc6db210b26bdbbd1acb7e799415df0680ed1e8dd5129f5 SHA512: 58536a8792b303eefd69ef5e0166b8314c232b8f96e976be03e6c41616c18111068aed188c30a83510f77f581217be984835e9698c2bc794611cdb23666f1684 Homepage: https://cran.r-project.org/package=regmed Description: CRAN Package 'regmed' (Regularized Mediation Analysis) Mediation analysis for multiple mediators by penalized structural equation models with different types of penalties depending on whether there are multiple mediators and only one exposure and one outcome variable (using sparse group lasso) or multiple exposures, multiple mediators, and multiple outcome variables (using lasso, L1, penalties). Package: r-cran-regmhmm Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-glmnetutils, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-regmhmm_1.0.0-1.ca2404.1_amd64.deb Size: 173170 MD5sum: 33c8739175c47203ed1a6f2ac5742f93 SHA1: 4840086344ea38dd8b863a640edc717ee119f911 SHA256: e0b5651f1f7cb986771b151d374dc03d581e80c82e582065766915f39f36f862 SHA512: 79d11b99517c4ca761103bf3a72a3ebe92b94dfeb3ebf4ff659ffc60d7bdd05b37dcc1433409b85197befe0feab99cac5ecb5c4cfd495fb9796006332f10f209 Homepage: https://cran.r-project.org/package=regmhmm Description: CRAN Package 'regmhmm' ('regmhmm' Fits Hidden Markov Models with Regularization) Designed for longitudinal data analysis using Hidden Markov Models (HMMs). Tailored for applications in healthcare, social sciences, and economics, the main emphasis of this package is on regularization techniques for fitting HMMs. Additionally, it provides an implementation for fitting HMMs without regularization, referencing Zucchini et al. (2017, ISBN:9781315372488). Package: r-cran-regnet Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2869 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-rcpp, r-cran-igraph, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-regnet_1.0.2-1.ca2404.1_amd64.deb Size: 2669364 MD5sum: ed75ed2fbf0bd0c59baa4b97723c3454 SHA1: 354ed14305d2357abe4a9de72f2a5915ec8ed4a0 SHA256: 42d5e8df2f467e646766c876d468e997bcfa1ae6f271a9e80dd33060f48a75ee SHA512: 38a3b0fef07a21f4777e407a75df6f4f545b84ec946e4047df70f018ed3601547e40a2552abd1c021c38828c06f2551db88ee4b4518fd2110048f484b97587bc Homepage: https://cran.r-project.org/package=regnet Description: CRAN Package 'regnet' (Network-Based Regularization for Generalized Linear Models) Network-based regularization has achieved success in variable selection for high-dimensional biological data due to its ability to incorporate correlations among genomic features. This package provides procedures of network-based variable selection for generalized linear models (Ren et al. (2017) and Ren et al.(2019) ). Continuous, binary, and survival response are supported. Robust network-based methods are available for continuous and survival responses. Package: r-cran-rego Architecture: amd64 Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5966 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rego_1.6.1-1.ca2404.1_amd64.deb Size: 588920 MD5sum: 297d66ba0c40ba423357b63511638122 SHA1: c04dcee0fc3739dc8860f76a42cc261c919c98f9 SHA256: 9ca7343a5bcc70420e8a0793cbc41baed9bab86c6f0a08e330d0b93865904876 SHA512: 7640aba8b25ce5661d913f81e7fee6c8922363faf1f50da6217cda8f75ed333aa7392d6fdfe1b07a547326ac65f4ebd4041664f1c540bf99fa2005f1cd8601f7 Homepage: https://cran.r-project.org/package=rego Description: CRAN Package 'rego' (Automatic Time Series Forecasting and Missing Value Imputation) Machine learning algorithm for predicting and imputing time series. It can automatically set all the parameters needed, thus in the minimal configuration it only requires the target variable and the dependent variables if present. It can address large problems with hundreds or thousands of dependent variables and problems in which the number of dependent variables is greater than the number of observations. Moreover it can be used not only for time series but also for any other real valued target variable. The algorithm implemented includes a Bayesian stochastic search methodology for model selection and a robust estimation based on bootstrapping. 'rego' is fast because all the code is C++. Package: r-cran-regsem Architecture: amd64 Version: 1.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 571 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan, r-cran-rcpp, r-cran-rsolnp, r-cran-rcpparmadillo Suggests: r-cran-snowfall, r-cran-markdown, r-cran-mass, r-cran-ga, r-cran-caret, r-cran-glmnet, r-cran-islr, r-cran-lbfgs, r-cran-numderiv, r-cran-psych, r-cran-knitr, r-cran-nloptr, r-cran-nlcoptim, r-cran-optimx, r-cran-semplot, r-cran-colorspace, r-cran-plyr, r-cran-matrixstats, r-cran-stringr Filename: pool/dists/noble/main/r-cran-regsem_1.9.5-1.ca2404.1_amd64.deb Size: 379740 MD5sum: 4a64b3d786d387344f2c2d7331912837 SHA1: 0b74634279ca2cf5482f8a76a8442a869c973b39 SHA256: 609ef4130921f8bd6c94e45c869a95482570fcfac0b1b07090f6f9471eadb4b2 SHA512: 20b8b3588b95717646de5c095396d7493650b6ef2b1e4f7c3d8eba1c852554c3e62b6564abed0d7f4af2e0d41ed18d0c6ecbe62076556dbe5c8f86e7f8d63033 Homepage: https://cran.r-project.org/package=regsem Description: CRAN Package 'regsem' (Regularized Structural Equation Modeling) Uses both ridge and lasso penalties (and extensions) to penalize specific parameters in structural equation models. The package offers additional cost functions, cross validation, and other extensions beyond traditional structural equation models. Also contains a function to perform exploratory mediation (XMed). Package: r-cran-rehh Architecture: amd64 Version: 3.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2359 Depends: libc6 (>= 2.4), libgomp1 (>= 6), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rehh.data Suggests: r-cran-ape, r-cran-bookdown, r-cran-data.table, r-cran-gap, r-cran-knitr, r-cran-qqman, r-cran-rmarkdown, r-cran-r.utils, r-cran-testthat, r-cran-vcfr Filename: pool/dists/noble/main/r-cran-rehh_3.2.3-1.ca2404.1_amd64.deb Size: 1583416 MD5sum: b3889fd3b92e0e77ee817b9afa80ea35 SHA1: 31fb59e1d4fcad1f60c75639b7580aa2c3dc5872 SHA256: 0ca49a07d359ec57e2d20ef0363273156714a92b9dccd64efe194dcbc674bcc4 SHA512: 8312018bdb40cd6df9214bcad6ac947a79bc32194a33d1676bbdf7d9d6197c8adcd164e005d37eae97d37b381b48b2ba246099fca463ce5c45a855e3d8a5ff8d Homepage: https://cran.r-project.org/package=rehh Description: CRAN Package 'rehh' (Searching for Footprints of Selection using 'Extended HaplotypeHomozygosity' Based Tests) Population genetic data such as 'Single Nucleotide Polymorphisms' (SNPs) is often used to identify genomic regions that have been under recent natural or artificial selection and might provide clues about the molecular mechanisms of adaptation. One approach, the concept of an 'Extended Haplotype Homozygosity' (EHH), introduced by (Sabeti 2002) , has given rise to several statistics designed for whole genome scans. The package provides functions to compute three of these, namely: 'iHS' (Voight 2006) for detecting positive or 'Darwinian' selection within a single population as well as 'Rsb' (Tang 2007) and 'XP-EHH' (Sabeti 2007) , targeted at differential selection between two populations. Various plotting functions are included to facilitate visualization and interpretation of these statistics. Package: r-cran-reins Architecture: amd64 Version: 1.0.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1793 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-foreach, r-cran-doparallel, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-interval, r-bioc-icens Filename: pool/dists/noble/main/r-cran-reins_1.0.16-1.ca2404.1_amd64.deb Size: 1372208 MD5sum: f24064faef8270f4f958a1eaeee4c060 SHA1: 56f4e47a57fd73d5c2fc3d757315d57af1319a1c SHA256: 0a6ca95250990a29f05ba5c6776b56cc5056315fbfbac00c0ad9207a64898a2a SHA512: 1cfd685b52559aece93de26e386aa5d2ca4e3f7dd0c73ef7e4d809e7f924faf8fe7080e7d8006372cdc80c6052ea94cb146728754b7d534682e27062ec4cff1f Homepage: https://cran.r-project.org/package=ReIns Description: CRAN Package 'ReIns' (Functions from "Reinsurance: Actuarial and Statistical Aspects") Functions from the book "Reinsurance: Actuarial and Statistical Aspects" (2017) by Hansjoerg Albrecher, Jan Beirlant and Jef Teugels . Package: r-cran-relatedness Architecture: amd64 Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-relatedness_2.0-1.ca2404.1_amd64.deb Size: 108740 MD5sum: 19cb9b4b35f769583f5ccbb001a84d7e SHA1: 630251808b776253bbf9e54994a6fe6f5ebfa0e8 SHA256: 55ef495d3f54679006a467c45702bf24e60248e9ded33b38977cddfed5a0eb63 SHA512: d9218d5baf334bfb784010e3bf790c6f87a98b384eceeb3183cce178568e76e6dc32ef74eea8fd211035b06ce952e3dddf2fb99e160e4c8f6b0fd73ccef810cc Homepage: https://cran.r-project.org/package=Relatedness Description: CRAN Package 'Relatedness' (Maximum Likelihood Estimation of Relatedness using EM Algorithm) Inference of relatedness coefficients from a bi-allelic genotype matrix using a Maximum Likelihood estimation, Laporte, F., Charcosset, A. and Mary-Huard, T. (2017) . Package: r-cran-relevent Architecture: amd64 Version: 1.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-trust, r-cran-sna, r-cran-coda Filename: pool/dists/noble/main/r-cran-relevent_1.2-1-1.ca2404.1_amd64.deb Size: 158930 MD5sum: 072384958fee9f0c13bffdb80cacc772 SHA1: 2fe41cbea94b4eb6c8747c8a6a7cca1ffeb65bd3 SHA256: 55fbadf733c17b0587f2f4ac0f9c596ec50585a998c70586d2e4b3639812e33b SHA512: d236d53edbb8aedbac34cca1ec67020ce9804d0c794079f43c8ed75c79e6fba14a3fb190b06cb3777923fd1cfb00b3b0ffffc5a4b350667a3691d6e2350d6e0d Homepage: https://cran.r-project.org/package=relevent Description: CRAN Package 'relevent' (Relational Event Models) Tools to fit and simulate realizations from relational event models. Package: r-cran-relliptical Architecture: amd64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fuzzynumbers.ext.2, r-cran-matrixcalc, r-cran-rcpp, r-cran-rdpack, r-cran-ryacas, r-cran-rcpparmadillo Suggests: r-cran-ggextra, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-relliptical_1.4.0-1.ca2404.1_amd64.deb Size: 253842 MD5sum: 9b5a6553dd3e94270bcc950524b17624 SHA1: 9774ddd0a9645c97da709fb10935a88f85c036ba SHA256: d222bea31c27e30a44b96dde9df17289fca5be229aba29d2bacd9147582767e0 SHA512: cafc6d61ba1476f7196ae1757003bab50826a26c87b7acffc01f87ce0a71cc1a8a8dedba05e1cbbef57f643e12e67a7bc7e4ca6fb49fb63986e68134be96b009 Homepage: https://cran.r-project.org/package=relliptical Description: CRAN Package 'relliptical' (The Truncated Elliptical Family of Distributions) It provides a function for random number generation from members of the truncated multivariate elliptical family of distributions, including truncated versions of the Normal, Student-t, Pearson type VII, Slash, Logistic, and related distributions. Additional distributions can be specified by supplying the density generating function. The package also computes first- and second-order moments, including the covariance matrix, for selected distributions. References used for this package: Galarza, C. E., Matos, L. A., Castro, L. M., & Lachos, V. H. (2022). Moments of the doubly truncated selection elliptical distributions with emphasis on the unified multivariate skew-t distribution. Journal of Multivariate Analysis, 189, 104944 ; Ho, H. J., Lin, T. I., Chen, H. Y., & Wang, W. L. (2012). Some results on the truncated multivariate t distribution. Journal of Statistical Planning and Inference, 142(1), 25-40 ; Valeriano, K. A., Galarza, C. E., & Matos, L. A. (2023). Moments and random number generation for the truncated elliptical family of distributions. Statistics and Computing, 33(1), 32 . Package: r-cran-relsim Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-xtable, r-cran-multicool, r-cran-rvest, r-cran-stringr, r-cran-xml2, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-relsim_1.0.0-1.ca2404.1_amd64.deb Size: 309378 MD5sum: ede285fef62beec472a0a23cc7d7c218 SHA1: 121101740f7292c2921d87ee1e5eadad330a8a96 SHA256: a5541df53fac3ba78612a00146f30c150bd909fe72a4d959bb074e55662e4655 SHA512: 57f65c6206b0d5af63339f72bf596bfdde215c223e263c6d54eaa8a7c79b6e6ec709b230fda13c6954c179084e2bad1207376b4cbad5a893b01feb8a3dfb66be Homepage: https://cran.r-project.org/package=relSim Description: CRAN Package 'relSim' (Relative Simulator) A set of tools to explore the behaviour statistics used for forensic DNA interpretation when close relatives are involved. The package also offers some useful tools for exploring other forensic DNA situations. Package: r-cran-relsurv Architecture: amd64 Version: 2.3-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1047 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-pammtools, r-cran-scales, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-relsurv_2.3-3-1.ca2404.1_amd64.deb Size: 858248 MD5sum: e5c8465c9d5b1948f2f64f663adf5148 SHA1: ae2cc34bd4967bfecdad1566e2c87b4d3539ec59 SHA256: f6f973a3ee394f7c28fa1a71c7c3bc1aee9a4dee825cc33275fd19f4bb4d36ab SHA512: 3d6279367fdea8798f12f46392af483172712b59f912c13bf12b8bbb7716bd90c98b9a4a452fc3a8d999b34a6abd3b55d10d09354f3b75227a8de017450681e4 Homepage: https://cran.r-project.org/package=relsurv Description: CRAN Package 'relsurv' (Relative Survival) Contains functions for analysing relative survival data, including nonparametric estimators of net (marginal relative) survival, relative survival ratio, crude mortality, methods for fitting and checking additive and multiplicative regression models, transformation approach, methods for dealing with population mortality tables. Work has been described in Pohar Perme, Pavlic (2018) . Package: r-cran-rem Architecture: amd64 Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-foreach, r-cran-doparallel Suggests: r-cran-texreg, r-cran-statnet, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-rem_1.3.1-1.ca2404.1_amd64.deb Size: 268078 MD5sum: c5b30243f34c8c642f3917157dc3ed43 SHA1: 54300767786335ab081f6dc64aada718265b8f9b SHA256: b5171cf14b19d987b870fb1e4b2a8d328c232654d313483fd33027dd86a4cda8 SHA512: 9816ee69c1419d9cb1765157aa6e9bfa0b7a0fceeb217e6db3da68f64790d3d6113a0d2a36f5fd78bb716a7964e44cac3f16c67c967df8c636df0a6c4a96099d Homepage: https://cran.r-project.org/package=rem Description: CRAN Package 'rem' (Relational Event Models (REM)) Calculate endogenous network effects in event sequences and fit relational event models (REM): Using network event sequences (where each tie between a sender and a target in a network is time-stamped), REMs can measure how networks form and evolve over time. Endogenous patterns such as popularity effects, inertia, similarities, cycles or triads can be calculated and analyzed over time. Package: r-cran-rema Architecture: amd64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-progress Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rema_0.0.1-1.ca2404.1_amd64.deb Size: 109506 MD5sum: 86a313cfe258cb76871d3e7a70e29b1c SHA1: eff02d5cfb647282d945be797446de3c7d06e2e1 SHA256: a0110b3b5658d8929ac04c6f11fdccf41ca3121ad46183041fa7dadbce62ff97 SHA512: 20e06bb9a108a557c09bbf649d3f97b622c3d0da5f5d2993e9aa4e1d20b941fcc02b0859dac781c46f6e8e5fb9c65ecb07b4ccacea1cc03f879221dec1370c74 Homepage: https://cran.r-project.org/package=rema Description: CRAN Package 'rema' (Rare Event Meta Analysis) The rema package implements a permutation-based approach for binary meta-analyses of 2x2 tables, founded on conditional logistic regression, that provides more reliable statistical tests when heterogeneity is observed in rare event data (Zabriskie et al. 2021 ). To adjust for the effect of heterogeneity, this method conditions on the sufficient statistic of a proxy for the heterogeneity effect as opposed to estimating the heterogeneity variance. While this results in the model not strictly falling under the random-effects framework, it is akin to a random-effects approach in that it assumes differences in variability due to treatment. Further, this method does not rely on large-sample approximations or continuity corrections for rare event data. This method uses the permutational distribution of the test statistic instead of asymptotic approximations for inference. The number of observed events drives the computation complexity for creating this permutational distribution. Accordingly, for this method to be computationally feasible, it should only be applied to meta-analyses with a relatively low number of observed events. To create this permutational distribution, a network algorithm, based on the work of Mehta et al. (1992) and Corcoran et al. (2001) , is employed using C++ and integrated into the package. Package: r-cran-remacor Architecture: amd64 Version: 0.0.20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1232 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mvtnorm, r-cran-reshape2, r-cran-rcpp, r-cran-envstats, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-runit, r-cran-clustergeneration, r-cran-metafor Filename: pool/dists/noble/main/r-cran-remacor_0.0.20-1.ca2404.1_amd64.deb Size: 721184 MD5sum: 771075277113d9387c869059fa368d55 SHA1: 014e54a6cfac59381a1daf21a6e2a604039c1a99 SHA256: 8f48c2ac2d127d496d6be367765178efda263c1cfffc8c43572a119b14f3b419 SHA512: 73498bb8ce08abf7efec377a5c1623800f0db0fa5215bc0b880b08405532bbd979fa7859c0a7cd776c201b3acb2c22272b178a71943010a75763385aebfa043b Homepage: https://cran.r-project.org/package=remaCor Description: CRAN Package 'remaCor' (Random Effects Meta-Analysis for Correlated Test Statistics) Meta-analysis is widely used to summarize estimated effects sizes across multiple statistical tests. Standard fixed and random effect meta-analysis methods assume that the estimated of the effect sizes are statistically independent. Here we relax this assumption and enable meta-analysis when the correlation matrix between effect size estimates is known. Fixed effect meta-analysis uses the method of Lin and Sullivan (2009) , and random effects meta-analysis uses the method of Han, et al. . Package: r-cran-remify Architecture: amd64 Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4499 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-igraph, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-remstats Filename: pool/dists/noble/main/r-cran-remify_4.0.0-1.ca2404.1_amd64.deb Size: 1545046 MD5sum: 484752263fbbca6795895adc4f97a912 SHA1: 861fb9bd870258ca0fe9a74786ea1c81579bd3bd SHA256: e37cdaa53f8ee62a28cdc6ab64d594ab182962a08f988eadb9c60fe940fada03 SHA512: b44e8812b3179e6652975300092fab5d8c6234f6e5c13091a911783b749d1d4b5b4a4b0ad063a394c63678093cc86bac4afacdc4e0224d8567f46d1f4bdd037c Homepage: https://cran.r-project.org/package=remify Description: CRAN Package 'remify' (Processing and Transforming Relational Event History Data) Efficiently processes relational event history data and transforms them into formats suitable for other packages. The primary objective of this package is to convert event history data into a format that integrates with the packages in 'remverse' and is compatible with various analytical tools (e.g., computing network statistics, estimating tie-oriented or actor-oriented social network models). Second, it can also transform the data into formats compatible with other packages out of 'remverse'. The package processes the data for two types of temporal social network models: tie-oriented modeling framework (Butts, C., 2008, ) and actor-oriented modeling framework (Stadtfeld, C., & Block, P., 2017, ). Package: r-cran-remote Architecture: amd64 Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2327 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-raster, r-cran-gridextra, r-cran-latticeextra, r-cran-mapdata, r-cran-scales Suggests: r-cran-maps, r-cran-lattice, r-cran-sp Filename: pool/dists/noble/main/r-cran-remote_1.2.3-1.ca2404.1_amd64.deb Size: 2049220 MD5sum: cc9f3a938f4071366cf93e587da34553 SHA1: a0909985518fc61387a22b29ef64af56cafc0b19 SHA256: 90ff97b6babc531d276bdbdda008f4793c60b0d84148fca535a70c3e1214f99c SHA512: 434e6c308aef786865b6ad7c1e3625f5542140a6746938afd632c6b68a8bcfd7cf2e3cfbd5b98dcf024064edca9f0c103b5fee92c7c74000a0bfad3b44b65819 Homepage: https://cran.r-project.org/package=remote Description: CRAN Package 'remote' (Empirical Orthogonal Teleconnections in R) Empirical orthogonal teleconnections in R. 'remote' is short for 'R(-based) EMpirical Orthogonal TEleconnections'. It implements a collection of functions to facilitate empirical orthogonal teleconnection analysis. Empirical Orthogonal Teleconnections (EOTs) denote a regression based approach to decompose spatio-temporal fields into a set of independent orthogonal patterns. They are quite similar to Empirical Orthogonal Functions (EOFs) with EOTs producing less abstract results. In contrast to EOFs, which are orthogonal in both space and time, EOT analysis produces patterns that are orthogonal in either space or time. Package: r-cran-remoteparts Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1801 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-geosphere, r-cran-rcpp, r-cran-compquadform, r-cran-foreach, r-cran-iterators, r-cran-doparallel, r-cran-rcppeigen Suggests: r-cran-dplyr, r-cran-data.table, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-sqldf, r-cran-devtools, r-cran-ggplot2, r-cran-reshape2, r-cran-sf Filename: pool/dists/noble/main/r-cran-remoteparts_1.0.4-1.ca2404.1_amd64.deb Size: 1405690 MD5sum: c109a00edd44ecafcc49693e662d57ab SHA1: 1181f2bec65c68c8404f29e9417e2fdd3a82d748 SHA256: 325d0c0cd2fad5b9b3978fe072b77d11a7f46c59f7adc6cb10a4ca2ef7114711 SHA512: e636d5faab2eebc55176a5c67e5e7e88236343949c071b6567db7f9a91c4b0de37a46b767a7119f23f6a341d2f99a8ffd1062f2c60c36bf448131361c77dede1 Homepage: https://cran.r-project.org/package=remotePARTS Description: CRAN Package 'remotePARTS' (Spatiotemporal Autoregression Analyses for Large Data Sets) These tools were created to test map-scale hypotheses about trends in large remotely sensed data sets but any data with spatial and temporal variation can be analyzed. Tests are conducted using the PARTS method for analyzing spatially autocorrelated time series (Ives et al., 2021: ). The method's unique approach can handle extremely large data sets that other spatiotemporal models cannot, while still appropriately accounting for spatial and temporal autocorrelation. This is done by partitioning the data into smaller chunks, analyzing chunks separately and then combining the separate analyses into a single, correlated test of the map-scale hypotheses. Package: r-cran-remstats Architecture: amd64 Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2367 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-remify, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-remstats_4.0.0-1.ca2404.1_amd64.deb Size: 987718 MD5sum: 04c7f90e6b37b88d2b653900681dc1eb SHA1: 7441d51fe51f1ac3a4a4e54f6106d01584399b8d SHA256: 545f8dca53fae081ceca06a2c596bdd2efac58274bbdcd926bfb502531564d08 SHA512: 844fbde7f9c79e5b733d6128066b3a1dc43f104b95b1914b88d2b80c5e6931fc5432552fa450c09b30829a48e4efc889e8813739f876ee360b622004a2367fda Homepage: https://cran.r-project.org/package=remstats Description: CRAN Package 'remstats' (Computes Statistics for Relational Event History Data) Computes a variety of statistics for relational event models (Meijerink et al., 2023, ). Relational event models enable researchers to investigate exogenous and endogenous factors, and interactions, influencing the evolution of a time-ordered sequence of events. These models are categorized into tie-oriented models (Butts, C., 2008, ), where the probability of a dyad interacting next is modeled in a single step, and actor-oriented models (Stadtfeld, C., & Block, P., 2017, ), which first model the probability of a sender initiating an interaction and subsequently the probability of the sender's choice of receiver. The package is designed to compute a variety of statistics that summarize exogenous and endogenous influences on the event stream for both types of models. 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The package accommodates both frequentist and Bayesian approaches. Maximum Likelihood Optimization (MLE) is supported. Bayesian estimation is done via Hamiltonian Monte Carlo (HMC). Package: r-cran-remulate Architecture: amd64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-remulate_2.1.0-1.ca2404.1_amd64.deb Size: 264146 MD5sum: 89a32bf607a3dc2bbe6883b7e7fcf42c SHA1: 303851a34cdb8847657b92e0f6910c1b200f9d7f SHA256: 0f74d20344043f0a2f4693653c17abbdba07d4435f208f7563bfb7a05a71be81 SHA512: cc3307c18f66ccc40c988543182a458df38ff381d7e271905c775fab7df69fe56f90a344556d4a6a6e40b00a2899f3d1e925cc5a9495a6ea32d89e0a5a2d4cc2 Homepage: https://cran.r-project.org/package=remulate Description: CRAN Package 'remulate' (Simulate Dynamic Networks from Relational Event Models) Model based simulation of dynamic networks under tie-oriented (Butts, C., 2008, ) and actor-oriented (Stadtfeld, C., & Block, P., 2017, ) relational event models. Supports simulation from a variety of relational event model extensions, including temporal variability in effects, heterogeneity through dyadic latent class relational event models (DLC-REM), random effects, blockmodels, and memory decay in relational event models (Lakdawala, R., 2024 ). The development of this package was supported by a Vidi Grant (452-17-006) awarded by the Netherlands Organization for Scientific Research (NWO) Grant and an ERC Starting Grant (758791). Package: r-cran-rena Architecture: amd64 Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1101 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rcpp, r-cran-r6, r-cran-plotly, r-cran-doparallel, r-cran-scales, r-cran-glmnet, r-cran-tma, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rena_0.3.1-1.ca2404.1_amd64.deb Size: 807372 MD5sum: 6b512843e9d7b6fda5b2d693fc812b29 SHA1: 8b723995529c5613fdde49d2e383ccdd5981c765 SHA256: 90351a1d0d67d35b94cf13e240595dda712f8b7aeea6bcbb2193f4ede85a8ec0 SHA512: 4800088aa0b0df18720e19c5d16384f14a4087dc7ee77ddac86067ebbd1608553d82c36be9129c6386db43cd35acfb29ac9b1caa66e29d1f45c34c4ac6fdcb27 Homepage: https://cran.r-project.org/package=rENA Description: CRAN Package 'rENA' (Epistemic Network Analysis) ENA (Shaffer, D. 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Package: r-cran-rendo Architecture: amd64 Version: 2.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1536 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rendo_2.5.0-1.ca2404.1_amd64.deb Size: 1436310 MD5sum: 72827bff2396321f754c632f6b3a4527 SHA1: 0ae97a50b5da8fcd52445cd2dd933f11823d9ea0 SHA256: b7ca4ddeceb7ee9d27a09a49f856dc785af955800e6f57aa9af4e00670ade0eb SHA512: 177f895e250579cd8bc22f04e37e887ed590522e5211cf510af7a478d4e77ee98fa61c70568f47d24084104cb228db08e3c1cbadefe56f964e27a0d50ba74ae7 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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(2023) . The modeling framework is based on a joint frailty scale-change model, that includes models described in Wang et al. (2001) , Huang and Wang (2004) , Xu et al. (2017) , and Xu et al. (2019) as special cases. The implemented estimating procedure does not require any parametric assumption on the frailty distribution. The package also allows the users to specify different model forms for both the recurrent event process and the terminal event. Package: r-cran-resemble Architecture: amd64 Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7115 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-iterators, r-cran-rhpcblasctl, r-cran-rcpp, r-cran-mathjaxr, r-cran-lifecycle, r-cran-rcpparmadillo Suggests: r-cran-prospectr, r-cran-doparallel, r-cran-testthat, r-cran-quarto, r-cran-knitr Filename: pool/dists/noble/main/r-cran-resemble_3.0.0-1.ca2404.1_amd64.deb Size: 3735526 MD5sum: 92dde7cd5b095ac02406c96632a620b1 SHA1: 718a0ecd70520b6b1b391ce216e51344cfbfadaa SHA256: 6357d492b5526ffb6dafd95a6f13c02f916ff28760a64485e89418dcd4603795 SHA512: 8ba31f762636973b006f4ffd6e340eb0154955d2035b48d0b7ad522ba9d8036b808380342c07276ba665e931c4b724b9f30ac30f617ca9e46745bf8e5999e94c Homepage: https://cran.r-project.org/package=resemble Description: CRAN Package 'resemble' (Similarity Retrieval and Local Learning for SpectralChemometrics) Functions for dissimilarity analysis and machine learning in complex spectral data sets, including memory-based learning (MBL), optimal subset search and selection, and retrieval-based modelling with model libraries. Supports local learning, optimisation of spectral libraries, and ensemble prediction from precomputed models. Most of these functions are based on the methods presented in Ramirez-Lopez et al. (2013) . Package: r-cran-reservr Architecture: amd64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3911 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-generics, r-cran-glue, r-cran-keras3, r-cran-matrixstats, r-cran-nloptr, r-cran-numderiv, r-cran-purrr, r-cran-r6, r-cran-rcpp, r-cran-rcppparallel, r-cran-rlang, r-cran-bh, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-callr, r-cran-colorspace, r-cran-data.table, r-cran-dplyr, r-cran-evmix, r-cran-fitdistrplus, r-cran-flextable, r-cran-formattable, r-cran-furrr, r-cran-ggplot2, r-cran-ggridges, r-cran-knitr, r-cran-logkde, r-cran-officer, r-cran-patchwork, r-cran-reticulate, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-tensorflow, r-cran-testthat, r-cran-tidyr, r-cran-tibble, r-cran-bench, r-cran-survival, r-cran-rticles, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-reservr_0.0.3-1.ca2404.1_amd64.deb Size: 2283066 MD5sum: 3c7630c8f2b46212c3c220a34b3e144e SHA1: e307cc73906e60e84adfd956e3f69ea0b07b192b SHA256: fd718fdc9cc1889c9a1fd0624faac1c3ef845df3e6d0fac07aae593672c9b224 SHA512: c1d5dffe8ae096539002bed67c67e23d7847d15224df28f45a036c1461281a157220627a4334507eedce41c82c17ebf5e960ce4aaf6eaba3c30f00564b2f41db Homepage: https://cran.r-project.org/package=reservr Description: CRAN Package 'reservr' (Fit Distributions and Neural Networks to Censored and TruncatedData) Define distribution families and fit them to interval-censored and interval-truncated data, where the truncation bounds may depend on the individual observation. The defined distributions feature density, probability, sampling and fitting methods as well as efficient implementations of the log-density log f(x) and log-probability log P(x0 <= X <= x1) for use in 'TensorFlow' neural networks via the 'tensorflow' package. Allows training parametric neural networks on interval-censored and interval-truncated data with flexible parameterization. Applications include Claims Development in Non-Life Insurance, e.g. modelling reporting delay distributions from incomplete data, see Bücher, Rosenstock (2022) . Package: r-cran-resevol Architecture: amd64 Version: 0.4.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2136 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-markdown Filename: pool/dists/noble/main/r-cran-resevol_0.4.0.2-1.ca2404.1_amd64.deb Size: 1109776 MD5sum: 41d29cde119131119042dfa5d704f893 SHA1: 9e2bdfebc5a932408f7a34830581fc53882b8b53 SHA256: c02e8c9277cec116ccb54173c3d837dfeb4b02c755b46bb313f3acbfe701f18f SHA512: 8d1112dc6cbb1060c622ab00b6997ff13e3c55bd053487e998713ae1dbda3dc12a565c395cfbfc199b2f8f41d713915543d3f57a7687ea21ef57818ede59c822 Homepage: https://cran.r-project.org/package=resevol Description: CRAN Package 'resevol' (Simulate Agricultural Production and Evolution of PesticideResistance) Simulates individual-based models of agricultural pest management and the evolution of pesticide resistance. Management occurs on a spatially explicit landscape that is divided into an arbitrary number of farms that can grow one of up to 10 crops and apply one of up to 10 pesticides. Pest genomes are modelled in a way that allows for any number of pest traits with an arbitrary covariance structure that is constructed using an evolutionary algorithm in the mine_gmatrix() function. Simulations are then run using the run_farm_sim() function. This package thereby allows for highly mechanistic social-ecological models of the evolution of pesticide resistance under different types of crop rotation and pesticide application regimes. 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Package: r-cran-reticulate Architecture: amd64 Version: 1.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2920 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcpptoml, r-cran-here, r-cran-jsonlite, r-cran-png, r-cran-rappdirs, r-cran-rlang, r-cran-withr Suggests: r-cran-callr, r-cran-knitr, r-cran-glue, r-cran-cli, r-cran-rmarkdown, r-cran-pillar, r-cran-testthat Filename: pool/dists/noble/main/r-cran-reticulate_1.46.0-1.ca2404.1_amd64.deb Size: 1868844 MD5sum: fec65a8efc29a97159ab6328db3878a0 SHA1: 68d49c22db42de221f5ee58623e6a6ba2cc79ddf SHA256: 399de786c99d16c55c98076ef11866a00bcc409b525830a136732cc157f57f98 SHA512: b1a162b9657eb242a892aeb7f77ad3e4786403c3d2a2325d7b35111f273e2370474904d410c1081206ea687733d61fb17430f68646b7f19f754c85b3346af87c Homepage: https://cran.r-project.org/package=reticulate Description: CRAN Package 'reticulate' (Interface to 'Python') Interface to 'Python' modules, classes, and functions. 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Package: r-cran-rexpokit Architecture: amd64 Version: 0.26.6.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1309 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rexpokit_0.26.6.15-1.ca2404.1_amd64.deb Size: 384016 MD5sum: 4bb78fac7321de5e43c069b13e975932 SHA1: 5ca2745c8f594d8e7bbb874b90005dac220c25f2 SHA256: 499e8811b718f7124b570be2ca758ca88506f76d9279dd8d5e7ef33193d0248b SHA512: 967d2b3a7bf398f8836207b27379938b75fc9034dd68d1c1c2130fe9698267449c59c58948c0f408cfcc079ccaefeb5d098ef2d477551cf6b5569eb26a602de3 Homepage: https://cran.r-project.org/package=rexpokit Description: CRAN Package 'rexpokit' (R Wrappers for EXPOKIT; Other Matrix Functions) Wraps some of the matrix exponentiation utilities from EXPOKIT (), a FORTRAN library that is widely recommended for matrix exponentiation (Sidje RB, 1998. 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Column and row wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 . b) Tsagris M. and Papadakis M. (2018). Forward regression in R: from the extreme slow to the extreme fast. Journal of Data Science, 16(4): 771--780. . c) Chatzipantsiou C., Dimitriadis M., Papadakis M. and Tsagris M. (2020). Extremely Efficient Permutation and Bootstrap Hypothesis Tests Using Hypothesis Tests Using R. Journal of Modern Applied Statistical Methods, 18(2), eP2898. . d) Tsagris M., Papadakis M., Alenazi A. and Alzeley O. (2024). Computationally Efficient Outlier Detection for High-Dimensional Data Using the MDP Algorithm. Computation, 12(9): 185. . e) Tsagris M. and Papadakis M. (2025). Fast and light-weight energy statistics using the R package Rfast. . 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Package: r-cran-riembase Architecture: amd64 Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 581 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-pracma, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-riembase_0.2.6-1.ca2404.1_amd64.deb Size: 242068 MD5sum: 6c07cac5b9dc13015dae7fdf5535ea52 SHA1: 14461c9dcbb48e8544d2905266654cc76acc4878 SHA256: 376c9bdfa55fefa8097b282069b18551901ec9d2a823332057fed249067ddc11 SHA512: 18c75ff2469098ae16286e32913476ba9dc8ba698a0d4549c9005c0cca1edd8d656916c913cceb614e164b264ac78bb41bb26165644071a4e249f15844482830 Homepage: https://cran.r-project.org/package=RiemBase Description: CRAN Package 'RiemBase' (Functions and C++ Header Files for Computation on Manifolds) We provide a number of algorithms to estimate fundamental statistics including Fréchet mean and geometric median for manifold-valued data. 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: amd64 Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1043 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-riemtan_0.2.5-1.ca2404.1_amd64.deb Size: 563368 MD5sum: 5bcc24b5a9cf5a13d5a3059441ba4186 SHA1: 774da6a0943dea4db962f27d38a34afa066696dd SHA256: e57a307d97f5a6b611d661cd727a32fe1e0a982f8ecc8e195bbd0d146c09cf44 SHA512: b93c02fd5ad3dd65abde5af79b3c709d198d2712f3b5d3641475c31c1e32e4502a88cf3048c27fbae7a0f76f5c2b8babf46bc860bcbb66f46c4f2b553b898e4b 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: amd64 Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1612 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-knitr, r-cran-globaloptions Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rim_0.8.1-1.ca2404.1_amd64.deb Size: 1034220 MD5sum: 02b9269cf9dbdec75502a8194a15ec85 SHA1: 7ba98d4fa9761d0ff278338276bb6eaa77380821 SHA256: e6fc7cdc7d1f4ca576909559ae425034fa19707d7d8f26139c74aeba49217824 SHA512: 76b2f9e5440ba2b2a290ccb05806d06c8df291c104c8caace1272bad81d60f20f378af7cdfa0104ae2a22cda7010ca6056ffbcfffdb3d6a7b7b56ebe00974797 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: amd64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 757 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ring_1.0.8-1.ca2404.1_amd64.deb Size: 376866 MD5sum: 4e8bd7264ce007509ef44c1e33e8ece2 SHA1: 8878524a671774e82ddadc085b6955de139c8f26 SHA256: 9801f4167b315d5f42a10028fb570a1fe2dc0af60f29f076c793f6c56ebeb080 SHA512: c3b379788ca31f9e29d4d4b61d5cd9c7d0265fd2161b8226232d7ba3e559c9a29fbb0cc18e6eceaf845ee468600b9379c1664124a7c5befd3dc63b2c0da69e11 Homepage: https://cran.r-project.org/package=ring Description: CRAN Package 'ring' (Circular / Ring Buffers) Circular / ring buffers in R and C. There are a couple of different buffers here with different implementations that represent different trade-offs. Package: r-cran-rinside Architecture: amd64 Version: 0.2.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 995 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rinside_0.2.19-1.ca2404.1_amd64.deb Size: 139826 MD5sum: 38e74f535a0ce27a3b23783402ea5f80 SHA1: fb73727801a4edd2d00f2963e401a262d30d7128 SHA256: 77f3f936f7ab5f068d7ec1eb8dea97f01b51ed0925e9418dcc8d53fdf8479285 SHA512: 4174eea2d9d93b901ddcaabe8f58950a6e2ab5fe36722b873a8c060f85a99cea00afe15fcb12933a94d83370b1ccba4326f4a0b3942b6fe181933ea5248b93b5 Homepage: https://cran.r-project.org/package=RInside Description: CRAN Package 'RInside' (C++ Classes to Embed R in C++ (and C) Applications) C++ classes to embed R in C++ (and C) applications A C++ class providing the R interpreter is offered by this package making it easier to have "R inside" your C++ application. As R itself is embedded into your application, a shared library build of R is required. This works on Linux, OS X and even on Windows provided you use the same tools used to build R itself. Numerous examples are provided in the nine subdirectories of the examples/ directory of the installed package: standard, 'mpi' (for parallel computing), 'qt' (showing how to embed 'RInside' inside a Qt GUI application), 'wt' (showing how to build a "web-application" using the Wt toolkit), 'armadillo' (for 'RInside' use with 'RcppArmadillo'), 'eigen' (for 'RInside' use with 'RcppEigen'), and 'c_interface' for a basic C interface and 'Ruby' illustration. The examples use 'GNUmakefile(s)' with GNU extensions, so a GNU make is required (and will use the 'GNUmakefile' automatically). 'Doxygen'-generated documentation of the C++ classes is available at the 'RInside' website as well. Package: r-cran-rinsp Architecture: amd64 Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rinsp_1.2.5-1.ca2404.1_amd64.deb Size: 167394 MD5sum: db8b3a3da6f2ef673dc6edeb8264081a SHA1: ef700f4fc1604ed286c572e9afb26adad909ac7e SHA256: 208c685c263761cddee80c30a7eea086f6ae249b3a1fb252e8d0576a130d342d SHA512: e6642c82f17d5725c611ab14f3fe27518eb6f815b0e116b935f3f804162ed55be7ce2394b73a2e4eb3ed5d5351d98619ced91f5901e1f5491b0fdae0bd326ddd 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: amd64 Version: 1.0-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 598 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-vegan, r-cran-mgcv Suggests: r-cran-foreach Filename: pool/dists/noble/main/r-cran-rioja_1.0-7-1.ca2404.1_amd64.deb Size: 465032 MD5sum: 88bcb8d6df3e556b0adf5ef0b6b3d9f3 SHA1: 6eea2a5da462df28024bb7d0778a104cdfa3a073 SHA256: 13230567785397473847d943b6128b6894af3ae3356c0b5588336a0d05c2882f SHA512: 8b3353cb30490572531dc8c623eca008391fd9bcee89df4b46aaf45f4fd5c357d56aa8cd245784231ba668c2aefe57a7b5dc4e2450bf6ab7f22c75d922c01662 Homepage: https://cran.r-project.org/package=rioja Description: CRAN Package 'rioja' (Analysis of Quaternary Science Data) Constrained clustering, transfer functions, and other methods for analysing Quaternary science data. Package: r-cran-rip.opencv Architecture: amd64 Version: 0.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3652 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libopencv-core406t64 (>= 4.6.0+dfsg), libopencv-imgcodecs406t64 (>= 4.6.0+dfsg), libopencv-imgproc406t64 (>= 4.6.0+dfsg), libopencv-photo406t64 (>= 4.6.0+dfsg), libopencv-videoio406t64 (>= 4.6.0+dfsg), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rip.opencv_0.3-1-1.ca2404.1_amd64.deb Size: 2001400 MD5sum: dcac3e69bccf7fb41587cdf94ac82918 SHA1: de731d5dd443bd96713b1c56bf6fc04d09ba6122 SHA256: f234796ec1abae2d94e1fd4317ab81db04dea7a23546199c395608d02991797d SHA512: 353536642a6ee07cad64b9816c3c635c89a8c1b8b95beaa0be52302bd71b6e54ee9c264e37d3954d272399399337358a0d6681fc0aa8329fca0197c88b9657c0 Homepage: https://cran.r-project.org/package=rip.opencv Description: CRAN Package 'rip.opencv' (Interface to 'OpenCV' Image Processing Routines) R interface for calling 'OpenCV' routines that works by translating R objects to 'OpenCV' classes and back. Low-level wrappers for several 'OpenCV' routines are provided as 'Rcpp' modules. In addition, high level interfaces are provided for a limited selection of common operations. Package: r-cran-ripserr Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 969 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lmtest Filename: pool/dists/noble/main/r-cran-ripserr_1.0.0-1.ca2404.1_amd64.deb Size: 538206 MD5sum: 872b1eb1481b46fb404ff74a54dfe991 SHA1: 5fe375b9e55aac14d6c48b4e3aa7447962d7ef91 SHA256: ba0c2510f1867f9c596f3174a43c2e9b85e6a27751cac958ea2b5bd688b5a260 SHA512: 944f2d99fb4d49800f863bf27526a8824af35cd7b33d60d49343254491f1f9405085a76fb7e9b0abe64847d6ca0dbdd79c850e6d69da578e87b25f5bc6c22d92 Homepage: https://cran.r-project.org/package=ripserr Description: CRAN Package 'ripserr' (Calculate Persistent Homology with Ripser-Based Engines) Ports the Ripser and Cubical Ripser persistent homology calculation engines from C++. Can be used as a rapid calculation tool in topological data analysis pipelines. Package: r-cran-rirt Architecture: amd64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp, r-cran-reshape2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rirt_0.0.2-1.ca2404.1_amd64.deb Size: 261888 MD5sum: 112d37ebc3f31127aab2f62e60cc65f7 SHA1: 263f29c82278e13c281e3f3e18ecb6a65248c715 SHA256: e28842175f1881a27d90d1fe8c7d9833e381a6534315705c8cc8dbc23053e890 SHA512: 819e29c18019c3d1f8fb3c8c205dcc7e05edb228c8c6d37057baa49cf0a12fa11f8a4b59a38a1eafc9b4f77186de6215bbd56aee591baab51231af03a7504e02 Homepage: https://cran.r-project.org/package=Rirt Description: CRAN Package 'Rirt' (Data Analysis and Parameter Estimation Using Item ResponseTheory) Parameter estimation, computation of probability, information, and (log-)likelihood, and visualization of item/test characteristic curves and item/test information functions for three uni-dimensional item response theory models: the 3-parameter-logistic model, generalized partial credit model, and graded response model. The full documentation and tutorials are at . Package: r-cran-rising Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-rcppeigen Suggests: r-cran-igraph, r-cran-isingsampler Filename: pool/dists/noble/main/r-cran-rising_0.1.0-1.ca2404.1_amd64.deb Size: 96676 MD5sum: a0e6f4996c1a0d38d2ced6a379212720 SHA1: 748a47edd06fde9719e410376be2b6b10da01991 SHA256: 011a30287e8a06cf1535af34ed2a6961486b08426c376a4ef9877e223b092ef7 SHA512: 62de818f1db855920a597f91e6a95338e0e78ef87b9e0aefd42f5457656fcddec6e7e31a0daef827a000ed639060194154b1236ebc6465e5480cad27c2a744fb Homepage: https://cran.r-project.org/package=rIsing Description: CRAN Package 'rIsing' (High-Dimensional Ising Model Selection) Fits an Ising model to a binary dataset using L1 regularized logistic regression and extended BIC. Also includes a fast lasso logistic regression function for high-dimensional problems. Uses the 'libLBFGS' optimization library by Naoaki Okazaki. Package: r-cran-riskparityportfolio Architecture: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1813 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-alabama, r-cran-matrix, r-cran-nloptr, r-cran-quadprog, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-numderiv, r-cran-portfoliobacktest, r-cran-prettydoc, r-cran-rmarkdown, r-cran-r.rsp, r-cran-testthat, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-riskparityportfolio_0.2.2-1.ca2404.1_amd64.deb Size: 1181522 MD5sum: 7a1ccfe1c353a6992b336dafe4008828 SHA1: 4a17e58b8ec5b88182127263708c62366c253959 SHA256: ece060314cc4200fedaf267a433feea810d3516ae9050d42566f64a2f1341c96 SHA512: 833a26d8f6b9754558683afb9f3fcd2b2eb4c289727edc6cca3ec6ef7edf5c359d0e2ea3c4ea8d9a75be14b11aa01ae143897f68915ffb50f191bd38fdaa4f5c Homepage: https://cran.r-project.org/package=riskParityPortfolio Description: CRAN Package 'riskParityPortfolio' (Design of Risk Parity Portfolios) Fast design of risk parity portfolios for financial investment. The goal of the risk parity portfolio formulation is to equalize or distribute the risk contributions of the different assets, which is missing if we simply consider the overall volatility of the portfolio as in the mean-variance Markowitz portfolio. In addition to the vanilla formulation, where the risk contributions are perfectly equalized subject to no shortselling and budget constraints, many other formulations are considered that allow for box constraints and shortselling, as well as the inclusion of additional objectives like the expected return and overall variance. See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the papers: Y. Feng, and D. P. Palomar (2015). SCRIP: Successive Convex Optimization Methods for Risk Parity Portfolio Design. IEEE Trans. on Signal Processing, vol. 63, no. 19, pp. 5285-5300. . F. Spinu (2013), An Algorithm for Computing Risk Parity Weights. . T. Griveau-Billion, J. Richard, and T. Roncalli (2013). A fast algorithm for computing High-dimensional risk parity portfolios. . Package: r-cran-riskregression Architecture: amd64 Version: 2026.03.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2326 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cmprsk, r-cran-data.table, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-lattice, r-cran-lava, r-cran-mets, r-cran-mvtnorm, r-cran-plotrix, r-cran-prodlim, r-cran-publish, r-cran-ranger, r-cran-rcpp, r-cran-rms, r-cran-hmisc, r-cran-glmnet, r-cran-survival, r-cran-timereg, r-cran-rcpparmadillo Suggests: r-cran-boot, r-cran-smcfcs, r-cran-casebase, r-cran-gbm, r-cran-flexsurv, r-cran-grpreg, r-cran-hal9001, r-cran-mgcv, r-cran-mstate, r-cran-nnls, r-cran-numderiv, r-cran-party, r-cran-pec, r-cran-penalized, r-cran-proc, r-cran-randomforest, r-cran-randomforestsrc, r-cran-rpart, r-cran-scam, r-cran-superlearner, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-riskregression_2026.03.11-1.ca2404.1_amd64.deb Size: 1741246 MD5sum: e491d3a38e186c34ee637b86e86d1e67 SHA1: f8fb3f815a1be5e410d846617736a13ffb8f04b1 SHA256: c191d2bd4edf9acf3acb3293224f067dbfcabd1c2497b2779968534ca2b94254 SHA512: 0864f7cc1a1534d6e52bf2c47a56164d8985efc4c9470012bef31b12576dda45ef6bfefd8636aca4914ed012ec572a0f3684ec401cf2b9c1682cbbc7747730b1 Homepage: https://cran.r-project.org/package=riskRegression Description: CRAN Package 'riskRegression' (Risk Regression Models and Prediction Scores for SurvivalAnalysis with Competing Risks) Implementation of the following methods for event history analysis. Risk regression models for survival endpoints also in the presence of competing risks are fitted using binomial regression based on a time sequence of binary event status variables. A formula interface for the Fine-Gray regression model and an interface for the combination of cause-specific Cox regression models. A toolbox for assessing and comparing performance of risk predictions (risk markers and risk prediction models). Prediction performance is measured by the Brier score and the area under the ROC curve for binary possibly time-dependent outcome. Inverse probability of censoring weighting and pseudo values are used to deal with right censored data. Lists of risk markers and lists of risk models are assessed simultaneously. Cross-validation repeatedly splits the data, trains the risk prediction models on one part of each split and then summarizes and compares the performance across splits. Package: r-cran-ritch Architecture: amd64 Version: 0.1.30-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1160 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rcpp, r-cran-nanotime, r-cran-bit64 Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-ritch_0.1.30-1.ca2404.1_amd64.deb Size: 581472 MD5sum: a3f31ca598863cba9faf9d6e1bfda9ff SHA1: 779c57e84c885650fa3691e36988d34207c05cb2 SHA256: ce7eb4a8a66a84df7848d6267826b4ce81f76e1da9fe54f7fa201258af5e6ba8 SHA512: 5c5560cb8c3a0adc0a0575411d7baef639176cf462c80cad809b2d5cd86ba46458e7f93b32eecf921d5276358ae1fc9b7fe6c522c1f16bdef5366c11a1311b86 Homepage: https://cran.r-project.org/package=RITCH Description: CRAN Package 'RITCH' (Parser for the ITCH Protocol) Efficiently parses, filters, and writes binary ITCH files (Version 5.0) containing detailed financial transactions as distributed by NASDAQ to a data.table. Includes functions to interact with NASDAQ data services at and . Package: r-cran-rivnet Architecture: amd64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2733 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-spam, r-cran-raster, r-cran-sf, r-cran-terra, r-cran-traudem, r-cran-elevatr, r-cran-ocnet, r-cran-rcpp, r-cran-curl, r-cran-fields, r-cran-parallelly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-rivnet_0.6.0-1.ca2404.1_amd64.deb Size: 2237528 MD5sum: 3b241577f4465443a33296069335c593 SHA1: 2203f35e5e2c5e487c00138d2232d163d81285c8 SHA256: dae87bbe4618f6d40967a607b6395d46974944ebb9fb88e360cc72bf50185bc7 SHA512: be56578936bc2560da505056174c9d21b27e5f1a2478a66678f9f446bc2965c82ea8c5d42bec4f1219b8e0b3764b50fada15c096686bd6c0a1f6326ccff25404 Homepage: https://cran.r-project.org/package=rivnet Description: CRAN Package 'rivnet' (Extract and Analyze Rivers from Elevation Data) Seamless extraction of river networks from digital elevation models data. The package allows analysis of digital elevation models that can be either externally provided or downloaded from open source repositories (thus interfacing with the 'elevatr' package). Extraction is performed via the 'D8' flow direction algorithm of TauDEM (Terrain Analysis Using Digital Elevation Models), thus interfacing with the 'traudem' package. Resulting river networks are compatible with functions from the 'OCNet' package. See Carraro (2023) for a presentation of the package. Package: r-cran-rivr Architecture: amd64 Version: 1.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 576 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny Filename: pool/dists/noble/main/r-cran-rivr_1.2-3-1.ca2404.1_amd64.deb Size: 225270 MD5sum: ddd2b68244a81d9dc6d0891ae7d624a9 SHA1: 7cf2ce41a32bdb02fe4825952ba0917692f792a4 SHA256: 720d3fe59b266455743ad3b675df024fb5f31aaa84b23681408642f1f5025706 SHA512: 6d1f1f4fc07a625af199e9801a309ea743bab72dba651b231bfc7e9ff9fcb8124abde6dfa60dd96345bb1184d2fc5f33c91a2b1a493c41e64fe8447d6c6bc5ac Homepage: https://cran.r-project.org/package=rivr Description: CRAN Package 'rivr' (Steady and Unsteady Open-Channel Flow Computation) A tool for undergraduate and graduate courses in open-channel hydraulics. Provides functions for computing normal and critical depths, steady-state water surface profiles (e.g. backwater curves) and unsteady flow computations (e.g. flood wave routing) as described in Koohafkan MC, Younis BA (2015). "Open-channel computation with R." The R Journal, 7(2), 249–262. . Package: r-cran-rjaf Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-tibble, r-cran-magrittr, r-cran-readr, r-cran-randomforest, r-cran-ranger, r-cran-forcats, r-cran-rlang, r-cran-tidyr, r-cran-stringr, r-cran-mass, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rjaf_0.1.3-1.ca2404.1_amd64.deb Size: 144764 MD5sum: 52257dea23453f11c03c88691b4b0b1d SHA1: dbc2ca053060427096e6a2f93ed685debca275fb SHA256: ddf62721f04de427a1a99bd410f5dc578a470f7508bb7a3c5916ca72383ec71e SHA512: 5fde40504c70e87903887dee30fcb59f1ba9ca7f0f3923963bfb38a6202e038e8026977411dcf5b8910769d92659d75e672b3a419b624d6ba0ded410e4a10255 Homepage: https://cran.r-project.org/package=rjaf Description: CRAN Package 'rjaf' (Regularized Joint Assignment Forest with Treatment ArmClustering) Personalized assignment to one of many treatment arms via regularized and clustered joint assignment forests as described in Ladhania, Spiess, Ungar, and Wu (2023) . The algorithm pools information across treatment arms: it considers a regularized forest-based assignment algorithm based on greedy recursive partitioning that shrinks effect estimates across arms; and it incorporates a clustering scheme that combines treatment arms with consistently similar outcomes. Package: r-cran-rjafroc Architecture: amd64 Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2724 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bbmle, r-cran-binom, r-cran-dplyr, r-cran-ggplot2, r-cran-mvtnorm, r-cran-numderiv, r-cran-openxlsx, r-cran-readxl, r-cran-rcpp, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-kableextra, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rjafroc_2.1.2-1.ca2404.1_amd64.deb Size: 1855368 MD5sum: b4890664787d9538131f67525a3c49c1 SHA1: 29b7a7f45f8b4efbc3be63c92934973c53b754e9 SHA256: 343beab089154ced7ddb3e65adbb80a473b597a37dffb5b8048f82e8a75ae4e9 SHA512: 8fea255cfdeff83b7e6b3d580f02aade0f57602137b15532bb6221ddee8b6e9147609fb6e469d9ca5179afe4f67e3cd44500b4cd166ce075a2f3b6eafac8166f Homepage: https://cran.r-project.org/package=RJafroc Description: CRAN Package 'RJafroc' (Artificial Intelligence Systems and Observer Performance) Analyzing the performance of artificial intelligence (AI) systems/algorithms characterized by a 'search-and-report' strategy. Historically observer performance has dealt with measuring radiologists' performances in search tasks, e.g., searching for lesions in medical images and reporting them, but the implicit location information has been ignored. The implemented methods apply to analyzing the absolute and relative performances of AI systems, comparing AI performance to a group of human readers or optimizing the reporting threshold of an AI system. In addition to performing historical receiver operating receiver operating characteristic (ROC) analysis (localization information ignored), the software also performs free-response receiver operating characteristic (FROC) analysis, where lesion localization information is used. A book using the software has been published: Chakraborty DP: Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and Applications with R-Based Examples, Taylor-Francis LLC; 2017: . Online updates to this book, which use the software, are at , and at . Supported data collection paradigms are the ROC, FROC and the location ROC (LROC). ROC data consists of single ratings per images, where a rating is the perceived confidence level that the image is that of a diseased patient. An ROC curve is a plot of true positive fraction vs. false positive fraction. FROC data consists of a variable number (zero or more) of mark-rating pairs per image, where a mark is the location of a reported suspicious region and the rating is the confidence level that it is a real lesion. LROC data consists of a rating and a location of the most suspicious region, for every image. Four models of observer performance, and curve-fitting software, are implemented: the binormal model (BM), the contaminated binormal model (CBM), the correlated contaminated binormal model (CORCBM), and the radiological search model (RSM). Unlike the binormal model, CBM, CORCBM and RSM predict 'proper' ROC curves that do not inappropriately cross the chance diagonal. Additionally, RSM parameters are related to search performance (not measured in conventional ROC analysis) and classification performance. Search performance refers to finding lesions, i.e., true positives, while simultaneously not finding false positive locations. Classification performance measures the ability to distinguish between true and false positive locations. Knowing these separate performances allows principled optimization of reader or AI system performance. This package supersedes Windows JAFROC (jackknife alternative FROC) software V4.2.1, . Package functions are organized as follows. Data file related function names are preceded by 'Df', curve fitting functions by 'Fit', included data sets by 'dataset', plotting functions by 'Plot', significance testing functions by 'St', sample size related functions by 'Ss', data simulation functions by 'Simulate' and utility functions by 'Util'. Implemented are figures of merit (FOMs) for quantifying performance and functions for visualizing empirical or fitted operating characteristics: e.g., ROC, FROC, alternative FROC (AFROC) and weighted AFROC (wAFROC) curves. For fully crossed study designs significance testing of reader-averaged FOM differences between modalities is implemented via either Dorfman-Berbaum-Metz or the Obuchowski-Rockette methods. Also implemented is single treatment analysis, which allows comparison of performance of a group of radiologists to a specified value, or comparison of AI to a group of radiologists interpreting the same cases. Crossed-modality analysis is implemented wherein there are two crossed treatment factors and the aim is to determined performance in each treatment factor averaged over all levels of the second factor. Sample size estimation tools are provided for ROC and FROC studies; these use estimates of the relevant variances from a pilot study to predict required numbers of readers and cases in a pivotal study to achieve the desired power. Utility and data file manipulation functions allow data to be read in any of the currently used input formats, including Excel, and the results of the analysis can be viewed in text or Excel output files. The methods are illustrated with several included datasets from the author's collaborations. This update includes improvements to the code, some as a result of user-reported bugs and new feature requests, and others discovered during ongoing testing and code simplification. Package: r-cran-rjags Architecture: amd64 Version: 4-17-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: jags, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda Filename: pool/dists/noble/main/r-cran-rjags_4-17-1.ca2404.2_amd64.deb Size: 132122 MD5sum: e95a46dcacd1349f99f5e665c9724adb SHA1: 7cbf1a2f16267aecd503893c523c67a78618d281 SHA256: 1a864978c938b31a930065f71239580c329fb943827704d73e91d47a915922ef SHA512: 484ad69c5b71a03b657c948c783442e04b69b8e853bd067d997805a49d6c854f5094be798dd196839c8e680663aca0e46f3cd28f82fdc6d37a6b8bc5adfaac7b Homepage: https://cran.r-project.org/package=rjags Description: CRAN Package 'rjags' (Bayesian Graphical Models using MCMC) Interface to the JAGS MCMC library. Package: r-cran-rjava Architecture: amd64 Version: 1.0-18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1305 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, default-jre Filename: pool/dists/noble/main/r-cran-rjava_1.0-18-1.ca2404.1_amd64.deb Size: 718426 MD5sum: 00b970bab6817828696283f44f5ee83e SHA1: 3392c5bb307ecb623dbfe21ba738d62551eb9352 SHA256: 608d459f4ed715fea27958a7a68b29eefc654ca0eca9b641f668959030e31afe SHA512: 7c52425a1c764012cd6b098dc129ee298a52d2ace0649947ca49d786a7d127c9fa2204c1c6408b16dcbe246e5d894d5d353bc6d273ede6052f19c38dfecae01a Homepage: https://cran.r-project.org/package=rJava Description: CRAN Package 'rJava' (Low-Level R to Java Interface) Low-level interface to Java VM very much like .C/.Call and friends. Allows creation of objects, calling methods and accessing fields. Package: r-cran-rjcluster Architecture: amd64 Version: 3.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 590 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrixstats, r-cran-infotheo, r-cran-rlang, r-cran-profvis, r-cran-mclust, r-cran-foreach, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rjcluster_3.2.4-1.ca2404.1_amd64.deb Size: 357214 MD5sum: 3358c7b7f4db930a614ee4b1d8facc79 SHA1: 8ee7a26c4b1f68bf28a6ce573f205804cae3c0cc SHA256: c9875582f56aa1d5ec5efa0d3bd6c37b8e13b1964c259c07a1662587a60fc11a SHA512: 33ea44804b66a34a3981ffd188119d47899aa8aff0478d28ef255bcaf8c0e9cebd057213d5472e7bcc1b48730b87d77dd1956663cd5b7bb9701de44e6156e9fd Homepage: https://cran.r-project.org/package=RJcluster Description: CRAN Package 'RJcluster' (A Fast Clustering Algorithm for High Dimensional Data Based onthe Gram Matrix Decomposition) Clustering algorithm for high dimensional data. Assuming that P feature measurements on N objects are arranged in an N×P matrix X, this package provides clustering based on the left Gram matrix XX^T. To simulate test data, type "help('simulate_HD_data')" and to learn how to use the clustering algorithm, type "help('RJclust')". To cite this package, type 'citation("RJcluster")'. 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Query and pivot support 'JSONpointer', 'JSONpath' or 'JMESpath' expressions. The implementation uses the 'jsoncons' header-only library; the library is easily linked to other packages for direct access to 'C++' functionality not implemented here. Package: r-cran-rjsonio Architecture: amd64 Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2595 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/noble/main/r-cran-rjsonio_2.0.5-1.ca2404.1_amd64.deb Size: 569484 MD5sum: 8cc6b31b87c796a392f3966d18c101cf SHA1: 84c1cefe4f3dc53b25a4012afab418d9ac3ba8fd SHA256: 3fefe864b5af154619f8e1ec69c673db401bbf7ebc4eaa1a3205d5ccdd7bda90 SHA512: a8d5dfd2b2f7f058cce716eb796336f7c3a2362db9c77dd6274bb652ede18caebb95fa6e1c5f2ee02ea262b73452f4ab6c4625d8a5240825ba655b15101a5585 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-rkhsmetamod Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1039 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppgsl Suggests: r-cran-lhs Filename: pool/dists/noble/main/r-cran-rkhsmetamod_1.1-1.ca2404.1_amd64.deb Size: 366084 MD5sum: 7840028f95e96d06e229ae9b040d106b SHA1: cf51b1a975ce020536060abac0dc5b63b7a247df SHA256: c2da4576155d73f003f8919cdea4b5ac281f9c8e258dc37def061b43b1649ad3 SHA512: b747bb1e4c65947074c5d7c910ccb4deb44de4de251b31bdb5f035d0f175659d7aabf36a8990c02fab9fc0371e50fc3efad7c32a476f1f8767839d64c92047a5 Homepage: https://cran.r-project.org/package=RKHSMetaMod Description: CRAN Package 'RKHSMetaMod' (Ridge Group Sparse Optimization Problem for Estimation of a MetaModel Based on Reproducing Kernel Hilbert Spaces) Estimates the Hoeffding decomposition of an unknown function by solving ridge group sparse optimization problem based on reproducing kernel Hilbert spaces, and approximates its sensitivity indices (see Kamari, H., Huet, S. and Taupin, M.-L. (2019) ). Package: r-cran-rkriging Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2005 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-nloptr, r-cran-rcppeigen, r-cran-bh Filename: pool/dists/noble/main/r-cran-rkriging_1.0.2-1.ca2404.1_amd64.deb Size: 483392 MD5sum: 8d22b0205010ce1edd0decbedb84089a SHA1: 6453a934fe8505d458e7bc4fc75631d3385786c6 SHA256: da89a9ca22258e25b711e0c07946c023ac148b2907c6283139b9e1353c685dda SHA512: 6ee5fd847c72b7d579ed6b72be3d892c6ebf7182d4a2751d5b9478250dd54af04ef9ad799f270f0c0fe15ca04e83334933b6b77a5a03ca1ae64621074923bd33 Homepage: https://cran.r-project.org/package=rkriging Description: CRAN Package 'rkriging' (Kriging Modeling) An 'Eigen'-based computationally efficient 'C++' implementation for fitting various kriging models to data. This research is supported by U.S. National Science Foundation grant DMS-2310637. Package: r-cran-rkvo Architecture: amd64 Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rkvo_0.1-1.ca2404.1_amd64.deb Size: 41346 MD5sum: 3dacac8df1cf6892b1c492210db146b2 SHA1: fa3867a7989e928fb1148448a35133abd8dfc605 SHA256: c59b14cf07ed5bbe075db53495b940062db4a9f75ac22a9db66617e7d0513fcb SHA512: 5ae43f80df3e52259323375890101edbec1a239e0d248d2331ac90a2950b7de8ba9fc37bab3e44d9d4400374c3e6fb5090ecc1f40b2367d33ed05f97bb0fbfda 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-rlft Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2167 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-sf Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-rlft_1.0.1-1.ca2404.1_amd64.deb Size: 197946 MD5sum: 76f26d644bb10cca4ef056873abc4175 SHA1: 9cfc68434c8cc892b57e5882d6c1f326264c6b66 SHA256: 91a1c675ff90a7c919fe427a870cda4fce23dfeff04dfb6758f5e1c8ff343898 SHA512: 984d7015d686839f6c715694978afc9bd0e19909ea1177afcb73629eabbd1152adc259110086b002970bb341a026314654cbe2fb214790968351c5192cf2f82c Homepage: https://cran.r-project.org/package=rLFT Description: CRAN Package 'rLFT' (Processing Linear Features) Assists in the manipulation and processing of linear features with the help of the 'sf' package. 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Package: r-cran-rlibeemd Architecture: amd64 Version: 1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rlibeemd_1.4.4-1.ca2404.1_amd64.deb Size: 98336 MD5sum: 92cd001ddc5003827fbb8bbe9459fa02 SHA1: bb3716ebae9a564399f3f85cd999a442f834bec6 SHA256: e773d44ebaef7d1fc09e81f58bcaf70df18189ceb0d4b700f512da84b1eb2c7b SHA512: fa099252edaa5970ec52a20f73dee84f96593eef183f48a2c8b5cf48695a954e487be36f0e02f4a2626bb55623625d4c3b0e24d558fa39486490ab41a6887b3a 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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Package: r-cran-rmodule Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-rmodule_1.0-1.ca2404.1_amd64.deb Size: 211778 MD5sum: cb46e8026d10bb8d7a4247deebd2a35b SHA1: 3aa00e123e559f597f99fd070b9edf13eaf31ae1 SHA256: 4741f53b82f589e06ccd6f38895f87bc30baab608c05f027caedaad91a431e70 SHA512: 222f94ef172cd90fc72e6a6d5d57a3952a15ed6e3b72626a386807d611dde037777785b14b90a951a3770f6ae264495d46abf7b1cd7e41c1442bb1dd621bc70d Homepage: https://cran.r-project.org/package=Rmodule Description: CRAN Package 'Rmodule' (Automated Markov Chain Monte Carlo for Arbitrarily StructuredCorrelation Matrices) Supports automated Markov chain Monte Carlo for arbitrarily structured correlation matrices. 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Package: r-cran-rmonocypher Architecture: amd64 Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmonocypher_0.1.8-1.ca2404.1_amd64.deb Size: 67204 MD5sum: 2914ef3f581912f9492aee95670ca0cb SHA1: 8df74cea738836a3eb144fa742edff8fc4f04ea2 SHA256: 18dcff7844c2926207389fe1360a0fe225e4839eba7708c41114d15445578ada SHA512: 00ea9666f14e21a759b86b4d669c438aa657fb56027f3f00d7a4026a0655f731a11b411dadcfddc8980927e2a91c4de65b740ba8af3b13756c9fa42e4374938c Homepage: https://cran.r-project.org/package=rmonocypher Description: CRAN Package 'rmonocypher' (Easy Encryption of R Objects using Strong Modern Cryptography) Encrypt R objects to a raw vector or file using modern cryptographic techniques. Password-based key derivation is with 'Argon2' (). 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Package: r-cran-rmp Architecture: amd64 Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rmp_2.2-1.ca2404.1_amd64.deb Size: 71260 MD5sum: ceace12d32c74cc4edabec1e15b28e2a SHA1: c3f5b3bef143f575c73d646a26201124a91ab53c SHA256: 494198239b8873d12895e5fd89a0269dcff900ced778ee36ef86e8e62d3fc480 SHA512: e600ad602f72750eb7c84010943770b42ab02ca3007c92431351e68b265df85a6e725059105301f92a03f711e3c65ad13ca94baaf1cd6a8b4b634027b0c80322 Homepage: https://cran.r-project.org/package=rmp Description: CRAN Package 'rmp' (Rounded Mixture Package) Performs univariate probability mass function estimation via Bayesian nonparametric mixtures of rounded kernels as in Canale and Dunson (2011) . Package: r-cran-rmpfr Architecture: amd64 Version: 1.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1729 Depends: libc6 (>= 2.4), libgmp10 (>= 2:6.3.0+dfsg), libmpfr6 (>= 4.0.0), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gmp Suggests: r-cran-dpqmpfr, r-cran-mass, r-cran-bessel, r-cran-polynom, r-cran-sfsmisc Filename: pool/dists/noble/main/r-cran-rmpfr_1.1-2-1.ca2404.1_amd64.deb Size: 1266782 MD5sum: afc30fc56518e69c82e54ee66e8cc39b SHA1: 5a2fbea550605c696c70831670b7d3907d738894 SHA256: ab32a37933c9106766142faf225637b93ceae1de27377e2f0e81daa404c746a5 SHA512: fe046e291f7509abecd60fc2f7b73871f672817da593e9d100a7216693ad506e9af1b471973dd51e5cb0eeac3d2c048bb9dbeeb9d67166dab6cba778f8ab1b78 Homepage: https://cran.r-project.org/package=Rmpfr Description: CRAN Package 'Rmpfr' (Interface R to MPFR - Multiple Precision Floating-Point Reliable) Arithmetic (via S4 classes and methods) for arbitrary precision floating point numbers, including transcendental ("special") functions. 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It also provides interactive R manager and worker environment. Package: r-cran-rmpsh Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rmpsh_1.1.1-1.ca2404.1_amd64.deb Size: 45454 MD5sum: 5b6b1a4b2e59c502a6d3cf5464566907 SHA1: 71d6100fd7f825bc6209b7aedcc9872c9176dc59 SHA256: 86fcde1ea0a1f2a2bf13d75e59f2e8077e055bcc43c155b77df71d6cdb3a09fa SHA512: 863d35c035664e644a460cfb247f38098546392bc950d0be34fb67f5d2c5d8a483df07cf8aba5dd20cabdbc76091442485445d04056351e7222bccd5aeb44c93 Homepage: https://cran.r-project.org/package=RMPSH Description: CRAN Package 'RMPSH' (Recursive Modified Pattern Search on Hyper-Rectangle) Optimization of any Black-Box/Non-Convex Function on Hyper-Rectangular Parameter Space. It uses a Variation of Pattern Search Technique. 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Package: r-cran-rms Architecture: amd64 Version: 8.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2855 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-survival, r-cran-quantreg, r-cran-ggplot2, r-cran-matrix, r-cran-sparsem, r-cran-rpart, r-cran-nlme, r-cran-polspline, r-cran-multcomp, r-cran-htmltable, r-cran-htmltools, r-cran-mass, r-cran-cluster, r-cran-digest, r-cran-colorspace, r-cran-knitr, r-cran-scales Suggests: r-cran-boot, r-cran-plotly, r-cran-mice, r-cran-icenreg, r-cran-rmsb, r-cran-nnet, r-cran-vgam, r-cran-lattice, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-rms_8.1-1-1.ca2404.1_amd64.deb Size: 2474644 MD5sum: 6381e5fb33cb0d5e110fcc8270140192 SHA1: 4310df0ae37711011d5ee4094990b4be97dafdcf SHA256: bd80659890ff0ddd60a62fd2877a488d97a015f3bcf7694de33ab44c191225ef SHA512: d179b08dcbd6e56f68be64c00ef53208ce11862f6595817bb7d81c5a9668e40cde63bcc953ff9c5ea50b3973e200324b267b1f8ad4a646f41a4e944ae6cd3dd5 Homepage: https://cran.r-project.org/package=rms Description: CRAN Package 'rms' (Regression Modeling Strategies) Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. 'rms' is a collection of functions that assist with and streamline modeling. It also contains functions for binary and ordinal logistic regression models, ordinal models for continuous Y with a variety of distribution families, and the Buckley-James multiple regression model for right-censored responses, and implements penalized maximum likelihood estimation for logistic and ordinary linear models. 'rms' works with almost any regression model, but it was especially written to work with binary or ordinal regression models, Cox regression, accelerated failure time models, ordinary linear models, the Buckley-James model, generalized least squares for serially or spatially correlated observations, generalized linear models, and quantile regression. Package: r-cran-rmsb Architecture: amd64 Version: 1.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3470 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rms, r-cran-rcpp, r-cran-rstan, r-cran-hmisc, r-cran-survival, r-cran-ggplot2, r-cran-mass, r-cran-cluster, r-cran-digest, r-cran-knitr, r-cran-loo, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-bayesplot, r-cran-mice Filename: pool/dists/noble/main/r-cran-rmsb_1.1-2-1.ca2404.1_amd64.deb Size: 1075566 MD5sum: 2ac967bcd65344e196f3c31ccf1e1414 SHA1: 314545beb8189202c0805cbad2b48cd51f6e188a SHA256: 65fcdcdc7f1f745e40304e34fa7320ec8e2b49e49327c579a5a13c8a1814f70b SHA512: 9af5f6380dd36149f5453b46aa2bf9c9f25725f77cfbd1761b08678c5c4703ff35b9f48ca06b16120b5707b525980254254ba73eba34f5b9a7809a10a4c1def7 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: amd64 Version: 1.0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readr, r-cran-purrr, r-cran-magrittr, r-cran-dplyr, r-cran-tbl2xts, r-cran-performanceanalytics, r-cran-xts, r-cran-zoo Suggests: r-cran-lubridate, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmsfuns_1.0.0.1-1.ca2404.1_amd64.deb Size: 33184 MD5sum: b8fef6c23447a59acb4b06b19e776790 SHA1: f673bb0e6f38530097dffa4a8397cf9e6e09d2d5 SHA256: fd2ee821f6ef9c3c9bfc5de8b270b02742ce582d219dbd4897eceaceb832bbc4 SHA512: 2ab65e108e67f401f0c08248e79722dc47d5e5b82847d5bb1c25b0f95a84fe3999bfbb99904fd51fb3ad561a0a89db3bab379b08039767a43dbd85f2815d77e2 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: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmsnumpress_1.0.1-1.ca2404.1_amd64.deb Size: 64928 MD5sum: 270b6ebebbf9e8dcca9a58d14a33e165 SHA1: 66f40856fa0380722177aaa63963132346a2d6d2 SHA256: 2b9f00247611a7b60c9f4b3b4fb81430489c70d7ac90d06e4552dbd27e36fca8 SHA512: 5b468e15e67c5940a86a1259ae13a431206cbc17835b79aa03c859f1ae8594dc3f3dffb8535b4077970b6f7d54468c68ca65c1232a8fc80def5907e2aed5bab7 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: amd64 Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-srlars, r-cran-robstepsplitreg, r-cran-cellwise, r-cran-robustbase, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-mvnfast Filename: pool/dists/noble/main/r-cran-rmss_1.2.4-1.ca2404.1_amd64.deb Size: 216630 MD5sum: 010f56e0480ff46452e36f64392e3ce9 SHA1: 93b3ea1c0a242f8806a140014593bd0b00f1e1a3 SHA256: c29de6cc7cf39ce3f2f49e07b7733b6210fe91dd5509c43f6798997a927e8010 SHA512: 40d610a89795bb6eb815e75a907c54c3d93b36e382b9fefc5df8d075ca5e5358a2755fda6e23697a251b482a1d941c584e03a3b0f1690931273f031ae0340f2c Homepage: https://cran.r-project.org/package=RMSS Description: CRAN Package 'RMSS' (Robust Multi-Model Subset Selection) Efficient algorithms for generating ensembles of robust, sparse and diverse models via robust multi-model subset selection (RMSS). The robust ensembles are generated by minimizing the sum of the least trimmed square loss of the models in the ensembles under constraints for the size of the models and the sharing of the predictors. Tuning parameters for the robustness, sparsity and diversity of the robust ensemble are selected by cross-validation. Package: r-cran-rmumps Architecture: amd64 Version: 5.2.1-41-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3097 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-matrix, r-cran-slam Filename: pool/dists/noble/main/r-cran-rmumps_5.2.1-41-1.ca2404.1_amd64.deb Size: 1234250 MD5sum: baa924625170353a1f8040f49b6f96b7 SHA1: cedc53ce5d130002e8881b69ea983e81d3a2c606 SHA256: 23510538e78f5eecb211dfde0650bf74147ae956ce96af83d01b6fadbdaaf3d7 SHA512: fdcdcd983361fffed122047fbb3d734880f79855eaf3a245cae1c92c2f264e0d6e9d0700525dbb43deabd668f62f24614b8e33c01b7a6c791b524369a7a9c329 Homepage: https://cran.r-project.org/package=rmumps Description: CRAN Package 'rmumps' (Wrapper for MUMPS Library) Some basic features of 'MUMPS' (Multifrontal Massively Parallel sparse direct Solver) are wrapped in a class whose methods can be used for sequentially solving a sparse linear system (symmetric or not) with one or many right hand sides (dense or sparse). There is a possibility to do separately symbolic analysis, LU (or LDL^t) factorization and system solving. Third part ordering libraries are included and can be used: 'PORD', 'METIS', 'SCOTCH'. 'MUMPS' method was first described in Amestoy et al. (2001) and Amestoy et al. (2006) . Package: r-cran-rmutil Architecture: amd64 Version: 1.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 845 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rmutil_1.1.10-1.ca2404.1_amd64.deb Size: 724806 MD5sum: 0dc8739907724840aeddf939dac52af4 SHA1: 646f97f0b06a57cf4ce4eef241ebfb417a80e91e SHA256: 3e2344bba41fd62e29b87b80bdd071cb566797d778bf28d0f4e4edf60df53218 SHA512: 52abcd10b6309357707526ecdef0f2b08dc587d92f1c1fe83f6da62ff769ed4e4870ee25448904027ba7a00dc8c54d157749a7701bf9d9dc61de4ee26c8b00c7 Homepage: https://cran.r-project.org/package=rmutil Description: CRAN Package 'rmutil' (Utilities for Nonlinear Regression and Repeated MeasurementsModels) A toolkit of functions for nonlinear regression and repeated measurements not to be used by itself but called by other Lindsey packages such as 'gnlm', 'stable', 'growth', 'repeated', and 'event' (available at ). Package: r-cran-rmvl Architecture: amd64 Version: 1.1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 414 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rmvl_1.1.0.3-1.ca2404.1_amd64.deb Size: 245578 MD5sum: 2b20b16917bc1182b4e8af09d294cf1e SHA1: b13de7a304a7b254360b1a31280d87f6315a4510 SHA256: 6b8863255d787e52fc7c68de5b6f7901412ecd04cdb4663179c26a5c3b9bb832 SHA512: 9a2fe16b302f6cc2ffb74cbcaf7843d07188742198c55eb709c6bd6c71df874d1b5496793886bd41c58abb2c33be7c06f5c1f4c670f85701f58ecebaeed91e8f Homepage: https://cran.r-project.org/package=RMVL Description: CRAN Package 'RMVL' (Mappable Vector Library for Handling Large Datasets) Mappable vector library provides convenient way to access large datasets. Use all of your data at once, with few limits. Memory mapped data can be shared between multiple R processes. Access speed depends on storage medium, so solid state drive is recommended, preferably with PCI Express (or M.2 nvme) interface or a fast network file system. The data is memory mapped into R and then accessed using usual R list and array subscription operators. Convenience functions are provided for merging, grouping and indexing large vectors and data.frames. The layout of underlying MVL files is optimized for large datasets. The vectors are stored to guarantee alignment for vector intrinsics after memory map. The package is built on top of libMVL, which can be used as a standalone C library. libMVL has simple C API making it easy to interchange datasets with outside programs. Large MVL datasets are distributed via Academic Torrents . Package: r-cran-rmvp Architecture: amd64 Version: 1.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1932 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-bigmemory, r-cran-rhpcblasctl, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-rcppprogress, r-cran-bh Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmvp_1.4.6-1.ca2404.1_amd64.deb Size: 1360932 MD5sum: 45cebd1465decb88a9e94f70d57c6243 SHA1: 8e02eba16c82cb57d4cfd0926a6fcf5d502373c3 SHA256: b22576dfee7e6a3d1cb3082ce3cfdf51c7617ff3759de4e01dbba8018756bd35 SHA512: 1aad6874f4bc98e1597c3e5c0c7df4a7c74232931bbd622a1d8c1e3f9099833315a12e708bf225d33d9d52dfd0847ac852a2551ecef980c686459ae5c635c546 Homepage: https://cran.r-project.org/package=rMVP Description: CRAN Package 'rMVP' (Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWASTool) A memory-efficient, visualize-enhanced, parallel-accelerated Genome-Wide Association Study (GWAS) tool. It can (1) effectively process large data, (2) rapidly evaluate population structure, (3) efficiently estimate variance components several algorithms, (4) implement parallel-accelerated association tests of markers three methods, (5) globally efficient design on GWAS process computing, (6) enhance visualization of related information. 'rMVP' contains three models GLM (Alkes Price (2006) ), MLM (Jianming Yu (2006) ) and FarmCPU (Xiaolei Liu (2016) ); variance components estimation methods EMMAX (Hyunmin Kang (2008) ;), FaSTLMM (method: Christoph Lippert (2011) , R implementation from 'GAPIT2': You Tang and Xiaolei Liu (2016) and 'SUPER': Qishan Wang and Feng Tian (2014) ), and HE regression (Xiang Zhou (2017) ). Package: r-cran-rmysql Architecture: amd64 Version: 0.11.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: libc6 (>= 2.38), libmysqlclient21 (>= 8.0.11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi Suggests: r-cran-testthat, r-cran-curl Filename: pool/dists/noble/main/r-cran-rmysql_0.11.3-1.ca2404.1_amd64.deb Size: 286518 MD5sum: 305fd1f25bf61a840ce13af962975502 SHA1: a7a6dd4b0ab6404fd4da109b0a4a2ce7bc85e89c SHA256: ce3803b9b186fd298f11724e14585296e0204634b3e062e72d505e9ae77f49aa SHA512: 10d40df78438421dee56d709e8dd3cf769cf277af8c73c56bf5ee9f03a533fa17b3b7275389e80779acb449e38b571f6f7871fb26215e0763df9a6bedd126620 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: amd64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1618 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-rnanoflann_0.0.3-1.ca2404.1_amd64.deb Size: 183826 MD5sum: 3e6c48a16d93c24ad26726931961f008 SHA1: 67dc01f85d1dbd6dbd862d4531b2d4560c110692 SHA256: 4e53304318c14b0d510b54646761b490d2242a5b0ead04e48e701cf5198908ee SHA512: 401d4039554108390e6a06df880be9103bb4cf5a7b165ec178f983769ee0d0440a1100e95cf57b3531fe3ca1159de32c19e24df0b9c141d76663f7fbbd345112 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: amd64 Version: 0.8.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1553 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-progress Suggests: r-cran-testthat, r-cran-ape Filename: pool/dists/noble/main/r-cran-rncl_0.8.9-1.ca2404.1_amd64.deb Size: 520302 MD5sum: b833d4b33c9dfafd52abe469644aba1e SHA1: 3402d90f5bda71be15ee5cf3ae5db676fcd1b373 SHA256: d46eb06878f60e6166e2b315897ea0b8468a652897d3e7cfca236965f3b5fc16 SHA512: faab313c8b08d9994e2d993993329fe54c45088e83b053fd2d8160d09e7440fc81906b7324a79a6bf7990bbecfbe3d3d10261f9572c814a89ae9a670b97e5ed1 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. 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It is a wrapper around the rgraph library (Guimera & Amaral, 2005, ). 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The input vector is transformed using AES (Advanced Encryption Standard) algorithm into an initial state of Mersenne-Twister random number generator. The function provides a better alternative to the R base function set.seed(), if the input vector is a single integer. Initializing a stream of random numbers with a vector is a convenient way to obtain several streams, each of which is identified by several integer indices. 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But this package is not intended to be used directly, you are strongly __encouraged__ to use the 'randtoolbox' package, which depends on this package. Package: r-cran-rnifti Architecture: amd64 Version: 1.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1647 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest, r-cran-covr, r-cran-reportr, r-cran-shades, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-rnifti_1.9.0-1.ca2404.1_amd64.deb Size: 909034 MD5sum: ed1359caaea1211d435fbd87d02ffe5e SHA1: 50829a2e399e0e6d7c0aeba8dac65444a8556610 SHA256: d29a2c0dd897551ec32dd31c5d6b6724bee4c7b7c977480d10a3c58d3d98a84e SHA512: 88e74a122f6061d891c73d2069fef0e24de529f63f11309330b2af02cc660cb2dbd44d8ec20ee20087e3fbad944d8ca26387a1e464e294df9a05e134698ff9ac 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. 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An additional file specifies all the spectra to be considered by associating their sample code as well as the levels of experimental factors to which they belong. More detail can be found in Jacob et al. (2017) . 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Based on the 'Python' package 'PyNNDescent' . Package: r-cran-rnomni Architecture: amd64 Version: 1.0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 413 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-withr, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-rnomni_1.0.1.2-1.ca2404.1_amd64.deb Size: 199728 MD5sum: 09f13362399f42556e24a345b46982e4 SHA1: 41f0bbc6778ce86217a4f6f63b190d5c0c55c53e SHA256: fabc30b9180b071daeb61c7f020deeca9b582b6c19832a0f09427f4b1e48b646 SHA512: 8bd54e0a48a3d17af93e45115be45c9f41616dee73dff0b9da10d38d37ae326d882ccf9755ab9bd5a65aae8c501eba4511f744ce01df7a40103ebc83bf117199 Homepage: https://cran.r-project.org/package=RNOmni Description: CRAN Package 'RNOmni' (Rank Normal Transformation Omnibus Test) Inverse normal transformation (INT) based genetic association testing. These tests are recommend for continuous traits with non-normally distributed residuals. INT-based tests robustly control the type I error in settings where standard linear regression does not, as when the residual distribution exhibits excess skew or kurtosis. Moreover, INT-based tests outperform standard linear regression in terms of power. These tests may be classified into two types. In direct INT (D-INT), the phenotype is itself transformed. In indirect INT (I-INT), phenotypic residuals are transformed. The omnibus test (O-INT) adaptively combines D-INT and I-INT into a single robust and statistically powerful approach. See McCaw ZR, Lane JM, Saxena R, Redline S, Lin X. "Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies" . Package: r-cran-robcat Architecture: amd64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 551 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp, r-cran-mvtnorm, r-cran-stringr, r-cran-matrix, r-cran-numderiv, r-cran-pracma Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-robcat_0.2-1.ca2404.1_amd64.deb Size: 313458 MD5sum: c1ebd35382a3e7f4438ab5351126790b SHA1: e40b9a9afb7f0c90ced594765b79c634d23bd523 SHA256: 179f47887236053d34795edadb485f501bd3397dd9ae008d75786815dfdbdcab SHA512: aafef5d9c6082bb54b19ce1f83cc488fdfedc3b7546e10aca952f87fc9a6d585311d41ffc3fca7393f72aa40d02e653bfdf408f95891756793dc3083fb400059 Homepage: https://cran.r-project.org/package=robcat Description: CRAN Package 'robcat' (Robust Categorical Data Analysis) Robust categorical data analysis based on the theory of C-estimation developed in Welz (2024) . For now, the package only implements robust estimation of polychoric correlation as proposed in Welz, Mair and Alfons (2026) and robust estimation of polyserial correlation (Welz, 2026 ) with methods for printing and plotting. We will implement further models in future releases. In addition, the package is still experimental, so input arguments and class structure may change in future releases. Package: r-cran-robcompositions Architecture: amd64 Version: 2.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3077 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-pls, r-cran-data.table, r-cran-cvtools, r-cran-fda, r-cran-rrcov, r-cran-cluster, r-cran-dplyr, r-cran-magrittr, r-cran-ggally, r-cran-ggfortify, r-cran-kernlab, r-cran-mass, r-cran-mclust, r-cran-tidyr, r-cran-robustbase, r-cran-robusthd, r-cran-sparsepca, r-cran-vim, r-cran-zcompositions, r-cran-reshape2, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-e1071, r-cran-fpc, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-robcompositions_2.4.2-1.ca2404.1_amd64.deb Size: 2623572 MD5sum: 03d79d907d92d260448cd3a68b76b9d4 SHA1: e8824039b16df7f405c383e1e58a04bd38bcd7bd SHA256: f679cde86aeb0049704030368c3092bebd0d7a7eefacb895d6920619985db02a SHA512: b64b197478039841d1e7d221bcad522f373befabcf508469fe3c918de14e52045caaa45f0f46ca8fa126cc3102afc84585cdb21767cf9de28be075306bd8db62 Homepage: https://cran.r-project.org/package=robCompositions Description: CRAN Package 'robCompositions' (Compositional Data Analysis) Methods for analysis of compositional data including robust methods (), imputation of missing values (), methods to replace rounded zeros (, , ), count zeros (), methods to deal with essential zeros (), (robust) outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors, functional data analysis () and p-splines (), contingency () and compositional tables (, , ) and (robust) Anderson-Darling normality tests for compositional data as well as popular log-ratio transformations (addLR, cenLR, isomLR, and their inverse transformations). In addition, visualisation and diagnostic tools are implemented as well as high and low-level plot functions for the ternary diagram. Package: r-cran-robcp Architecture: amd64 Version: 0.3.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2164 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-mvtnorm, r-cran-pracma Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-robcp_0.3.10-1.ca2404.1_amd64.deb Size: 2079466 MD5sum: a789233cbaeb07d67e2aef2c5c0b39fd SHA1: c984d7eaf7e74fee0403854f8f45ef54efe6c001 SHA256: 719c27b8d58dad63362da2e49bc0db0bb0504dde13e9fa42389821162b6e4139 SHA512: eca0aaf419719e506c81aa3c2ef57e703423b6e36db65d27d5812b71ab8ee880a1e1bf3d71d63c0d83ae4d37fc8cd2dea209ce53af7b2b80983c429122af0734 Homepage: https://cran.r-project.org/package=robcp Description: CRAN Package 'robcp' (Robust Change-Point Tests) Provides robust methods to detect change-points in uni- or multivariate time series. They can cope with corrupted data and heavy tails. Focus is on the detection of abrupt changes in location, but changes in the scale or dependence structure can be detected as well. This package provides tests for change detection in uni- and multivariate time series based on Huberized versions of CUSUM tests proposed in Duerre and Fried (2019) , and tests for change detection in univariate time series based on 2-sample U-statistics or 2-sample U-quantiles as proposed by Dehling et al. (2015) and Dehling, Fried and Wendler (2020) . Furthermore, the packages provides tests on changes in the scale or the correlation as proposed in Gerstenberger, Vogel and Wendler (2020) , Dehling et al. (2017) , and Wied et al. (2014) . Package: r-cran-roben Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1144 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-roben_0.1.2-1.ca2404.1_amd64.deb Size: 688076 MD5sum: 6c323f7d7acfee11bda12849463277eb SHA1: 9921b49e3f3e62f54febb6b84c2b1af45ae1c61e SHA256: 514cbb59a1fc8032e77bdd98a43ccfc191b77bf230a07d6c3fa37e30dc4d834d SHA512: d2bbbadc121631072b85d72ef355074b40e93688643a2cda0ae834ab1e5b8cce198825b9bb4b6a161d7a6ffdadad425d4317a6d4a6cc02383c3cee6a62aa482e Homepage: https://cran.r-project.org/package=roben Description: CRAN Package 'roben' (Robust Bayesian Variable Selection for Gene-EnvironmentInteractions) Gene-environment (G×E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G×E studies have been commonly encountered, leading to the development of a broad spectrum of robust penalization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a robust Bayesian variable selection method for G×E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects. An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++. Package: r-cran-robeth Architecture: amd64 Version: 2.7-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1014 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-robeth_2.7-8-1.ca2404.1_amd64.deb Size: 681350 MD5sum: d045af5092b9f63dfcef10343d67bb38 SHA1: c1297c9d14fd8d14d302d5246a2d44c4a63a12b2 SHA256: e6eab20be5dbc2321d948257d83e9e279925d33753a0b3c7d4e39a75c5aec0a5 SHA512: c92ea0b726e1db70d5f676d2c088a007f9fd7e8fd24d772cc9c07beda697077cf288175c691070edcb1c7cd35c1465bba2aa4903c929f2ddf845b87ca25cfa86 Homepage: https://cran.r-project.org/package=robeth Description: CRAN Package 'robeth' (R Functions for Robust Statistics) Locations problems, M-estimates of coefficients and scale in linear regression, Weights for bounded influence regression, Covariance matrix of the coefficient estimates, Asymptotic relative efficiency of regression M-estimates, Robust testing in linear models, High breakdown point regression, M-estimates of covariance matrices, M-estimates for discrete generalized linear models. Package: r-cran-robextremes Architecture: amd64 Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1583 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-distrmod, r-cran-roptest, r-cran-robustbase, r-cran-evd, r-cran-robastrda, r-cran-distr, r-cran-distrex, r-cran-randvar, r-cran-robastbase, r-cran-startupmsg, r-cran-actuar Suggests: r-cran-runit, r-cran-ismev Filename: pool/dists/noble/main/r-cran-robextremes_1.3.2-1.ca2404.1_amd64.deb Size: 1088542 MD5sum: e4f814e7b53c1415e6daa82cb7d70958 SHA1: f3cfe6dfdab01ec8303e5f232bde31cd4abcea36 SHA256: 7e203604d80e2ebd1c093f5af3c2d412fb2a2cab24e3a856d451718c819dff59 SHA512: 55048107c8507535e380a25a82ee2cced0d77b87ab2d7f6f452f86ce738296e3f076f0b5f358fc648c779470c797447480f175e319f4b386f59afe47ffee304d Homepage: https://cran.r-project.org/package=RobExtremes Description: CRAN Package 'RobExtremes' (Optimally Robust Estimation for Extreme Value Distributions) Optimally robust estimation for extreme value distributions using S4 classes and methods (based on packages 'distr', 'distrEx', 'distrMod', 'RobAStBase', and 'ROptEst'); the underlying theoretic results can be found in Ruckdeschel and Horbenko, (2013 and 2012), \doi{10.1080/02331888.2011.628022} and \doi{10.1007/s00184-011-0366-4}. Package: r-cran-robfilter Architecture: amd64 Version: 4.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 611 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase, r-cran-mass, r-cran-lattice Filename: pool/dists/noble/main/r-cran-robfilter_4.1.6-1.ca2404.1_amd64.deb Size: 463362 MD5sum: 8e0f3caf4526437abb8f8afc1d1c620f SHA1: dc7f44aaa01f9058719a12c2131eddc40c1f5218 SHA256: 47089171f9da6d94c2b96948f41c45cd24fc0f5c1ff00d0b61a97fc525d3ac49 SHA512: 51cc5461f61b561ea7c90296954cb732e34fca0347c91538b41157b33a7f64275d24a50ac07a870c4c97c27ad1a44aef1e1b6a92ecfb08aecd364411ae99e2d5 Homepage: https://cran.r-project.org/package=robfilter Description: CRAN Package 'robfilter' (Robust Time Series Filters) Implementations for several robust procedures that allow for (online) extraction of the signal of univariate or multivariate time series by applying robust regression techniques to a moving time window are provided. Included are univariate filtering procedures based on repeated-median regression as well as hybrid and trimmed filters derived from it; see Schettlinger et al. (2006) . The adaptive online repeated median by Schettlinger et al. (2010) and the slope comparing adaptive repeated median by Borowski and Fried (2013) choose the width of the moving time window adaptively. Multivariate versions are also provided; see Borowski et al. (2009) for a multivariate online adaptive repeated median and Borowski (2012) for a multivariate slope comparing adaptive repeated median. Furthermore, a repeated-median based filter with automatic outlier replacement and shift detection is provided; see Fried (2004) . Package: r-cran-robgarchboot Architecture: amd64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-foreach, r-cran-doparallel, r-cran-dorng, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-robgarchboot_1.2.0-1.ca2404.1_amd64.deb Size: 82554 MD5sum: 6e51d9b6d958dd77d54dd587b68c2d66 SHA1: 1784e91e7ab56ce6d2d5601d3be8be9b1b6f566d SHA256: e9ba276655473832e5788fc3e1d4a42063e6672b173ff2e2be385cd21a7f6e7a SHA512: 594ef9ef11a09c386fe5fae162029b415b21060d553be012954f1368982366bb0b5d55aae17658365e01c89b3b553bc8c41c7c009cd82a9a96725b7ad74773d3 Homepage: https://cran.r-project.org/package=RobGARCHBoot Description: CRAN Package 'RobGARCHBoot' (Robust Bootstrap Forecast Densities for GARCH Models) Bootstrap forecast densities for GARCH (Generalized Autoregressive Conditional Heteroskedastic) returns and volatilities using the robust residual-based bootstrap procedure of Trucios, Hotta and Ruiz (2017) . Package: r-cran-robkf Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 821 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-ggplot2, r-cran-reshape2, r-cran-matrix, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-robkf_1.0.2-1.ca2404.1_amd64.deb Size: 288622 MD5sum: 5fd518594f373b7b32f3ac641fa13ce4 SHA1: 1f570f2a1dcd5d67beaa940646fe33b6082a88fe SHA256: 66e3d13525cd52da47cdeb05b475d5dafe6b33d18211f554a1a46d14cdf93353 SHA512: 0f1011c043788a7d546c0b675f893a0229214513a0a3f600910c774e382353742a750dbf07c091df5698b860b2c24dfabf4ecec0c3d0d8fd13f6a332ec5c2c54 Homepage: https://cran.r-project.org/package=RobKF Description: CRAN Package 'RobKF' (Innovative and/or Additive Outlier Robust Kalman Filtering) Implements a series of robust Kalman filtering approaches. It implements the additive outlier robust filters of Ruckdeschel et al. (2014) and Agamennoni et al. (2018) , the innovative outlier robust filter of Ruckdeschel et al. (2014) , as well as the innovative and additive outlier robust filter of Fisch et al. (2020) . Package: r-cran-robma Architecture: amd64 Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10341 Depends: jags, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-bayestools, r-cran-bridgesampling, r-cran-loo, r-cran-mass, r-cran-runjags, r-cran-rjags, r-cran-mvtnorm, r-cran-scales, r-cran-rdpack, r-cran-rlang, r-cran-coda, r-cran-ggplot2 Suggests: r-cran-metafor, r-cran-posterior, r-cran-weightr, r-cran-lme4, r-cran-fixest, r-cran-metabma, r-cran-emmeans, r-cran-metadat, r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-robma_4.0.0-1.ca2404.1_amd64.deb Size: 5045626 MD5sum: d1b89450b8277fdaed3d6bb8c43dad01 SHA1: ee6835d3e248841cfd248589ca6dab03216f6ac3 SHA256: a4c2aa201d695f981d133f3ddfa09add580eda8ca8995bfe3f2e564ff73f3970 SHA512: 42e385523f52b839cc52f003f8127ee9d37d77b23660d2e342b27b61474a91aefd87f897ece9d04a1008b00a8693a2005cd40008ac447d4083a1753b3e75267e Homepage: https://cran.r-project.org/package=RoBMA Description: CRAN Package 'RoBMA' (Robust Bayesian Meta-Analyses) A framework for Bayesian meta-analysis, including model estimation, prior specification, model comparison, prediction, summaries, visualizations, and diagnostics. The package fits single and model-averaged meta-analytic, meta-regression, multilevel, publication bias adjusted, and generalized linear mixed models The model-averaged meta-analytic models combine competing models based on their predictive performance, weight inference by posterior model probabilities, and test model components using Bayes factors (e.g., effect vs. no effect; Bartoš et al., 2022, ; Maier, Bartoš & Wagenmakers, 2022, ; Bartoš et al., 2025, ). Users can specify flexible prior distributions for effect sizes, heterogeneity, publication bias (including selection models and PET-PEESE), and moderators. Package: r-cran-robmixglm Architecture: amd64 Version: 1.2-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 530 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastghquad, r-cran-bbmle, r-cran-vgam, r-cran-actuar, r-cran-rcpp, r-cran-boot, r-cran-numderiv, r-cran-doparallel, r-cran-foreach, r-cran-dorng, r-cran-mass Suggests: r-cran-r.rsp, r-cran-robustbase, r-cran-lattice, r-cran-forward Filename: pool/dists/noble/main/r-cran-robmixglm_1.2-5-1.ca2404.1_amd64.deb Size: 418170 MD5sum: 0570c49cc16371577a87ce5d48dd3d37 SHA1: 0d257d33906d4b093e942974d8a94101c881115f SHA256: 671e52b53adfe3dd82ae531a97a02d2e3b91484e74d36d9478c1d038421cafba SHA512: da12e756fc74fd50de92cb6bf5c9bf95fcb3acfdeea0602bb0532441b0bd68d852b924116cff687c8e6ae276a152a6db3f1af9ab18610b19de8d4af45101378f Homepage: https://cran.r-project.org/package=robmixglm Description: CRAN Package 'robmixglm' (Robust Generalized Linear Models (GLM) using Mixtures) Robust generalized linear models (GLM) using a mixture method, as described in Beath (2018) . This assumes that the data are a mixture of standard observations, being a generalised linear model, and outlier observations from an overdispersed generalized linear model. The overdispersed linear model is obtained by including a normally distributed random effect in the linear predictor of the generalized linear model. Package: r-cran-robobayes Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 556 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-robobayes_1.3-1.ca2404.1_amd64.deb Size: 213352 MD5sum: 6fb17b6e0a3646189fcbc9570a094f9b SHA1: e559b4bd7c3c72df94b2cde488f3b19ecfa6d9e3 SHA256: e85454f583bec96bd3a7d8edaec1f70edc6e40eea55731f9dcfeb4973b8860ec SHA512: 58326c232247e6614626ce3a86c9b022222d259e093eff3fa3882182b6386e6b6e4392723704855238d470db248f3f761e09b98d7cda6f454905fd2fd6db5aab Homepage: https://cran.r-project.org/package=roboBayes Description: CRAN Package 'roboBayes' (Robust Online Bayesian Monitoring) An implementation of Bayesian online changepoint detection (Adams and MacKay (2007) ) with an option for probability based outlier detection and removal (Wendelberger et. al. (2021) ). Building on the independent multivariate constant mean model implemented in the 'R' package 'ocp', this package models multivariate data as multivariate normal about a linear trend, defined by user input covariates, with an unstructured error covariance. Changepoints are identified based on a probability threshold for windows of points. Package: r-cran-robregcc Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-magrittr, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-robregcc_1.1-1.ca2404.1_amd64.deb Size: 392662 MD5sum: 03d293e18c7d866d3757d8a6a787829a SHA1: 9710c23d40293ff8b65c557c9dccbcd0a35a7a32 SHA256: 65f89bfc7a4aceaa334ac7979a3e489ddfc99b4312f30fc7e8a5d794f0723450 SHA512: 62d77f4de09a5949af17caeabf31a38464652c5462a5fb041b2e519ba8cde76111f97104a49ea5a9667d1e56869757abca3d7af42f5cce1e917e2ea943906732 Homepage: https://cran.r-project.org/package=robregcc Description: CRAN Package 'robregcc' (Robust Regression with Compositional Covariates) We implement the algorithm estimating the parameters of the robust regression model with compositional covariates. The model simultaneously treats outliers and provides reliable parameter estimates. Publication reference: Mishra, A., Mueller, C.,(2019) . 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(2017) . 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It allows for emulation, calibration and prediction using complex mathematical model outputs and experimental data. See the reference: Mengyang Gu and Long Wang, 2018, Journal of Uncertainty Quantification; Mengyang Gu, Fangzheng Xie and Long Wang, 2022, Journal of Uncertainty Quantification; Mengyang Gu, Kyle Anderson and Erika McPhillips, 2023, Technometrics. 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Package: r-cran-robustgasp Architecture: amd64 Version: 0.6.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1074 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-nloptr, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-robustgasp_0.6.8-1.ca2404.1_amd64.deb Size: 665920 MD5sum: 773c6191a7e8adb6d6981dce4566ef75 SHA1: e62ccbdd15ecfc072f84cc3bc94d38f6bec2a7e0 SHA256: 9030a2b03b8228158db3b07b834f763853e0a96c86a8101b0a1b64b142ef597c SHA512: b7844d47a5dca309329b276c6350b6ca89a06d56a6929cbe0370c2d69675ee58596b8ebd3ea46f1d54f1d205579492ea2c9f8b39a9b35be6b6ee8700d71f0b14 Homepage: https://cran.r-project.org/package=RobustGaSP Description: CRAN Package 'RobustGaSP' (Robust Gaussian Stochastic Process Emulation) Robust parameter estimation and prediction of Gaussian stochastic process emulators. 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A time-invariant partition scheme on the survivor population was considered to incorporate time-dependent covariates. Motivated by ideas of randomized tests, generalized time-dependent ROC curves were used to evaluate the performance of survival trees and establish the optimality of the target hazard/survival function. The optimality of the target hazard function motivates us to use a weighted average of the time-dependent area under the curve (AUC) on a set of time points to evaluate the prediction performance of survival trees and to guide splitting and pruning. A detailed description of the implemented methods can be found in Sun et al. (2019) . 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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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This package provides a method called OptSpace, which was proposed by Keshavan, R.H., Oh, S., and Montanari, A. (2009) for a case under low-rank assumption. Package: r-cran-rotasym Architecture: amd64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2077 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-dplyr, r-cran-scatterplot3d, r-cran-testthat, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-rotasym_1.2.0-1.ca2404.1_amd64.deb Size: 1862360 MD5sum: d5440f94cfeb216614a466c1aad3a80a SHA1: 714598b16fbfc7653a893c9bb4352f1efba5b4c1 SHA256: 62b2e32424143b8f60ff607140cb84225740f055595d1cc95ccdafdb6a0ead6a SHA512: af982d7fa6a4238ce9b6866ed235fa8112d19234fbfde58414778087bf3733f3077eef44aa06dcbd0b5de216253df7af325efbaf57f87479d3bde15c9a3dd664 Homepage: https://cran.r-project.org/package=rotasym Description: CRAN Package 'rotasym' (Tests for Rotational Symmetry on the Hypersphere) Implementation of the tests for rotational symmetry on the hypersphere proposed in García-Portugués, Paindaveine and Verdebout (2020) . The package also implements the proposed distributions on the hypersphere, based on the tangent-normal decomposition, and allows for the replication of the data application considered in the paper. Package: r-cran-rotations Architecture: amd64 Version: 1.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5540 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-onion, r-cran-orientlib, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rotations_1.6.6-1.ca2404.1_amd64.deb Size: 5105284 MD5sum: eae43ea8701e1f3a4861020e6324749d SHA1: 347b6c88c23245ef42432a05173226b3bce17b27 SHA256: 497b07065495c03b62423217969462fcb0ab1c0ca09867c49c29b27732fddf6f SHA512: 48811b6eb74cd5df6cd1d6722b3d45758308f14d65a8c7fdf6b42599f20533424182e3c1b5b9a7b60dbf26dcd3c3b01a35a6de1e843c1129a90a9591817cda30 Homepage: https://cran.r-project.org/package=rotations Description: CRAN Package 'rotations' (Working with Rotation Data) Tools for working with rotational data, including simulation from the most commonly used distributions on SO(3), methods for different Bayes, mean and median type estimators for the central orientation of a sample, confidence/credible regions for the central orientation based on those estimators and a novel visualization technique for rotation data. Most recently, functions to identify potentially discordant (outlying) values have been added. References: Bingham, Melissa A. and Nordman, Dan J. and Vardeman, Steve B. (2009), Bingham, Melissa A and Vardeman, Stephen B and Nordman, Daniel J (2009), Bingham, Melissa A and Nordman, Daniel J and Vardeman, Stephen B (2010), Leon, C.A. and Masse, J.C. and Rivest, L.P. (2006), Hartley, R and Aftab, K and Trumpf, J. (2011), Stanfill, Bryan and Genschel, Ulrike and Hofmann, Heike (2013), Maonton, Jonathan (2004), Mardia, KV and Jupp, PE (2000, ISBN:9780471953333), Rancourt, D. and Rivest, L.P. and Asselin, J. (2000), Chang, Ted and Rivest, Louis-Paul (2001), Fisher, Nicholas I. (1996, ISBN:0521568900). Package: r-cran-roughsets Architecture: amd64 Version: 1.3-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 853 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-class Filename: pool/dists/noble/main/r-cran-roughsets_1.3-8-1.ca2404.1_amd64.deb Size: 692724 MD5sum: 61955ef54fe0ae5c94871eaef3888acf SHA1: be0252c6308c4bd84b312834a8b203d6c44dbdcf SHA256: d6e93af2a20a3918e13b527166e977aefd0cc8aed62e3f55b0456449df3f4fb7 SHA512: 2fedf4924a18239ea85f7040e8c36af7ce43cf38509a153e39cfc56aa7dd1c6a23bf5800a6fd9e40295f1e612691c611af59bb24c3183109920b407b89694c7e Homepage: https://cran.r-project.org/package=RoughSets Description: CRAN Package 'RoughSets' (Data Analysis Using Rough Set and Fuzzy Rough Set Theories) Implementations of algorithms for data analysis based on the rough set theory (RST) and the fuzzy rough set theory (FRST). 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See Morlon et al. (2010) , Morlon et al. (2011) , Condamine et al. (2013) , Morlon et al. (2014) , Manceau et al. (2015) , Lewitus & Morlon (2016) , Drury et al. (2016) , Manceau et al. (2016) , Morlon et al. (2016) , Clavel & Morlon (2017) , Drury et al. (2017) , Lewitus & Morlon (2017) , Drury et al. (2018) , Clavel et al. (2019) , Maliet et al. (2019) , Billaud et al. (2019) , Lewitus et al. (2019) , Aristide & Morlon (2019) , Maliet et al. (2020) , Drury et al. (2021) , Perez-Lamarque & Morlon (2022) , Perez-Lamarque et al. (2022) , Mazet et al. (2023) , Drury et al. (2024) . 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Package: r-cran-rpbk Architecture: amd64 Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3328 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), 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/noble/main/r-cran-rpbk_0.2.5-1.ca2404.1_amd64.deb Size: 1449830 MD5sum: 6fce620a3b6b8f3fd3509e25d1fa1a34 SHA1: bdfc1b8c7613bdeb2f641cc47a2e956f473d6816 SHA256: 45b960876fa9990b090f64e5a5ae664832a107910decc5d0e6a6fab1f8eda56c SHA512: fd625f9ab8ab2cd052eb920953f554f61422341b8d2791497393a48f79eed9a2e6a228eef4403a3a61b0c95753607cc492d9e7305fabfd95c29303eaa92cb607 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: amd64 Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Suggests: r-cran-matrixextra Filename: pool/dists/noble/main/r-cran-rpc_2.0.3-1.ca2404.1_amd64.deb Size: 73010 MD5sum: 7ca47309547f9d9d7875387019b88787 SHA1: f83f981778774061f9fa5b0e81b9be9465558460 SHA256: a3ccc3fdd0371dca213f82ae44407010c407711fdc67aa2e0c41aa4e77165803 SHA512: b28e2d285e43992f6de3440838a41a3a7778543824dd86891ebee44eb77c745a5360b77d8ea1fcad36614bd6a0566258f279522e3dc1aa351845280d4c5f1b62 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: amd64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 456 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-rpeif, r-cran-rcppeigen Suggests: r-cran-r.rsp, r-cran-testthat, r-cran-performanceanalytics Filename: pool/dists/noble/main/r-cran-rpeglmen_1.1.4-1.ca2404.1_amd64.deb Size: 289976 MD5sum: 0e80467bbb1d435295af253c7e0527b5 SHA1: 3072e8349832fe30ac4aa089dc310a54af6b33a9 SHA256: 04bc1ee298e18c3a4ddf67cf1c9d4d3796b59895c669888e881f6c9c3aa6a61c SHA512: efa7e85840122ceeb6e5687cd8223716605dad510862a8d8fdb73f4c43a292fdacb5b559293b04d949fe791f9643b29e0a50d88e4775bd9c46838de5b6fae9e7 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: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4321 Depends: libc6 (>= 2.34), 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/noble/main/r-cran-rpesto_0.1.4-1.ca2404.1_amd64.deb Size: 1265552 MD5sum: 2ef7cb5c36b8a05f4bb6cd96cf684503 SHA1: 95a90266325cef7f2aa6c142bdd66411f30750d1 SHA256: d6007adf48341959df38af906c19460782091d79eb6410a5fd47e996b25266aa SHA512: 8088e663845a25ba296b220633c0d6481ac6ae71741a148336bfc2021727ff3d14ca48ab171146c0039df32e77035751ae44ba6ff6b4d6143db3526b73058970 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: amd64 Version: 1.0.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1524 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-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/noble/main/r-cran-rpf_1.0.15-1.ca2404.1_amd64.deb Size: 916510 MD5sum: de19551f84928c7b8c624388be30bde5 SHA1: 19b896618dee20323847c226cb3fff58e442b5a2 SHA256: f77a9789f02f8f765e098935388cf9a6635d9c39824cd6dbdf9e505109e25729 SHA512: 1a274b14093e8eb547d4136962b5ae922fdd0e59a2d4559374755377b780da1f5237caed5590dc9749e70402c97185ad7c5bcd988808fd44e37f049ad6c02c8a 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: amd64 Version: 2.0.1-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2147 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-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/noble/main/r-cran-rphosfate_2.0.1-1.ca2404.2_amd64.deb Size: 1538174 MD5sum: 87b7e1c250637f1ff9e92d75fd9e7e4e SHA1: c2ae22873b7103cc3705334c22c3208cf4a7ea53 SHA256: c85aed6817c8bca2dad336a273c6fa040a1b808b2db1df2c692e10ec0141a96e SHA512: 17e0d8effcda1607c234cb75902ec20a62f91fd5f8d5905f43fed9850bfb0a3fbe8cd712ce4df196c9f31ea5a4221c2e75c1e100fe16ef01c8da342a7a0411bb 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: amd64 Version: 0.3.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 603 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-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/noble/main/r-cran-rphylopars_0.3.10-1.ca2404.1_amd64.deb Size: 332174 MD5sum: 7256d14d01e550bc2e92deed3bcf0d56 SHA1: 9b9d5c2ec5437adc0bfb4d3339b05c1f71d7db97 SHA256: bc783417742748de05ea60441c1a0dcf65bd87ce5a84550f4f2327c8e0069ee7 SHA512: bba74a9edf0777bcc0345a5ee9a3d0a36ff816e8bd576520ad7ada0440fdf15e5bc559415d041f99233cdbdd210ab7600ca789239c118766c44edc80e7051aed 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. 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The models are for matchings within a bipartite population where individuals have utility for people based on known and unknown characteristics. People can form a partnership or remain unpartnered. The model represents both the availability of potential partners of different types and preferences of individuals for such people. The software estimates preference parameters based on sample survey data on partnerships and population composition. The simulation of matchings and goodness-of-fit are considered. See Goyal, Handcock, Jackson, Rendall and Yeung (2022) . Package: r-cran-rpms Architecture: amd64 Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4401 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-rpms_0.5.1-1.ca2404.1_amd64.deb Size: 2935624 MD5sum: eb6edb4378ee7409a2a11e1376a545f0 SHA1: bce78467d3d504e9ba892eded29a69c29e671f13 SHA256: 3c17fc6df96264e41a0d0ef50f288f6b5ed43d755512f067338130c3c7920b26 SHA512: 750fdaaef3086a52730dd50a8b2dac318cd78387373331e4ea3f0847ba134d2463d44069b6915e7d58c0c33176210826f821b12cfd5974e892ef3e11fdd707f5 Homepage: https://cran.r-project.org/package=rpms Description: CRAN Package 'rpms' (Recursive Partitioning for Modeling Survey Data) Functions to allow users to build and analyze design consistent tree and random forest models using survey data from a complex sample design. The tree model algorithm can fit a linear model to survey data in each node obtained by recursively partitioning the data. The splitting variables and selected splits are obtained using a randomized permutation test procedure which adjusted for complex sample design features used to obtain the data. Likewise the model fitting algorithm produces design-consistent coefficients to any specified least squares linear model between the dependent and independent variables used in the end nodes. The main functions return the resulting binary tree or random forest as an object of "rpms" or "rpms_forest" type. The package also provides methods modeling a "boosted" tree or forest model and a tree model for zero-inflated data as well as a number of functions and methods available for use with these object types. Package: r-cran-rpoppler Architecture: amd64 Version: 0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: libc6 (>= 2.4), libglib2.0-0t64 (>= 2.12.0), libpoppler-glib8t64 (>= 0.18.0), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rpoppler_0.1-3-1.ca2404.1_amd64.deb Size: 29128 MD5sum: a970ca0b65c6bed712be45e11d59964b SHA1: b0a9fef1a6ec5b622d343ec97c159e2b827f6e34 SHA256: c4f6725aaaafb68bddb5919847f5a57f74a8a4d1f633fc5469d228ac2f9aea91 SHA512: a09bb3b7a12cfd322c1487f8b0ca4c5913de0f42c4a69f44859abc8cda6439711fe2a7074690d7b66ceef343a16fd661b9ec8d3fd552d489150b21bcf49f1237 Homepage: https://cran.r-project.org/package=Rpoppler Description: CRAN Package 'Rpoppler' (PDF Tools Based on Poppler) PDF tools based on the Poppler PDF rendering library. See for more information on Poppler. Package: r-cran-rpostgres Architecture: amd64 Version: 1.4.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 753 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libpq5 (>= 9.2~beta3), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bit64, r-cran-blob, r-cran-dbi, r-cran-hms, r-cran-lubridate, r-cran-withr, r-cran-cpp11 Suggests: r-cran-callr, r-cran-covr, r-cran-dbitest, r-cran-knitr, r-cran-rlang, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rpostgres_1.4.10-1.ca2404.1_amd64.deb Size: 439182 MD5sum: cb3aab0babf6133915a81e407dae5f43 SHA1: bf46f2f0744fbe2e936f9e0d4ca66f43f1d0a77f SHA256: fc6951655229abeb5766adc79ad351db4e68af153308f0945e9944f42560e770 SHA512: a9a094d1eab8f81fac497f40b136ab041435342d320221dfec27c99b24ed3cc84fabf3d19ef1562a5fed4089696d3f0a4ceffadd6247137bf41bdb16389a9e69 Homepage: https://cran.r-project.org/package=RPostgres Description: CRAN Package 'RPostgres' (C++ Interface to PostgreSQL) Fully DBI-compliant C++-backed interface to PostgreSQL , an open-source relational database. Package: r-cran-rpostgresql Architecture: amd64 Version: 0.7-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 538 Depends: libc6 (>= 2.14), libpq5, r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi Filename: pool/dists/noble/main/r-cran-rpostgresql_0.7-8-1.ca2404.1_amd64.deb Size: 363594 MD5sum: 106521ceb1c9310a9e880bd4e2ae979f SHA1: d38466f1a07cbb90a5b150a161c8177c37104edc SHA256: 68e4ecd9cc286b01cb4bb2e659a2a743ade87c7355df62d5111fa699650f9554 SHA512: 227cd1cd5abbeb298f8cc4fcf40bd6f5d03dfac5164092dfa5a6c98ff24f3bf0c310014ba329305f77bc526ad7db032d2d919a8f071aa36a6f0d2ce8289692b3 Homepage: https://cran.r-project.org/package=RPostgreSQL Description: CRAN Package 'RPostgreSQL' (R Interface to the 'PostgreSQL' Database System) Database interface and 'PostgreSQL' driver for 'R'. This package provides a Database Interface 'DBI' compliant driver for 'R' to access 'PostgreSQL' database systems. In order to build and install this package from source, 'PostgreSQL' itself must be present your system to provide 'PostgreSQL' functionality via its libraries and header files. These files are provided as 'postgresql-devel' package under some Linux distributions. On 'macOS' and 'Microsoft Windows' system the attached 'libpq' library source will be used. Package: r-cran-rpql Architecture: amd64 Version: 0.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gamlss.dist, r-cran-lme4, r-cran-matrix, r-cran-mass, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-nlme Filename: pool/dists/noble/main/r-cran-rpql_0.8.3-1.ca2404.1_amd64.deb Size: 185932 MD5sum: c04b33f4021c163d5548ed13c6637915 SHA1: 11a23d02124f17bb85aacad7da51c185f61b034c SHA256: c36e367ccf7ba972c6c4efc8c66b0e2f711cb6bcc9ab0606bcd6bdcfe2fe7743 SHA512: ed413b1a0eec993da53661aa0cc84cdb99a2d7c339bbd8014f0ef0b078387bcec1c86207c7badf78e4ade26b8a1b0be84463f427a56d9d86c00fa09143ff1c40 Homepage: https://cran.r-project.org/package=rpql Description: CRAN Package 'rpql' (Regularized PQL for Joint Selection in GLMMs) Performs joint selection in Generalized Linear Mixed Models (GLMMs) using penalized likelihood methods. Specifically, the Penalized Quasi-Likelihood (PQL) is used as a loss function, and penalties are then augmented to perform simultaneous fixed and random effects selection. Regularized PQL avoids the need for integration (or approximations such as the Laplace's method) during the estimation process, and so the full solution path for model selection can be constructed relatively quickly. Package: r-cran-rpref Architecture: amd64 Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 819 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-dplyr, r-cran-igraph, r-cran-lazyeval Suggests: r-cran-testthat, r-bioc-graph, r-bioc-rgraphviz, r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rpref_1.5.0-1.ca2404.1_amd64.deb Size: 470110 MD5sum: 8a53e60222dc87b1ebb73e4fefc46742 SHA1: 434134af8d9eae881e49dbd1f5f355762b3def4a SHA256: 3ee00e8313460151399c4e2c94b78c937943de5aadc421a8160fc79048763e83 SHA512: 1c443bc0a00abfd3c701480436d6987cdee47ce711e3090ad7392ad0b5a4d512574e912b07614bf11b21643038e307c13efff5d2fdeed9ea47f4eff364830825 Homepage: https://cran.r-project.org/package=rPref Description: CRAN Package 'rPref' (Database Preferences and Skyline Computation) Routines to select and visualize the maxima for a given strict partial order. This especially includes the computation of the Pareto frontier, also known as (Top-k) Skyline operator (see Börzsönyi, et al. (2001) ), and some generalizations known as database preferences (see Kießling (2002) ). 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Package: r-cran-rsghb Architecture: amd64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-rsghb_1.2.2-1.ca2404.1_amd64.deb Size: 309260 MD5sum: 98744d6454d3004c89eea0f5bf5277b7 SHA1: 210d772db29617793e76e8992f0ba15ace59d4c9 SHA256: 2a5d71d944e011323976c0c7dee2549769d8d44b6c1316863681647ec5dc2548 SHA512: 22b24641095001324b0e08d2f0077692c6d2635aff598f6ab9d1ef5c06f33eded2bd72772e7897dc86f52122fe80688d88738b4f38b0eaa0e1df440ae16fae80 Homepage: https://cran.r-project.org/package=RSGHB Description: CRAN Package 'RSGHB' (Functions for Hierarchical Bayesian Estimation: A FlexibleApproach) Functions for estimating models using a Hierarchical Bayesian (HB) framework. The flexibility comes in allowing the user to specify the likelihood function directly instead of assuming predetermined model structures. Types of models that can be estimated with this code include the family of discrete choice models (Multinomial Logit, Mixed Logit, Nested Logit, Error Components Logit and Latent Class) as well ordered response models like ordered probit and ordered logit. In addition, the package allows for flexibility in specifying parameters as either fixed (non-varying across individuals) or random with continuous distributions. Parameter distributions supported include normal, positive/negative log-normal, positive/negative censored normal, and the Johnson SB distribution. Kenneth Train's Matlab and Gauss code for doing Hierarchical Bayesian estimation has served as the basis for a few of the functions included in this package. These Matlab/Gauss functions have been rewritten to be optimized within R. Considerable code has been added to increase the flexibility and usability of the code base. Train's original Gauss and Matlab code can be found here: See Train's chapter on HB in Discrete Choice with Simulation here: ; and his paper on using HB with non-normal distributions here: . The authors would also like to thank the invaluable contributions of Stephane Hess and the Choice Modelling Centre: . 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Citations: Rodionov() from Rodionov (2004) Lanzante() from Lanzante (1996) Hellinger_trans from Numerical Ecology, Legendre & Legendre (ISBN 9780444538680) rolling_autoc from Liu, Gao & Wang (2018) Sample data sets lake_data & lake_RSI processed from Bush, Silman & Urrego (2004) Sample data set January_PDO from NOAA: . 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(2017) ). Package: r-cran-rsiena Architecture: amd64 Version: 1.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3446 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/noble/main/r-cran-rsiena_1.6.6-1.ca2404.1_amd64.deb Size: 2130686 MD5sum: 8776ef9ebad82ca01f7e901e4ea54b12 SHA1: fcfcc4ece30a1c41691af34b3142a731512b0868 SHA256: 95c9b5dc37a88987df2c29b6e0a51dbd0419cacba474ff68dee210d90863b99a SHA512: 98c2e48d42835c0ca26af308999b898c400d0ab858ae66833e7f3dccd0ee042019ccd807aa6560e973c191aa79d53c0739141eac9cba9bff0352df79b10208aa 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. 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Package: r-cran-rsixel Architecture: amd64 Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-png Suggests: r-cran-jpeg, r-cran-magick Filename: pool/dists/noble/main/r-cran-rsixel_0.0.4-1.ca2404.1_amd64.deb Size: 184176 MD5sum: a2e1eb7716dd35561909d03b813dee46 SHA1: f4048804a55e1bb2f6d004aed78f7651addb5c5a SHA256: d0f836682d56deb3832e6ae02063713ae26464042146fbe7c552d477b2b2cf3a SHA512: 0d26431ddc152ba97ea7ab702b936fb44ce911bfb774f4290a4061e2b7b0fbe7b14fb33b7878aec9de06585524da373318fd439f390d41cbfd699426d1acbbfd 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. 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Package: r-cran-rsnns Architecture: amd64 Version: 0.4-18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1738 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/noble/main/r-cran-rsnns_0.4-18-1.ca2404.1_amd64.deb Size: 1076624 MD5sum: 641ee8f7d1f34497f5ccb103fb9e5d93 SHA1: 8afd9faa726789e51428f0f0cc6d620bf3e1e605 SHA256: 4c289ff8eff0e327978b0c09c3a4d029e6117871ab8666ee80b71716b74f925e SHA512: 4a88dbbf300b52840ac304c7b0931299e4ddd4649df6de0ed354897a7c01a66d26bdc2aea3dad3ccb667f8f9caca43fead9c83de1817cb7fd5be022df9b91f02 Homepage: https://cran.r-project.org/package=RSNNS Description: CRAN Package 'RSNNS' (Neural Networks using the Stuttgart Neural Network Simulator(SNNS)) The Stuttgart Neural Network Simulator (SNNS) is a library containing many standard implementations of neural networks. 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Package: r-cran-rsofun Architecture: amd64 Version: 5.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3125 Depends: libc6 (>= 2.27), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-magrittr, r-cran-gensa, r-cran-bayesiantools, r-cran-lubridate, r-cran-multidplyr Suggests: r-cran-covr, r-cran-constructive, r-cran-cowplot, r-cran-rcmdcheck, r-cran-testthat, r-cran-rmarkdown, r-cran-ggplot2, r-cran-knitr, r-cran-sensitivity, r-cran-rpmodel, r-cran-rlang Filename: pool/dists/noble/main/r-cran-rsofun_5.1.0-1.ca2404.1_amd64.deb Size: 2100484 MD5sum: 6485449c176fb993844ec7a45f1e6182 SHA1: 618fb2a18029044bb616597719c2c9c66036452d SHA256: 7ec546371ce25015e9964af8628f84404f61fb1caeba891f1621e3c3ac86e103 SHA512: 263c952ed17571b39dc0f6ace040e560a069909a30a71de046a8a462213f9b49bdacb46b8290825fbba57b703fc6ff1c1a855b69dcce5715fd945c4ef6e9650f 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. 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Package: r-cran-rsomoclu Architecture: amd64 Version: 1.7.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-kohonen, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rsomoclu_1.7.7-1.ca2404.1_amd64.deb Size: 61646 MD5sum: 1b108dd30e63910e1bf2bc4e7ea8cca0 SHA1: 1d74ebe22e627069706ac9a743a53fad1a9def16 SHA256: 98c98ce24689beb20c33ecab0db2a5f724b3f05b3281344372fcdc07794477e4 SHA512: 8eadb010c020217b86d1a9ac55ab92cff5e00732f990157db6a30102a68a9ec33987361330d06ea8cb4edcbcd83a727b80b1e9fe13ad21be2c65f6277e17dbc2 Homepage: https://cran.r-project.org/package=Rsomoclu Description: CRAN Package 'Rsomoclu' (Somoclu) Somoclu is a massively parallel implementation of self-organizing maps. It exploits multicore CPUs and it can be accelerated by CUDA. The topology of the map can be planar or toroid and the grid of neurons can be rectangular or hexagonal . Details refer to (Peter Wittek, et al (2017)) . Package: r-cran-rspa Architecture: amd64 Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-validate, r-cran-lintools Suggests: r-cran-editrules, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rspa_0.2.8-1.ca2404.1_amd64.deb Size: 86776 MD5sum: 7b68fe0ceed221be44c7e2107ba93d60 SHA1: 32c162897568333baf5ccca55ca3f2693f370af1 SHA256: 4ed7eac1c94e94c7e9772397b4d55ae5bdebbdf7c71b534667bf527521fab6f4 SHA512: 9156eef418640b0288769dc4707905589700fc9d5bbbb383dfa5409a536b519d42f3d6f0ea97dc421230157abe32525600fedf0c20be3415b696f2a392b44abb Homepage: https://cran.r-project.org/package=rspa Description: CRAN Package 'rspa' (Adapt Numerical Records to Fit (in)Equality Restrictions) Minimally adjust the values of numerical records in a data.frame, such that each record satisfies a predefined set of equality and/or inequality constraints. The constraints can be defined using the 'validate' package. The core algorithms have recently been moved to the 'lintools' package, refer to 'lintools' for a more basic interface and access to a version of the algorithm that works with sparse matrices. Package: r-cran-rsparse Architecture: amd64 Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1329 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libopenblas0, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-matrixextra, r-cran-rcpp, r-cran-data.table, r-cran-float, r-cran-rhpcblasctl, r-cran-lgr, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-rsparse_0.5.3-1.ca2404.1_amd64.deb Size: 811822 MD5sum: 927edab35fd8b2acad856d197314f401 SHA1: 59e6374ff68cb0b199e6168795e6371383db3585 SHA256: 4f63471b9907d8204d92972d22697543fdb77c5445632604aed24d4de192a6be SHA512: 1806e9b758e3c187f3c1762daa9e49937e1da7ba7880723ed6729f63381aa35ee10f2f6b102df67f86170f83340cf92cb0c35d41104e45fe2e3c60e7ce2460d1 Homepage: https://cran.r-project.org/package=rsparse Description: CRAN Package 'rsparse' (Statistical Learning on Sparse Matrices) Implements many algorithms for statistical learning on sparse matrices - matrix factorizations, matrix completion, elastic net regressions, factorization machines. Also 'rsparse' enhances 'Matrix' package by providing methods for multithreaded matrix products and native slicing of the sparse matrices in Compressed Sparse Row (CSR) format. List of the algorithms for regression problems: 1) Elastic Net regression via Follow The Proximally-Regularized Leader (FTRL) Stochastic Gradient Descent (SGD), as per McMahan et al(, ) 2) Factorization Machines via SGD, as per Rendle (2010, ) List of algorithms for matrix factorization and matrix completion: 1) Weighted Regularized Matrix Factorization (WRMF) via Alternating Least Squares (ALS) - paper by Hu, Koren, Volinsky (2008, ) 2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro (2005, ) 3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD, Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder et al. (2014, ) 4) Linear-Flow matrix factorization, from 'Practical linear models for large-scale one-class collaborative filtering' by Sedhain, Bui, Kawale et al (2016, ISBN:978-1-57735-770-4) 5) GlobalVectors (GloVe) matrix factorization via SGD, paper by Pennington, Socher, Manning (2014, ) Package is reasonably fast and memory efficient - it allows to work with large datasets - millions of rows and millions of columns. This is particularly useful for practitioners working on recommender systems. Package: r-cran-rspectra Architecture: amd64 Version: 0.16-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1529 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc Filename: pool/dists/noble/main/r-cran-rspectra_0.16-2-1.ca2404.1_amd64.deb Size: 443396 MD5sum: da77a14024c2fb0911ad4775a96016b2 SHA1: a36539cb33f3546b6f8097ac105a80b81702671d SHA256: 43619e976e27cd7307228885ea242f4b4420302f3796bb826789af930c2db302 SHA512: 517b8f2cfb282858901b1fd22e5c154ff826654ed21225280cb1f06381cc3ba1003d2f098cb3c80487a2e5c8d1a26849622fae8140e67e932e9f72cd1521a89b 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. 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Package: r-cran-rspectral Architecture: amd64 Version: 1.0.0.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 978 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-igraph, r-bioc-graph, r-cran-rcpparmadillo Suggests: r-cran-rcolorbrewer, r-bioc-rgraphviz, r-cran-igraphdata, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rspectral_1.0.0.14-1.ca2404.1_amd64.deb Size: 743724 MD5sum: b57ea66d199dff466a2c4ba5d7fcc52c SHA1: 12d5a3ab7219de696770d6f33b6c2940eb79ab28 SHA256: ed66fe7268ae9923ca6d4c754a6d3181ab3122d3afe9e33b2c39d8b175122ce5 SHA512: 2921c33eb5a19fea60d81f426920a1343fec646e2849078a047b2ac4fd5dd80d987856b6cd8275942e4dc82eb397e47436022e2744cf8822317ffbc395cd8572 Homepage: https://cran.r-project.org/package=rSpectral Description: CRAN Package 'rSpectral' (Spectral Modularity Clustering) Implements the network clustering algorithm described in Newman (2006) . The complete iterative algorithm comprises of two steps. In the first step, the network is expressed in terms of its leading eigenvalue and eigenvector and recursively partition into two communities. Partitioning occurs if the maximum positive eigenvalue is greater than the tolerance (10e-5) for the current partition, and if it results in a positive contribution to the Modularity. Given an initial separation using the leading eigen step, 'rSpectral' then continues to maximise for the change in Modularity using a fine-tuning step - or variate thereof. The first stage here is to find the node which, when moved from one community to another, gives the maximum change in Modularity. This node’s community is then fixed and we repeat the process until all nodes have been moved. The whole process is repeated from this new state until the change in the Modularity, between the new and old state, is less than the predefined tolerance. A slight variant of the fine-tuning step, which can improve speed of the calculation, is also provided. Instead of moving each node into each community in turn, we only consider moves of neighbouring nodes, found in different communities, to the community of the current node of interest. The two steps process is repeatedly applied to each new community found, subdivided each community into two new communities, until we are unable to find any division that results in a positive change in Modularity. 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The source for the SQLite engine and for various extensions is included. System libraries will never be consulted because this package relies on static linking for the plugins it includes; this also ensures a consistent experience across all installations. Package: r-cran-rsrd Architecture: amd64 Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-janitor, r-cran-tibble, r-cran-ggplot2, r-cran-stringr, r-cran-rlang, r-cran-ggrepel, r-cran-gplots Filename: pool/dists/noble/main/r-cran-rsrd_0.1.8-1.ca2404.1_amd64.deb Size: 162646 MD5sum: f8f9fcf12e6b2d5f04908b73b81e05f1 SHA1: 3d0a2e77d279a46877efa44de47a7ce2e68b96c0 SHA256: c1201d221f54855404bab08f83ef6541279a4d125981cc1d31ba1a1b2bc095e0 SHA512: 0bd4d86a4123852a6f55f622a307ccb9363eb55c75b131e6f92e2136f02d1d163c055b6f3975d83a7217c37be23f6835d721865fec52b1aa613e894a394292cd Homepage: https://cran.r-project.org/package=rSRD Description: CRAN Package 'rSRD' (Sum of Ranking Differences Statistical Test) We provide an implementation for Sum of Ranking Differences (SRD), a novel statistical test introduced by Héberger (2010) . The test allows the comparison of different solutions through a reference by first performing a rank transformation on the input, then calculating and comparing the distances between the solutions and the reference - the latter is measured in the L1 norm. The reference can be an external benchmark (e.g. an established gold standard) or can be aggregated from the data. The calculated distances, called SRD scores, are validated in two ways, see Héberger and Kollár-Hunek (2011) . A randomization test (also called permutation test) compares the SRD scores of the solutions to the SRD scores of randomly generated rankings. The second validation option is cross-validation that checks whether the rankings generated from the solutions come from the same distribution or not. For a detailed analysis about the cross-validation process see Sziklai, Baranyi and Héberger (2021) . The package offers a wide array of features related to SRD including the computation of the SRD scores, validation options, input preprocessing and plotting tools. Package: r-cran-rssa Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1630 Depends: libc6 (>= 2.14), libfftw3-double3 (>= 3.3.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-svd, r-cran-forecast, r-cran-lattice Suggests: r-cran-testthat, r-cran-rspectra, r-cran-primme, r-cran-irlba Filename: pool/dists/noble/main/r-cran-rssa_1.1-1.ca2404.1_amd64.deb Size: 1498372 MD5sum: 966f8ec9d6d34e17dd5e6eb2ac774197 SHA1: 6fbc685971af0c19d29c212923753206958aa91f SHA256: 4567a10e08b240bce0511ff7f71a6574815fc7da68a33e5767ef3ae477b2c71b SHA512: 6fb9c45aa16fed05ed71b20a2378c19f44c1a0092f26f9edfbd3135c3892b9272c8f640bc9236a888a22be06b0023929ffbf8cb377e202eab2b2e82fc7bec469 Homepage: https://cran.r-project.org/package=Rssa Description: CRAN Package 'Rssa' (A Collection of Methods for Singular Spectrum Analysis) Methods and tools for Singular Spectrum Analysis including decomposition, forecasting and gap-filling for univariate and multivariate time series. General description of the methods with many examples can be found in the book Golyandina (2018, ). See 'citation("Rssa")' for details. Package: r-cran-rssl Architecture: amd64 Version: 0.9.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2230 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-kernlab, r-cran-quadprog, r-cran-matrix, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-reshape2, r-cran-scales, r-cran-cluster, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-sparsem, r-cran-numderiv, r-cran-liblinear, r-cran-covr Filename: pool/dists/noble/main/r-cran-rssl_0.9.8-1.ca2404.1_amd64.deb Size: 1858164 MD5sum: f68a72e57fe37a0c185f344149dc18cf SHA1: fc5618d71590abf7b38046d85e205f7fcc4f61ba SHA256: 297bd2c48daf0500800cdeb9533bb08c05f634d68f511149e97f189dce065fe2 SHA512: 967cf1e61b8ff071e37ce80d193401fdc40e829b464abf066064cb0ec7a8c9b2ffa8d2fa4e72125b57908b24bdd73c4e95279a0a04be7123af033efde023e8df Homepage: https://cran.r-project.org/package=RSSL Description: CRAN Package 'RSSL' (Implementations of Semi-Supervised Learning Approaches forClassification) A collection of implementations of semi-supervised classifiers and methods to evaluate their performance. The package includes implementations of, among others, Implicitly Constrained Learning, Moment Constrained Learning, the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic Likelihood estimation, S4VM and WellSVM. Package: r-cran-rstan Architecture: amd64 Version: 2.32.7-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5995 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-stanheaders, r-cran-inline, r-cran-gridextra, r-cran-rcpp, r-cran-rcppparallel, r-cran-loo, r-cran-pkgbuild, r-cran-quickjsr, r-cran-ggplot2, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-testthat, r-cran-kernsmooth, r-cran-shinystan, r-cran-bayesplot, r-cran-rmarkdown, r-cran-rstantools, r-cran-rstudioapi, r-cran-matrix, r-cran-knitr, r-cran-coda, r-cran-v8 Filename: pool/dists/noble/main/r-cran-rstan_2.32.7-1.ca2404.2_amd64.deb Size: 2031394 MD5sum: 1df3ae536c61019e755ac1263d81c832 SHA1: 5b88b85461c1fae7e22f6d2ef5c101cd77493290 SHA256: 03bf23dada0c562f2d8a36b5baaf3e709aadfa091539bebef969d199e7637675 SHA512: 399cfb2d0aefed45afab502ca415c55da26e172df9259aaed72d94153c1607d8596af9b7649cc3c281e796e3fb31a477fc8ccb288b168d368ddba26bb5104933 Homepage: https://cran.r-project.org/package=rstan Description: CRAN Package 'rstan' (R Interface to Stan) User-facing R functions are provided to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the 'StanHeaders' package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via 'variational' approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives. Package: r-cran-rstanarm Architecture: amd64 Version: 2.32.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 21204 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bayesplot, r-cran-ggplot2, r-cran-lme4, r-cran-loo, r-cran-matrix, r-cran-nlme, r-cran-posterior, r-cran-rstan, r-cran-rstantools, r-cran-shinystan, r-cran-survival, r-cran-reformulas, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-biglm, r-cran-betareg, r-cran-data.table, r-cran-digest, r-cran-gridextra, r-cran-hsaur3, r-cran-knitr, r-cran-mass, r-cran-mgcv, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-gamm4, r-cran-shiny, r-cran-v8 Filename: pool/dists/noble/main/r-cran-rstanarm_2.32.2-1.ca2404.1_amd64.deb Size: 7989508 MD5sum: a0539119a76907888b1410a2e613e719 SHA1: e90bf05daebff06481039158883d4ba0b548e5f2 SHA256: a315e54ad5617b7ce219e1b5849b5c1f0c9358ae2e7acca2ae970d171266c6c0 SHA512: dda24bba82e702b8bcb27ba013544e41b3ff8f087141864f9ab99d6d1481e5e5878dc5db9fd9e2b10525a25857a174a99e874783af6a5e5603733afe3829727f Homepage: https://cran.r-project.org/package=rstanarm Description: CRAN Package 'rstanarm' (Bayesian Applied Regression Modeling via Stan) Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors. Package: r-cran-rstanbdp Architecture: amd64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4494 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-rrcov, r-cran-mixtools, r-cran-bayestestr, r-cran-kernsmooth, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/noble/main/r-cran-rstanbdp_0.0.3-1.ca2404.1_amd64.deb Size: 968640 MD5sum: c64490e750409b8760f5838e475553f0 SHA1: 53f8ba9f4e7563649f784ea6fece8fd7d3a9e663 SHA256: 2bfd9e519ebdf597ba6b4ffde3f624e8bdb981e444e8521a29fa808964bf1447 SHA512: 2dc2cb9157534f265ac353f2e9eabae5c83bb1f5c76f0f26df3b3806e0009d79503aa5386d7f94c243fcaa92ead47ed98f4a648ff1bc1a9434cc332ae482c8e5 Homepage: https://cran.r-project.org/package=rstanbdp Description: CRAN Package 'rstanbdp' (Bayesian Deming Regression for Method Comparison) Regression methods to quantify the relation between two measurement methods are provided by this package. The focus is on a Bayesian Deming regressions family. With a Bayesian method the Deming regression can be run in a traditional fashion or can be run in a robust way just decreasing the degree of freedom d.f. of the sampling distribution. With d.f. = 1 an extremely robust Cauchy distribution can be sampled. Moreover, models for dealing with heteroscedastic data are also provided. For reference see G. Pioda (2024) . Package: r-cran-rstanemax Architecture: amd64 Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2797 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-ggplot2, r-cran-posterior, r-cran-lifecycle, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-tibble, r-cran-withr, r-cran-tidybayes Filename: pool/dists/noble/main/r-cran-rstanemax_0.1.9-1.ca2404.1_amd64.deb Size: 956202 MD5sum: 729ebeddb30124bae8cdd4d80fb79c3f SHA1: 90a3de56ce8a74f45e1293d441c5e1fda173c260 SHA256: fae358e9978c4a576ae7a503ccd0bd8bfb280ffea9a22b304745ee22b4526ba3 SHA512: 805686ec3be5482eddf8cd1cac7d0717c2efeeb4d965d77273568a5097141e55ee67e3619d3ccd39995518d6078ec49c2647a6d0f342c342423506ed99b5bf0f Homepage: https://cran.r-project.org/package=rstanemax Description: CRAN Package 'rstanemax' (Emax Model Analysis with 'Stan') Perform sigmoidal Emax model fit using 'Stan' in a formula notation, without writing 'Stan' model code. Package: r-cran-rstiefel Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 569 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-rstiefel_1.0.1-1.ca2404.1_amd64.deb Size: 499546 MD5sum: 0ef6139c3f2f89e59f97ea3fa20f0627 SHA1: cd5b9d9452325425929ec46d46e1c2d15ab0daeb SHA256: 45104811690464e7b6d118ae14e00a3537af0758b4a825ed18c508fd32ae47e4 SHA512: f675ec42f8328105e4efdae050c47733b864c5302a451854b89a4b45e590096e66f353de07e187625688ba27090bbd58ebb8b5ba1da441b05e1063e19369936e Homepage: https://cran.r-project.org/package=rstiefel Description: CRAN Package 'rstiefel' (Random Orthonormal Matrix Generation and Optimization on theStiefel Manifold) Simulation of random orthonormal matrices from linear and quadratic exponential family distributions on the Stiefel manifold. The most general type of distribution covered is the matrix-variate Bingham-von Mises-Fisher distribution. Most of the simulation methods are presented in Hoff(2009) "Simulation of the Matrix Bingham-von Mises-Fisher Distribution, With Applications to Multivariate and Relational Data" . The package also includes functions for optimization on the Stiefel manifold based on algorithms described in Wen and Yin (2013) "A feasible method for optimization with orthogonality constraints" . Package: r-cran-rstoolbox Architecture: amd64 Version: 1.0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2445 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-sf, r-cran-terra, r-cran-xml, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-reshape2, r-cran-lifecycle, r-cran-exactextractr, r-cran-rcpp, r-cran-magrittr, r-cran-rcpparmadillo Suggests: r-cran-randomforest, r-cran-lattice, r-cran-kernlab, r-cran-e1071, r-cran-gridextra, r-cran-pls, r-cran-testthat, r-cran-themis, r-cran-rose Filename: pool/dists/noble/main/r-cran-rstoolbox_1.0.2.2-1.ca2404.1_amd64.deb Size: 2073034 MD5sum: 3991ede3d2a0690df6d1e0bfa3fecbfd SHA1: b61a4cd12dd280f09d876c5d0c59291b8040f3ff SHA256: 68cae79ab13b748ac6fefba8542de06f861c2c6361899e11233b449a148c0c9e SHA512: d1f81337585d7b4bf01181d0a2c951ee214a10c76ab4186857ff470077cc330788df37929bf10f3c4b3951d79c248dcda68521d05c5a5913db42e6e26559e6d6 Homepage: https://cran.r-project.org/package=RStoolbox Description: CRAN Package 'RStoolbox' (Remote Sensing Data Analysis) Toolbox for remote sensing image processing and analysis such as calculating spectral indexes, principal component transformation, unsupervised and supervised classification or fractional cover analyses. Package: r-cran-rstpm2 Architecture: amd64 Version: 1.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3764 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-mgcv, r-cran-bbmle, r-cran-fastghquad, r-cran-mvtnorm, r-cran-numderiv, r-cran-lsoda, r-cran-rcpparmadillo Suggests: r-cran-eha, r-cran-testthat, r-cran-ggplot2, r-cran-lattice, r-cran-readstata13, r-cran-mstate, r-cran-scales, r-cran-survpen, r-cran-flexsurv, r-cran-timereg Filename: pool/dists/noble/main/r-cran-rstpm2_1.7.1-1.ca2404.1_amd64.deb Size: 2170806 MD5sum: 06d659c723e600ffffb3692abe07ecfd SHA1: 11a78cb799ce94d2a0f257d6ee7f1174c9d5fd95 SHA256: b1f195d5b7b5ce9eadfb47181ebbf3216db865a6272e488da4d495eb52c1a0f7 SHA512: 327e1014785a09fc0ede909c970a2d8af312a2e89fdd7783f7cd6516cfff688c7f270b6abe66203d19ee0c2cbbd339db5a4c5ee414decde00d52b7b3ae1126c3 Homepage: https://cran.r-project.org/package=rstpm2 Description: CRAN Package 'rstpm2' (Smooth Survival Models, Including Generalized Survival Models) R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth . For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects , and copulas. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation. Package: r-cran-rstr Architecture: amd64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2627 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-matrixstats, r-cran-spdep, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rstr_1.1.4-1.ca2404.1_amd64.deb Size: 1859128 MD5sum: f55c8c088acb03ddfaad6a89100453ca SHA1: dafc179a7d9504b51f3c0f0859ff6f3fe0432d12 SHA256: 29ebca607b92d8634dfd211c7bbc47bd2cb9485a183da0d828ab53b66953e83e SHA512: caee5310be3d236e63c52b1ef9fe98cd5d8084c8d69c6ef64236db8a07d511a682db937171e9e9cc1cb9e3dac1fc4b3858474e8b9ef046dfe6483a008892a0db Homepage: https://cran.r-project.org/package=RSTr Description: CRAN Package 'RSTr' (Gibbs Samplers for Discrete Bayesian Spatiotemporal Models) Takes Poisson or Binomial discrete spatial data and runs a Gibbs sampler for a variety of Spatiotemporal Conditional Autoregressive (CAR) models. Includes measures to prevent estimate over-smoothing through a restriction of model informativeness for select models. Also provides tools to load output and get median estimates. Implements methods from Besag, York, and Mollié (1991) "Bayesian image restoration, with two applications in spatial statistics" , Gelfand and Vounatsou (2003) "Proper multivariate conditional autoregressive models for spatial data analysis" , Quick et al. (2017) "Multivariate spatiotemporal modeling of age-specific stroke mortality" , and Quick et al. (2021) "Evaluating the informativeness of the Besag-York-Mollié CAR model" . Package: r-cran-rstream Architecture: amd64 Version: 1.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rstream_1.3.7-1.ca2404.1_amd64.deb Size: 356712 MD5sum: fd03594ded6200bdc71419db3a3b92cc SHA1: 3acb3dae4836f3706b7e68e09ec9113cb9fa2a85 SHA256: 5394f79fb763c87dabc835a14c6ba94b639c91a6f326515d6682bc82e4a1af63 SHA512: 23cca116b9c9c7ebd4135469e8053e69b8b76668eb0d43c841a1c9ce0ef898832fe94426f5520ec445a0343160dcc3e19aaf84912df3bde58a2b0d227132ad47 Homepage: https://cran.r-project.org/package=rstream Description: CRAN Package 'rstream' (Streams of Random Numbers) Unified object oriented interface for multiple independent streams of random numbers from different sources. Package: r-cran-rsubbotools Architecture: amd64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 771 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppgsl Suggests: r-cran-testthat, r-cran-usethis, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rsubbotools_0.0.1-1.ca2404.1_amd64.deb Size: 318864 MD5sum: 136fc6257e721c539271b1e232938774 SHA1: d03a4f052d65c72429543968e836c8843e29564e SHA256: 4c95b0e514bfef8893541a958fbe3987e29a0a71f8b57db94755e8aa3ec1d4fc SHA512: 18c92836db969b52b12b223ba6bfd5f21d24a379293e338d5b4da2c668292a856c4aee7ec724a7aec693b88b8b245c6344b03d71cc670d5acff74e338c158e2a Homepage: https://cran.r-project.org/package=Rsubbotools Description: CRAN Package 'Rsubbotools' (Fast Estimation of Subbottin and AEP Distributions (GeneralizedError Distribution)) Create densities, probabilities, random numbers, quantiles, and maximum likelihood estimation for several distributions, mainly the symmetric and asymmetric power exponential (AEP), a.k.a. the Subbottin family of distributions, also known as the generalized error distribution. Estimation is made using the design of Bottazzi (2004) , where the likelihood is maximized by several optimization procedures using the 'GNU Scientific Library (GSL)', translated to 'C++' code, which makes it both fast and accurate. The package also provides methods for the gamma, Laplace, and Asymmetric Laplace distributions. Package: r-cran-rsvddpd Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-matrixstats, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark, r-bioc-pcamethods, r-cran-v8 Filename: pool/dists/noble/main/r-cran-rsvddpd_1.0.1-1.ca2404.1_amd64.deb Size: 109920 MD5sum: cccfb6ab56f1188e0ada5da3a2d025fc SHA1: 6beea09a4772fcd85535a31e079b728024d5ff9c SHA256: b1b85a56307012470425601cfe4371d899ec60c7c79d799b855f6fe838d12a3d SHA512: 316bbfb52702f6aa4157510b5ce50c232654ee6a0ca0a9b637bd98a6cf288711b0e9350cf0ac76092c4de9a8cb9d2ed148e0c4e9d660f6fd2fc4a307df0bdc68 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-rsvg Architecture: amd64 Version: 2.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 Depends: libc6 (>= 2.14), libcairo2 (>= 1.6.0), libglib2.0-0t64 (>= 2.12.0), librsvg2-2 (>= 2.47.3), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-magick, r-cran-rmarkdown, r-cran-spelling, r-cran-svglite, r-cran-testthat, r-cran-webp, r-cran-png Filename: pool/dists/noble/main/r-cran-rsvg_2.7.0-1.ca2404.1_amd64.deb Size: 268652 MD5sum: 326cf1cd020e53724cc78a195a012ce9 SHA1: df42dec053c6deadc31c4b2d543fd0448e03b638 SHA256: 953bcdf33a6fdcd8d2417d149768da3fec2e9b01950c68fa4ff42ef3d60dba2e SHA512: 544977a37a7032e9845b160a43ebfdfab9ebbe2fb6e138ec35ad494ae8e001d90dc4dfa326b0f13c8413ea7abb24b84efdd499fdef739cab6889bc10a0568327 Homepage: https://cran.r-project.org/package=rsvg Description: CRAN Package 'rsvg' (Render SVG Images into PDF, PNG, (Encapsulated) PostScript, orBitmap Arrays) Renders vector-based svg images into high-quality custom-size bitmap arrays using 'librsvg2'. 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Package: r-cran-rsyslog Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rsyslog_1.0.3-1.ca2404.1_amd64.deb Size: 19642 MD5sum: 328e28144450dfbcd569da2ff88e08c5 SHA1: 6c7ef063203669721cb1793e64d75021dce68145 SHA256: 1493cbc3bb276a4870dc4cfb27e35899086e721762a6ead979094559f7bdcd9d SHA512: 72d6cd4dadf42161e0b7795d8717baf484eed1b9cb717c61c7548319510c774f18573e526499d135ea8af51566fc58d3d628563f0d3cdbe6fcf11e62daf366e0 Homepage: https://cran.r-project.org/package=rsyslog Description: CRAN Package 'rsyslog' (Interface to the 'syslog' System Logger) Functions to write messages to the 'syslog' system logger API, available on all 'POSIX'-compatible operating systems. Features include tagging messages with a priority level and application type, as well as masking (hiding) messages below a given priority level. Package: r-cran-rtcc Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats, r-cran-vegan, r-cran-rcpp, r-cran-testthat Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rtcc_0.1.1-1.ca2404.1_amd64.deb Size: 114528 MD5sum: bc03af94fa0b5d9370441d18872f6ace SHA1: 400adfaf536e297e329e2e91131e0d101b47410a SHA256: a9fdb150a3ce678935e3cbf3c6ff3a8f4426058289dc93f5a595fbc95521a3a8 SHA512: 1628a708c94ec3010fce695c47da99ce9837e75e27ab5803e442d78c54f8fb3730dde9d388a600f9cc5d6d5c23217622d36435ef387afef3cf75532fb814bfc2 Homepage: https://cran.r-project.org/package=RTCC Description: CRAN Package 'RTCC' (Detecting Trait Clustering in Environmental Gradients) The Randomized Trait Community Clustering method (Triado-Margarit et al., 2019, ) is a statistical approach which allows to determine whether if an observed trait clustering pattern is related to an increasing environmental constrain. The method 1) determines whether exists or not a trait clustering on the sampled communities and 2) assess if the observed clustering signal is related or not to an increasing environmental constrain along an environmental gradient. Also, when the effect of the environmental gradient is not linear, allows to determine consistent thresholds on the community assembly based on trait-values. Package: r-cran-rtdists Architecture: amd64 Version: 0.11-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1228 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-evd, r-cran-msm, r-cran-gsl, r-cran-rcpp Suggests: r-cran-testthat, r-cran-glba, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-lattice, r-cran-latticeextra, r-cran-binom, r-cran-rwiener Filename: pool/dists/noble/main/r-cran-rtdists_0.11-5-1.ca2404.1_amd64.deb Size: 725210 MD5sum: 55da23a170f22194fe1150e41790851a SHA1: 8453fd11780a8de0767f62a965e0e9cee58c34f1 SHA256: 36a9a6d257d3c72f4a00c4da587ad67b40f9ed673f19d8f4a44d80f6a4dde78a SHA512: 89e9d167797c0b2a86e00e5c3d63e0b421a6b0163ea17c84728b541ba18311caf1e2fe47c08672f6ec2b3cdd30dc217e7306b36306e3526d53d9958c7e49631e Homepage: https://cran.r-project.org/package=rtdists Description: CRAN Package 'rtdists' (Response Time Distributions) Provides response time distributions (density/PDF, distribution function/CDF, quantile function, and random generation): (a) Ratcliff diffusion model (Ratcliff & McKoon, 2008, ) based on C code by Andreas and Jochen Voss and (b) linear ballistic accumulator (LBA; Brown & Heathcote, 2008, ) with different distributions underlying the drift rate. Package: r-cran-rtestim Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1701 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dspline, r-cran-ggplot2, r-cran-matrix, r-cran-rcpp, r-cran-rlang, r-cran-tibble, r-cran-tvdenoising, r-cran-vctrs, r-cran-bh, r-cran-rcppeigen, r-cran-testthat Suggests: r-cran-dplyr, r-cran-forcats, r-cran-knitr, r-cran-nnet, r-cran-rmarkdown, r-cran-tidyr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-rtestim_1.0.2-1.ca2404.1_amd64.deb Size: 1207596 MD5sum: b88d63005fba9b4e44da931ef1ce46ce SHA1: 9fbff563d9787b8f737ca14eecc2920fa3d9ebd6 SHA256: fcf59895bf7edd98c351aa22332a7cedd0717ed821c19dc14718a6a983502a49 SHA512: a4e513be7ae2acfd1c3b4c8bf1fe1b22a5478a96efb7cf5cdabe71fbaf823d9f4a71f25cc8d8a355d1ad67240aba1324c417b676d519f80686fef91505547a16 Homepage: https://cran.r-project.org/package=rtestim Description: CRAN Package 'rtestim' (Estimate the Effective Reproductive Number with Trend Filtering) Use trend filtering, a type of regularized nonparametric regression, to estimate the instantaneous reproduction number, also called Rt. This value roughly says how many new infections will result from each new infection today. Values larger than 1 indicate that an epidemic is growing while those less than 1 indicate decline. For more details about this methodology, see Liu, Cai, Gustafson, and McDonald (2024) . 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Package: r-cran-rtiktoken Architecture: amd64 Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11015 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rtiktoken_0.0.7-1.ca2404.1_amd64.deb Size: 3346258 MD5sum: 90ee081e6c42d01348a9e50ca6403a65 SHA1: 815e48bd3f17f7d504c331b5bc3683ecd5b3b321 SHA256: b52ae7a509e9f3fad5fd87bbf172a9a024f223bbbcc69db6e4bb06b975269a61 SHA512: 61f10f9d1784871fba3ce61144bd8c217820100df0e479519709f81f46b8b828df3bf63ba41343468bfa3d375e368a64533f6730790ef5b78f8c780277caf330 Homepage: https://cran.r-project.org/package=rtiktoken Description: CRAN Package 'rtiktoken' (A Byte-Pair-Encoding (BPE) Tokenizer for OpenAI's Large LanguageModels) A thin wrapper around the tiktoken-rs crate, allowing to encode text into Byte-Pair-Encoding (BPE) tokens and decode tokens back to text. 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The package compiles 'TinyCC' from source and provides R functions to interact with the compiler. 'TinyCC' can be used for header preprocessing, just-in-time compilation of 'C' code in 'R', and lightweight 'C' scripting workflows. 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Package: r-cran-rtkore Architecture: amd64 Version: 1.6.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3593 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-inline Filename: pool/dists/noble/main/r-cran-rtkore_1.6.13-1.ca2404.1_amd64.deb Size: 907934 MD5sum: ba8540f0f37700a5aaee9f9737a37804 SHA1: b5ef738a075010c25f8671f118134843f0e584bb SHA256: 3407b5e82a2212415df3f7be028fc5e1e69f653b57196a215d337889a1ea3166 SHA512: 521579cb1c21bee3887a1853732387e1c6a5e6252b417b5fb83f58bc4079e06cb5e436c06757f7a57881edfeea2c7ccf31f2ef969f3b05c6433ff9c5eb6ed67d Homepage: https://cran.r-project.org/package=rtkore Description: CRAN Package 'rtkore' ('STK++' Core Library Integration to 'R' using 'Rcpp') 'STK++' is a collection of C++ classes for statistics, clustering, linear algebra, arrays (with an 'Eigen'-like API), regression, dimension reduction, etc. The integration of the library to 'R' is using 'Rcpp'. The 'rtkore' package includes the header files from the 'STK++' core library. All files contain only template classes and/or inline functions. 'STK++' is licensed under the GNU LGPL version 2 or later. 'rtkore' (the 'stkpp' integration into 'R') is licensed under the GNU GPL version 2 or later. See file LICENSE.note for details. Package: r-cran-rtl Architecture: amd64 Version: 1.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3316 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-plotly, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-timetk, r-cran-tsibble, r-cran-xts, r-cran-zoo, r-cran-glue, r-cran-rcpp, r-cran-lifecycle, r-cran-ttr, r-cran-tidyselect, r-cran-performanceanalytics, r-cran-numderiv Suggests: r-cran-testthat, r-cran-covr, r-cran-lpsolve, r-cran-rugarch, r-cran-tidyquant, r-cran-feasts, r-cran-fabletools, r-cran-mass, r-cran-sf Filename: pool/dists/noble/main/r-cran-rtl_1.3.7-1.ca2404.1_amd64.deb Size: 3235304 MD5sum: 5b9a9247a70dafd03825ea5f53d51cdd SHA1: d2f8e5a7e21082b24f379c616eaca85e41d225da SHA256: 1929cd76ce07bd9b399ef94a310b414aa64afd290e7a37c0ce9ef1f6e65570cc SHA512: 613fbde53e413f13303049016200a647673852f48a136951bbd2219da9c49dfcf441353d009116ff9d2c8f736d8e35bb829d36c06618cc2350461c84133f2c44 Homepage: https://cran.r-project.org/package=RTL Description: CRAN Package 'RTL' (Risk Tool Library - Trading, Risk, Analytics for Commodities) A toolkit for Commodities 'analytics', risk management and trading professionals. 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Package: r-cran-rtls Architecture: amd64 Version: 0.2.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5394 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-alphashape3d, r-cran-boot, r-cran-dosnow, r-cran-foreach, r-cran-rcpphnsw, r-cran-rgl, r-cran-sf, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rtls_0.2.6.1-1.ca2404.1_amd64.deb Size: 4128090 MD5sum: 7ac9865f58c7fbc208de5e1bce6b2919 SHA1: f3c8faf3fe0cd3dc26d96291538ea875c0284d6b SHA256: 64d206c7605be153e61201f1163da83a9ab8063a9d9ab901945012e724a7729b SHA512: dda32daba7b2cce31b19fc72bc734e39e52155379409bb062184d36031d25d9349b3ef48a90f55528a7165a2a064fc034a59e3ea674d2bcbc597685df6a537b5 Homepage: https://cran.r-project.org/package=rTLS Description: CRAN Package 'rTLS' (Tools to Process Point Clouds Derived from Terrestrial LaserScanning) A set of tools to process and calculate metrics on point clouds derived from terrestrial LiDAR (Light Detection and Ranging; TLS). Its creation is based on key aspects of the TLS application in forestry and ecology. Currently, the main routines are based on filtering, neighboring features of points, voxelization, canopy structure, and the creation of artificial stands. It is written using data.table and C++ language and in most of the functions it is possible to use parallel processing to speed-up the routines. Package: r-cran-rtmb Architecture: amd64 Version: 1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10141 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-tmb, r-cran-mass, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-igraph, r-cran-tinytest, r-cran-numderiv, r-cran-tinyplot, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-rtmb_1.9-1.ca2404.1_amd64.deb Size: 3388932 MD5sum: 10fbacdc0509e8721ef9fc373a78da7a SHA1: 08920d9981a86186d96f0c03138cb337c9566ed7 SHA256: 5e365742028364cd05a8e94f586933826e44b86bc0b1a3cf59b89a14e56d6550 SHA512: 976359a9657a2a484184e9a137275bf8654b4fb2a08a1425ea4134d5f1433a0967845a8cac8b468a45cacad505896f61ef9eb632de5e147a9aaf72cb967c0217 Homepage: https://cran.r-project.org/package=RTMB Description: CRAN Package 'RTMB' ('R' Bindings for 'TMB') Native 'R' interface to 'TMB' (Template Model Builder) so models can be written entirely in 'R' rather than 'C++'. Automatic differentiation, to any order, is available for a rich subset of 'R' features, including linear algebra for dense and sparse matrices, complex arithmetic, Fast Fourier Transform, probability distributions and special functions. 'RTMB' provides easy access to model fitting and validation following the principles of Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H., & Bell, B. M. (2016) and Thygesen, U.H., Albertsen, C.M., Berg, C.W. et al. (2017) . Package: r-cran-rtmpt Architecture: amd64 Version: 2.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1446 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-data.table, r-cran-loo, r-cran-ryacas, r-cran-stringr, r-cran-truncnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rtmpt_2.0-3-1.ca2404.1_amd64.deb Size: 786350 MD5sum: 5fa613c92f770bbf98705733cb882a1a SHA1: b2faaf1ae76d6a0a5f6165ef75d2b575e4243502 SHA256: 0d9de0ed4a010998e5210fafdf63a6ff62533e5fdd92e069d945fbde7520042a SHA512: 3e1880e1312ec59fb04b4fd321bed18004005f1412739a1acede6a7d7f14f87918de9669b864e11320204c3ca967f6620b80c1e6e66bb4a8e1e2807dd34f24d7 Homepage: https://cran.r-project.org/package=rtmpt Description: CRAN Package 'rtmpt' (Fitting (Exponential/Diffusion) RT-MPT Models) Fit (exponential or diffusion) response-time extended multinomial processing tree (RT-MPT) models by Klauer and Kellen (2018) and Klauer, Hartmann, and Meyer-Grant (submitted). The RT-MPT class not only incorporate frequencies like traditional multinomial processing tree (MPT) models, but also latencies. This enables it to estimate process completion times and encoding plus motor execution times next to the process probabilities of traditional MPTs. 'rtmpt' is a hierarchical Bayesian framework and posterior samples are sampled using a Metropolis-within-Gibbs sampler (for exponential RT-MPTs) or Hamiltonian-within-Gibbs sampler (for diffusion RT-MPTs). Package: r-cran-rtop Architecture: amd64 Version: 0.6-17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1982 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gstat, r-cran-sf, r-cran-units, r-cran-sp Suggests: r-cran-intamap, r-cran-spacetime, r-cran-data.table, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-rtop_0.6-17-1.ca2404.1_amd64.deb Size: 932256 MD5sum: 25077f671967b0330fcdc972d6aa820f SHA1: d408d280f45ca4600cdb35995c598d987336215b SHA256: d6290e46e2457c152544dbf1cece8b66ab10c2be9c078586123a7ff91ded6a58 SHA512: da3bf206e8817ca5b62f6f9a1bd377bca6e4cc3c4228c575460efd227fee807c9a03e20ad024093a7769a07cd4d3750cd23b07157f8fe56da41ed35db39f1784 Homepage: https://cran.r-project.org/package=rtop Description: CRAN Package 'rtop' (Interpolation of Data with Variable Spatial Support) Data with irregular spatial support, such as runoff related data or data from administrative units, can with 'rtop' be interpolated to locations without observations with the top-kriging method. A description of the package is given by Skøien et al (2014) . Package: r-cran-rtpcr Architecture: amd64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-multcomp, r-cran-multcompview, r-cran-ggplot2, r-cran-lmertest, r-cran-purrr, r-cran-reshape2, r-cran-tidyr, r-cran-dplyr, r-cran-emmeans Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rtpcr_2.0.2-1.ca2404.1_amd64.deb Size: 245792 MD5sum: 10469949c17daf80bf409f3140d0c54e SHA1: ea7b92de2d3a3de00edc0e01b9ab8eab640037d8 SHA256: 00036104ee09653249e7aeeabe86a632edca190edc2d6be0809648ae0b694bc0 SHA512: b3f921a4f7c79ac38671e81a73a9a8f953a2829d14965b6d94adf20727d1714192cdb740bd0879bfa0dd3c9a0345b692fd5d1490d8a7d569d4c437b4f023e583 Homepage: https://cran.r-project.org/package=rtpcr Description: CRAN Package 'rtpcr' (qPCR Data Analysis) Various methods are employed for statistical analysis and graphical presentation of real-time PCR (quantitative PCR or qPCR) data. 'rtpcr' handles amplification efficiency calculation, statistical analysis and graphical representation of real-time PCR data based on up to two reference genes. By accounting for amplification efficiency values, 'rtpcr' was developed using a general calculation method described by Ganger et al. (2017) and Taylor et al. (2019) , covering both the Livak and Pfaffl methods. Based on the experimental conditions, the functions of the 'rtpcr' package use t-test (for experiments with a two-level factor), analysis of variance (ANOVA), analysis of covariance (ANCOVA) or analysis of repeated measure data to calculate the fold change (FC, Delta Delta Ct method) or relative expression (RE, Delta Ct method). The functions further provide standard errors and confidence intervals for means, apply statistical mean comparisons and present significance. To facilitate function application, different data sets were used as examples and the outputs were explained. ‘rtpcr’ package also provides bar plots using various controlling arguments. The 'rtpcr' package is user-friendly and easy to work with and provides an applicable resource for analyzing real-time PCR data. Package: r-cran-rtransferentropy Architecture: amd64 Version: 0.2.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 894 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-future, r-cran-future.apply, r-cran-rcpp Suggests: r-cran-data.table, r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-quantmod, r-cran-rmarkdown, r-cran-testthat, r-cran-vars, r-cran-xts, r-cran-zoo Filename: pool/dists/noble/main/r-cran-rtransferentropy_0.2.21-1.ca2404.1_amd64.deb Size: 619892 MD5sum: 2f87c7c9e2a21afa3600e6c1c6649315 SHA1: f5c64be10d3a8d90e400db31d82ff72f2d388230 SHA256: d013f03300810298f71b16a6c136eac1b5f289ca1cc47498bb1fdc83c0b8d1e2 SHA512: b18b5359bbb36e3eff02dfc802c31b42e464fc03a38619121db8722efc1a69a1ed608796034fa6c581d17006cf95ee63bc3b76c76917797cfa1f1e992a8304ed Homepage: https://cran.r-project.org/package=RTransferEntropy Description: CRAN Package 'RTransferEntropy' (Measuring Information Flow Between Time Series with Shannon andRenyi Transfer Entropy) Measuring information flow between time series with Shannon and Rényi transfer entropy. See also Dimpfl and Peter (2013) and Dimpfl and Peter (2014) for theory and applications to financial time series. Additional references can be found in the theory part of the vignette. Package: r-cran-rtrend Architecture: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 965 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-fftwtools, r-cran-boot, r-cran-magrittr, r-cran-matrixstats, r-cran-lubridate, r-cran-terra, r-cran-plyr, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rtrend_0.1.5-1.ca2404.1_amd64.deb Size: 747956 MD5sum: a2fc608b9b031b4fa827fece5ccdc7f1 SHA1: c715352f866e979c52a39766a84a05730beb1ebe SHA256: 1cc1ddf6c096c5ba23d8755c3868607ae4fa1a1cab5a098c887e1f0216b99dca SHA512: 90b19e211731f9841f2ae915bd6d6b717324c061545bc4b2a28adfdc25dac72bfdf91682474593dd3fce026f9ebb6d0e76074f5d81da9dea2b0dfca9cafff9c1 Homepage: https://cran.r-project.org/package=rtrend Description: CRAN Package 'rtrend' (Trend Estimating Tools) The traditional linear regression trend, Modified Mann-Kendall (MK) non-parameter trend and bootstrap trend are included in this package. Linear regression trend is rewritten by '.lm.fit'. MK trend is rewritten by 'Rcpp'. Finally, those functions are about 10 times faster than previous version in R. Reference: Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated data. Journal of hydrology, 204(1-4), 182-196. . Package: r-cran-rtriangle Architecture: amd64 Version: 1.6-0.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-geometry Filename: pool/dists/noble/main/r-cran-rtriangle_1.6-0.15-1.ca2404.1_amd64.deb Size: 170574 MD5sum: 0db08e871597140df14bf8cbc71da92b SHA1: 79a52d3c0351e1a94136835d248c475006dbe598 SHA256: 493d19e8ee7a31c670cf1e2ff83b33540b128e8606de2775b608c55fa3cea737 SHA512: 94fdfc74f99363ab6f6afe27b43ec20a03033adeae9f03f8dbd477ea2b5185cd1e9874afca05babac683c2950b1aecee4b1f81cdf83a2e55c6925dbab3b140b9 Homepage: https://cran.r-project.org/package=RTriangle Description: CRAN Package 'RTriangle' (Triangle - A 2D Quality Mesh Generator and Delaunay Triangulator) This is a port of Jonathan Shewchuk's Triangle library to R. From his description: "Triangle generates exact Delaunay triangulations, constrained Delaunay triangulations, conforming Delaunay triangulations, Voronoi diagrams, and high-quality triangular meshes. The latter can be generated with no small or large angles, and are thus suitable for finite element analysis." Package: r-cran-rtrng Architecture: amd64 Version: 4.23.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10842 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Suggests: r-cran-covr, r-cran-knitr, r-cran-r.rsp, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rtrng_4.23.1-5-1.ca2404.1_amd64.deb Size: 903542 MD5sum: e3e7b025625098f826e15e32846424a1 SHA1: 8405b0c09303e3813582b1ed6e0ef31ff876d0b0 SHA256: b3a2f68cc165796141d3f449865029b8f5504765c2beeecedc75f25a62abe443 SHA512: f790b1b60074566e148e8bdc8e55c42a8c85a22362f279556985757f15538f2cb7086feae25b911938dd5764babb32f4f86fa5c76799f62bcdb3c6dc6954a853 Homepage: https://cran.r-project.org/package=rTRNG Description: CRAN Package 'rTRNG' (Advanced and Parallel Random Number Generation via 'TRNG') Embeds sources and headers from Tina's Random Number Generator ('TRNG') C++ library. Exposes some functionality for easier access, testing and benchmarking into R. Provides examples of how to use parallel RNG with 'RcppParallel'. The methods and techniques behind 'TRNG' are illustrated in the package vignettes and examples. Full documentation is available in Bauke (2021) . Package: r-cran-rts2 Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5653 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-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/noble/main/r-cran-rts2_1.0.2-1.ca2404.1_amd64.deb Size: 3037290 MD5sum: 300d3260f95f13bc8ababb1a43e20bb4 SHA1: 683326af88aa68b6fbdfa8c15ff97c2619be5110 SHA256: ea7121a8b1c3b9a8927afeafd69deb1611cf9ac88d7838d002d6d688bc0cbf8a SHA512: 371101668e1d5f9da04e2391904ad3e006a8852bf6587005eab34dbf8d87c8d0aa44249fde680ca10740b671f961ee818e838792a9b8d101eb0c3d482d5f6773 Homepage: https://cran.r-project.org/package=rts2 Description: CRAN Package 'rts2' (Real-Time Disease Surveillance) Supports modelling real-time case data to facilitate the real-time surveillance of infectious diseases and other point phenomena. The package provides automated computational grid generation over an area of interest with methods to map covariates between geographies, model fitting including spatially aggregated case counts, and predictions and visualisation. Both Bayesian and maximum likelihood methods are provided. Log-Gaussian Cox Processes are described by Diggle et al. (2013) and we provide both the low-rank approximation for Gaussian processes described by Solin and Särkkä (2020) and Riutort-Mayol et al (2023) and the nearest neighbour Gaussian process described by Datta et al (2016) . Package: r-cran-rtsa Architecture: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3522 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-metafor, r-cran-ggplot2, r-cran-scales, r-cran-rcpp Suggests: r-cran-gsdesign, r-cran-compquadform, r-cran-dplyr, r-cran-kableextra, r-cran-rmarkdown, r-cran-knitr, r-cran-bookdown, r-cran-gridextra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rtsa_0.2.2-1.ca2404.1_amd64.deb Size: 884960 MD5sum: 3973ee8404e6ade63192353a9d14f63e SHA1: 42bf4cb66baa76f9795c07b5b953a6f6134037d9 SHA256: 03252366986f00358efea9ddf3e38a0d7d1a4f2b0a03bbd0e5c9e4efe40cd723 SHA512: f80a23f05ab5615be2d4938c0526dc7ea23520a7ab3ccdea4e791f4a8a6e89384d12f26acca03153af8a1b7bf7e91ad89e43ead13677053ba6181e3af9651776 Homepage: https://cran.r-project.org/package=RTSA Description: CRAN Package 'RTSA' ('Trial Sequential Analysis' for Error Control and Inference inSequential Meta-Analyses) Frequentist sequential meta-analysis based on 'Trial Sequential Analysis' (TSA) in programmed in Java by the Copenhagen Trial Unit (CTU). The primary function is the calculation of group sequential designs for meta-analysis to be used for planning and analysis of both prospective and retrospective sequential meta-analyses to preserve type-I-error control under sequential testing. 'RTSA' includes tools for sample size and trial size calculation for meta-analysis and core meta-analyses methods such as fixed-effect and random-effects models and forest plots. TSA is described in Wetterslev et. al (2008) . The methods for deriving the group sequential designs are based on Jennison and Turnbull (1999, ISBN:9780849303166). Package: r-cran-rtsne Architecture: amd64 Version: 0.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-irlba, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rtsne_0.17-1.ca2404.1_amd64.deb Size: 107978 MD5sum: 6e4439f03ed379a71e2a96c3abc9009e SHA1: a92d99558934d9e19f7cf4817825b25a4adf74cd SHA256: 0fd75ec18b1c9137598eff1b9b4b1549ed8d20fe643a8743d2fe8df16acc7d9b SHA512: 7e499beaeee3dee5823f6cfa6706ebd9e3ebb08210cb1f20b188acfdc7664415a2266e84a99451baea0bb882da69e189b626a0211ae95e3e67c9fdad89800f52 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). Package: r-cran-rttf2pt1 Architecture: amd64 Version: 1.3.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-rttf2pt1_1.3.14-1.ca2404.1_amd64.deb Size: 99378 MD5sum: fd0cbb9c707564045f87fce33637f8f7 SHA1: ea14941b2b4790985e37b76101d5a94623543bcf SHA256: d835ea539fdc9b85a8084b09bb713e4f4feaa9847931971efa53892fd597b789 SHA512: 6c3a006b0ff847f917e26819d7378ad851e494305685716fec822710eecf9ec3e5f6edd05cb8f2a88adbf275dc9f92d32986414bcac5499148d81ea0e917132b Homepage: https://cran.r-project.org/package=Rttf2pt1 Description: CRAN Package 'Rttf2pt1' ('ttf2pt1' Program) Contains the program 'ttf2pt1', for use with the 'extrafont' package. This product includes software developed by the 'TTF2PT1' Project and its contributors. 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Package: r-cran-rubias Architecture: amd64 Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1942 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-gtools, r-cran-magrittr, r-cran-rcpp, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-rcppparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-rubias_0.3.4-1.ca2404.1_amd64.deb Size: 1311678 MD5sum: d83e4a206bd8ece717c97adafaf9fdc4 SHA1: c261dcdb28feaba996dd68cd9f5cc2ee0b7baf70 SHA256: 24a63dbb50054c7f430b4de6dceba4d29d3ba7e083bd15ba7628f1d6b0afba6f SHA512: 19a16785a39b2ed6973fc091749885fb5d2b39857ad5c23f6ad83beb39de0885be832ca6342de885a89abea22dd4d1136db21698a3285f7f3351eeb60e19e58a Homepage: https://cran.r-project.org/package=rubias Description: CRAN Package 'rubias' (Bayesian Inference from the Conditional Genetic StockIdentification Model) Implements Bayesian inference for the conditional genetic stock identification model. 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Package: r-cran-rucrdtw Architecture: amd64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 746 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dtw, r-cran-rbenchmark Filename: pool/dists/noble/main/r-cran-rucrdtw_0.1.7-1.ca2404.1_amd64.deb Size: 372202 MD5sum: 7dd309d153ca0c55ce9412999278749b SHA1: 0e6076da27cb04ec5cf631de15bb170f9b94adf7 SHA256: 65679c4e1d56e0744b1d95a5416253343396f3ad69947ec41d186d131ae8e4bc SHA512: d434b1623fad9a3a7d768f02d8b00c4459fa2d1a9a36c17a96a23fe8c4783f342245be87b6264094ce3799ab72810e8386a66f93459cf410c560b768a9f29ff8 Homepage: https://cran.r-project.org/package=rucrdtw Description: CRAN Package 'rucrdtw' (R Bindings for the UCR Suite) R bindings for functions from the UCR Suite by Rakthanmanon et al. 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Package: r-cran-rugarch Architecture: amd64 Version: 1.5-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5452 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsolnp, r-cran-ks, r-cran-numderiv, r-cran-spd, r-cran-xts, r-cran-zoo, r-cran-chron, r-cran-skewhyperbolic, r-cran-rcpp, r-cran-fracdiff, r-cran-nloptr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rugarch_1.5-5-1.ca2404.1_amd64.deb Size: 4606854 MD5sum: 65d7b96cbdb518cda5b980acdb9fc718 SHA1: 9145d3b72cf390c28ef77d501826fcd299351aea SHA256: 12c6d6587c9203e125891f70b1383502868286795295da1a85baa04087982a19 SHA512: 888c9ed35bb50ac1d178b537335f62afed021c332182d39e92b9917453e32192b4432c704587ea94369aabcb171081d9d4af9926b5c686846c7e2b0b88887358 Homepage: https://cran.r-project.org/package=rugarch Description: CRAN Package 'rugarch' (Univariate GARCH Models) ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting. 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Package: r-cran-ruimtehol Architecture: amd64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4865 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-udpipe, r-cran-data.table Filename: pool/dists/noble/main/r-cran-ruimtehol_0.3.2-1.ca2404.1_amd64.deb Size: 4549534 MD5sum: 9217ba9691073658f881b0e6ccde85f1 SHA1: b60075d1c7a5eaa6241adec157a5ba50b67232fb SHA256: 25e07806a07158f6076fb18ba4f62aa2879eaf32d619510904b9488ef3173931 SHA512: f5aeadae538b2d7cb95b72f26eca753a40d59e4b83cd242576c578f54ddfd7899c6caf8ba7bc26baa4644a89a0390009c9a598a3060f717af8b357d272af7991 Homepage: https://cran.r-project.org/package=ruimtehol Description: CRAN Package 'ruimtehol' (Learn Text 'Embeddings' with 'Starspace') Wraps the 'StarSpace' library allowing users to calculate word, sentence, article, document, webpage, link and entity 'embeddings'. 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Package: r-cran-rupturesrcpp Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1786 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-ggplot2, r-cran-patchwork, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-reticulate, r-cran-binsegrcpp Filename: pool/dists/noble/main/r-cran-rupturesrcpp_1.0.2-1.ca2404.1_amd64.deb Size: 585902 MD5sum: c300584610c19f8d6bb80a2a9e38ca2b SHA1: 1b97d94fdd534049d02b43e4c8bc93371b74f774 SHA256: 4c0a39e48d319dbc77807f41f06401ca83fd223f13ee946e629ddc55b87c2562 SHA512: f2e08eb889dbc23810556d0051325aefb5700f1ecb0c0ff8aef18f79e32f5a48924c0b88f2ce34e762e8aa530c79111e1b307f15cd525e4a2179c2c9e980351d Homepage: https://cran.r-project.org/package=rupturesRcpp Description: CRAN Package 'rupturesRcpp' (Object-Oriented Interface for Offline Change-Point Detection) A collection of efficient implementations of popular offline change-point detection algorithms, featuring a consistent, object-oriented interface for practical use. 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Package: r-cran-rust Architecture: amd64 Version: 1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1054 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-bang, r-cran-knitr, r-cran-microbenchmark, r-cran-revdbayes, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rust_1.4.4-1.ca2404.1_amd64.deb Size: 463094 MD5sum: 2f33376a756d83a886bb6b3fe690ec6a SHA1: 0c8e4cfc550e183da766b3fb31dce1afa2b89760 SHA256: cc9134b663bb411c0a9f9f71b70614ce20efe9c8cdfde7e2b21e1234fbb8a8d2 SHA512: c1e39cd8811f81ec016d5f2fbf4ba668e2c9a2b9ac6d07ae327177ddcfc862ab2d6e5a8e8726aef4395c4746561f262b1bf176d75107c47b9a2b212eefe82fce Homepage: https://cran.r-project.org/package=rust Description: CRAN Package 'rust' (Ratio-of-Uniforms Simulation with Transformation) Uses the generalized ratio-of-uniforms (RU) method to simulate from univariate and (low-dimensional) multivariate continuous distributions. The user specifies the log-density, up to an additive constant. The RU algorithm is applied after relocation of mode of the density to zero, and the user can choose a tuning parameter r. For details see Wakefield, Gelfand and Smith (1991) , Efficient generation of random variates via the ratio-of-uniforms method, Statistics and Computing (1991) 1, 129-133. A Box-Cox variable transformation can be used to make the input density suitable for the RU method and to improve efficiency. In the multivariate case rotation of axes can also be used to improve efficiency. From version 1.2.0 the 'Rcpp' package can be used to improve efficiency. Package: r-cran-ruv Architecture: amd64 Version: 0.9.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-gridextra Suggests: r-cran-shiny, r-cran-colourpicker Filename: pool/dists/noble/main/r-cran-ruv_0.9.7.1-1.ca2404.1_amd64.deb Size: 283094 MD5sum: 8717041183ca7bf71694d613d80918c3 SHA1: 94c4c57fc8a0a022ba457ec4d8d7735d86fbc4fa SHA256: 583c02ba56f53cabbb43cb60f73a5c0bc251eab38bc2d47d6316afc7e5a0f2cb SHA512: a0e7df95ab87041594f75f28516596ef50fdd2e7bb523728a5625c9f3ce6c2ee0825b9412272e81e9999362567be255d8ecb87cdb5771c5f1644a8c557529bfc Homepage: https://cran.r-project.org/package=ruv Description: CRAN Package 'ruv' (Detect and Remove Unwanted Variation using Negative Controls) Implements the 'RUV' (Remove Unwanted Variation) algorithms. These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data. The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well. These algorithms were proposed in Gagnon-Bartsch and Speed (2012) , Gagnon-Bartsch, Jacob and Speed (2013), and Molania, et. al. (2019) . The algorithms require the user to specify a set of negative control variables, as described in the references. The algorithms included in this package are 'RUV-2', 'RUV-4', 'RUV-inv', 'RUV-rinv', 'RUV-I', and RUV-III', along with various supporting algorithms. Package: r-cran-rvalues Architecture: amd64 Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2779 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rvalues_0.7.1-1.ca2404.1_amd64.deb Size: 2777294 MD5sum: bc84bdae6e3d4aba490b88824443dc94 SHA1: 19af503b17b0eb9808fd45878d2900e88b9ffc1d SHA256: f778751fd05c3f9c74789222f4298f98e662709e43ba0cd4c8765cec2400692d SHA512: 767a63aaeaf49db83eff36a984cd3c22735e02ee23984598cc832b880378cc9b625df9b60cb8e00335cfb52fc8457b151b9f1595c84d7fd6964d095260e90700 Homepage: https://cran.r-project.org/package=rvalues Description: CRAN Package 'rvalues' (R-Values for Ranking in High-Dimensional Settings) A collection of functions for computing "r-values" from various kinds of user input such as MCMC output or a list of effect size estimates and associated standard errors. Given a large collection of measurement units, the r-value, r, of a particular unit is a reported percentile that may be interpreted as the smallest percentile at which the unit should be placed in the top r-fraction of units. Package: r-cran-rvcg Architecture: amd64 Version: 0.25-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3451 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcpparmadillo Suggests: r-cran-morpho, r-cran-rgl Filename: pool/dists/noble/main/r-cran-rvcg_0.25-1.ca2404.1_amd64.deb Size: 1888316 MD5sum: 9f24b20f597af30218858d6e2749adb1 SHA1: bb62472a0a523836d47fba59c4f4644df3c3ccfa SHA256: 21ccbfb8c2fc6e22d49b527f9485cc0327c9e2cefe3be0de36d6e9c963ab9187 SHA512: c3e366d4f53aa4f399576b1d78ff44df23f8396803b1d8a1ec176d6594f708c62c8a7d0bdf43f36362d959a3aef3ef660c9a59bcf7ce7a3b37128fc45910abb3 Homepage: https://cran.r-project.org/package=Rvcg Description: CRAN Package 'Rvcg' (Manipulations of Triangular Meshes Based on the 'VCGLIB' API) Operations on triangular meshes based on 'VCGLIB'. This package integrates nicely with the R-package 'rgl' to render the meshes processed by 'Rvcg'. The Visualization and Computer Graphics Library (VCG for short) is an open source portable C++ templated library for manipulation, processing and displaying with OpenGL of triangle and tetrahedral meshes. The library, composed by more than 100k lines of code, is released under the GPL license, and it is the base of most of the software tools of the Visual Computing Lab of the Italian National Research Council Institute ISTI , like 'metro' and 'MeshLab'. The 'VCGLIB' source is pulled from trunk and patched to work with options determined by the configure script as well as to work with the header files included by 'RcppEigen'. Package: r-cran-rvcompare Architecture: amd64 Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-ggplot2, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rvcompare_0.1.8-1.ca2404.1_amd64.deb Size: 126884 MD5sum: 75d345432422a84c2655f6968bb2a018 SHA1: 32a02a7a6c36c202441c766e628872a311920cce SHA256: a6331cb558710d85bbe12e031c8aa7efb5bd86784a5937060b0a13b8554d004e SHA512: 952249c5421f4c0f28fb83234cb5e210a3ed705a4cad695c01014513cf3b3eb8ba912d35316c1264d774941e84b16c34cb6d725336070ab4450297002a7a02ac Homepage: https://cran.r-project.org/package=RVCompare Description: CRAN Package 'RVCompare' (Compare Real Valued Random Variables) A framework with tools to compare two random variables via stochastic dominance. See the README.md at for a quick start guide. It can compute the Cp and Cd of two probability distributions and the Cumulative Difference Plot as explained in E. Arza (2022) . Uses bootstrap or DKW-bounds to compute the confidence bands of the cumulative distributions. These two methods are described in B. Efron. (1979) and P. Massart (1990) . Package: r-cran-rvg Architecture: amd64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libpng16-16t64 (>= 1.6.2), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gdtools, r-cran-officer, r-cran-rcpp, r-cran-rlang, r-cran-systemfonts, r-cran-xml2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rvg_0.4.2-1.ca2404.1_amd64.deb Size: 144900 MD5sum: 833c94addeb6a0c1fe28a7e8c3f2d673 SHA1: ffd40eb72407884ff0fb250c5a3e491bfff52025 SHA256: c559aacd7b0a0893ae8dd67a98e319e05f3fe9058cc33a8e8a8f80181f3b9e84 SHA512: 2e0a627d6f30daf622813e7590cb0fa9c4d713ff64ecf43afe048700f65430c9171ee901539589c1604bce713a6ad606641714b664c01ac0f37d756f8fa1398d Homepage: https://cran.r-project.org/package=rvg Description: CRAN Package 'rvg' (R Graphics Devices for 'Office' Vector Graphics Output) Vector Graphics devices for 'Microsoft PowerPoint' and 'Microsoft Excel'. 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Package: r-cran-rvinecopulib Architecture: amd64 Version: 0.7.3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10107 Depends: libc6 (>= 2.35), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-kde1d, r-cran-lattice, r-cran-rcpp, r-cran-bh, r-cran-rcppeigen, r-cran-rcppthread, r-cran-wdm Suggests: r-cran-igraph, r-cran-ggplot2, r-cran-ggraph, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rvinecopulib_0.7.3.1.0-1.ca2404.1_amd64.deb Size: 2132706 MD5sum: 38d0a3cb6740eb6e5ca470ec759593bf SHA1: 7afc3bfdbd92ae2d3a6103057afc745d3cb80fbb SHA256: dfc3d8c20855f2ac509fc7ef1db37a7da30efcfdace615b9e3fa59c53d14f559 SHA512: 433bb8873d7768bc0ad1383e17ce30073e3e06b0e1175db2eecf9faefe01355095f76851cda87cc4d81d0b7dcbb425e2839d6345c0ffac109b6a64183e616838 Homepage: https://cran.r-project.org/package=rvinecopulib Description: CRAN Package 'rvinecopulib' (High Performance Algorithms for Vine Copula Modeling) Provides an interface to 'vinecopulib', a C++ library for vine copula modeling. The 'rvinecopulib' package implements the core features of the popular 'VineCopula' package, in particular inference algorithms for both vine copula and bivariate copula models. Advantages over 'VineCopula' are a sleeker and more modern API, improved performances, especially in high dimensions, nonparametric and multi-parameter families, and the ability to model discrete variables. The 'rvinecopulib' package includes 'vinecopulib' as header-only C++ library (currently version 0.7.2). Thus users do not need to install 'vinecopulib' itself in order to use 'rvinecopulib'. Since their initial releases, 'vinecopulib' is licensed under the MIT License, and 'rvinecopulib' is licensed under the GNU GPL version 3. Package: r-cran-rvmf Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libmpfr6 (>= 3.1.3), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bessel, r-cran-rcpp, r-cran-rfast Filename: pool/dists/noble/main/r-cran-rvmf_0.1.2-1.ca2404.1_amd64.deb Size: 52754 MD5sum: b82ea7f8ea391af5802b4a8e3d6141de SHA1: 9c914c7a7c3784530873c125a7493abe66f18823 SHA256: aea38099085894de26087f43c1adfa0a034077a5434094b8878aa31671d8760a SHA512: 1cfe74c564792efc605228b4eb9e57bb8ed61534d626d82749829c629626f36264f00e0e142a1cdd5f4f6f1ff8f483cef8bfb00a1fadaf0f8e3f1233c3d9aede Homepage: https://cran.r-project.org/package=rvMF Description: CRAN Package 'rvMF' (Fast Generation of von Mises-Fisher Distributed Pseudo-RandomVectors) Generates pseudo-random vectors that follow an arbitrary von Mises-Fisher distribution on a sphere. This method is fast and efficient when generating a large number of pseudo-random vectors. Functions to generate random variates and compute density for the distribution of an inner product between von Mises-Fisher random vector and its mean direction are also provided. Details are in Kang and Oh (2024) . Package: r-cran-rwave Architecture: amd64 Version: 2.6-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1222 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rwave_2.6-5-1.ca2404.1_amd64.deb Size: 1014474 MD5sum: ae6ced0fa0b4ce05e0be452742ea490c SHA1: c635f56f0c5ac2e4f9706de82b9242c8be9a6499 SHA256: 0d9ae117e7ff81074c4915a49d081875082dbb3aeef16bd11ce58a35e95b67cd SHA512: d1e852b1ad430b192bd276bdfb5f5f0013eece16cbe066f6de5dae3c831c59a4a8e8e1101ea883375536a9d2e1e6ade1e967a4d19c3af5f547ecfdd22dd6b486 Homepage: https://cran.r-project.org/package=Rwave Description: CRAN Package 'Rwave' (Time-Frequency Analysis of 1-D Signals) A set of R functions which provide an environment for the Time-Frequency analysis of 1-D signals (and especially for the wavelet and Gabor transforms of noisy signals). It was originally written for Splus by Rene Carmona, Bruno Torresani, and Wen L. Hwang, first at the University of California at Irvine and then at Princeton University. Credit should also be given to Andrea Wang whose functions on the dyadic wavelet transform are included. Rwave is based on the book: "Practical Time-Frequency Analysis: Gabor and Wavelet Transforms with an Implementation in S", by Rene Carmona, Wen L. Hwang and Bruno Torresani (1998, eBook ISBN:978008053942), Academic Press. Package: r-cran-rwbo Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2379 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rwbo_0.1.2-1.ca2404.1_amd64.deb Size: 840358 MD5sum: e47b93b1a775d7284bbd7e36c5e087b8 SHA1: 13b2814671900a336a14b5789e837836d5c1e0aa SHA256: 528a32bca4b276f3f5f626ae43ce019629ba23597826c8b229e0aff15579ddec SHA512: 7339de2d86530d9ff1065cebd28ed0a3a062f9554bd15d0809235a2050b88f84f7d216f8f634afc754e8fde06933d40fe854a56f3ac638f46f127375735b9bd5 Homepage: https://cran.r-project.org/package=Rwbo Description: CRAN Package 'Rwbo' (Run the 'Open-WBO' MaxSAT Solver) Provides a wrapper for running the bundled 'Open-WBO' Maximum Satisfiability (MaxSAT) solver (). Users can pass command-line arguments to the solver and capture its output as a character string or file. 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'RiverWare' and 'RiverSMART' generate data in "rdf", "csv", and "nc" format. This package provides an interface to read, aggregate, and summarize data from one or more simulations in a 'dplyr' pipeline. Package: r-cran-rwfec Architecture: amd64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rwfec_0.2-1.ca2404.1_amd64.deb Size: 36194 MD5sum: 24afd0a10acc99b112d6d294ce06d3cc SHA1: 13008a894a6c6a6272b09a95b2ef7ae665a1cc1c SHA256: 4f8a96bbf21377ed29a72c88ac289e6d60ef611304b7e8c65086710deac3769d SHA512: 68498214650d421fad4ecd8a85ea73148df89f9ee2e187ed10dd5ef1691ef2270f6399df7b736be843441dff4a1b152545b85a0b0b37b6e9cd75ed1cc4dbe887 Homepage: https://cran.r-project.org/package=rwfec Description: CRAN Package 'rwfec' (R Wireless, Forward Error Correction) Communications simulation package supporting forward error correction. Package: r-cran-rwiener Architecture: amd64 Version: 1.3-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-rwiener_1.3-3-1.ca2404.1_amd64.deb Size: 107746 MD5sum: 0c45c2f8d8bc9b635e989afd93d0a36f SHA1: 0d819f764abc729343223b29765be4aef9ac8c6e SHA256: 1005a8fc146de55f875ff1c226d491a5721ee14696ce20768a9617cadd1d361e SHA512: 6f0aae698cdb55ca55dc8c8236a5501a82606ea1ac4ac064f2ebd6487adae7f005d25e21030645487761d2c9db54fcae9cd50eac3e12609c82313165e547b603 Homepage: https://cran.r-project.org/package=RWiener Description: CRAN Package 'RWiener' (Wiener Process Distribution Functions) Provides Wiener process distribution functions, namely the Wiener first passage time density, CDF, quantile and random functions. Additionally supplies a modelling function (wdm) and further methods for the resulting object. Package: r-cran-rwig Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1294 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-cli, r-cran-lubridate, r-cran-rcpp, r-cran-rhpcblasctl, r-cran-word2vec, r-cran-tokenizers, r-cran-stopwords, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rwig_0.1.0-1.ca2404.1_amd64.deb Size: 746254 MD5sum: 458e2e985bc855153127446d1ec8e801 SHA1: 5d84a8742d04d6ed657bdd236e71b108a8428813 SHA256: f31fe829e4ed78ffc85953cb8df6268a1fc2e6d9681d91d4497811d4394a9ba9 SHA512: 4ee85775775fc719462268b1272c8d5482510e8c5ca787db3adea8a72808a0b940b9be258c8b87fadbf70664b8a94eef2405e8fc08c05ed6432e1eac69d8abda Homepage: https://cran.r-project.org/package=rwig Description: CRAN Package 'rwig' (Wasserstein Index Generation (WIG) Model) Efficient implementation of several Optimal Transport algorithms in Fangzhou Xie (2025) and the Wasserstein Index Generation (WIG) model in Fangzhou Xie (2020) . Package: r-cran-rwnn Architecture: amd64 Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog, r-cran-randtoolbox, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rwnn_0.4-1.ca2404.1_amd64.deb Size: 346378 MD5sum: b7a17386296ba75149c4ce198318746e SHA1: 56f32404c702663a7f6c74136f16ee5dc5b8358c SHA256: 1fd0230cfb1dc4fda27e80b91ec2ccc1e7312bda8da8ea664a7afce42e0bd634 SHA512: efbed319c0335ffa0f11d3ab30a2e163f5f11e2f4a88134f3700a12158046ae42e69df7c3cfdc42310d0b4958f90d74f566d096945cf50ba20154d6b4205ee1b Homepage: https://cran.r-project.org/package=RWNN Description: CRAN Package 'RWNN' (Random Weight Neural Networks) Creation, estimation, and prediction of random weight neural networks (RWNN), Schmidt et al. (1992) , including popular variants like extreme learning machines, Huang et al. (2006) , sparse RWNN, Zhang et al. (2019) , and deep RWNN, Henríquez et al. (2018) . It further allows for the creation of ensemble RWNNs like bagging RWNN, Sui et al. (2021) , boosting RWNN, stacking RWNN, and ensemble deep RWNN, Shi et al. (2021) . Package: r-cran-rwofost Architecture: amd64 Version: 0.8-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1667 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-meteor, r-cran-rcpp Suggests: r-cran-terra, r-cran-raster Filename: pool/dists/noble/main/r-cran-rwofost_0.8-7-1.ca2404.1_amd64.deb Size: 387062 MD5sum: e1c3717d2c7002020fc78bbd71ea9e84 SHA1: 421ee8c6af960b92895a700c626f010fc3a0b79f SHA256: f1582af82eb59ef4ffb083cd1db56197707f0f539e6c3f9ecd1342c34242ee4b SHA512: 623f996d1c79501b776d08424f85562fb5e40968f722a736b0e597d23cc2e085d162cf63b2378e550515ada3f642d785c8d404182af1955a0c5c2fd07b1289d3 Homepage: https://cran.r-project.org/package=Rwofost Description: CRAN Package 'Rwofost' (WOFOST Crop Growth Simulation Model) An implementation of the WOFOST ("World Food Studies") crop growth model. WOFOST is a dynamic simulation model that uses daily weather data, and crop, soil and management parameters to simulate crop growth and development. See De Wit et al. (2019) for a recent review of the history and use of the model. Package: r-cran-rxode2 Architecture: amd64 Version: 5.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8805 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-precisesums, r-cran-rcpp, r-cran-backports, r-cran-cli, r-cran-checkmate, r-cran-ggplot2, r-cran-inline, r-cran-lotri, r-cran-memoise, r-cran-rex, r-cran-sys, r-cran-dparser, r-cran-rxode2ll, r-cran-data.table, r-cran-qs2, r-cran-sitmo, r-cran-rcpparmadillo, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-matrix, r-cran-dt, r-cran-covr, r-cran-crayon, r-cran-curl, r-cran-digest, r-cran-dplyr, r-cran-ggrepel, r-cran-gridextra, r-cran-htmltools, r-cran-knitr, r-cran-learnr, r-cran-microbenchmark, r-cran-nlme, r-cran-remotes, r-cran-rlang, r-cran-rmarkdown, r-cran-scales, r-cran-shiny, r-cran-stringi, r-cran-symengine, r-cran-testthat, r-cran-tidyr, r-cran-usethis, r-cran-withr, r-cran-xgxr, r-cran-pillar, r-cran-tibble, r-cran-units, r-cran-rsconnect, r-cran-devtools, r-cran-patchwork, r-cran-nlmixr2data, r-cran-lifecycle, r-cran-kableextra, r-cran-pmxtools, r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-rxode2_5.0.2-1.ca2404.1_amd64.deb Size: 3945590 MD5sum: ce7e08864c051d735ba09ba23adde0eb SHA1: 34ebd0374b4163d328830c3f54e695000dfe89fb SHA256: fee788d665f1a62e4ffa515bb7313b2036a0e218f4774603a4987fbdb82ba2da SHA512: 26b060ed28e45ab15f239b9d588bbe2c6d3f72bf0afbf2c357e707520b192561f47195dd88391271262c59097a24d1b90446ab057f2d182dbed824f490a3808e Homepage: https://cran.r-project.org/package=rxode2 Description: CRAN Package 'rxode2' (Facilities for Simulating from ODE-Based Models) Facilities for running simulations from ordinary differential equation ('ODE') models, such as pharmacometrics and other compartmental models. A compilation manager translates the ODE model into C, compiles it, and dynamically loads the object code into R for improved computational efficiency. An event table object facilitates the specification of complex dosing regimens (optional) and sampling schedules. NB: The use of this package requires both C and Fortran compilers, for details on their use with R please see Section 6.3, Appendix A, and Appendix D in the "R Administration and Installation" manual. Also the code is mostly released under GPL. The 'VODE' and 'LSODA' are in the public domain. The information is available in the inst/COPYRIGHTS. 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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) . 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This method extends the supervised elastic-net problem, and thus it is a practical solution to the problem of feature selection in semi-supervised contexts. Its mathematical formulation is presented from a general perspective, covering a wide range of models. We focus on linear and logistic responses, but the implementation could be easily extended to other losses in generalized linear models. We develop a flexible and fast implementation, written in 'C++' using 'RcppArmadillo' and integrated into R via 'Rcpp' modules. See Culp, M. 2013 for references on the Joint Trained Elastic-Net. Package: r-cran-s7 Architecture: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 614 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bench, r-cran-callr, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-s7_0.2.2-1.ca2404.1_amd64.deb Size: 299056 MD5sum: 5eeb4913a6e9f7d7d45bb767c0dce5a4 SHA1: 003d25e5e4fc051d31ce80036f5ad439f781c57e SHA256: 0392bda34d0262c235fd9fccbeb318cb557876b98e7c3cb6d75b70820d883ff1 SHA512: 7eaf33ec578616076eade46a8429d416789b2dcb37b27edcba373f8a41d02ce452bedac44392ced4b5fd55faed49cc4f2394598025d301680cb1fff8f7a2c991 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: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-cluster, r-cran-signal, r-cran-tidyr, r-cran-magrittr, r-cran-rcpp Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-ggplot2, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-saccadr_0.1.3-1.ca2404.1_amd64.deb Size: 268658 MD5sum: c2f2daa0a560090174f49b1776016219 SHA1: 263829e22a59c5a54ada025003972b551eeec73c SHA256: cda2b16fd7ef0ffe11b6ded1b08682b39f5d672610590bc776bd55f4e97acc03 SHA512: 829923b926c98c9ff96a30c4d75d29429c01b561960e9f2339285393f0ede027d5dbe714a2ae57b10cdebde3b87d167bd779ea30103b57e2d6800ead02c6dd2f 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: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vctrs, r-cran-withr Filename: pool/dists/noble/main/r-cran-sacrebleu_0.2.0-1.ca2404.1_amd64.deb Size: 63524 MD5sum: f6391b98a967fb84019a8c0730498ab4 SHA1: 69e8056e355bb0b2f46bd9e446a832d1a961c18b SHA256: e9cdef9edc8feb38e4bcd1782732a8e6e3d51cc3c6ab27108f2dacfbe04afede SHA512: ab33b32a77cfd09932f57362104b7967fcbd90c8de75162495c832db0f47bb2c029e2b2083700d07a308858101ddaa48ccebcf589f3d08a4be7477463373d357 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: amd64 Version: 0.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1154 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmle, r-cran-mass, r-cran-vgam, r-cran-poilog, r-cran-guilds, r-cran-powerlaw Suggests: r-cran-vegan Filename: pool/dists/noble/main/r-cran-sads_0.6.5-1.ca2404.1_amd64.deb Size: 897782 MD5sum: d874a8c52ecb22ba0ab6c79e2ee89342 SHA1: 8e1f7b99b869a1e360bc15caa483a8b0a87a6878 SHA256: 2e8fd1f7876bb01bdb6d997833417d0a00934ef0b085e8f3061134c0a22146b8 SHA512: a1892def53ac7c7f622794b3932294677e8c85d8fe51caa1ac94e62e8e06906d4298fe1968ce717fc679c963aaac4f97b9c2b3e54a69b394691cebb34c7fae6c Homepage: https://cran.r-project.org/package=sads Description: CRAN Package 'sads' (Maximum Likelihood Models for Species Abundance Distributions) Maximum likelihood tools to fit and compare models of species abundance distributions and of species rank-abundance distributions. Package: r-cran-saeczi Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 859 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lme4, r-cran-purrr, r-cran-progressr, r-cran-furrr, r-cran-future, r-cran-rlang, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-saeczi_0.2.0-1.ca2404.1_amd64.deb Size: 401540 MD5sum: d64c0bdf7e66529aa49ffa185fd54905 SHA1: 758f095f99ac5158616d44c078543e98f03f19fc SHA256: cff51a80346498fbab31bc151e161b34c4c4b7790495ee29e4f4894816684775 SHA512: 475129bdf2c805404403b467b77b8c4eb2c758b0486b1f1635a61e17963c8c2df1fd569a899d04f73af0fda8c9a84c39e77f8136fa97e590ba02cf23d3dbcb2e Homepage: https://cran.r-project.org/package=saeczi Description: CRAN Package 'saeczi' (Small Area Estimation for Continuous Zero Inflated Data) Provides functionality to fit a zero-inflated estimator for small area estimation. This estimator is a combines a linear mixed effects regression model and a logistic mixed effects regression model via a two-stage modeling approach. The estimator's mean squared error is estimated via a parametric bootstrap method. Chandra and others (2012, ) introduce and describe this estimator and mean squared error estimator. White and others (2024+, ) describe the applicability of this estimator to estimation of forest attributes and further assess the estimator's properties. Package: r-cran-saehb.tf.beta Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2568 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bayesplot, r-cran-stringr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-saehb.tf.beta_0.2.0-1.ca2404.1_amd64.deb Size: 802758 MD5sum: 4bdcea7f3e57bfce923efe597b405e06 SHA1: b76e7ca65f89cdbe80cb0a543e35cda486c9aea9 SHA256: 2dd420009360b7735099a0227ecd61eb5dedde2e0b53a91df2340aff08d2be8e SHA512: e545432a08bd43c7c524df4eaa44be851b783fc3d861e8efc6e4e1b6e40b34e48e1ff13ae3ead5d19845e9ba9e3479d6ad43fa373742185e5bb169e23c1adfc7 Homepage: https://cran.r-project.org/package=saeHB.TF.beta Description: CRAN Package 'saeHB.TF.beta' (SAE using HB Twofold Subarea Model under Beta Distribution) Estimates area and subarea level proportions using the Small Area Estimation (SAE) Twofold Subarea Model with a hierarchical Bayesian (HB) approach under Beta distribution. A number of simulated datasets generated for illustration purposes are also included. The 'rstan' package is employed to estimate parameters via the Hamiltonian Monte Carlo and No U-Turn Sampler algorithm. The model-based estimators include the HB mean, the variation of the mean, and quantiles. For references, see Rao and Molina (2015) , Torabi and Rao (2014) , Leyla Mohadjer et al.(2007) , Erciulescu et al.(2019) , and Yudasena (2024). Package: r-cran-saemspe Architecture: amd64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 746 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-smallarea, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-saemspe_1.4-1.ca2404.1_amd64.deb Size: 437898 MD5sum: 2b52175b3b0cc147b051d08e6fac90da SHA1: bc0a21215f18d4d7a308d7152bad888f5bf1abc7 SHA256: 46ce472cacb98e73b6b3bd5b456aedfb9b0d240e9dea8f2873971923320ce556 SHA512: e6de7d7576cbb6ab1adc22a04690bd22f87308055a1ecef12532f39ecf3bcf875607f58e8e8b1622ce3f2a2262bc4f5adc745ef4feb7b84658fa94e0af5ecfaa Homepage: https://cran.r-project.org/package=saeMSPE Description: CRAN Package 'saeMSPE' (Computing MSPE Estimates in Small Area Estimation) Compute various common mean squared predictive error (MSPE) estimators, as well as several existing variance component predictors as a byproduct, for FH model (Fay and Herriot, 1979) and NER model (Battese et al., 1988) in small area estimation. Package: r-cran-saerobust Architecture: amd64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-aoos, r-cran-assertthat, r-cran-ggplot2, r-cran-matrix, r-cran-magrittr, r-cran-mass, r-cran-modules, r-cran-memoise, r-cran-pbapply, r-cran-rcpp, r-cran-spdep, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sae, r-cran-saesim, r-cran-testthat Filename: pool/dists/noble/main/r-cran-saerobust_0.5.0-1.ca2404.1_amd64.deb Size: 265108 MD5sum: e75b401284ebdd53d8d2fcc45d72d0f1 SHA1: 04c8b390650696cd0cbf78258aeed9d8f34241ed SHA256: f359c0d9ad5d305866845e424eab74fcdee75dbe58dbf033b7b7139b1c0d5c67 SHA512: 1c511ef1e55b4e259ba6b5e6236f002c6de46e8185aa0e415aaa40131afd8e8526a2bd4b7a85ebefbf41f9d434c61aee341cdee209da3acd33f147e63fe75cbd Homepage: https://cran.r-project.org/package=saeRobust Description: CRAN Package 'saeRobust' (Robust Small Area Estimation) Methods to fit robust alternatives to commonly used models used in Small Area Estimation. The methods here used are based on best linear unbiased predictions and linear mixed models. At this time available models include area level models incorporating spatial and temporal correlation in the random effects. Package: r-cran-safepg Architecture: amd64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-safepg_0.0.1-1.ca2404.1_amd64.deb Size: 54266 MD5sum: 18aa480f733b1fd375c8bfa241fc62fe SHA1: 091acdab949835d1f56b7c3456cd04ab1c7e70c1 SHA256: 7f144a01c673580b387c85126d9beb2b153d990e86b71b51a8f2978bce9714f1 SHA512: a864b4d6144dba46e109c0649caea4e082dccabf39065ae355da6c5143685b7e97753973bcde8085922ce78654fcdf1360fc2cd2fd6cbbb9fdcc99d237dd17d1 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. 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Package: r-cran-sagmm Architecture: amd64 Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mixsim, r-cran-mclust, r-cran-lowmemtkmeans, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-sagmm_0.2.5-1.ca2404.1_amd64.deb Size: 75926 MD5sum: b39d1e07a9287e66f2bd0537243724ed SHA1: 7581ac89b119f2fe950133b7272836ea9208c392 SHA256: 740dca91ed8cdeda1d8755ade4050c1a86ab975cb6d1bf5aac5ac8a8ae850545 SHA512: f787cf97a7a442bc443949b44f3feb0eb964c5d1ba77b91088fac9e21c7b46a06663b45f1150cf2c331c79881f468b3d4f3b62e79cd2f3f263d480176c0e06f2 Homepage: https://cran.r-project.org/package=SAGMM Description: CRAN Package 'SAGMM' (Clustering via Stochastic Approximation and Gaussian MixtureModels) Computes clustering by fitting Gaussian mixture models (GMM) via stochastic approximation following the methods of Nguyen and Jones (2018) . It also provides some test data generation and plotting functionality to assist with this process. Package: r-cran-sakura Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-arrow Filename: pool/dists/noble/main/r-cran-sakura_0.1.0-1.ca2404.1_amd64.deb Size: 20498 MD5sum: 8ccb7306c9f8f97227807959666e088e SHA1: 915b0c1bd868463dc5d20b9872924f4cfbd2ea3b SHA256: 057ae6079ccfd8b9f0bbe0a464d9a876ad5d2b3aa5aadaa835ad9c2c98cee4f6 SHA512: 43c19e5794462c82d1edb665b3b5ef5ceee8aaa0144f4bec5835476e20d99c12899badce88bc6d68cf5f4572bcc90421155260d1abff0b4cfb21ccc321153ac9 Homepage: https://cran.r-project.org/package=sakura Description: CRAN Package 'sakura' (Extension to R Serialization) Extends the functionality of R serialization by augmenting the built-in reference hook system. This enhanced implementation allows optimal, one-pass integrated serialization that combines R serialization with third-party serialization methods. Facilitates the serialization of even complex R objects, which contain non-system reference objects, such as those accessed via external pointers, for use in parallel and distributed computing. Package: r-cran-sales Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sales_1.0.2-1.ca2404.1_amd64.deb Size: 150128 MD5sum: bd87dd4f5f74bc98890e5c4f990d7ff7 SHA1: 86b7b1c19a768aa8ba786e6a9d5388034d6f69a2 SHA256: 3ed77e6812638ec9d3d1045993c9912bd2d14de824935fc2eae8198f7f949972 SHA512: c2d5e31e886bf523df4d7e7a4e3e7f7fdb7577162df3e92a308e730172a729e3a395f63c49bb79bd85d4d8c8ef3a3e2d13ff32529dab1b29c1f1ea89559918c3 Homepage: https://cran.r-project.org/package=SALES Description: CRAN Package 'SALES' (The (Adaptive) Elastic Net and Lasso Penalized Sparse AsymmetricLeast Squares (SALES) and Coupled Sparse Asymmetric LeastSquares (COSALES) using Coordinate Descent and ProximalGradient Algorithms) A coordinate descent algorithm for computing the solution paths of the sparse and coupled sparse asymmetric least squares, including the (adaptive) elastic net and Lasso penalized SALES and COSALES regressions. Package: r-cran-salso Architecture: amd64 Version: 0.3.78-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1495 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-salso_0.3.78-1.ca2404.1_amd64.deb Size: 708052 MD5sum: a8bb9feb3fc8bf8ce49e6949bdb1dfad SHA1: 049be6cdddd0925cd8145c7acfad0456340c9019 SHA256: dfa348a4a8a30757dc3c98bb8cae7e175c54643c28e93735f133cb338bd15d74 SHA512: 6feb0e119b38196e338d28e6edb296c7c0c655b928fbfab6e93af926a38e7e64a26daeb66ccf19d88d1bfa30dcbe7275a9598782886b920f7ec5d06073b9045f Homepage: https://cran.r-project.org/package=salso Description: CRAN Package 'salso' (Search Algorithms and Loss Functions for Bayesian Clustering) The SALSO algorithm is an efficient randomized greedy search method to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. The algorithm is implemented for many loss functions, including the Binder loss and a generalization of the variation of information loss, both of which allow for unequal weights on the two types of clustering mistakes. Efficient implementations are also provided for Monte Carlo estimation of the posterior expected loss of a given clustering estimate. See Dahl, Johnson, Müller (2022) . Package: r-cran-sam Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-sam_1.3-1.ca2404.1_amd64.deb Size: 200382 MD5sum: decb525446a69f07034cea5c2a9dedd3 SHA1: e081a4d7618dae531512b9ed9eb5bba78a615f21 SHA256: 324f9a5619b3faa708236b681f99939ac1835024857ea5e7e8a534122f590649 SHA512: f06565b3530c1ecc15d39234c360d402c8134da559e11261fea4135ef782d2eb617784f23be41a64e3c13a381b7512eb6af36eea6b24f4487582f467a6f89fc0 Homepage: https://cran.r-project.org/package=SAM Description: CRAN Package 'SAM' (Sparse Additive Modelling) Computationally efficient tools for high dimensional predictive modeling (regression and classification). SAM is short for sparse additive modeling, and adopts the computationally efficient basis spline technique. We solve the optimization problems by various computational algorithms including the block coordinate descent algorithm, fast iterative soft-thresholding algorithm, and newton method. The computation is further accelerated by warm-start and active-set tricks. Package: r-cran-samc Architecture: amd64 Version: 4.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1572 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-raster, r-cran-terra, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppthread Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-gdistance, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-samc_4.2.1-1.ca2404.1_amd64.deb Size: 1055632 MD5sum: c3c48aa4c03fd598a93720b4ec94d003 SHA1: 6c4025a00e1f3e9e52d8caa8e1a33338d8d6e480 SHA256: 36810abdc1f8d7bef2cbd09e8779cea3d24bf417fe6d3e37384efe69736054ac SHA512: 1a4f96c8a76030f7b43be5e76812eee45d4ce5bc79796bae7a0d3a05fd9e9daf1ba5d9066861dd4e6ccdc7a90f63843322dadbf8efc47643ecf2ccc3ff83f053 Homepage: https://cran.r-project.org/package=samc Description: CRAN Package 'samc' (Spatial Absorbing Markov Chains) Implements functions for working with absorbing Markov chains. The implementation is based on the framework described in "Toward a unified framework for connectivity that disentangles movement and mortality in space and time" by Fletcher et al. (2019) , which applies them to spatial ecology. This framework incorporates both resistance and absorption with spatial absorbing Markov chains (SAMC) to provide several short-term and long-term predictions for metrics related to connectivity in landscapes. Despite the ecological context of the framework, this package can be used in any application of absorbing Markov chains. Package: r-cran-samgep Architecture: amd64 Version: 0.1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2316 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-nlme, r-cran-proc, r-cran-abind, r-cran-nloptr, r-cran-foreach, r-cran-doparallel, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-samgep_0.1.0-1-1.ca2404.1_amd64.deb Size: 2217876 MD5sum: 0f91d47dd1364c1480ac741e79ec84cc SHA1: e86577ee892e4a346225df3b4af159cb8d7e1e45 SHA256: fc2d5a8e7472417f951a99b04d6f60cd2f0aa16c15a725d3fc1f7c87ee33c7c7 SHA512: 99dbcd0f03fa2f9cd59f4b6e5d84181080255d004fc31c19297e8a14b5c6f2012dbf455a08d08918dc4bba450dafae650ddeeed78c9ecdadcf47ab3a951a7543 Homepage: https://cran.r-project.org/package=SAMGEP Description: CRAN Package 'SAMGEP' (A Semi-Supervised Method for Prediction of Phenotype Event Times) A novel semi-supervised machine learning algorithm to predict phenotype event times using Electronic Health Record (EHR) data. Package: r-cran-samon Architecture: amd64 Version: 4.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2538 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-samon_4.0.2-1.ca2404.1_amd64.deb Size: 2168962 MD5sum: 5152a1d463fec461dc2792d5404948b6 SHA1: 8760bbf1d23e2f96222e7fc2002759a7ce258d84 SHA256: 944388ca60d96a778008b6bd43ba9ed3bf25b7dd035534730f1a33993a3c5b13 SHA512: e63736abebe21ba6e01870c0bdfd6e863691fffb726db19858a054a1fe358e51522737d63bdecfc0ce75e9e3732282b95559c0fdd5f47d7901f5cf2179f39827 Homepage: https://cran.r-project.org/package=samon Description: CRAN Package 'samon' (Sensitivity Analysis for Missing Data) In a clinical trial with repeated measures designs, outcomes are often taken from subjects at fixed time-points. The focus of the trial may be to compare the mean outcome in two or more groups at some pre-specified time after enrollment. In the presence of missing data auxiliary assumptions are necessary to perform such comparisons. One commonly employed assumption is the missing at random assumption (MAR). The 'samon' package allows the user to perform a (parameterized) sensitivity analysis of this assumption. In particular it can be used to examine the sensitivity of tests in the difference in outcomes to violations of the MAR assumption. The sensitivity analysis can be performed under two scenarios, a) where the data exhibit a monotone missing data pattern (see the samon() function), and, b) where in addition to a monotone missing data pattern the data exhibit intermittent missing values (see the samonIM() function). Package: r-cran-sampling Architecture: amd64 Version: 2.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 984 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-lpsolve Filename: pool/dists/noble/main/r-cran-sampling_2.11-1.ca2404.1_amd64.deb Size: 757990 MD5sum: 6699cd4f5518e55331e30b589cbe4c84 SHA1: 2932c58f96968c26ba2f9aeff40315a9024c9f86 SHA256: 4765285287f1bf6a066c97699426157ce1a80c28b97336f5597a777d3506f22a SHA512: 1795b84892528871ee0b65ea94aa52d80f96a5fd9ae0048c7fcf9b1f533a484a836f9942745719eaa1f6905a51c41cec3eecb9ad61254f905ab97f30bb6b780f Homepage: https://cran.r-project.org/package=sampling Description: CRAN Package 'sampling' (Survey Sampling) Functions to draw random samples using different sampling schemes are available. Functions are also provided to obtain (generalized) calibration weights, different estimators, as well some variance estimators. Package: r-cran-samplingbigdata Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-samplingbigdata_1.0.0-1.ca2404.1_amd64.deb Size: 30686 MD5sum: 3d51d179a1ee0df1457cd80c4cd3105c SHA1: 8818bbe521acf4487c682cd7bce12638b47f676f SHA256: 1cf278e438468d570e37f68a01a07cfd3680f63b3bfd89e3301f39601a8d7a4e SHA512: 5295e3970c01389a89599971e183ab75ba83eac2f99c05833163e1840af0ca604825c4ceb5c6f9fea1a3a55e51eb1ecac641def9994700e43801c91b6cd90143 Homepage: https://cran.r-project.org/package=SamplingBigData Description: CRAN Package 'SamplingBigData' (Sampling Methods for Big Data) Select sampling methods for probability samples using large data sets. This includes spatially balanced sampling in multi-dimensional spaces with any prescribed inclusion probabilities. All implementations are written in C with efficient data structures such as k-d trees that easily scale to several million rows on a modern desktop computer. Package: r-cran-samplingvarest Architecture: amd64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 534 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-samplingvarest_1.5-1.ca2404.1_amd64.deb Size: 447632 MD5sum: 3ef9a969c5d56ea9344d81ca197b51fb SHA1: 7df7be4b35cfeecbea031fe2df8af526434d62f2 SHA256: 09bf2251eec03582dd5a3c11d1465fa5f9e1816da72d323864c94a3be7f8fbcd SHA512: 4d1e58ab005d65455b3d34854c5ea6d6b6b8fa8749151784dbb2291ca65a560a761b67e4f308cb06826acbd19d636847ea8c82a8ca31a0008be485c35eef8146 Homepage: https://cran.r-project.org/package=samplingVarEst Description: CRAN Package 'samplingVarEst' (Sampling Variance Estimation) Functions to calculate some point estimators and estimate their variance under unequal probability sampling without replacement. Single and two-stage sampling designs are considered. Some approximations for the second-order inclusion probabilities (joint inclusion probabilities) are available (sample and population based). A variety of Jackknife variance estimators are implemented. Almost every function is written in C (compiled) code for faster results. The functions incorporate some performance improvements for faster results with large datasets. Package: r-cran-samplr Architecture: amd64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2020 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-pracma, r-cran-lme4, r-cran-rdpack, r-cran-r6, r-cran-rcpparmadillo, r-cran-rcppdist, r-cran-testthat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-vdiffr, r-cran-bench, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-mvtnorm, r-cran-xml2, r-cran-withr, r-cran-samplrdata Filename: pool/dists/noble/main/r-cran-samplr_1.1.2-1.ca2404.1_amd64.deb Size: 990222 MD5sum: 834538b2bdcb33ded6f89b6ed24c8a24 SHA1: 749448fbf48fd206004ec48f187e3ae794828965 SHA256: 375fe05a4e3d0b555f5b43e4fdde2c94d89147d4f51ad7ddc2cb4901358d19fe SHA512: f08046523d6bd0d18d8e745b773101a3ca70b62f5688da64833f0d1a6e0e6240abc23fb2cb73141688135c72e5ba480fcd7046f5d78b88967813e4662fd0c3f4 Homepage: https://cran.r-project.org/package=samplr Description: CRAN Package 'samplr' (Compare Human Performance to Sampling Algorithms) Understand human performance from the perspective of sampling, both looking at how people generate samples and how people use the samples they have generated. A longer overview and other resources can be found at . Package: r-cran-samr Architecture: amd64 Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3907 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-impute, r-cran-matrixstats, r-cran-shiny, r-cran-shinyfiles, r-cran-openxlsx, r-cran-gsa Filename: pool/dists/noble/main/r-cran-samr_3.0.1-1.ca2404.1_amd64.deb Size: 3775368 MD5sum: fd58668e03d76d5107fb35165720b11d SHA1: 1eb1e3d76bc5ff8f456032fcd5e33cf0e224d1c1 SHA256: 1325bcea45e779b47ecfb696159bf76f50c669c6bf13a654d0744139fdb100ae SHA512: 4db1ae1ab99b74d46eb7eb0cfbbb07bf2f0bc33cf0156babb7c3df57f50bcd52cacf3ed6b0f63d9f49980d5a92e07d756c5f8e40af3b1cacab25ca2b8bdd3c30 Homepage: https://cran.r-project.org/package=samr Description: CRAN Package 'samr' (SAM: Significance Analysis of Microarrays) Significance Analysis of Microarrays for differential expression analysis, RNAseq data and related problems. Package: r-cran-samsaralight Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 745 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-concaveman, r-cran-data.table, r-cran-dplyr, r-cran-ggforce, r-cran-ggnewscale, r-cran-ggplot2, r-cran-httr, r-cran-patchwork, r-cran-rcpp, r-cran-rhpcblasctl, r-cran-scales, r-cran-sf, r-cran-sfheaders, r-cran-tidyr Suggests: r-cran-cowplot, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-samsaralight_1.0.0-1.ca2404.1_amd64.deb Size: 506040 MD5sum: e9fb0322b6f6f52e5d635a1b46abe71d SHA1: 911c871f29016ed85d15d780e90f67332b7eb245 SHA256: d6deb9ef6a8ad92f8945f567786ecf3b2c1685704e625cadf818f9c07c641188 SHA512: db76830a26f37d9c40f989ce036cc253fada84b17c8b165f37df22043b724424916c75e62915c3401a97664c7258ebdef870895bb666ebdfa1514a539fd8aabe Homepage: https://cran.r-project.org/package=SamsaRaLight Description: CRAN Package 'SamsaRaLight' (Simulate Tree Light Transmission Using Ray-Tracing) Provides tools to simulate light transmission in forest stands using three-dimensional ray-tracing. The package allows users to build virtual stands from tree inventories and to estimate (1) light intercepted by individual trees, (2) light reaching the forest floor, and (3) light at virtual sensors. The package is designed for ecological and forestry applications, including the analysis of light competition, tree growth, and forest regeneration. The implementation builds on the individual-based ray-tracing model SamsaraLight developed by Courbaud et al. (2003) . Package: r-cran-samtool Architecture: amd64 Version: 1.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3708 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-msetool, r-cran-tmb, r-cran-abind, r-cran-dplyr, r-cran-gplots, r-cran-pbapply, r-cran-rmarkdown, r-cran-snowfall, r-cran-vars, r-cran-rcppeigen Suggests: r-cran-caret, r-cran-corpcor, r-cran-covr, r-cran-extradistr, r-cran-ggplot2, r-cran-gmisc, r-cran-knitr, r-cran-mvtnorm, r-cran-numderiv, r-cran-reshape2, r-cran-shiny, r-cran-testthat, r-cran-tmbstan, r-cran-usethis Filename: pool/dists/noble/main/r-cran-samtool_1.9.1-1.ca2404.1_amd64.deb Size: 2234974 MD5sum: 02995aca0b2e9f5a987487cc79b9a576 SHA1: 718382267b28e6749c4bca48420b7b66fa41efc2 SHA256: bfe0d1f2944dd6db3c6ef36c0efa7545708c6e04e5621eccb76a3a972fb37f44 SHA512: 8052bf195bc087c960ba3e4fa0926a4c94dd086df7059a39cda3d274fd594a56dbca9f8aec47ff16e3e3feae793224b979fe944d88103ee9ac225a2295d7a4e7 Homepage: https://cran.r-project.org/package=SAMtool Description: CRAN Package 'SAMtool' (Stock Assessment Methods Toolkit) Simulation tools for closed-loop simulation are provided for the 'MSEtool' operating model to inform data-rich fisheries. 'SAMtool' provides a conditioning model, assessment models of varying complexity with standardized reporting, model-based management procedures, and diagnostic tools for evaluating assessments inside closed-loop simulation. Package: r-cran-samurais Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6390 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-samurais_0.1.0-1.ca2404.1_amd64.deb Size: 4327696 MD5sum: 7b2db4ea70b29fac0ca1d33654f57c2a SHA1: c402097118b61284d5f34d0632fefdb9bdf697d2 SHA256: bbc8e1ba9d52d874d51936fdf9cbb7051fe788323b350891560f814d89de4678 SHA512: f1dd03ac24f6331938def9de350f521c4ae821c197acae4234337d8ec4c95be33ff4881fd1ac3e57bdfcc9ed49007a0744798b36765cd3edaea78dda543e2fee Homepage: https://cran.r-project.org/package=samurais Description: CRAN Package 'samurais' (Statistical Models for the Unsupervised Segmentation ofTime-Series ('SaMUraiS')) Provides a variety of original and flexible user-friendly statistical latent variable models and unsupervised learning algorithms to segment and represent time-series data (univariate or multivariate), and more generally, longitudinal data, which include regime changes. 'samurais' is built upon the following packages, each of them is an autonomous time-series segmentation approach: Regression with Hidden Logistic Process ('RHLP'), Hidden Markov Model Regression ('HMMR'), Multivariate 'RHLP' ('MRHLP'), Multivariate 'HMMR' ('MHMMR'), Piece-Wise regression ('PWR'). For the advantages/differences of each of them, the user is referred to our mentioned paper references. Package: r-cran-sanba Architecture: amd64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 983 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrixstats, r-cran-salso, r-cran-scales, r-cran-rcolorbrewer, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-sanba_0.0.3-1.ca2404.1_amd64.deb Size: 530054 MD5sum: 21fc3dd2286956ea05809759ae16b214 SHA1: 2f282e7c57b6c765d347c8d6197406915daef169 SHA256: 4c2769a5f8b45e12b012725fcdc5dd8d899c4f34a7edfd2833b5946431023cb0 SHA512: ae4d502cf60f42df87ad58576495f0c1b93ea3de152589354c125fd11884ddc3acc831440166ea0c2ba04b0ef1655f0f8dc0347e07a034f9f72e60bbe2249e8f Homepage: https://cran.r-project.org/package=sanba Description: CRAN Package 'sanba' (Fitting Shared Atoms Nested Models via MCMC or Variational Bayes) An efficient tool for fitting nested mixture models based on a shared set of atoms via Markov Chain Monte Carlo and variational inference algorithms. Specifically, the package implements the common atoms model (Denti et al., 2023), its finite version (similar to D'Angelo et al., 2023), and a hybrid finite-infinite model (D'Angelo and Denti, 2024). All models implement univariate nested mixtures with Gaussian kernels equipped with a normal-inverse gamma prior distribution on the parameters. Additional functions are provided to help analyze the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) , D’Angelo, Canale, Yu, Guindani (2023) , D’Angelo, Denti (2024) . Package: r-cran-sanic Architecture: amd64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 799 Depends: libc6 (>= 2.14), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-sanic_0.0.2-1.ca2404.1_amd64.deb Size: 303676 MD5sum: 5ca4d81f31e3344ba1b8678ac89ad9a4 SHA1: 33fb87a663eece132a7dfb70412f0c5d8bb0b652 SHA256: e542ce68008f07af162fbf6e37d91548f00fadee00f09161a40f1ddfc5f1785c SHA512: c940436d4bc91c929dd206f39da59f678a4e30af42219a5a9206e4cae0f5442e5a2dc7aab10b20eca0ad7d19160ce50438a73e2ce6fee2168750c7358b6b7770 Homepage: https://cran.r-project.org/package=sanic Description: CRAN Package 'sanic' (Solving Ax = b Nimbly in C++) Routines for solving large systems of linear equations and eigenproblems in R. Direct and iterative solvers from the Eigen C++ library are made available. Solvers include Cholesky, LU, QR, and Krylov subspace methods (Conjugate Gradient, BiCGSTAB). Dense and sparse problems are supported. Package: r-cran-sanitizers Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: libc6 (>= 2.4), libstdc++6 (>= 4.1.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sanitizers_0.1.1-1.ca2404.1_amd64.deb Size: 16002 MD5sum: 26ebfb2dc14633e273f59a9729514512 SHA1: be4cad1ce895ce3613c9b5983dd52a391116c6d8 SHA256: c1f27ca67a2504ec7506577ab203242df626883cdb03dec99442a855f38fc311 SHA512: 99550424dea41bb880338ab601d83ec7de27cccdd03c017ebf295a17905aa406eea17a9a2c70dd35869ea9f9199baae177422142e55a06693524193cfdd5c98b Homepage: https://cran.r-project.org/package=sanitizers Description: CRAN Package 'sanitizers' (C/C++ Source Code to Trigger Address and Undefined BehaviourSanitizers) Recent gcc and clang compiler versions provide functionality to test for memory violations and other undefined behaviour; this is often referred to as "Address Sanitizer" (or 'ASAN') and "Undefined Behaviour Sanitizer" ('UBSAN'). The Writing R Extension manual describes this in some detail in Section 4.3 title "Checking Memory Access". This feature has to be enabled in the corresponding binary, eg in R, which is somewhat involved as it also required a current compiler toolchain which is not yet widely available, or in the case of Windows, not available at all (via the common Rtools mechanism). As an alternative, pre-built Docker containers such as the Rocker container 'r-devel-san' or the multi-purpose container 'r-debug' can be used. This package then provides a means of testing the compiler setup as the known code failures provides in the sample code here should be detected correctly, whereas a default build of R will let the package pass. The code samples are based on the examples from the Address Sanitizer Wiki at . Package: r-cran-sanple Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 713 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-scales, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-salso, r-cran-rcpparmadillo, r-cran-rcppprogress Filename: pool/dists/noble/main/r-cran-sanple_0.2.0-1.ca2404.1_amd64.deb Size: 499582 MD5sum: b3326526863a5f1d6e3996a3fa6a9f09 SHA1: 3aae4159f1e0573f5bb3b8fa09094d62e2826165 SHA256: 30f5d50d36297a914aefc8aa2b06495bfd44e615aae56446df02cf8aad2e1cde SHA512: 200ef497c1cec8b7ca0df722c23d7aaf8c9057765d0c81cb42bdc22e27f77f26d392ed4c79189c87f92a1c12255b4e1030fbbce09247637bdac23df015e31d20 Homepage: https://cran.r-project.org/package=SANple Description: CRAN Package 'SANple' (Fitting Shared Atoms Nested Models via Markov Chains Monte Carlo) Estimate Bayesian nested mixture models via Markov Chain Monte Carlo methods. Specifically, the package implements the common atoms model (Denti et al., 2023), and hybrid finite-infinite models. All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyzing the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) , D’Angelo, Denti (2024) . Package: r-cran-santoku Architecture: amd64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 648 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-assertthat, r-cran-glue, r-cran-lifecycle, r-cran-rlang, r-cran-vctrs Suggests: r-cran-bench, r-cran-bit64, r-cran-covr, r-cran-haven, r-cran-hmisc, r-cran-hms, r-cran-knitr, r-cran-lubridate, r-cran-purrr, r-cran-rmarkdown, r-cran-scales, r-cran-stringi, r-cran-testthat, r-cran-units, r-cran-withr, r-cran-xts, r-cran-zoo Filename: pool/dists/noble/main/r-cran-santoku_1.2.0-1.ca2404.1_amd64.deb Size: 419640 MD5sum: 26fd71f42c5b33b6268a8ab42993adf5 SHA1: a8ad914a7c16774af999c6bc12118aa8d6191a02 SHA256: 93b39a2c25aeb65f334da35292cc70e88110f86b88df9a8d3bd7093948bb2598 SHA512: 73b21260dff1a1aa095d2f4deb542458f81d9a5e01429d397bc042dc7e51c4550429723e8a3ef152c18c6adc05a64e04de87040c825e4eedce19982758f504b9 Homepage: https://cran.r-project.org/package=santoku Description: CRAN Package 'santoku' (A Versatile Cutting Tool) A tool for cutting data into intervals. 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Package: r-cran-sanvi Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 861 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-scales, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-matrixstats, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-sanvi_0.1.1-1.ca2404.1_amd64.deb Size: 586060 MD5sum: ccdc14d8e6777ade6999f4678572f6a3 SHA1: 0208fd6a637115af3bb875b04ee83c1f407a66ac SHA256: 84fca336b3b7984dfc2519cbf23a4782cc45a3c28c9ef8b0122f05acfd44b0f2 SHA512: c3ba25287412749d266a6b05c1335eb9614d58991eef3e77c52c4dbd5d93b0eee34c081617460a136f834f9fe14fbb185ecab628f69df785a31e2e74033c935b 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. 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Package: r-cran-sapp Architecture: amd64 Version: 1.0.9-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 589 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sapp_1.0.9-4-1.ca2404.1_amd64.deb Size: 470988 MD5sum: 5a34aaed7f42b10ae55c0372349e36db SHA1: 1b1128f17b748c37cf29c0ac79cf5a9597ef32c2 SHA256: 3548ffb5ffdae9ad35766396a865d2601487fc06631ea46eb3c0c84ac79d1c7d SHA512: 1860e7bc8b8906835b2b14e606f6c335ed15198c8b669bf5d99ef86f31ffdfa11489b8a0943df56872e8d0153bf96e921aa06fc8ca3d8c45ec8f2b4320b444a6 Homepage: https://cran.r-project.org/package=SAPP Description: CRAN Package 'SAPP' (Statistical Analysis of Point Processes) Functions for statistical analysis of point processes. 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Package: r-cran-sarima Architecture: amd64 Version: 0.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1795 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-polynomf, r-cran-formula, r-cran-lagged, r-cran-rcpp, r-cran-rdpack, r-cran-numderiv, r-cran-ltsa, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-kfas, r-cran-fkf, r-cran-fgarch, r-cran-forecast Filename: pool/dists/noble/main/r-cran-sarima_0.9.5-1.ca2404.1_amd64.deb Size: 1431532 MD5sum: 2d89f6c89e5e9f4a11bb3a46202d6893 SHA1: b43f4d292c87bc0e4006a482cdb3aa4f08d65fba SHA256: de1f8c11354f8ce1229d4962ed7894466c4c148b6dc43ff3d9c8802762edf76d SHA512: da94400ee6b4237e0c19cc4763eed2a2373b2cbfcd1ec4e1a7cb1be0faafbce33bbae1c5f9285a633b7040b0a23dc48a1c9cb47e3403149ac044f408dbebc8a9 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. 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Package: r-cran-sarsop Architecture: amd64 Version: 0.6.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4006 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2, r-cran-processx, r-cran-digest, r-cran-matrix, r-cran-bh Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-covr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-sarsop_0.6.16-1.ca2404.1_amd64.deb Size: 860142 MD5sum: 325b0d140a769fa65f65b26dd0950160 SHA1: 49a7d673bf8f77d8b8358eec8ab728a5a66fd34e SHA256: 2388877caa7ab7200df6955d33556980fcb0b6821126a53b8ea550f23ff976e0 SHA512: 86eaf680c85cf69d3a7784588458e16fcade13e3af461e91d7657244d554e3e441632221fd755b2ed5bf728316e439bbe0bc7185a96c432d6dc436a63df0e848 Homepage: https://cran.r-project.org/package=sarsop Description: CRAN Package 'sarsop' (Approximate POMDP Planning Software) A toolkit for Partially Observed Markov Decision Processes (POMDP). 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Package: r-cran-sasfunclust Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 951 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-fda, r-cran-mclust, r-cran-matrixcalc, r-cran-mass, r-cran-matrix, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sasfunclust_1.0.0-1.ca2404.1_amd64.deb Size: 688500 MD5sum: 97769e58772f357b3e1fa3b9ea27029c SHA1: 49d45bb85fe06c1bc18b9b6006a6a4844554a63e SHA256: b1dce96cbb7c4bb22602e46a30ad740ba60c8c0ce49d55bd1a6e2721b2580143 SHA512: 27b888c4a860a59f8bba87c06826c5e41b451a192c0e013642482c1153ded6dccb5dc3cb840eab5e1ae7757a32ff477a8846641c2dd1153ba3eee5d919156a9f Homepage: https://cran.r-project.org/package=sasfunclust Description: CRAN Package 'sasfunclust' (Sparse and Smooth Functional Clustering) Implements the sparse and smooth functional clustering (SaS-Funclust) method (Centofanti et al. 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Package: r-cran-satdad Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4450 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-maps, r-cran-partitions, r-cran-graphicalextremes, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-satdad_1.1-1.ca2404.1_amd64.deb Size: 2705440 MD5sum: 777ab44c2ec90f2b37edab9ed9348450 SHA1: 301d6bc82b83ca9380329eabeb26e64e6ffc8595 SHA256: a8d0d11d5752638102aef0aca8af04f91f7b0df87bcff346ace2a51715f186fb SHA512: 7a50dfc63b2ca3b23d50460f463d8d31bdef481eaaf60acb99deed26f30dafcef3544c9ac0801b91f043c35d22c12a2e0cd139d39be427dd2e98a97b1f95afa7 Homepage: https://cran.r-project.org/package=satdad Description: CRAN Package 'satdad' (Sensitivity Analysis Tools for Dependence and AsymptoticDependence) Tools for analyzing tail dependence in any sample or in particular theoretical models. The package uses only theoretical and non parametric methods, without inference. The primary goals of the package are to provide: (a)symmetric multivariate extreme value models in any dimension; theoretical and empirical indices to order tail dependence; theoretical and empirical graphical methods to visualize tail dependence. Package: r-cran-satellite Architecture: amd64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3595 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-plyr, r-cran-rcpp, r-cran-terra Suggests: r-cran-devtools, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-satellite_1.0.6-1.ca2404.1_amd64.deb Size: 2811940 MD5sum: 80d10d517a77330fc8fe9b334f8014bc SHA1: 5632e879aa509fa6004ca8adcacd6f9a102a7e89 SHA256: 77b9f284de78f19fd28b9e23dd2956867fb99954fb366e5f49cc236bb6b4d0b3 SHA512: 8047a3893d399f55b6717d9671404036b25cd4dc0fa5b3ec56e7363b7d5b9397bfde7824c88242a5e47b62b95b33971db3c9bcd7742f135a08b74f94fe44b35d Homepage: https://cran.r-project.org/package=satellite Description: CRAN Package 'satellite' (Handling and Manipulating Remote Sensing Data) Herein, we provide a broad variety of functions which are useful for handling, manipulating, and visualizing satellite-based remote sensing data. 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Based on Tokdar et al. (2022) . 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An implementation of SBFC by Krakovna, Du and Liu (2015), . 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Package: r-cran-sbim Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-devtools, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-pomp, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-sbim_1.0.0-1.ca2404.1_amd64.deb Size: 182432 MD5sum: b8719c48f5d09ffc66bfe3df607dd31d SHA1: a25d4fa20cd16ccd5404cf3591cd6314b10d8f82 SHA256: eb963ea58a9f98091260f9f18425931a245b0d2e763cb6b0517fe8141ce8766c SHA512: 309fce0b4044b5aff39ca15f7969dfe78266af130264638ddeb7b7c47942a8aad5434362848a86c13fac6440b4989fabd63f74b468e168af11aba5a03c924679 Homepage: https://cran.r-project.org/package=sbim Description: CRAN Package 'sbim' (Simulation-Based Inference using a Metamodel for Log-LikelihoodEstimator) Parameter inference methods for models defined implicitly using a random simulator. Inference is carried out using simulation-based estimates of the log-likelihood of the data. The inference methods implemented in this package are explained in Park, J. (2025) . These methods are built on a simulation metamodel which assumes that the estimates of the log-likelihood are approximately normally distributed with the mean function that is locally quadratic around its maximum. Parameter estimation and uncertainty quantification can be carried out using the ht() function (for hypothesis testing) and the ci() function (for constructing a confidence interval for one-dimensional parameters). 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For a description of AdaBoost, see Freund and Schapire (1997) . This type of classifier is nonlinear, but easy to interpret and visualize. Feature vectors may be a combination of continuous (numeric) and categorical (string, factor) elements. Methods for classifier assessment, predictions, and cross-validation also included. Package: r-cran-sbrl Architecture: amd64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-arules Filename: pool/dists/noble/main/r-cran-sbrl_1.4-1.ca2404.1_amd64.deb Size: 93558 MD5sum: 859ed3054285e60d665d39d27729ad7a SHA1: eba10afaf3bd1745311dfeaadf4db6c79e5b66f0 SHA256: 5371530aa18b10dfbe01ddbe4fc880605d0f44063edc6d676fe4805474586bd2 SHA512: bced33fdd109085bae16955d541d973d37ac3636e94da910fbfeb77bd4f5b930d3966f1bfc063576a0cfc2a880a0fa4f6771585d583556c2855476b4fb76a259 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. 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Package: r-cran-scalablebayesm Architecture: amd64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 810 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bayesm, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-scalablebayesm_0.2-1.ca2404.1_amd64.deb Size: 393106 MD5sum: a751a29248f6874c5f0a8877e74ee4dd SHA1: a13a3a6304177a1da1be8f27af4dc717b0757cce SHA256: a9e339907648a8e95d80907a93db4d54062914dcdfcf6e33d0c7495f827e24f4 SHA512: a73e6c9cd449ae6006cc540a0f6664f0b623ef7e607fccbf7e42a1d16896441a5d6aaf1e366699dfd5c36857187935e69cce6bd88ae37674d54239165eb142ce 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. 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Niloy Biswas, Lester Mackey and Xiao-Li Meng, "Scalable Spike-and-Slab" (2022) . Package: r-cran-scalpel Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3753 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-r.matlab, r-cran-protoclust, r-cran-igraph, r-cran-gam Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-scalpel_1.0.3-1.ca2404.1_amd64.deb Size: 3791472 MD5sum: f2ed30672307aec493940d730c4c2c6d SHA1: 7bc900490536f97533d10a797ec3284294eb64ef SHA256: 445a1de866799082f6f6af06029fcbf044f07c7b8033c97f7334c77eb142905f SHA512: 86c5e668a52f1d0963f95d03d6c0880cd5a94426858e84d073a0087c50245937e58737cad54377e3da646ae36f778df8f2dad5b1c8245106ed45a837e6d31718 Homepage: https://cran.r-project.org/package=scalpel Description: CRAN Package 'scalpel' (Processes Calcium Imaging Data) Identifies the locations of neurons, and estimates their calcium concentrations over time using the SCALPEL method proposed in Petersen, Ashley; Simon, Noah; Witten, Daniela. SCALPEL: Extracting neurons from calcium imaging data. Ann. Appl. Stat. 12 (2018), no. 4, 2430--2456. . . 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The 'scDHA' software package can perform clustering, dimension reduction and visualization, classification, and time-trajectory inference on single-cell data (Tran et.al. (2021) ). 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Package: r-cran-scepter Architecture: amd64 Version: 0.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3933 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-scepter_0.2-4-1.ca2404.1_amd64.deb Size: 3976920 MD5sum: 31147bbb8d851b9a00397063faa1f9fd SHA1: 2612fbc4fd5556026c6f7ed7e93be709a9f2718d SHA256: f73f5b415dfb4d5f9623a5306afc4e57995fb657958f764f873662b7d167fd14 SHA512: 37152b11c512248394c9b314c3e7e20b9c7adec17728504800f2820ce4677cc175e94f7c5957d4aeffbff076f1074fbc0d904215c15f0b2d4bd2f85e45efd1c1 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. 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Package: r-cran-scepterbinary Architecture: amd64 Version: 0.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-scepter Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-scepterbinary_0.1-1-1.ca2404.1_amd64.deb Size: 39506 MD5sum: d5e1b8f7e1ed9a7a967f6966d3e9f60d SHA1: 69ccaff3d684aadba8c0ca0b6d0742ddac5743a9 SHA256: 66e7dc4c603333a7c8875cb945fa6a5ba2f72bbb67901e9dff189f16b224fd1a SHA512: 63805fc73b0055802583c6a9ec382d5f7fa0e4a76472e4be5da06992fca05d0d3e0e0d1bea09afa0717fdc63b737ca3bff95b29a51cf7f125e18d320da5cdb5c Homepage: https://cran.r-project.org/package=SCEPtERbinary Description: CRAN Package 'SCEPtERbinary' (Stellar CharactEristics Pisa Estimation gRid for Binary Systems) SCEPtER pipeline for estimating the stellar age for double-lined detached binary systems. The observational constraints adopted in the recovery are the effective temperature, the metallicity [Fe/H], the mass, and the radius of the two stars. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models. Package: r-cran-schangeblock Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-nortest, r-cran-expm, r-cran-robcp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-schangeblock_0.1.0-1.ca2404.1_amd64.deb Size: 148258 MD5sum: b6cab6f3ef677db54a3b44c3cf7940e6 SHA1: 187ad02964013e0058c4af5544efd157e628a54b SHA256: 1c3d55f21127c613a8c4b659c1d7cd7a31f9d04468809306117eb67199c74f98 SHA512: 38f27aea5d6197845c9650066a3c6130109689cf3e3d30f0b5e31c5dc8c083bf67972a1edddae4b76bc7ba39d32aeebf1e26c90adfbb0db7f9a650f6608d51ef Homepage: https://cran.r-project.org/package=SChangeBlock Description: CRAN Package 'SChangeBlock' (Spatial Structural Change Detection by an Analysis ofVariability Between Blocks of Observations) Provides methods to detect structural changes in time series or random fields (spatial data). Focus is on the detection of abrupt changes or trends in independent data, but the package also provides a function to de-correlate data with dependence. The functions are based on the test suggested in Schmidt (2024) and the work in Görz and Fried (2025) . Package: r-cran-scinsight Architecture: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rann, r-cran-igraph, r-cran-stringr, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-scinsight_0.1.5-1.ca2404.1_amd64.deb Size: 155066 MD5sum: c58d87cc34c3013721bc75098b7f101e SHA1: 10aa17894865d0c79c53e0370c99d38221bb48c6 SHA256: 50fb8c79e9944d9449e391e855f2bcb85e9fa6d39062ae1651f3d530f6e9545d SHA512: 49961fd57caf07b95815e872f4e047e8efb2f4e0bf1bd319fa2abb26b34bb49d36ea12a8401cbd6ab49185a6da9c9d020e5d282d9a4862de5c7ceb027e078cbe Homepage: https://cran.r-project.org/package=scINSIGHT Description: CRAN Package 'scINSIGHT' (Interpretation of Heterogeneous Single-Cell Gene Expression Data) We develop a novel matrix factorization tool named 'scINSIGHT' to jointly analyze multiple single-cell gene expression samples from biologically heterogeneous sources, such as different disease phases, treatment groups, or developmental stages. Given multiple gene expression samples from different biological conditions, 'scINSIGHT' simultaneously identifies common and condition-specific gene modules and quantify their expression levels in each sample in a lower-dimensional space. With the factorized results, the inferred expression levels and memberships of common gene modules can be used to cluster cells and detect cell identities, and the condition-specific gene modules can help compare functional differences in transcriptomes from distinct conditions. Please also see Qian K, Fu SW, Li HW, Li WV (2022) . Package: r-cran-scintruler Architecture: amd64 Version: 0.99.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2902 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-bioc-batchelor, r-cran-seurat, r-cran-seuratobject, r-bioc-matrixgenerics, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-dplyr, r-cran-coin, r-cran-harmony, r-cran-ggplot2, r-cran-gridextra, r-cran-cowplot, r-cran-magrittr Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scintruler_0.99.6-1.ca2404.1_amd64.deb Size: 2111904 MD5sum: 8b3782bf9394cb18fbaeabbf99b2d7d1 SHA1: 28f1a8138233882c4ad380ad87fc16b9f9a64c11 SHA256: 118aacdb93d27dc6adb118eb8ab6a5e0b9ce7ae5f349653d1c9e9560a8042c05 SHA512: 86a34a12c074c205d79dee1d91e997fc27d11378f517f32e574cd5072747872de7e23fca8697059e44fec3133cede3bde214b784e9667d6d053b1bdd0bd513ec Homepage: https://cran.r-project.org/package=SCIntRuler Description: CRAN Package 'SCIntRuler' (Guiding the Integration of Multiple Single-Cell RNA-Seq Datasets) The accumulation of single-cell RNA-seq ('scRNA-seq') studies highlights the potential benefits of integrating multiple datasets. By augmenting sample sizes and enhancing analytical robustness, integration can lead to more insightful biological conclusions. However, challenges arise due to the inherent diversity and batch discrepancies within and across studies. 'SCIntRuler', a novel R package, addresses these challenges by guiding the integration of multiple 'scRNA-seq' datasets. Package: r-cran-scip Architecture: amd64 Version: 1.10.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10159 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-tinytest, r-cran-slam, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-scip_1.10.0-3-1.ca2404.1_amd64.deb Size: 4160054 MD5sum: 66a5db09232810818764efd9133d3596 SHA1: cac232116da6b858763b52189c0803131d2da313 SHA256: a8b60b9c00272c0045c67ee9b12f4bc8bedbe958a35bda181a74f2603b25c888 SHA512: efac4cc39f465efc19823145b1e617689fe8888c8560ae94e6961167f8802ed82317a5e9f34b0e2e2ddf4e670e67a2c91630d39374997a01fabb108495a1c124 Homepage: https://cran.r-project.org/package=scip Description: CRAN Package 'scip' (Interface to the SCIP Optimization Suite) Provides an R interface to SCIP (Solving Constraint Integer Programs), a framework for mixed-integer programming (MIP), mixed-integer nonlinear programming (MINLP), and constraint integer programming (2025, ). Supports linear, quadratic, SOS, indicator, and knapsack constraints with continuous, binary, and integer variables. Includes a one-shot solver interface and a model-building API for incremental problem construction. Package: r-cran-scistreer Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-dplyr, r-cran-ggplot2, r-bioc-ggtree, r-cran-igraph, r-cran-paralleldist, r-cran-patchwork, r-cran-phangorn, r-cran-rcpp, r-cran-reshape2, r-cran-rcppparallel, r-cran-rhpcblasctl, r-cran-stringr, r-cran-tidygraph, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-scistreer_1.2.1-1.ca2404.1_amd64.deb Size: 249794 MD5sum: 782eb9ddd10e020d398ba21f77fa0840 SHA1: 830d1bba1b269a3562ea20f7cf8e04d44e4ccce6 SHA256: 0c23c3864320c63c37670408a0c9e71399130cac78526e7bc90c335dddde87ab SHA512: f4a88784950813b2eab8065ee552450babe13bbf4792f8d9b13ee081e722d74606ae1bb7de57a055ca53bb3656f77dfdf04637c96ed2215520c1320194e3c9b2 Homepage: https://cran.r-project.org/package=scistreer Description: CRAN Package 'scistreer' (Maximum-Likelihood Perfect Phylogeny Inference at Scale) Fast maximum-likelihood phylogeny inference from noisy single-cell data using the 'ScisTree' algorithm by Yufeng Wu (2019) . 'scistreer' provides an 'R' interface and improves speed via 'Rcpp' and 'RcppParallel', making the method applicable to massive single-cell datasets (>10,000 cells). Package: r-cran-scitd Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1240 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rtensor, r-cran-ica, r-bioc-fgsea, r-cran-circlize, r-cran-reshape2, r-bioc-complexheatmap, r-cran-ggplot2, r-cran-mgcv, r-cran-rcpp, r-cran-rcolorbrewer, r-cran-dplyr, r-bioc-edger, r-bioc-sva, r-cran-rmisc, r-cran-ggpubr, r-cran-msigdbr, r-cran-sccore, r-cran-nmf, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-coda.base, r-bioc-simplifyenrichment, r-cran-wgcna, r-cran-cowplot, r-cran-matrixstats, r-cran-stringr, r-cran-zoo, r-cran-rlang, r-bioc-annotationdbi, r-bioc-go.db, r-cran-conos, r-cran-pagoda2, r-cran-betareg, r-cran-slam, r-cran-tm Filename: pool/dists/noble/main/r-cran-scitd_1.0.4-1.ca2404.1_amd64.deb Size: 1069334 MD5sum: afda4bdef7dd3f5c8c52646f69a46b56 SHA1: 7ed9def0f71a14fb76847f18c359ba3dfefb3553 SHA256: d7b24c69a46f34d194486396589a8e4e3ed9518836c713d8ded5615d1ec353b4 SHA512: 7dcf86eb733bb4c22892e4673d08e953dabf83a9c8bf432b524349c477355a396a3200c3ce86db73e04268196f9ba6d55810dd7f9cd4bbcfb2bd82d7695f429e Homepage: https://cran.r-project.org/package=scITD Description: CRAN Package 'scITD' (Single-Cell Interpretable Tensor Decomposition) Single-cell Interpretable Tensor Decomposition (scITD) employs the Tucker tensor decomposition to extract multicell-type gene expression patterns that vary across donors/individuals. This tool is geared for use with single-cell RNA-sequencing datasets consisting of many source donors. The method has a wide range of potential applications, including the study of inter-individual variation at the population-level, patient sub-grouping/stratification, and the analysis of sample-level batch effects. Each "multicellular process" that is extracted consists of (A) a multi cell type gene loadings matrix and (B) a corresponding donor scores vector indicating the level at which the corresponding loadings matrix is expressed in each donor. Additional methods are implemented to aid in selecting an appropriate number of factors and to evaluate stability of the decomposition. Additional tools are provided for downstream analysis, including integration of gene set enrichment analysis and ligand-receptor analysis. Tucker, L.R. (1966) . Unkel, S., Hannachi, A., Trendafilov, N. T., & Jolliffe, I. T. (2011) . Zhou, G., & Cichocki, A. (2012) . Package: r-cran-scmodels Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libmpfr6 (>= 3.1.3), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-gamlss.dist Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scmodels_1.0.4-1.ca2404.1_amd64.deb Size: 143414 MD5sum: 1126f307e98800177f69f74331931df6 SHA1: de6aca932bcf843b9d092e237bab7e72db91f6e2 SHA256: a6facb1e473114c6af8da681ecf2f176153ca18b664880c7abfab55c94cc9854 SHA512: d9c9f08f220a5480fc8aba3bb34e5c9d67ec18153ba1ae10604f8121d4dba75a63040b7c7b1a262fc06a9577683c934d2c6342a8ff8115d2151e279473bfef05 Homepage: https://cran.r-project.org/package=scModels Description: CRAN Package 'scModels' (Fitting Discrete Distribution Models to Count Data) Provides functions for fitting discrete distribution models to count data. Included are the Poisson, the negative binomial, the Poisson-inverse gaussian and, most importantly, a new implementation of the Poisson-beta distribution (density, distribution and quantile functions, and random number generator) together with a needed new implementation of Kummer's function (also: confluent hypergeometric function of the first kind). Three different implementations of the Gillespie algorithm allow data simulation based on the basic, switching or bursting mRNA generating processes. Moreover, likelihood functions for four variants of each of the three aforementioned distributions are also available. The variants include one population and two population mixtures, both with and without zero-inflation. The package depends on the 'MPFR' libraries () which need to be installed separately (see description at ). This package is supplement to the paper "A mechanistic model for the negative binomial distribution of single-cell mRNA counts" by Lisa Amrhein, Kumar Harsha and Christiane Fuchs (2019) available on bioRxiv. Package: r-cran-scoper Architecture: amd64 Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 797 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-alakazam, r-cran-shazam, r-cran-data.table, r-cran-doparallel, r-cran-dplyr, r-cran-fastcluster, r-cran-foreach, r-cran-rcpp, r-cran-rlang, r-cran-scales, r-cran-stringi, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scoper_1.5.0-1.ca2404.1_amd64.deb Size: 599168 MD5sum: f837a1899690651e25459130dff20975 SHA1: 310634757de7b0704e3efba58c1c2505d0fae285 SHA256: db12f9aa94c1ca35416c7d5efd0f5a2d62dcb13a73fa3be3d69eacf5ac8ae32a SHA512: d3232d144015b3b3bc948e43884dd33fed63b825601da0eca59eee53d18906079f7100d3fb177e573c9b24b483d4d8cd074c17ebcbef9ed5d09c958a8071432d Homepage: https://cran.r-project.org/package=scoper Description: CRAN Package 'scoper' (Spectral Clustering-Based Method for Identifying B Cell Clones) Provides a computational framework for identification of B cell clones from Adaptive Immune Receptor Repertoire sequencing (AIRR-Seq) data. Three main functions are included (identicalClones, hierarchicalClones, and spectralClones) that perform clustering among sequences of BCRs/IGs (B cell receptors/immunoglobulins) which share the same V gene, J gene and junction length. Nouri N and Kleinstein SH (2018) . Nouri N and Kleinstein SH (2019) . Gupta NT, et al. (2017) . Package: r-cran-scoredec Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rfast, r-cran-igraph Suggests: r-cran-rfast2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scoredec_0.1.2-1.ca2404.1_amd64.deb Size: 159284 MD5sum: 9a556626a1d6f34bcfed42550fd879d8 SHA1: a1dbc7eea8531c9fd23d5c03e6d40b10606a4485 SHA256: d0ecdf02189d4f7c94188cf3948a788185b04ab7246e4ed80ea610094c575b3a SHA512: b7ca3f723bd616ea25dba236e422f96af91c2dcca37b6af7c1a934cac8dc011b8b6960a0edafd8e23b4bcb62a50f0b01b7ec9f21ee444a4d162b22c49fee4464 Homepage: https://cran.r-project.org/package=scoredec Description: CRAN Package 'scoredec' (S-Core Graph Decomposition) S-Core Graph Decomposition algorithm for graphs. This is a method for decomposition of a weighted graph, as proposed by Eidsaa and Almaas (2013) . The high speed and the low memory usage make it suitable for large graphs. Package: r-cran-scoreeb Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Filename: pool/dists/noble/main/r-cran-scoreeb_0.1.1-1.ca2404.1_amd64.deb Size: 58904 MD5sum: 13533631addf1124cd691a0d0764a08b SHA1: cb68df36e49e43deb7d984796635fbd84d28e345 SHA256: f43019f425bb197e486dddfc8ed412a64ae7cc12962b9ec428683885bac343bf SHA512: 6edf566ed32adb3e7558e0752900d7b864653ab29bc7d9c0507202ed9ea19800bc9754d3ed5d684560ae2d9020ab298d2911737e7db26f50157b53e9caf4d139 Homepage: https://cran.r-project.org/package=ScoreEB Description: CRAN Package 'ScoreEB' (Score Test Integrated with Empirical Bayes for Association Study) Perform association test within linear mixed model framework using score test integrated with Empirical Bayes for genome-wide association study. Firstly, score test was conducted for each marker under linear mixed model framework, taking into account the genetic relatedness and population structure. And then all the potentially associated markers were selected with a less stringent criterion. Finally, all the selected markers were placed into a multi-locus model to identify the true quantitative trait nucleotide. Package: r-cran-scorematchingad Architecture: amd64 Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13234 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcppeigen, r-cran-mcmcpack, r-cran-optimx, r-cran-fixedpoint, r-cran-rdpack, r-cran-rcpp, r-cran-rlang Suggests: r-cran-testthat, r-cran-ks, r-cran-movmf, r-cran-cubature, r-cran-simdd, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-scorematchingad_0.1.6-1.ca2404.1_amd64.deb Size: 1734638 MD5sum: 12f852ec71202d144c071e17105837bf SHA1: f87504a927d5c2c1b74322078895cced1c83216a SHA256: 437209bdb64f92b8a029fa9ceea96c5da7f872e99a6ee63beaa2276ad37b96fb SHA512: 1644c8e96cd3496520ca86e8027a12dfd56b340baab5bef347e59af3711f52be45e369440a6f2473c72e6238022ac635ccd0d9e3ab92cde8b683b4bf01768c2e Homepage: https://cran.r-project.org/package=scorematchingad Description: CRAN Package 'scorematchingad' (Score Matching Estimation by Automatic Differentiation) Hyvärinen's score matching (Hyvärinen, 2005) is a useful estimation technique when the normalising constant for a probability distribution is difficult to compute. This package implements score matching estimators using automatic differentiation in the 'CppAD' library and is designed for quickly implementing score matching estimators for new models. Also available is general robustification (Windham, 1995) . Already in the package are estimators for directional distributions (Mardia, Kent and Laha, 2016) and the flexible Polynomially-Tilted Pairwise Interaction model for compositional data. The latter estimators perform well when there are zeros in the compositions (Scealy and Wood, 2023) , even many zeros (Scealy, Hingee, Kent, and Wood, 2024) . A partial interface to CppAD's ADFun objects is also available. Package: r-cran-scorepeak Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-cluster Filename: pool/dists/noble/main/r-cran-scorepeak_0.1.2-1.ca2404.1_amd64.deb Size: 168086 MD5sum: 02825ddac55b8f2679ca0851c3125a60 SHA1: cc35fbaa56cdd1b09816241496965b7b92e870d2 SHA256: 315945da3e54ca9b65f80dce8382badf4127833a039461580a08c0900f453673 SHA512: f2c36526338f927902400f8839ae31f4c137555b76425bb353e3d0b1c4cd0d57ddcb5870af8bda8aff398d3723ea3e532a8a0ac9323948a254819961b81069a4 Homepage: https://cran.r-project.org/package=scorepeak Description: CRAN Package 'scorepeak' (Peak Functions for Peak Detection in Univariate Time Series) Provides peak functions, which enable us to detect peaks in time series. The methods implemented in this package are based on Girish Keshav Palshikar (2009) . Package: r-cran-scoringrules Architecture: amd64 Version: 1.1.3-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-knitr, r-cran-rcpparmadillo Suggests: r-cran-gsl, r-cran-hypergeo, r-cran-rmarkdown, r-cran-testthat, r-cran-crch, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-scoringrules_1.1.3-1.ca2404.1_amd64.deb Size: 2061552 MD5sum: a3b5e61422b9ec9881b9e5d314218a57 SHA1: b45413541b503835cf206bfd993475e9b4612253 SHA256: 26dabe68d1c68c0937bd767bba687a323344c83d18419c83813cc2a99059dfec SHA512: ad5d591c1c046600b8acf065330a1b3ab21f00c9ab407d595e8e4401594970748230afb1c00f123b8045ff27d348d766f9b3481af7c57ef9bdabe293a00c5252 Homepage: https://cran.r-project.org/package=scoringRules Description: CRAN Package 'scoringRules' (Scoring Rules for Parametric and Simulated DistributionForecasts) Dictionary-like reference for computing scoring rules in a wide range of situations. Covers both parametric forecast distributions (such as mixtures of Gaussians) and distributions generated via simulation. Further details can be found in the package vignettes , . Package: r-cran-scornet Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-survival, r-cran-pracma, r-cran-foreach, r-cran-doparallel, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-scornet_0.1.1-1.ca2404.1_amd64.deb Size: 74254 MD5sum: 018e7d119aca470ccdd29d6944edf8e0 SHA1: 0d6eef511fd1c72d316ec4f6f489cc34a09c7c11 SHA256: ab6e46a5b15dd963999e964eca2b2944c2907cc977b20f71276f7669be559e18 SHA512: ef401ef5c053f914d746b138378b9ea3b16c820ff4a087119f83dba57f393bcef2c645b448b5d9142aba08d2475419ece89465f99160d973c6368a55fa34cf82 Homepage: https://cran.r-project.org/package=SCORNET Description: CRAN Package 'SCORNET' (Semi-Supervised Calibration of Risk with Noisy Event Times) A consistent, semi-supervised, non-parametric survival curve estimator optimized for efficient use of Electronic Health Record (EHR) data with a limited number of current status labels. See van der Laan and Robins (1997) . Package: r-cran-scout Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-glasso Suggests: r-cran-lars Filename: pool/dists/noble/main/r-cran-scout_1.0.4-1.ca2404.1_amd64.deb Size: 71406 MD5sum: 74b4c27e5a447df23d8ffb08dcd2ddbf SHA1: c52569a8fab09ead28b230cf4effc8f8b17029ad SHA256: 64a877820ef556285843ff5d906cf7d3c800472164a149f8aa4b42c2f05f5c79 SHA512: 171a3835d1d0dfe02b9d1105f0e3f459afa6e63374537c5782cdfa14208f8eaa34ca25b409ce2eeafebd10679ad9c9f7b719f51389c1deca4b7115b678b71450 Homepage: https://cran.r-project.org/package=scout Description: CRAN Package 'scout' (Implements the Scout Method for Covariance-RegularizedRegression) Implements the Scout method for regression, described in "Covariance-regularized regression and classification for high-dimensional problems", by Witten and Tibshirani (2008), Journal of the Royal Statistical Society, Series B 71(3): 615-636. Package: r-cran-scquantum Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-scquantum_1.0.0-1.ca2404.1_amd64.deb Size: 37398 MD5sum: 8db991d4505bdb38f89270ef379dc1e3 SHA1: c911c43bd5d888a83ac9d79aa7d55d11899f96d9 SHA256: 2102c27ecddbe5a9990aa999b05a246d124a963b8b0d01e0f1743bc5edeb852a SHA512: fa7de10f2cc1bdb294e2a7a923468ed45daf8876b3f8ed56e8af55b5ec84db72d3d8633a8aaaa537a860f72ecb652c8e2a4b7a56bfc5abea10036182338ec640 Homepage: https://cran.r-project.org/package=scquantum Description: CRAN Package 'scquantum' (Estimate Ploidy and Absolute Copy Number from Single CellSequencing) Given bincount data from single-cell copy number profiling (segmented or unsegmented), estimates ploidy, and uses the ploidy estimate to scale the data to absolute copy numbers. Uses the modular quantogram proposed by Kendall (1986) , modified by weighting segments according to confidence, and quantifying confidence in the estimate using a theoretical quantogram. Includes optional fused-lasso segmentation with the algorithm in Johnson (2013) , using the implementation from glmgen by Arnold, Sadhanala, and Tibshirani. Package: r-cran-scregclust Architecture: amd64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 707 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-reshape, r-cran-igraph, r-cran-cli, r-cran-prettyunits, r-cran-ggplot2, r-cran-rlang, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-quarto, r-cran-seurat, r-cran-hdf5r, r-bioc-glmgampoi, r-bioc-geoquery Filename: pool/dists/noble/main/r-cran-scregclust_0.2.4-1.ca2404.1_amd64.deb Size: 513908 MD5sum: e6b6d660fb760c20ddb8c3b0ea9f1238 SHA1: d38a05ca22442d2f59cf98d8954c5b0e23e720c5 SHA256: 85c33d05c6bc0b9f761633a06b8f377d1d524915b9942f299dcda3e84663c82b SHA512: a6132ec1312dcba5d43723debef131b675a5650aee18afca8e79615f6dda2d5c9ea6cb383b1e6e28b9c1e976b289e96f413e189061d125ebe04c6791b0ab728d Homepage: https://cran.r-project.org/package=scregclust Description: CRAN Package 'scregclust' (Reconstructing the Regulatory Programs of Target Genes inscRNA-Seq Data) Implementation of the scregclust algorithm described in Larsson, Held, et al. (2024) which reconstructs regulatory programs of target genes in scRNA-seq data. Target genes are clustered into modules and each module is associated with a linear model describing the regulatory program. Package: r-cran-scrm Architecture: amd64 Version: 1.7.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-ape, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scrm_1.7.5-1.ca2404.1_amd64.deb Size: 161978 MD5sum: 50678111ac176852ad7277503badc753 SHA1: 7543590f9434a1ad9a1ac9faaf9652b614c3a0f8 SHA256: f7abfea993bbeefbe3f0a06317fe7f79eddadd0f601fa20db762c24cc4d29095 SHA512: 3392fea16467e8b2666ff85f98eaa0644c6b68120d7e05da017613a3caa9a1b6a19b9dfe4f84540c7f8c5723ae675073f06ae036e8a0eddc2b28ba84ab68616f Homepage: https://cran.r-project.org/package=scrm Description: CRAN Package 'scrm' (Simulating the Evolution of Biological Sequences) A coalescent simulator that allows the rapid simulation of biological sequences under neutral models of evolution, see Staab et al. (2015) . Different to other coalescent based simulations, it has an optional approximation parameter that allows for high accuracy while maintaining a linear run time cost for long sequences. It is optimized for simulating massive data sets as produced by Next- Generation Sequencing technologies for up to several thousand sequences. Package: r-cran-scrypt Architecture: amd64 Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-scrypt_0.1.6-1.ca2404.1_amd64.deb Size: 53622 MD5sum: f86bf92cf06853f8d3d856cb4ad6cdcb SHA1: b1e042576a6d315dd7397d836160a4b0124dab9b SHA256: 04e520c5c5d83426446f6c1708d81fa067d9fa110f308a3a4291f7a641628308 SHA512: 6cd0ebb710c65407449d8993523f0fc3a8787bb78ba105210b9c954ffc90ae4942d87fab29e70e065b469abc926b6eea59652360edf519df9ccf8ad4980c674a Homepage: https://cran.r-project.org/package=scrypt Description: CRAN Package 'scrypt' (Key Derivation Functions for R Based on Scrypt) Functions for working with the scrypt key derivation functions originally described by Colin Percival and in Percival and Josefsson (2016) . Scrypt is a password-based key derivation function created by Colin Percival. The algorithm was specifically designed to make it costly to perform large-scale custom hardware attacks by requiring large amounts of memory. Package: r-cran-scs Architecture: amd64 Version: 3.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1567 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-matrix, r-cran-slam, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-scs_3.2.7-1.ca2404.1_amd64.deb Size: 1281358 MD5sum: a2ddf6a73b035227820b8df6d8f2edeb SHA1: c9bf3d106f98ae7d7311db4bf3451662620c68cb SHA256: 64949d515a0b3171a46bde7ff3c2179f6b6813589ef4c55df8e39214b187bed9 SHA512: 02dd0b872bfe0cf2fd5cbc33353507b55efac74f1180ca59208fb3176ba7f099445514d8f4c9ccdc555568c808915cc7515a83b2f6b2928b5604306ccfe485b4 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: amd64 Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 768 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-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/noble/main/r-cran-sctransform_0.4.3-1.ca2404.1_amd64.deb Size: 508084 MD5sum: e45118d306a8a9df1399fedff78681a9 SHA1: ff48ae33e7219e11da969e3b342a5caeaf67206e SHA256: 0c8493b187c12af7011100d13176874f45199dbbb0a2ad7d8911d38f81ae8df4 SHA512: c7e72b063825d8a6a51543ccc04d6534a8bb4e21c488abe88b950cd0f770a0abd3cfd99685eb7cbc50c9370437f3d86bc78ff745a17e02e7a2036cc78ea386f2 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: amd64 Version: 1.11-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1034 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-scuba_1.11-1-1.ca2404.1_amd64.deb Size: 682516 MD5sum: 739318bb328577910406dad222f8b123 SHA1: bd2102f5caea3ac98b3e5d061cb5e106bc39a470 SHA256: 8d92ee07146c7098abe2051cd6e7c4abd90f9cd5b1273ef93709eebd7f2a1ce9 SHA512: 5e29f04190f4c2fc40ecaf8097b97b6d261bd959192878b46ee9c308c8679adb74bc203684e9eaaacb960bec908301e824b60f67fa26b0867f1fad386de288f7 Homepage: https://cran.r-project.org/package=scuba Description: CRAN Package 'scuba' (Diving Calculations and Decompression Models) Code for describing and manipulating scuba diving profiles (depth-time curves) and decompression models, for calculating the predictions of decompression models, for calculating maximum no-decompression time and decompression tables, and for performing mixed gas calculations. Package: r-cran-sd2r Architecture: amd64 Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 25927 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggmlr Suggests: r-cran-testthat, r-cran-callr, r-cran-png, r-cran-shiny, r-cran-base64enc, r-cran-plumber, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-sd2r_0.1.9-1.ca2404.1_amd64.deb Size: 6676244 MD5sum: 2dc5ec9c86bed2b7031f5f2941935b3d SHA1: b5dd77ad7d10b9b1b29829d90e16b04c02917f35 SHA256: d2b3bc22f2e05821db7103848359b6cff3ffc0d1797977430d4f021ce12debc8 SHA512: 6a7be5d930ee1a9f48f15f48c90c1b81dd7da10b715554f4a95b51446b953288bd1a7ef133d5a742af278003a8ab0dd4ee71ad27994210082d0a23820044986c 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: amd64 Version: 0.23.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 541 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sdchierarchies_0.23.1-1.ca2404.1_amd64.deb Size: 244820 MD5sum: c1508de0d225e064e52f66ee03f1624c SHA1: 3926a760e2783fff3aeb11afe36f6f81e8546751 SHA256: c2f421a9918b489299e04b671b2072a269b48cd4397b20d69e6a1bac0e6efe00 SHA512: db7399e376595ab8297479c7cc34d7b1380e92a94e3edbc933c757a9426de4e957b47fc3e4eab7bb0d92f7397a10a9c70647b008663747a8131ebb087568a7f9 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: amd64 Version: 5.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3318 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-cardata, r-cran-rmarkdown, r-cran-knitr, r-cran-data.table, r-cran-xtable, r-cran-robustbase, r-cran-cluster, r-cran-mass, r-cran-e1071, r-cran-rcpp, r-cran-ggplot2, r-cran-shiny, r-cran-haven, r-cran-rhandsontable, r-cran-dt, r-cran-prettydoc, r-cran-vim, r-cran-httr, r-cran-jsonlite Suggests: r-cran-laeken, r-cran-testthat, r-cran-pdftools, r-cran-yaml Filename: pool/dists/noble/main/r-cran-sdcmicro_5.8.1-1.ca2404.1_amd64.deb Size: 1660490 MD5sum: 3fd7d42e73d455fe4b6c1c1de3a7b176 SHA1: 9a5810436fde4169a0908010d4f238ca39492d84 SHA256: bcee9a42e384681833a8c303fdd5332fe1233f2afa9dcc82ad63038d48b6a282 SHA512: 5c5765d7c362121d7e19f243310a9fc958ae8a6549344f22e326d531bc60b15ca3330d0f9deaf95fe395bfde034a017880e7498e266f1efbd1d5921283b4c29d 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: amd64 Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2162 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-sp, r-cran-sf, r-cran-fnn Filename: pool/dists/noble/main/r-cran-sdcspatial_0.6.1-1.ca2404.1_amd64.deb Size: 1966890 MD5sum: ac6450cdf0e00a4cff83f8a6da634c5b SHA1: bde9a55fce87c72b6af06f5a62978e6e2c390d54 SHA256: a679d50bb871ef6f762fa26ac77460e698621ad98f65b7149d81f14c087e0315 SHA512: 58186823d03798db902bf951c503274b12a91eb1b618753e84449979fdacb878aed0ddcd5c0f7f60710df508fc5a91816b8e4cc6e25b6f3817ce96d0d16ef0da 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: amd64 Version: 2.0.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-fda, r-cran-zoo Filename: pool/dists/noble/main/r-cran-sde_2.0.21-1.ca2404.1_amd64.deb Size: 457266 MD5sum: 93a1c38316a90434b9a17990221f40e6 SHA1: 11ddadba542ac1f3200c9cffbe70f103428a80fb SHA256: 89bfe5903aea9a3edd0a435cae27a5aaa642a1498e8303636c7e3c729fc217d6 SHA512: 7eaf03f56499b99c8bf41c45d30d135c2c38feb5a046968cd5f77c5777576289b9c420b4a00ab111ecabb7717310e9536ff945fc01654d5551d349a0f9792943 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). 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Several diffusive models are provided, most of them belonging to the Langevin family of diffusions on the torus. Specifically, the wrapped normal and von Mises processes are included, which can be seen as toroidal analogues of the Ornstein-Uhlenbeck diffusion. A collection of methods for approximate maximum likelihood estimation, organized in four blocks, is given: (i) based on the exact transition probability density, obtained as the numerical solution to the Fokker-Plank equation; (ii) based on wrapped pseudo-likelihoods; (iii) based on specific analytic approximations by wrapped processes; (iv) based on maximum likelihood of the stationary densities. The package allows the replicability of the results in García-Portugués et al. (2019) . Package: r-cran-sdmtmb Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5343 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-abind, r-cran-cli, r-cran-fmesher, r-cran-fishmod, r-cran-generics, r-cran-lifecycle, r-cran-matrix, r-cran-mgcv, r-cran-mvtnorm, r-cran-nlme, r-cran-reformulas, r-cran-rlang, r-cran-tmb, r-cran-rcppeigen Suggests: r-cran-dharma, r-cran-dplyr, r-cran-effects, r-cran-estimability, r-cran-emmeans, r-cran-future, r-cran-future.apply, r-cran-ggeffects, r-cran-ggforce, r-cran-glmmtmb, r-cran-ggplot2, r-cran-knitr, r-cran-lme4, r-cran-rmarkdown, r-cran-sf, r-cran-spatstat.data, r-cran-splancs, r-cran-testthat, r-cran-tibble, r-cran-visreg, r-cran-waywiser Filename: pool/dists/noble/main/r-cran-sdmtmb_1.0.0-1.ca2404.1_amd64.deb Size: 2146178 MD5sum: 239048ca4b64c0a7f54e4f7274ec6244 SHA1: 4950992d5f523a84e64f90c4de850dafd412d8eb SHA256: 9e63b899805f50b924492873095363e9f8a738cd113ace54c43d15d7f5ad662d SHA512: 83b6ce744f0230fc4e53655241f857f0f78c3854d6c3ceb72ee1112c96876a6c7c219b45d2c2daa8e9a2f269173c506e614a81df9f85ce0e158d2aeb2c33e6c5 Homepage: https://cran.r-project.org/package=sdmTMB Description: CRAN Package 'sdmTMB' (Spatial and Spatiotemporal SPDE-Based GLMMs with 'TMB') Implements spatial and spatiotemporal GLMMs (Generalized Linear Mixed Effect Models) using 'TMB', 'fmesher', and the SPDE (Stochastic Partial Differential Equation) Gaussian Markov random field approximation to Gaussian random fields. One common application is for spatially explicit species distribution models (SDMs). See Anderson et al. (2025) . 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The package implements functions for data driven variable selection and model tuning and includes numerous utilities to display the results. All the functions used to select variables or to tune model hyperparameters have an interactive real-time chart displayed in the 'RStudio' viewer pane during their execution. Package: r-cran-sdpdth Architecture: amd64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcma, r-cran-matrixcalc, r-cran-rjava, r-cran-matrix, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-sdpdth_0.2-1.ca2404.1_amd64.deb Size: 304284 MD5sum: 2b946f6d28c806483e5988723f785948 SHA1: fe4962536e7597da505a778cbefcbe06c69dafae SHA256: 5bdfea66dba76ccb270738b0ace61601e84fa2bd92722315b292da35cd61780c SHA512: ea35c73af34852dc79921cb78634bab7f6497e4dbccc8ab47ab5fd1e19dd238464007319ba5cdde52d9a31c444bf92c2e1aa561e8be8edc2b49d9965d526bbae Homepage: https://cran.r-project.org/package=sdpdth Description: CRAN Package 'sdpdth' (M-Estimator for Threshold Spatial Dynamic Panel Data Model) M-estimator for threshold and non-threshold spatial dynamic panel data model. Yang, Z (2018) . Wu, J., Matsuda, Y (2021) . Package: r-cran-sdprisk Architecture: amd64 Version: 1.1-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv, r-cran-polynomf, r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-sdprisk_1.1-6-1.ca2404.1_amd64.deb Size: 97256 MD5sum: 6cac1fc518f8d9d6de8e66ddef06da01 SHA1: fdca5e63e5ed63261b335de703a20d0311c87911 SHA256: e3a46bf91597568783e8ee90aa9b2e6a4c2e71a89d4120152022ddcde7eee172 SHA512: 685ec0661ce005e1046cc79a1f7634a4b9c6fe7569139bcc1e549ce20d5b30442660878a952aac094c895d86af2bb54a71fcaeece7d0b39789e724cbab05eba9 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) . 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Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection. 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This is primarily a simulation manager for package 'secr'. Extensions in version 2.5.0 include costing and evaluation of detector spacing. Package: r-cran-secretbase Architecture: amd64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-secretbase_1.2.2-1.ca2404.1_amd64.deb Size: 75664 MD5sum: 0b4ba3e20556d2630addcdde1d8c7559 SHA1: 146af289a182f1cd7e912562a10c5a1142f6567e SHA256: fb6e44c09855cbc21ec3352f5e2bc055a4f9ae47575ce97caa9b97a3ab9e823c SHA512: 2ccda5871ce15de74acf198714336353180dd1a400199f851e116f7bd93bb0a0000f961813ed6b0ca6701901763295c35a615050c09dc6e32a0340336b356bd3 Homepage: https://cran.r-project.org/package=secretbase Description: CRAN Package 'secretbase' (Cryptographic Hash Functions and Data Encoding) Fast and memory-efficient streaming hash functions, binary/text encoding and serialization. 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Package: r-cran-secrfunc Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 548 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-rcppnumerical, r-cran-rcppparallel, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-secrfunc_1.0.0-1.ca2404.1_amd64.deb Size: 163778 MD5sum: ee3ae459eae44144d2dfb4e6f915dac6 SHA1: f5919b972f8f79e2d713f2a5c694459a0e03088d SHA256: daae81c1cd363e7c52502e29220fc7277d51786e7a2994fd5d53fd902e1d0f3d SHA512: 56d5c9433245a4c64a84284e69586348490ef53d425d956afff28e6c17196794cdde55e2fa3ea32bd59ac07b2aed650a03733bb2a98707136d375d163fc768b4 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). 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See Herrera-Alsina et al. (2019) . Package: r-cran-secure Architecture: amd64 Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1287 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-secure_0.6-1.ca2404.1_amd64.deb Size: 1114538 MD5sum: ff61bf9b3e092660cf7f20376dc1d27a SHA1: 9278562daf23a9621b807113a0d441658559ca42 SHA256: e32c4b86f266f53edd081ed1558a04e7933659dabe7ef8ac38e91bc4ab9ed86d SHA512: 4d46a6f1b7f558d8e243d5fc9451bb2699a94a5a0499af0280d5c9c497b27b7fcce78ac605f841e17cc1a7b27893dda9c360ad486741312ff7bf0dca331a817d Homepage: https://cran.r-project.org/package=secure Description: CRAN Package 'secure' (Sequential Co-Sparse Factor Regression) Sequential factor extraction via co-sparse unit-rank estimation (SeCURE). Package: r-cran-seededlda Architecture: amd64 Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3830 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-quanteda, r-cran-proxyc, r-cran-matrix, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-testthat Suggests: r-cran-spelling, r-cran-topicmodels Filename: pool/dists/noble/main/r-cran-seededlda_1.4.3-1.ca2404.1_amd64.deb Size: 3360874 MD5sum: 57ec328e7af5b2116d76ebf0871851a7 SHA1: ffda0931fc7cb5d4063e2e1b448c1497e4aadcb0 SHA256: 40d65f20ab0f5cbe98f2151933855966222659eac13815265e47f31e1f468ae8 SHA512: eeb7866420b18b7b4cf7a3b3880e69e412819ad01af8a4106df61d2e382b8e13e3661ad03677e9657506da625d8346f9b498ba46c6eb411881c15d4baae14e60 Homepage: https://cran.r-project.org/package=seededlda Description: CRAN Package 'seededlda' (Seeded Sequential LDA for Topic Modeling) Seeded Sequential LDA can classify sentences of texts into pre-define topics with a small number of seed words (Watanabe & Baturo, 2023) . Implements Seeded LDA (Lu et al., 2010) and Sequential LDA (Du et al., 2012) with the distributed LDA algorithm (Newman, et al., 2009) for parallel computing. Package: r-cran-seerabomb Architecture: amd64 Version: 2019.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1222 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rgl, r-cran-demography, r-cran-rcpp, r-cran-reshape2, r-cran-mgcv, r-cran-tibble, r-cran-laf, r-cran-dbi, r-cran-rsqlite, r-cran-openxlsx, r-cran-writexls, r-cran-labelled, r-cran-scales, r-cran-forcats, r-cran-purrr, r-cran-readr, r-cran-tidyr, r-cran-stringr, r-cran-plyr, r-cran-survival Suggests: r-cran-bbmle Filename: pool/dists/noble/main/r-cran-seerabomb_2019.2-1.ca2404.1_amd64.deb Size: 807840 MD5sum: 64ffeae83659143f981e20f4ecfb6cff SHA1: 661a5312ed750e672744bacbac432ea3fcfff626 SHA256: 185ac68064973812f8970917e501d848bbf1e6cf26a99d0fc01054e49123a8c6 SHA512: 6c4541b8bc45f54a06774afc1f7a3742b49debe9554ad052514ccaf4b842792979b870fa5217c59dbe8546783179a9fd527dbb4e36231414948fd81ec032e348 Homepage: https://cran.r-project.org/package=SEERaBomb Description: CRAN Package 'SEERaBomb' (SEER and Atomic Bomb Survivor Data Analysis Tools) Creates SEER (Surveillance, Epidemiology and End Results) and A-bomb data binaries from ASCII sources and provides tools for estimating SEER second cancer risks. Methods are described in . Package: r-cran-segclust2d Architecture: amd64 Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1520 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-dplyr, r-cran-plyr, r-cran-reshape2, r-cran-ggplot2, r-cran-magrittr, r-cran-rcpp, r-cran-zoo, r-cran-scales, r-cran-rlang, r-cran-cli, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dygraphs, r-cran-xts, r-cran-leaflet, r-cran-sp, r-cran-adehabitatlt, r-cran-depmixs4, r-cran-movehmm, r-cran-htmltools, r-cran-move, r-cran-devtools, r-cran-spelling Filename: pool/dists/noble/main/r-cran-segclust2d_0.3.3-1.ca2404.1_amd64.deb Size: 905934 MD5sum: 441efd594529cc2af755b2b835e9bffe SHA1: 7f88b160db6d1a8f7c471bea5251f40b43523eed SHA256: 2ab61600fa30681045c92ea933270287202ff079031f63e2bb3ce39f975fb539 SHA512: 84c8bc4084a23d28537d7955051a6ba30d404405f7cac06286fb401a659e2b054821f650b0c5d4f0187d5e2bff14fe1cd44c229316ef0a11d7a8158e21eb14df Homepage: https://cran.r-project.org/package=segclust2d Description: CRAN Package 'segclust2d' (Bivariate Segmentation/Clustering Methods and Tools) Provides two methods for segmentation and joint segmentation/clustering of bivariate time-series. Originally intended for ecological segmentation (home-range and behavioural modes) but easily applied on other series, the package also provides tools for analysing outputs from R packages 'moveHMM' and 'marcher'. The segmentation method is a bivariate extension of Lavielle's method available in 'adehabitatLT' (Lavielle, 1999 and 2005 ). This method rely on dynamic programming for efficient segmentation. The segmentation/clustering method alternates steps of dynamic programming with an Expectation-Maximization algorithm. This is an extension of Picard et al (2007) method (formerly available in 'cghseg' package) to the bivariate case. The method is fully described in Patin et al (2018) . Package: r-cran-segmag Architecture: amd64 Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-plyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-segmag_1.2.4-1.ca2404.1_amd64.deb Size: 71548 MD5sum: 467671a791cb83477035396d77d13632 SHA1: dbf72172eaeb58226d2a53b2ee3416fe3bcfae55 SHA256: f8a454484ee531dd51cbf034ab48c6fe98eea90cb2b0fb61fb9d5901c7c8a795 SHA512: e15b076def05abf8a8e7715493744f61516c5b549020ee984ba55b98ae869d0068047346f76ee0adb0d0ed0e36610b28695340e191242fb16b82ba31e0fe5b6f Homepage: https://cran.r-project.org/package=segmag Description: CRAN Package 'segmag' (Determine Event Boundaries in Event Segmentation Experiments) Contains functions that help to determine event boundaries in event segmentation experiments by bootstrapping a critical segmentation magnitude under the null hypothesis that all key presses were randomly distributed across the experiment. Segmentation magnitude is defined as the sum of Gaussians centered at the times of the segmentation key presses performed by the participants. Within a participant, the maximum of the overlaid Gaussians is used to prevent an excessive influence of a single participant on the overall outcome (e.g. if a participant is pressing the key multiple times in succession). Further functions are included, such as plotting the results. Package: r-cran-segmentier Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 887 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-bioc-flowmerge, r-bioc-flowclust, r-bioc-flowcore, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-segmentier_0.1.2-1.ca2404.1_amd64.deb Size: 648844 MD5sum: b791f500b8fd13baa0166a17dc9e8b0a SHA1: 2491dac8e2d657d2a2f5e30867ef2621f123fca8 SHA256: 4bdcde8ad07a7b902df2df79dd1f335eb33b67339777e46ee48b0e8809069774 SHA512: afb746b9359b732151be765885e5dab68c1da9b8a3cd606209369945143b6e669a056572cab800ef7fa81b750b2b5d9c3e036cc5c96cab1b4c6dfbb83ddf60c0 Homepage: https://cran.r-project.org/package=segmenTier Description: CRAN Package 'segmenTier' (Similarity-Based Segmentation of Multidimensional Signals) A dynamic programming solution to segmentation based on maximization of arbitrary similarity measures within segments. The general idea, theory and this implementation are described in Machne, Murray & Stadler (2017) . In addition to the core algorithm, the package provides time-series processing and clustering functions as described in the publication. These are generally applicable where a `k-means` clustering yields meaningful results, and have been specifically developed for clustering of the Discrete Fourier Transform of periodic gene expression data (`circadian' or `yeast metabolic oscillations'). This clustering approach is outlined in the supplemental material of Machne & Murray (2012) ), and here is used as a basis of segment similarity measures. Notably, the time-series processing and clustering functions can also be used as stand-alone tools, independent of segmentation, e.g., for transcriptome data already mapped to genes. Package: r-cran-segmentr Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 489 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-foreach, r-cran-glue Suggests: r-cran-testthat, r-cran-doparallel, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-tibble, r-cran-dplyr, r-cran-lubridate, r-cran-magrittr, r-cran-rdwd, r-cran-purrr Filename: pool/dists/noble/main/r-cran-segmentr_0.2.0-1.ca2404.1_amd64.deb Size: 266068 MD5sum: c0a86b80dc7dd205e36609dfc6bd2c6f SHA1: ace29e94448c4b14f8cba70cb9b12f10143ce57b SHA256: d1cd80ff93fa7914b8fc6b89db55b366b7b56b9f935f986744eb52b742f79a0d SHA512: b24a6138456c649a21069769db6af4ebe4cc09883257e85e3a1d11ab580335b41bf59223bc349a725894b0ce208cc1f2509e6125a28fb64ba46702e2bea3f32b Homepage: https://cran.r-project.org/package=segmentr Description: CRAN Package 'segmentr' (Segment Data With Maximum Likelihood) Given a likelihood provided by the user, this package applies it to a given matrix dataset in order to find change points in the data that maximize the sum of the likelihoods of all the segments. This package provides a handful of algorithms with different time complexities and assumption compromises so the user is able to choose the best one for the problem at hand. The implementation of the segmentation algorithms in this package are based on the paper by Bruno M. de Castro, Florencia Leonardi (2018) . The Berlin weather sample dataset was provided by Deutscher Wetterdienst . You can find all the references in the Acknowledgments section of this package's repository via the URL below. Package: r-cran-segmgarch Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-foreach, r-cran-iterators, r-cran-doparallel, r-cran-fgarch, r-cran-corpcor, r-cran-mvtnorm, r-cran-rcpparmadillo Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-segmgarch_1.3-1.ca2404.1_amd64.deb Size: 156460 MD5sum: 98fdf4571319654646bedf155fa245e5 SHA1: 3e13b84d0a6c14024af3b38b2e4b512f48775cd7 SHA256: 6b12a95fe87d7a262f3d66b601622f4fdb39a9297d805aed7049c9a31816247b SHA512: a538aa8c52fdd444d975ae711d2ae92945f1efcc7547e2075a41c3f00fdf89c4e31696255fe52e2bb94d0f797f4d72ecb455437d4448703b90cc088a9face6ff Homepage: https://cran.r-project.org/package=segMGarch Description: CRAN Package 'segMGarch' (Multiple Change-Point Detection for High-Dimensional GARCHProcesses) Implements a segmentation algorithm for multiple change-point detection in high-dimensional GARCH processes. It simultaneously segments GARCH processes by identifying 'common' change-points, each of which can be shared by a subset or all of the component time series as a change-point in their within-series and/or cross-sectional correlation structure. Package: r-cran-segregation Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1045 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-checkmate, r-cran-rcpp, r-cran-rcppprogress Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-scales, r-cran-tidycensus, r-cran-tigris, r-cran-rrapply, r-cran-dendextend, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-segregation_1.1.0-1.ca2404.1_amd64.deb Size: 649546 MD5sum: 55260d84d892457f369a7a1490073908 SHA1: d90e22bc7364f893fd258a923b620b65052a815d SHA256: f49ebdbbef3cfbf73b91c3750fcdb7c68c6a3b6c39796aa69fc7c0563eadebca SHA512: e46302908cfe081dcbeb1e7071e5b6aafe7d3c697e072acdce09862affe9a194c52b0ea9760970a6fba8b4d53bca8178e2868953adeae532c54d170e9d252b3a Homepage: https://cran.r-project.org/package=segregation Description: CRAN Package 'segregation' (Entropy-Based Segregation Indices) Computes segregation indices, including the Index of Dissimilarity, as well as the information-theoretic indices developed by Theil (1971) , namely the Mutual Information Index (M) and Theil's Information Index (H). The M, further described by Mora and Ruiz-Castillo (2011) and Frankel and Volij (2011) , is a measure of segregation that is highly decomposable. The package provides tools to decompose the index by units and groups (local segregation), and by within and between terms. The package also provides a method to decompose differences in segregation as described by Elbers (2021) . The package includes standard error estimation by bootstrapping, which also corrects for small sample bias. The package also contains functions for visualizing segregation patterns. Package: r-cran-segtest Architecture: amd64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1187 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dofuture, r-cran-dorng, r-cran-foreach, r-cran-future, r-cran-iterators, r-cran-minqa, r-cran-nloptr, r-cran-rcpp, r-cran-updog, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-polymapr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-segtest_2.0.0-1.ca2404.1_amd64.deb Size: 972494 MD5sum: 15c99c1140e7598d28a06371d0912ef1 SHA1: 13b0731780e25fe3d1291483746f08b2cd6c24ff SHA256: 8ef105b3fddfdb08da02a2eef843cf3fbdbe55ccf32a7928e270c54a5a6a1134 SHA512: 400c6ef4777b60f9503f7d67f355bf27456e53b48502691e702bd555c72495dde0fec6797d6276835e44d0b02ec2d59b64459267ab84a6cad4fba67d96628312 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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Package: r-cran-sensiat Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 523 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-generics, r-cran-ggplot2, r-cran-glue, r-cran-kernsmooth, r-cran-mass, r-cran-mave, r-cran-orthogonalsplinebasis, r-cran-pracma, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-survival, r-cran-tibble, r-cran-tidyr Suggests: r-cran-dfoptim, r-cran-inline, r-cran-manifoldoptim, r-cran-metr, r-cran-progress, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-sensiat_0.3.0-1.ca2404.1_amd64.deb Size: 307240 MD5sum: 39a4a071a63681432f2da41153a8ba3a SHA1: 88a6f0ca0b24ce99cde69f41f4e087b9069d8a7c SHA256: 92b107c9d614a4ec946a600d1899b3bed70302d12f3fdf26ef55bda3d9f2ab23 SHA512: 5b78dfa867450fb47c5ae308348724bd70da7f8405b06cc5169c1fa1731389cbbafbea8f5dcb14184b5c81d10d6bda236e3b5d88ca49bc9f559084af7a4c32b3 Homepage: https://cran.r-project.org/package=SensIAT Description: CRAN Package 'SensIAT' (Sensitivity Analysis for Irregular Assessment Times) Sensitivity analysis for trials with irregular and informative assessment times, based on a new influence function-based, augmented inverse intensity-weighted estimator. Package: r-cran-sensitivity Architecture: amd64 Version: 1.31.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2920 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sensitivity_1.31.0-1.ca2404.1_amd64.deb Size: 2595814 MD5sum: f58064068a4073293ba4643ad277ca1d SHA1: 28da2bd62cb9e5a7780f0bb02e6858f585833eb1 SHA256: cbb2f49514e38e85f49aebeb8f2b56d4f82d8a2d7d362b4cdfd424d5e818d175 SHA512: b5fc369b0f13dffd2215d046e244e5ad4be9c0667aebcd2cfcf3e0ee43543377f0bba0fd5036c30f477e98d3a287b527670887e283c00b164031b007aed7f31e Homepage: https://cran.r-project.org/package=sensitivity Description: CRAN Package 'sensitivity' (Global Sensitivity Analysis of Model Outputs and ImportanceMeasures) A collection of functions for sensitivity analysis of model outputs (factor screening, global sensitivity analysis and robustness analysis), for variable importance measures of data, as well as for interpretability of machine learning models. Most of the functions have to be applied on scalar output, but several functions support multi-dimensional outputs. Package: r-cran-sensitivityixj Architecture: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-rbounds Filename: pool/dists/noble/main/r-cran-sensitivityixj_0.1.5-1.ca2404.1_amd64.deb Size: 235264 MD5sum: 8ddd71056a91af3fca90464b799cd0fa SHA1: ee26eb341d29fa26b61e2e5ccefef368c3017d81 SHA256: 1ca93804194c39bd3cdd1da3f5fc43e5138d46bb61dc33e46dce62dba3b50d2b SHA512: ae35ea866fd58502d9fd54c05db3da3f97520dec536fb1d1e5fbcbfce94f7710cf04be00ad255db071caa40208c3dcae08ef2993d0c52d7c16ef5415c6bacd4b 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: amd64 Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1626 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sensobol_1.1.9-1.ca2404.1_amd64.deb Size: 1497144 MD5sum: b2d96bf0edd7f5d1c1b7e2e4443ad6e4 SHA1: 3d07ed46ecf8ca8fa79468f3f1ad17f4f951eb39 SHA256: ae077a59b3729511d7e4289ece112c8d49452cbfdc72849811d85a004251db69 SHA512: 28395af34c975e492aa58542091645a385783074b5ea9ae5b5cfb8737b424177f0d653942c45ca7642da4d40b909d74425644146f2e204c20cb83c7810a5edc4 Homepage: https://cran.r-project.org/package=sensobol Description: CRAN Package 'sensobol' (Computation of Variance-Based Sensitivity Indices) It allows to rapidly compute, bootstrap and plot up to fourth-order Sobol'-based sensitivity indices using several state-of-the-art first and total-order estimators. Sobol' indices can be computed either for models that yield a scalar as a model output or for systems of differential equations. The package also provides a suit of benchmark tests functions and several options to obtain publication-ready figures of the model output uncertainty and sensitivity-related analysis. An overview of the package can be found in Puy et al. (2022) . Package: r-cran-senspe Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-senspe_1.3-1.ca2404.1_amd64.deb Size: 49806 MD5sum: f965994ecb20589df1cd352193c4dc76 SHA1: da021ecf66dc005e62e189d7ccd58eae32f5de0c SHA256: 8e135da8070be36ff8bc64c1f3f43c2becd8d0178ff8e4eb36870f534069e7e3 SHA512: 4c7fbccebda761f59367d7067e30ece9ddb3c8f76f6345e208ac393fef3d2fcdacd96ec283df91a0b252591c6a0a89a72ee6d6c569d09cdc835c19419144ea79 Homepage: https://cran.r-project.org/package=SenSpe Description: CRAN Package 'SenSpe' (Estimating Specificity at Controlled Sensitivity, or Vice Versa) Perform biomarker evaluation and comparison in terms of specificity at a controlled sensitivity level, or sensitivity at a controlled specificity level. Point estimation and exact bootstrap of Huang, Parakati, Patil, and Sanda (2023) for the one- and two-biomarker problems are implemented. Package: r-cran-sentencepiece Architecture: amd64 Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4314 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-tokenizers.bpe, r-cran-word2vec Filename: pool/dists/noble/main/r-cran-sentencepiece_0.2.5-1.ca2404.1_amd64.deb Size: 1420200 MD5sum: b25890f62eb6231ad0b76ee3d4a9f31a SHA1: e0b6ca299e9f5b0b08c4e558ff28437038fedd8c SHA256: aded215e26e64a20923f78bd4e13f2da9a246755f62cab9dacb7959863edb64a SHA512: 60cedb83e367f68a866d83730ebcc532b39999823b53ff38ca5b6bbd6abeb70fb00604cffe915a9bab78760094f363a4c10a92896b66fa00149806789dd0b64e Homepage: https://cran.r-project.org/package=sentencepiece Description: CRAN Package 'sentencepiece' (Text Tokenization using Byte Pair Encoding and Unigram Modelling) Unsupervised text tokenizer allowing to perform byte pair encoding and unigram modelling. Wraps the 'sentencepiece' library which provides a language independent tokenizer to split text in words and smaller subword units. The techniques are explained in the paper "SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing" by Taku Kudo and John Richardson (2018) . Provides as well straightforward access to pretrained byte pair encoding models and subword embeddings trained on Wikipedia using 'word2vec', as described in "BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages" by Benjamin Heinzerling and Michael Strube (2018) . Package: r-cran-sentometrics Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3767 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-data.table, r-cran-foreach, r-cran-ggplot2, r-cran-glmnet, r-cran-isoweek, r-cran-quanteda, r-cran-rcpp, r-cran-rcpproll, r-cran-rcppparallel, r-cran-stringi, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-doparallel, r-cran-e1071, r-cran-lexicon, r-cran-mcs, r-cran-nlp, r-cran-randomforest, r-cran-stopwords, r-cran-testthat, r-cran-tm Filename: pool/dists/noble/main/r-cran-sentometrics_1.0.1-1.ca2404.1_amd64.deb Size: 3513126 MD5sum: 9da263e62278969f037e7ada4ba7b87f SHA1: a1a5fe133fa7f75ba849c15777f4b6eeccf1fa97 SHA256: 9227cd30139185d3d17d6862904ec0d615d06f16b97684cf62d5979488eebdd9 SHA512: 06e339cdcf265a88d4fdb0f72786038cb7eba76f8f1480f769cd309ceb1a2ea28fbd0dc976a20cfddd8e631de980b1686c63de0b6ffbcc2ed42b4d272065b726 Homepage: https://cran.r-project.org/package=sentometrics Description: CRAN Package 'sentometrics' (An Integrated Framework for Textual Sentiment Time SeriesAggregation and Prediction) Optimized prediction based on textual sentiment, accounting for the intrinsic challenge that sentiment can be computed and pooled across texts and time in various ways. See Ardia et al. (2021) . Package: r-cran-sentopics Architecture: amd64 Version: 0.7.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2635 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-generics, r-cran-quanteda, r-cran-data.table, r-cran-rcpphungarian, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-ggplot2, r-cran-ggridges, r-cran-plotly, r-cran-rcolorbrewer, r-cran-xts, r-cran-zoo, r-cran-future, r-cran-future.apply, r-cran-progressr, r-cran-progress, r-cran-testthat, r-cran-covr, r-cran-stm, r-cran-lda, r-cran-topicmodels, r-cran-seededlda, r-cran-keyatm, r-cran-ldavis, r-cran-servr, r-cran-textcat, r-cran-stringr, r-cran-sentometrics, r-cran-spacyr, r-cran-knitr, r-cran-rmarkdown, r-cran-webshot Filename: pool/dists/noble/main/r-cran-sentopics_0.7.6-1.ca2404.1_amd64.deb Size: 2077726 MD5sum: 8ad7741bfd047b1d5c9d815a6d82c02d SHA1: 962c4bcf50eefb8f398a89c94fab15c5345b02ce SHA256: a0222c8c80738172553408a438a470ec59a82f38f80c2c3498f4c938faebb184 SHA512: 8ead73a768b442f0def9812a20caa4caa48d09e0124d4c1970d3e5d7d854ae2301bed44f51faa8ce2a3b7e6860f0ef740588a6cf213199021b28ba07e89b8667 Homepage: https://cran.r-project.org/package=sentopics Description: CRAN Package 'sentopics' (Tools for Joint Sentiment and Topic Analysis of Textual Data) A framework that joins topic modeling and sentiment analysis of textual data. The package implements a fast Gibbs sampling estimation of Latent Dirichlet Allocation (Griffiths and Steyvers (2004) ) and Joint Sentiment/Topic Model (Lin, He, Everson and Ruger (2012) ). It offers a variety of helpers and visualizations to analyze the result of topic modeling. The framework also allows enriching topic models with dates and externally computed sentiment measures. A flexible aggregation scheme enables the creation of time series of sentiment or topical proportions from the enriched topic models. Moreover, a novel method jointly aggregates topic proportions and sentiment measures to derive time series of topical sentiment. Package: r-cran-seq2r Architecture: amd64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-seqinr Filename: pool/dists/noble/main/r-cran-seq2r_2.0.1-1.ca2404.1_amd64.deb Size: 94998 MD5sum: 9637c38ca08b77b1ff2fc94acc5a5a11 SHA1: 9863e675a29df630630ad0ebdff3de6acf6a9245 SHA256: 8a1083b680a0d03151207e3b653495e13d4ee22dc0d4fb584e2a75fa1b5dbf6f SHA512: 4808fc54a5335dbc2765b3f4a3efe241c05e520890ffb75843e71dbe81d286febee474abf977176b9daa08be4f8ece31b95fc77d9e270bbf00690449a66bcd78 Homepage: https://cran.r-project.org/package=seq2R Description: CRAN Package 'seq2R' (Simple Method to Detect Compositional Changes in GenomicSequences) This software is useful for loading '.fasta' or '.gbk' files, and for retrieving sequences from 'GenBank' dataset . This package allows to detect differences or asymmetries based on nucleotide composition by using local linear kernel smoothers. Also, it is possible to draw inference about critical points (i. e. maximum or minimum points) related with the derivative curves. Additionally, bootstrap methods have been used for estimating confidence intervals and speed computational techniques (binning techniques) have been implemented in 'seq2R'. Package: r-cran-seqdetect Architecture: amd64 Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1948 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-eventdatar, r-cran-igraph, r-cran-dplyr Suggests: r-cran-xtable Filename: pool/dists/noble/main/r-cran-seqdetect_1.0.7-1.ca2404.1_amd64.deb Size: 1082918 MD5sum: 22e8350f8dbf7cfbaf25c65aff886ada SHA1: 83eaeaf0d48c4f8bff8a91215f860923d9833875 SHA256: b2ffd42fb899f85f8125e4e509f99cf8580b5b1148e2bd5ac409878948011cab SHA512: ffeb66902127e68c062d210d932fc1cc5a1f7bf90d2e23b29aa791e7c06dd180512c2a712d537e609808d8a7bc5479879a5b62c1ed1fb0116c18afcaa5fcfdc3 Homepage: https://cran.r-project.org/package=SeqDetect Description: CRAN Package 'SeqDetect' (Sequence and Latent Process Detector) Sequence detector in this package contains a specific automaton model that can be used to learn and detect data and process sequences. Automaton model in this package is capable of learning and tracing sequences. Automaton model can be found in Krleža, Vrdoljak, Brčić (2019) . This research has been partly supported under Competitiveness and Cohesion Operational Programme from the European Regional and Development Fund, as part of the Integrated Anti-Fraud System project no. KK.01.2.1.01.0041. This research has also been partly supported by the European Regional Development Fund under the grant KK.01.1.1.01.0009. Package: r-cran-seqest Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 511 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-geepack, r-cran-mvtnorm, r-cran-nnet, r-cran-vgam, r-cran-mass, r-cran-foreach, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-seqest_1.0.1-1.ca2404.1_amd64.deb Size: 244216 MD5sum: ac690d6428a422d2e753b584d81f0e3f SHA1: 287824061598dbca815b55a52bbde230cd8f67f1 SHA256: 2436f3ba38b730a56ac1d71f3b4cf19a3cfbe788a9e3d81337f32a8ba5b40fec SHA512: 9f2f4239880c5de0dfe2edc3b9a719ff874ceb61d0dc408a4060076b5306a346f7641d450d8d5301e023b750e539787b8570b269bab1091bd9cbfe52a1cec0f3 Homepage: https://cran.r-project.org/package=seqest Description: CRAN Package 'seqest' (Sequential Method for Classification and Generalized EstimatingEquations Problem) Sequential method to solve the the binary classification problem by Wang (2019) , multi-class classification problem by Li (2020) and the highly stratified multiple-response problem by Chen (2019) . Package: r-cran-seqhmm Architecture: amd64 Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3967 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-collapse, r-cran-data.table, r-cran-future.apply, r-cran-ggplot2, r-cran-ggseqplot, r-cran-gridbase, r-cran-igraph, r-cran-lhs, r-cran-matrix, r-cran-nloptr, r-cran-numderiv, r-cran-patchwork, r-cran-progressr, r-cran-rcpp, r-cran-rcpphungarian, r-cran-rlang, r-cran-traminer, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-knitr, r-cran-mass, r-cran-nnet, r-cran-testthat Filename: pool/dists/noble/main/r-cran-seqhmm_2.2.0-1.ca2404.1_amd64.deb Size: 2647366 MD5sum: f7708c08791721b1fb0e39205c4dfaa7 SHA1: 649333c4f5b09ade450147d8d5d187a92cc4d6f9 SHA256: 5e7ea0b2eab3ed298deba99b0479f757a25ab41bf49cf36e1785ac8e1cf27d48 SHA512: d2c16987c054d26552d8197b2f30b2b35d3b2a920495723bb17a6c2e36085ad8444ff2b18a372858f96e60d4bd817ce0591b48f226838938c974d692a78b9bae Homepage: https://cran.r-project.org/package=seqHMM Description: CRAN Package 'seqHMM' (Mixture Hidden Markov Models for Social Sequence Data and OtherMultivariate, Multichannel Categorical Time Series) Designed for estimating variants of hidden (latent) Markov models (HMMs), mixture HMMs, and non-homogeneous HMMs (NHMMs) for social sequence data and other categorical time series. Special cases include feedback-augmented NHMMs, Markov models without latent layer, mixture Markov models, and latent class models. The package supports models for one or multiple subjects with one or multiple parallel sequences (channels). External covariates can be added to explain cluster membership in mixture models as well as initial, transition and emission probabilities in NHMMs. The package provides functions for evaluating and comparing models, as well as functions for visualizing of multichannel sequence data and HMMs. For NHMMs, methods for computing average causal effects and marginal state and emission probabilities are available. Models are estimated using maximum likelihood via the EM algorithm or direct numerical maximization with analytical gradients. Documentation is available via several vignettes, and Helske and Helske (2019, ). For methodology behind the NHMMs, see Helske (2025, ). Package: r-cran-seqinr Architecture: amd64 Version: 4.2-44-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5310 Depends: libc6 (>= 2.38), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4, r-cran-segmented Filename: pool/dists/noble/main/r-cran-seqinr_4.2-44-1.ca2404.1_amd64.deb Size: 4075580 MD5sum: 213e8d8820d1b18e468f82e1c7aeccb2 SHA1: 0ec8d4c44081bb18a2a44585c1a678e0b1d4394a SHA256: 5993f12045f65a8bca741a359a50c23fed03f383d68de2ad451a91d4a5ab9813 SHA512: 7464450cee479e9c29fc0769365c692396c6544dec3ce70d4cdc4ea422ea8558ca7811ca549001720348c5b1386e8ea5cc2c634042ed06c127128c5e76e8c63f Homepage: https://cran.r-project.org/package=seqinr Description: CRAN Package 'seqinr' (Biological Sequences Retrieval and Analysis) Exploratory data analysis and data visualization for biological sequence (DNA and protein) data. Seqinr includes utilities for sequence data management under the ACNUC system described in Gouy, M. et al. (1984) Nucleic Acids Res. 12:121-127 . Package: r-cran-seqkat Architecture: amd64 Version: 0.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2518 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-rcpp Suggests: r-cran-testthat, r-cran-domc, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-seqkat_0.0.9-1.ca2404.1_amd64.deb Size: 889254 MD5sum: a421874e74d42759b154b52545996d36 SHA1: 5115a328a2e6780fb3a8c56d56bf81b4836f36f8 SHA256: 01fbd71d7ca2b505fe77c0edfbbf49e9c6181cb3e5ee490c53ce2208f238fe34 SHA512: 93279a7cd3d3b32617c30bc1304bfb8e8fbc3d536eedb86919b4fbf258ad739befb1d2e560468daff39d202b90c01007f5a9337bdb55064f590362fcd1bb6464 Homepage: https://cran.r-project.org/package=SeqKat Description: CRAN Package 'SeqKat' (Detection of Kataegis) Kataegis is a localized hypermutation occurring when a region is enriched in somatic SNVs. Kataegis can result from multiple cytosine deaminations catalyzed by the AID/APOBEC family of proteins. This package contains functions to detect kataegis from SNVs in BED format. This package reports two scores per kataegic event, a hypermutation score and an APOBEC mediated kataegic score. Yousif, F. et al.; The Origins and Consequences of Localized and Global Somatic Hypermutation; Biorxiv 2018 . Package: r-cran-seqminer Architecture: amd64 Version: 9.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3535 Depends: libbz2-1.0, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), libzstd1 (>= 1.5.5), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-skat Filename: pool/dists/noble/main/r-cran-seqminer_9.9-1.ca2404.1_amd64.deb Size: 2210998 MD5sum: bb3389691144a4819ab0e57a18c4c2a1 SHA1: f76bb5cafd3b9271e8d0851ec2cb0dedcde001e6 SHA256: f3be42eb55f67b4b5a4da849ba897a136240d376ab2719aebec78ee9ef345afb SHA512: 3f11cfb15797d429b87a2f5c19b747144b872bbb1ffe2d6da60b3dfdaa8a781387af8025b0ac1548fb249e6e94fbb65f46a5d1b9cceac68449447372fac18bc3 Homepage: https://cran.r-project.org/package=seqminer Description: CRAN Package 'seqminer' (Efficiently Read Sequence Data (VCF Format, BCF Format, METALFormat and BGEN Format) into R) Integrate sequencing data (Variant call format, e.g. VCF or BCF) or meta-analysis results in R. This package can help you (1) read VCF/BCF/BGEN files by chromosomal ranges (e.g. 1:100-200); (2) read 'RareMETAL' summary statistics files; (3) read tables from a 'tabix'-indexed files; (4) annotate VCF/BCF files; (5) create customized workflow based on Makefile. Package: r-cran-seqnet Architecture: amd64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5323 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fitdistrplus, r-cran-ggplot2, r-cran-igraph, r-cran-mvtnorm, r-cran-purrr, r-cran-tibble, r-cran-rcpp, r-cran-rlang, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-seqnet_1.1.3-1.ca2404.1_amd64.deb Size: 5307040 MD5sum: 0f7845b07e9e1631529fc9513c039d3c SHA1: f594caca49eb39952762df18baddbf9b9d8d8173 SHA256: e6c100f1d87b8723e88893ba60ca0fbbfd094dc8fbb79151007e8da769e494ae SHA512: d85384f8f4129575e9bb8cb4cc064dfbb9ecbacf94106eb6c45c29d6b080c714e4e1c1c29ed9252ee17d447c6726a70ddd1cfbe21427b1c55b93140c5b023e47 Homepage: https://cran.r-project.org/package=SeqNet Description: CRAN Package 'SeqNet' (Generate RNA-Seq Data from Gene-Gene Association Networks) Methods to generate random gene-gene association networks and simulate RNA-seq data from them, as described in Grimes and Datta (2021) . Includes functions to generate random networks of any size and perturb them to obtain differential networks. Network objects are built from individual, overlapping modules that represent pathways. The resulting network has various topological properties that are characteristic of gene regulatory networks. RNA-seq data can be generated such that the association among gene expression profiles reflect the underlying network. A reference RNA-seq dataset can be provided to model realistic marginal distributions. Plotting functions are available to visualize a network, compare two networks, and compare the expression of two genes across multiple networks. Package: r-cran-seqtrie Architecture: amd64 Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1914 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-r6, r-cran-rlang, r-cran-dplyr, r-cran-stringi, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stringdist, r-bioc-pwalign, r-cran-igraph, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-seqtrie_0.3.5-1.ca2404.1_amd64.deb Size: 1430314 MD5sum: da24f84f02582730118f12ac1b31a172 SHA1: bf31301be1ea9d60ff19523deae3c85e33b244cc SHA256: 1a8f5160afe5395235431ec65fae9d082f64f1c009e20414c2d370d60af22ca3 SHA512: 609e876b57772068694744f7d8c9899be94a02a670dfd5f4381e912d62bef13973a76ef2b7c354ccd64c0f6ba5ed0f14026c5525922269404c7865caf8e66d94 Homepage: https://cran.r-project.org/package=seqtrie Description: CRAN Package 'seqtrie' (Radix Tree and Trie-Based String Distances) A collection of Radix Tree and Trie algorithms for finding similar sequences and calculating sequence distances (Levenshtein and other distance metrics). This work was inspired by a trie implementation in Python: "Fast and Easy Levenshtein distance using a Trie." Hanov (2011) . Package: r-cran-sequencespikeslab Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppprogress, r-cran-selectiveinference Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sequencespikeslab_1.0.1-1.ca2404.1_amd64.deb Size: 99096 MD5sum: e7f4f5366fb8e26a5c53bb0cc4a71457 SHA1: b31dd1c9ce3cde93d11d03c8d66ca43f645125fe SHA256: ef3c2c3ceeefd09a59f4cd15cf13686b350238f9ababeba3f45bb2520c4ec1f6 SHA512: 6624eb7d205738555cb6b5e4c1110446e91ca9a675b60118871fbf30e22646001a5d87fc1c35c1c03897aa1a110e2102a94dc28b3685fb695d4af00f245f5d71 Homepage: https://cran.r-project.org/package=SequenceSpikeSlab Description: CRAN Package 'SequenceSpikeSlab' (Exact Bayesian Model Selection Methods for the Sparse NormalSequence Model) Contains fast functions to calculate the exact Bayes posterior for the Sparse Normal Sequence Model, implementing the algorithms described in Van Erven and Szabo (2021, ). For general hierarchical priors, sample sizes up to 10,000 are feasible within half an hour on a standard laptop. For beta-binomial spike-and-slab priors, a faster algorithm is provided, which can handle sample sizes of 100,000 in half an hour. In the implementation, special care has been taken to assure numerical stability of the methods even for such large sample sizes. Package: r-cran-sequoia Architecture: amd64 Version: 3.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3388 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-cli Suggests: r-cran-openxlsx, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-kinship2, r-cran-r.rsp, r-cran-hexbin, r-cran-data.table, r-cran-vcfr, r-cran-adegenet Filename: pool/dists/noble/main/r-cran-sequoia_3.2.0-1.ca2404.1_amd64.deb Size: 2657054 MD5sum: e5ff4cd27ed687cd9f3a1ab7ecc1fbbd SHA1: f59d986ea834bc06894236c79428cbb82b8c3aa7 SHA256: 3344b887d24bc688796a96ffa6354b5352a00b3a25a367b4776dc1699a58cebc SHA512: d4e056ea4d4b2cd7341b9bfc930b3d02e016d6a2d0c8fdf837cbb85ca463073a7a0a54be1ec1f0cb3e232b208eeb2b6088c3cd27c476ae6bdee13499bc74d79a Homepage: https://cran.r-project.org/package=sequoia Description: CRAN Package 'sequoia' (Pedigree Inference from SNPs) Multi-generational pedigree inference from incomplete data on hundreds of SNPs, including parentage assignment and sibship clustering. See Huisman (2017) () for more information. Package: r-cran-seriation Architecture: amd64 Version: 1.5.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1551 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ca, r-cran-cluster, r-cran-colorspace, r-cran-foreach, r-cran-gclus, r-cran-mass, r-cran-qap, r-cran-registry, r-cran-tsp, r-cran-vegan Suggests: r-cran-dbscan, r-cran-dendser, r-cran-dendextend, r-cran-doparallel, r-cran-ga, r-cran-ggplot2, r-cran-keras, r-cran-rtsne, r-cran-scales, r-cran-smacof, r-cran-tensorflow, r-cran-testthat, r-cran-umap Filename: pool/dists/noble/main/r-cran-seriation_1.5.8-1.ca2404.1_amd64.deb Size: 1348270 MD5sum: 0d1beeb7df48e533136a35d2f8ce3493 SHA1: b0a2d2ae14bd2c682311d5d2eb6d27537305374a SHA256: ca81e984ea8debb2ec7432e445c9cce5c39830ab01abcdc3925049780732d8b3 SHA512: 8cac6e87812f4d8c87782f353b698e2f2cb4d1379e0b7c4ec979d05d4060ce2da6e6a034e332c717b4ea9373d31770f24ad944d770ba5072d8eb6f3734cccb45 Homepage: https://cran.r-project.org/package=seriation Description: CRAN Package 'seriation' (Infrastructure for Ordering Objects Using Seriation) Infrastructure for ordering objects with an implementation of several seriation/sequencing/ordination techniques to reorder matrices, dissimilarity matrices, and dendrograms. Also provides (optimally) reordered heatmaps, color images and clustering visualizations like dissimilarity plots, and visual assessment of cluster tendency plots (VAT and iVAT). Hahsler et al (2008) . Package: r-cran-serocalculator Architecture: amd64 Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 754 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-patchwork, r-cran-lifecycle, r-cran-magrittr, r-cran-rcpp, r-cran-rlang, r-cran-rngtools, r-cran-scales, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-purrr, r-cran-and, r-cran-glue, r-cran-stringr, r-cran-labelled Suggests: r-cran-bookdown, r-cran-dt, r-cran-fs, r-cran-ggbeeswarm, r-cran-knitr, r-cran-mixtools, r-cran-pak, r-cran-quarto, r-cran-rmarkdown, r-cran-spelling, r-cran-ssdtools, r-cran-testthat, r-cran-tidyverse, r-cran-qrcode, r-cran-vdiffr, r-cran-withr, r-cran-forcats, r-cran-rex, r-cran-readr Filename: pool/dists/noble/main/r-cran-serocalculator_1.4.1-1.ca2404.1_amd64.deb Size: 495272 MD5sum: dd9e8835dafffbf7961222bce84ac643 SHA1: eef5c7b88bedc757013e5a7a634bee83f63e3f1e SHA256: 477ec478772dbec687318109a3570580e6c90b16c254409c37c65ddb8f223331 SHA512: 6f6f972d7e7dab6fc3ea311d91889730f88ec9b7e0bb3d4cded588dba5c25b444670a78d77cebd3cb5e10f94461defd6ad403ed2f34fc867509ab85f503d220a Homepage: https://cran.r-project.org/package=serocalculator Description: CRAN Package 'serocalculator' (Estimating Infection Rates from Serological Data) Translates antibody levels measured in cross-sectional population samples into estimates of the frequency with which seroconversions (infections) occur in the sampled populations. Replaces the previous `seroincidence` package. Package: r-cran-serofoi Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7193 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bayesplot, r-cran-checkmate, r-cran-config, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-loo, r-cran-expm, r-cran-purrr, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-tibble, r-cran-tidyr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rlang, r-cran-rmarkdown, r-cran-scales, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-serofoi_1.0.3-1.ca2404.1_amd64.deb Size: 1951472 MD5sum: 44d757ff5e845520915374ff4b2cb1a7 SHA1: 097ebd0cc79f5b0627a2ebc87de0681e7190f0b5 SHA256: 02055dc1e55edef8847600d0c19c9a71bb3f01e69fa697debedab6753c25b9b4 SHA512: e82d619294f62d28596b8a1a49743fdb449fc4736b105dda5a926c70bbcb22c7f668bc66aa1919a832d0b24805ddd9a2c8da9006328d122c0f574184d49b03e5 Homepage: https://cran.r-project.org/package=serofoi Description: CRAN Package 'serofoi' (Bayesian Estimation of the Force of Infection from SerologicalData) Estimating the force of infection from time varying, age varying, or constant serocatalytic models from population based seroprevalence studies using a Bayesian framework, including data simulation functions enabling the generation of serological surveys based on this models. This tool also provides a flexible prior specification syntax for the force of infection and the seroreversion rate, as well as methods to assess model convergence and comparison criteria along with useful visualisation functions. Package: r-cran-seroreconstruct Architecture: amd64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1173 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-seroreconstruct_1.1.5-1.ca2404.1_amd64.deb Size: 817024 MD5sum: e236d2c752724a563ea977e9b006d52f SHA1: 4270a09f991f3535b02f6a6d34a882f7edd9f80b SHA256: 84dff0dca153c8e0fdf52ef888d78078959de6612aa67423d684ae9d2389d280 SHA512: 788d6aa242d9946c450dc1e5a142c0e3a3666c96ff468255e41a89ff358a246e833060ad7c1ab78d7e42020495b49d258a26816feb0701b113a50310c2ce1b58 Homepage: https://cran.r-project.org/package=seroreconstruct Description: CRAN Package 'seroreconstruct' (Reconstructing Antibody Dynamics to Estimate the Risk ofInfluenza Virus Infection) A Bayesian framework for inferring influenza infection status from serial antibody measurements. Jointly estimates season-specific infection probabilities, antibody boosting and waning after infection, and baseline hemagglutination inhibition (HAI) titer distributions via Markov chain Monte Carlo (MCMC). Supports multi-season analysis and subgroup comparisons via a group_by interface. See Tsang et al. (2022) for methodological details. Package: r-cran-serosv Architecture: amd64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7805 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-janitor, r-cran-ggplot2, r-cran-locfit, r-cran-purrr, r-cran-stringr, r-cran-magrittr, r-cran-mgcv, r-cran-mixdist, r-cran-scam, r-cran-mvtnorm, r-cran-patchwork, r-cran-assertthat, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-boot, r-cran-proc, r-cran-rlang, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-serosv_1.3.0-1.ca2404.1_amd64.deb Size: 3269022 MD5sum: b60158aa567dea6cb8d973e20dda1f8e SHA1: a8bece941c8efc3eadf6be870ee68fc824ff3265 SHA256: cb233a80b990619bbf183d500038df03d605c0d03c463a4635368bf54352ec66 SHA512: 40a924634b63f49a3e1a3c1d83d282142da99c51282a699170fd7390cccfdd4e2927547433a9c0719cedd098f55f85f2c0f4aedda440428df897a9841a9a7c0b Homepage: https://cran.r-project.org/package=serosv Description: CRAN Package 'serosv' (Model Infectious Disease Parameters from Serosurveys) An easy-to-use and efficient tool to estimate infectious diseases parameters using serological data. Implemented models include SIR models (basic_sir_model(), static_sir_model(), mseir_model(), sir_subpops_model()), parametric models (polynomial_model(), fp_model()), nonparametric models (lp_model()), semiparametric models (penalized_splines_model()), hierarchical models (hierarchical_bayesian_model()). The package is based on the book "Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective" (Hens, Niel & Shkedy, Ziv & Aerts, Marc & Faes, Christel & Damme, Pierre & Beutels, Philippe., 2013) . Package: r-cran-serrsbayes Architecture: amd64 Version: 0.5-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1679 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-truncnorm, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-serrsbayes_0.5-0-1.ca2404.1_amd64.deb Size: 1115210 MD5sum: 9a6ec560289b32b9925c89a5cdc93321 SHA1: 178f9576da04ed9e37116f4cefad0c3d953922e4 SHA256: ca53d8d94595d1503bf306c917a841a6532c85abf70edf87fa84bace581713a9 SHA512: 0a010ad3b1349c2c7ba9e0bac7ba9ba0111d7af254f5b706190262c9cc8e18e7114792cae6ead03ad76e32a5ec59e06c87277c9ae894b1b33310bb8bfb424577 Homepage: https://cran.r-project.org/package=serrsBayes Description: CRAN Package 'serrsBayes' (Bayesian Modelling of Raman Spectroscopy) Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive mixed model (GAMM) to surface-enhanced resonance Raman spectroscopy (SERRS), using the method of Moores et al. (2016) . Multivariate observations of SERRS are highly collinear and lend themselves to a reduced-rank representation. The GAMM separates the SERRS signal into three components: a sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a smoothly-varying baseline; and additive white noise. The parameters of each component of the model are estimated iteratively using SMC. The posterior distributions of the parameters given the observed spectra are represented as a population of weighted particles. Package: r-cran-sets Architecture: amd64 Version: 1.0-25-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 800 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-proxy Filename: pool/dists/noble/main/r-cran-sets_1.0-25-1.ca2404.1_amd64.deb Size: 626178 MD5sum: c6c7d298fac8759d9a23024dcd8e50f4 SHA1: ec613386c491327deb184ac6293a6ab88d8a325f SHA256: 1a350171c1be152ee031c5de6bbbc4a4e05cb05b072c2d25dca9be233788e90e SHA512: b4d21fb739e8f26e2e0720e346f51167c05e2f31b73d2b8a15119a48299577fcae6345305179bca68a2c0169d30fe74ab78b24fb1b717b7476ccef4962f56248 Homepage: https://cran.r-project.org/package=sets Description: CRAN Package 'sets' (Sets, Generalized Sets, Customizable Sets and Intervals) Data structures and basic operations for ordinary sets, generalizations such as fuzzy sets, multisets, and fuzzy multisets, customizable sets, and intervals. Package: r-cran-setwidth Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-setwidth_1.1.0-1.ca2404.1_amd64.deb Size: 15552 MD5sum: fbc79af2aca19e2cda59059c8216d775 SHA1: 569f33da7a8df54b009d20b6fa4927827bb097fa SHA256: f43cb96cae5a040edd86135ca2bd987d386182436da6fdbcd30507dd89160e8c SHA512: 2521a1ca93f4b2cd4210de08d1fcb5a2dbf1b6ce46fce4cc7b5fb2b68170aac8ab7b80992ea84ef1a530809a1a5df2802d5e1a953765cabce03f2280f5301d43 Homepage: https://cran.r-project.org/package=setwidth Description: CRAN Package 'setwidth' (Automatically Set the Width Option on Terminal Emulators) Automatically sets the value of options("width") when the terminal emulator is resized. The functions of this package only work if R is compiled for Unix systems and it is running interactively in a terminal emulator. Package: r-cran-seurat Architecture: amd64 Version: 5.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3133 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-seuratobject, r-cran-cluster, r-cran-cowplot, r-cran-fastdummies, r-cran-fitdistrplus, r-cran-future, r-cran-future.apply, r-cran-generics, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggridges, r-cran-httr, r-cran-ica, r-cran-igraph, r-cran-irlba, r-cran-jsonlite, r-cran-kernsmooth, r-cran-lifecycle, r-cran-lmtest, r-cran-mass, r-cran-matrix, r-cran-matrixstats, r-cran-miniui, r-cran-patchwork, r-cran-pbapply, r-cran-plotly, r-cran-png, r-cran-progressr, r-cran-rann, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-rcppannoy, r-cran-rcpphnsw, r-cran-reticulate, r-cran-rlang, r-cran-rocr, r-cran-rspectra, r-cran-rtsne, r-cran-scales, r-cran-scattermore, r-cran-sctransform, r-cran-shiny, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-tibble, r-cran-uwot, r-cran-rcppeigen, r-cran-rcppprogress Suggests: r-cran-ape, r-cran-arrow, r-cran-base64enc, r-bioc-biobase, r-bioc-biocgenerics, r-cran-data.table, r-bioc-deseq2, r-bioc-delayedarray, r-cran-enrichr, r-bioc-genomicranges, r-bioc-genomeinfodb, r-bioc-glmgampoi, r-cran-ggrastr, r-cran-harmony, r-cran-hdf5r, r-bioc-iranges, r-cran-leidenbase, r-bioc-limma, r-cran-magrittr, r-bioc-mast, r-cran-metap, r-cran-mixtools, r-bioc-monocle, r-cran-rsvd, r-cran-r.utils, r-cran-rfast2, r-bioc-rtracklayer, r-bioc-s4vectors, r-cran-sf, r-cran-sp, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-testthat, r-cran-vgam Filename: pool/dists/noble/main/r-cran-seurat_5.5.0-1.ca2404.1_amd64.deb Size: 2574694 MD5sum: 87f05681099cff98f5310ece217d22cb SHA1: cdcbc4ece6863c5b9dd846e767daedadad150aed SHA256: 150ca8c7611b0f85e3a17df007b0be261e76cc850a1fe681a8fc5c5ce930de3a SHA512: 3fa3f3ca141f84218cc28e73d9aa06a1addf1d3610c6b1eefb18b73aec50e349a8f918b0150faf5ec731d862899b720653f4426751abc51d01881437a36d9c06 Homepage: https://cran.r-project.org/package=Seurat Description: CRAN Package 'Seurat' (Tools for Single Cell Genomics) A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) , Macosko E, Basu A, Satija R, et al (2015) , Stuart T, Butler A, et al (2019) , and Hao, Hao, et al (2020) for more details. Package: r-cran-seuratobject Architecture: amd64 Version: 5.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2489 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp, r-cran-future, r-cran-future.apply, r-cran-generics, r-cran-lifecycle, r-cran-matrix, r-cran-progressr, r-cran-rcpp, r-cran-rlang, r-cran-spam, r-cran-rcppeigen Suggests: r-bioc-delayedarray, r-cran-fs, r-cran-sf, r-cran-ggplot2, r-bioc-hdf5array, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-seuratobject_5.4.0-1.ca2404.1_amd64.deb Size: 1819484 MD5sum: 0e9b10d8463f9c5e996db9249053cf98 SHA1: 8485f8aaa790a871fee4cb23eae279396774c93a SHA256: e71bbf2630bb37cda5a32d850b95ab0fa565f2576f27242b22b6d53703935927 SHA512: c0ccacbc99e45ca1fbb517bae1d919b81b4e395247a0beca01c9bbe8a9785a481248babca6467c2800ca27c212cca694549efb0d521db35ad8329e378765eece Homepage: https://cran.r-project.org/package=SeuratObject Description: CRAN Package 'SeuratObject' (Data Structures for Single Cell Data) Defines S4 classes for single-cell genomic data and associated information, such as dimensionality reduction embeddings, nearest-neighbor graphs, and spatially-resolved coordinates. Provides data access methods and R-native hooks to ensure the Seurat object is familiar to other R users. See Satija R, Farrell J, Gennert D, et al (2015) , Macosko E, Basu A, Satija R, et al (2015) , and Stuart T, Butler A, et al (2019) , Hao Y, Hao S, et al (2021) and Hao Y, et al (2023) for more details. Package: r-cran-sf Architecture: amd64 Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8452 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgdal34t64 (>= 3.8.0), libgeos-c1t64 (>= 3.11.0), libproj25 (>= 7.1.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-classint, r-cran-dbi, r-cran-s2, r-cran-units, r-cran-rcpp Suggests: r-cran-blob, r-cran-nanoarrow, r-cran-covr, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-lwgeom, r-cran-maps, r-cran-mapview, r-cran-matrix, r-cran-microbenchmark, r-cran-odbc, r-cran-pbapply, r-cran-pillar, r-cran-pool, r-cran-raster, r-cran-rlang, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rpostgresql, r-cran-rsqlite, r-cran-sp, r-cran-spatstat, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.linnet, r-cran-spatstat.utils, r-cran-stars, r-cran-terra, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-tmap, r-cran-vctrs, r-cran-wk Filename: pool/dists/noble/main/r-cran-sf_1.1-1-1.ca2404.1_amd64.deb Size: 3689162 MD5sum: fc4767a387e53e0ab890ba1c1f0e5063 SHA1: 55dd3a3a9d7e75188a10939dbe0cc7aa8cb5c305 SHA256: d3083e5f6084a0c38bd1318b55c839247aa4aff6567a3e3cba74d4dbac9c529c SHA512: 69da263d52b9353b7fada27fc2f096735869f30e456ebe6e10162eb5cc458cbb7339285842f894d7dfa51a40dbb69772b2cb542225d56aad3adb3c13fe246e49 Homepage: https://cran.r-project.org/package=sf Description: CRAN Package 'sf' (Simple Features for R) Support for simple feature access, a standardized way to encode and analyze spatial vector data. 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Package: r-cran-sfcr Architecture: amd64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 458 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-expm, r-cran-forcats, r-cran-igraph, r-cran-kableextra, r-cran-magrittr, r-cran-purrr, r-cran-rdpack, r-cran-rootsolve, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-stringr, r-cran-vctrs, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ggraph, r-cran-ggplot2, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-rcolorbrewer, r-cran-testthat, r-cran-tidygraph, r-cran-tidyverse, r-cran-networkd3 Filename: pool/dists/noble/main/r-cran-sfcr_0.2.3-1.ca2404.1_amd64.deb Size: 323450 MD5sum: ad38727a12b96880b27a4592a1117794 SHA1: cac92edbd48e0fdd7d2bf40dab3ec7dff746c703 SHA256: eb2e240d9ec14cec3e2a84863fb126c3416cf7cc5fa18a947b7ce1bd9c60c324 SHA512: b8da36a720e2902b653b738957f472368171670db6ee6b7b48ed67b661424d51d695d0160ea23a5403fae0d0ae4434691beba514b944a39fa5c6a779dc576771 Homepage: https://cran.r-project.org/package=sfcr Description: CRAN Package 'sfcr' (Simulate Stock-Flow Consistent Models) Routines to write, simulate, and validate stock-flow consistent (SFC) models. The accounting structure of SFC models are described in Godley and Lavoie (2007, ISBN:978-1-137-08599-3). The algorithms implemented to solve the models (Gauss-Seidel and Broyden) are described in Kinsella and O'Shea (2010) and Peressini and Sullivan (1988, ISBN:0-387-96614-5). Package: r-cran-sfcurve Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 988 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-colorramp2 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-rgl, r-cran-testthat, r-bioc-complexheatmap, r-cran-igraph, r-cran-digest Filename: pool/dists/noble/main/r-cran-sfcurve_1.0.1-1.ca2404.1_amd64.deb Size: 614146 MD5sum: a34caf343e858f7b8762792fbf47e6ce SHA1: ffc8433c44b40a5bc8713a40f1b0c9a9e963f51c SHA256: 2943387d4c9b20099270c41228230b03c759d4e12de88488c7134f86549d7490 SHA512: 18aaef7ccc42ccd6a24662a84c18fc0bc4f58cfb74ceb08aae0297df77232e4882922b65d7ad45372013c74a409300bc41384f7501ecaa2a0366310e6edc9348 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. 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It also offers the option to optimize designs based on user-defined criteria. This work is supported by U.S. National Science Foundation grant DMS-2310637. Package: r-cran-sffdr Architecture: amd64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 642 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-locfit, r-cran-ggplot2, r-cran-patchwork, r-bioc-qvalue, r-cran-fastglm, r-cran-withr, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sffdr_1.1.2-1.ca2404.1_amd64.deb Size: 499256 MD5sum: 68ca5c8d5b3705458abf7c5451d02840 SHA1: de5ac6ac70e1aa83b4f36666948f7e0f88d8fd61 SHA256: fdc63779db94846a9c2a544d9060298f140eda66132e4f73ee028fdd9c39d9dd SHA512: 4281550c4bb6b86ba6d191670d38cd7c06a091df082c940ad261c796a513a68aac692ad5fdf12e05ef0fe6f35bec2f4aae2b091b79181906a9547b1e61ef9f73 Homepage: https://cran.r-project.org/package=sffdr Description: CRAN Package 'sffdr' (Surrogate Functional False Discovery Rates for Genome-WideAssociation Studies) Pleiotropy-informed significance analysis of genome-wide association studies with surrogate functional false discovery rates (sfFDR). The sfFDR framework adapts the fFDR to leverage informative data from multiple sets of GWAS summary statistics to increase power in study while accommodating for linkage disequilibrium. sfFDR provides estimates of key FDR quantities in a significance analysis such as the functional local FDR and $q$-value, and uses these estimates to derive a functional $p$-value for type I error rate control and a functional local Bayes' factor for post-GWAS analyses (e.g., fine mapping and colocalization). Package: r-cran-sfheaders Architecture: amd64 Version: 0.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1200 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-geometries Suggests: r-cran-covr, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sfheaders_0.4.5-1.ca2404.1_amd64.deb Size: 423834 MD5sum: 0f7f8b33e676e6fad887bf6e021452ca SHA1: 383bb0f128394e12eaa2746e32d6fed00da105cb SHA256: 920c002e57047a6db0ed8a26e454d2db0665f5835116c9956f513a57aeff8c80 SHA512: e9c87d0a5688216416ba9485ce200bb96da726597889485483f40d4122af61cb82e58c4731895467f0cc1fdd6ce572c782a3ea79e942c488e2a31503eb7cc41e Homepage: https://cran.r-project.org/package=sfheaders Description: CRAN Package 'sfheaders' (Converts Between R Objects and Simple Feature Objects) Converts between R and Simple Feature 'sf' objects, without depending on the Simple Feature library. Conversion functions are available at both the R level, and through 'Rcpp'. Package: r-cran-sfs Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-seriation Filename: pool/dists/noble/main/r-cran-sfs_0.1.4-1.ca2404.1_amd64.deb Size: 111776 MD5sum: 4de8ba6ef3ee8006b2551a030b29ac3a SHA1: c29b0210b7adb83622ff90d34148284566d353ea SHA256: 57a4ced2b2772e16bb745481b76a52e1bc25fba9701043263bc04b1be1ea1adc SHA512: b1975433e1f16b1f8a903e7fc2335e0ab0441541d88a021d6e167a5adfef899aadaf033f8e10417219def470f84201bdb33f80b2ae9d03afb980272fff271d05 Homepage: https://cran.r-project.org/package=SFS Description: CRAN Package 'SFS' (Similarity-First Search Seriation Algorithm) An implementation of the Similarity-First Search algorithm (SFS), a combinatorial algorithm which can be used to solve the seriation problem and to recognize some structured weighted graphs. The SFS algorithm represents a generalization to weighted graphs of the graph search algorithm Lexicographic Breadth-First Search (Lex-BFS), a variant of Breadth-First Search. The SFS algorithm reduces to Lex-BFS when applied to binary matrices (or, equivalently, unweighted graphs). Hence this library can be also considered for Lex-BFS applications such as recognition of graph classes like chordal or unit interval graphs. In fact, the SFS seriation algorithm implemented in this package is a multisweep algorithm, which consists in repeating a finite number of SFS iterations (at most n sweeps for a matrix of size n). If the data matrix has a Robinsonian structure, then the ranking returned by the multistep SFS algorithm is a Robinson ordering of the input matrix. Otherwise the algorithm can be used as a heuristic to return a ranking partially satisfying the Robinson property. Package: r-cran-sfsi Architecture: amd64 Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4817 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-scales, r-cran-tensorevd, r-cran-reshape2, r-cran-viridis, r-cran-igraph, r-cran-stringr, r-cran-ggplot2 Suggests: r-cran-bglr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sfsi_1.4.1-1.ca2404.1_amd64.deb Size: 2661352 MD5sum: fdf35fa05f015ae16c7811275decf5ee SHA1: 5cb208325d2776a74edadc99cca283d172ed8cab SHA256: ae734ab06c92407023971dfc5cf6d21ec10eda7dbd2d63ab0fb20011c62d1833 SHA512: 9796b77cd3292075d6eacbbbdbd89576c1ff559103ec4569ad458f9f506a4969a07a357eba6e4bea4e4d8755ec745a394bc907e8e6dc4159aed78d7107124b58 Homepage: https://cran.r-project.org/package=SFSI Description: CRAN Package 'SFSI' (Sparse Family and Selection Index) Here we provide tools for the estimation of coefficients in penalized regressions when the (co)variance matrix of predictors and the covariance vector between predictors and response, are provided. These methods are extended to the context of a Selection Index (commonly used for breeding value prediction). The approaches offer opportunities such as the integration of high-throughput traits in genetic evaluations ('Lopez-Cruz et al., 2020') and solutions for training set optimization in Genomic Prediction ('Lopez-Cruz & de los Campos, 2021') . Package: r-cran-sgd Architecture: amd64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1103 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-rcpp, r-cran-bh, r-cran-bigmemory, r-cran-rcpparmadillo Suggests: r-cran-glmnet, r-cran-gridextra, r-cran-r.rsp, r-cran-testthat, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-sgd_1.1.3-1.ca2404.1_amd64.deb Size: 817540 MD5sum: 0df424f30d9e632ad037c663db276d71 SHA1: 88da11d95bc521f50cc0d53f1c9629f5904f4f39 SHA256: bc54daf38599ae7c7b6b7922946964086f501a91b8d3ebcccdf89c1305995919 SHA512: 6a1655a0decc5bbca60c7fbcabe9bb301c8dd48f2f10d448f27ed8c6dad350fe1a3bd0c3c0257d1dfaf555b91133e2b847ce4b78fec092ad8501645c8d5ee99f Homepage: https://cran.r-project.org/package=sgd Description: CRAN Package 'sgd' (Stochastic Gradient Descent for Scalable Estimation) A fast and flexible set of tools for large scale estimation. It features many stochastic gradient methods, built-in models, visualization tools, automated hyperparameter tuning, model checking, interval estimation, and convergence diagnostics. Package: r-cran-sgdgmf Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1464 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rspectra, r-cran-doparallel, r-cran-foreach, r-cran-mass, r-cran-suppdists, r-cran-generics, r-cran-reshape2, r-cran-ggpubr, r-cran-viridislite Suggests: r-cran-testthat, r-cran-rtsne, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sgdgmf_1.0.1-1.ca2404.1_amd64.deb Size: 731212 MD5sum: 1586dab2ee3b3b8016290acf84e1ea27 SHA1: d8c952297dcbc737d3d5ecc5c8c10b55da64ec1a SHA256: b5852e7f7e3f505101da2b55b6bb131e930617819630236d36d1258a6efb0f2e SHA512: bb16880e0209762af0c87b1f494dc2148bdc2327a8de036000181859bd27fa1b6339cadf3df6962426197acba96e856c22036598a2f6c70f68fba86ce7ef7d9a Homepage: https://cran.r-project.org/package=sgdGMF Description: CRAN Package 'sgdGMF' (Estimation of Generalized Matrix Factorization Models viaStochastic Gradient Descent) Efficient framework to estimate high-dimensional generalized matrix factorization models using penalized maximum likelihood under a dispersion exponential family specification. Either deterministic and stochastic methods are implemented for the numerical maximization. In particular, the package implements the stochastic gradient descent algorithm with a block-wise mini-batch strategy to speed up the computations and an efficient adaptive learning rate schedule to stabilize the convergence. All the theoretical details can be found in Castiglione et al. (2024, ). Other methods considered for the optimization are the alternated iterative re-weighted least squares and the quasi-Newton method with diagonal approximation of the Fisher information matrix discussed in Kidzinski et al. (2022, ). Package: r-cran-sgdinference Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 645 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lmtest, r-cran-sandwich, r-cran-microbenchmark, r-cran-conquer Filename: pool/dists/noble/main/r-cran-sgdinference_0.1.0-1.ca2404.1_amd64.deb Size: 421066 MD5sum: 1e11a5e1f107b748b9149201e43bf54e SHA1: f2dca73c99bd9efd5ee10327fe862b1b1ebd4b53 SHA256: 0cbe5f3278f00992cbb4d78d5d271a5b6dc245a3714b6ecb8bca3ea0d26ffee9 SHA512: e52f26f09d0f9f849d3df1d9da9f2351f9740ce001233d77d101feb47437839b1f425c53a046c3f33cc3902f9e4fb0ed4337bd7a96b7449bbad6f59feed7277f Homepage: https://cran.r-project.org/package=SGDinference Description: CRAN Package 'SGDinference' (Inference with Stochastic Gradient Descent) Estimation and inference methods for large-scale mean and quantile regression models via stochastic (sub-)gradient descent (S-subGD) algorithms. The inference procedure handles cross-sectional data sequentially: (i) updating the parameter estimate with each incoming "new observation", (ii) aggregating it as a Polyak-Ruppert average, and (iii) computing an asymptotically pivotal statistic for inference through random scaling. The methodology used in the 'SGDinference' package is described in detail in the following papers: (i) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2022) "Fast and robust online inference with stochastic gradient descent via random scaling". (ii) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2023) "Fast Inference for Quantile Regression with Tens of Millions of Observations". Package: r-cran-sgeostat Architecture: amd64 Version: 1.0-27-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sgeostat_1.0-27-1.ca2404.1_amd64.deb Size: 144148 MD5sum: 761226b1d6967d7b07adf47765fcbe8f SHA1: cf6a782017f34b5d070c28990e5072f25b509d11 SHA256: 1c78a608e0bf775ff6ac56c96746cffad93d974495f0665e2fdad9fcb3caee3e SHA512: b64cfbdee22aea51b448d5df53d973d50fa89b80534ec698a8f8363e83b79cf4f8a36e983aef94fe1a7c735c152a881ce7fd6c25c9f2a17e7077529c8e644af0 Homepage: https://cran.r-project.org/package=sgeostat Description: CRAN Package 'sgeostat' (An Object-Oriented Framework for Geostatistical Modeling in S+) An Object-oriented Framework for Geostatistical Modeling in S+ containing functions for variogram estimation, variogram fitting and kriging as well as some plot functions. Written entirely in S, therefore works only for small data sets in acceptable computing time. Package: r-cran-sgl Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sgl_1.3-1.ca2404.1_amd64.deb Size: 98644 MD5sum: b5d19c07ddfd5a9ebe114b732c25fba5 SHA1: 9fc69136965cd780b15d987120be28e9b08ffd58 SHA256: e7a291c7d764cbb3e9ab1ce182d4db0bef0f9fb0a47ffc309e1031e61db37d8f SHA512: 527d1e3592e6224008affee967e52d0773668947eb0b87812e0208c08165cb89bb9b4f148fd84c30b6dff2569c155d4b4498968bdd8b7ef22b86c731f7b8700d Homepage: https://cran.r-project.org/package=SGL Description: CRAN Package 'SGL' (Fit a GLM (or Cox Model) with a Combination of Lasso and GroupLasso Regularization) Fit a regularized generalized linear model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. Fits linear, logistic and Cox models. Package: r-cran-sglasso Architecture: amd64 Version: 1.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-igraph Filename: pool/dists/noble/main/r-cran-sglasso_1.2.6-1.ca2404.1_amd64.deb Size: 132006 MD5sum: cf483dac969f28a8908906e4b054b197 SHA1: 59fce4227dbedc1b345910e6d3bc5bce13b72434 SHA256: 7f395d45f2858dff88330fbc53e40d672e65e63802f2092d1dc9e2fca91cce66 SHA512: 283e13c336923624c43e8e7d34017a2f125ab37dee04a0750d41c1f736662db61dfb208819a2aacb87ff3bc769ca5d98762064e8590302b0ea7bf2af7508d9d5 Homepage: https://cran.r-project.org/package=sglasso Description: CRAN Package 'sglasso' (Lasso Method for RCON(V,E) Models) RCON(V, E) models are a kind of restriction of the Gaussian Graphical Models defined by a set of equality constraints on the entries of the concentration matrix. 'sglasso' package implements the structured graphical lasso (sglasso) estimator proposed in Abbruzzo et al. (2014) for the weighted l1-penalized RCON(V, E) model. Two cyclic coordinate algorithms are implemented to compute the sglasso estimator, i.e. a cyclic coordinate minimization (CCM) and a cyclic coordinate descent (CCD) algorithm. Package: r-cran-sgolay Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-signal Suggests: r-cran-covr, r-cran-runit Filename: pool/dists/noble/main/r-cran-sgolay_1.0.3-1.ca2404.1_amd64.deb Size: 37472 MD5sum: 6b87ae8745f0eb7d6f0d35b7eb1df39f SHA1: 81bc380f19b31bbd27151102b748363a200d59d0 SHA256: 78cf9e0d7d26febb0595e1de50c9ec3855319eac23e197bfebd5782540f29aac SHA512: 134bd224662f125a7662ae6af40fe857d6ba180a0745372cb9d6cd05ebb0c10eca349f47b82efadfa270ff88c24a46cbccaaedfac1a7e0b8662fc0fade5bbc78 Homepage: https://cran.r-project.org/package=sgolay Description: CRAN Package 'sgolay' (Efficient Savitzky-Golay Filtering) Smoothing signals and computing their derivatives is a common requirement in signal processing workflows. Savitzky-Golay filters are a established method able to do both (Savitzky and Golay, 1964 ). This package implements one dimensional Savitzky-Golay filters that can be applied to vectors and matrices (either row-wise or column-wise). Vectorization and memory allocations have been profiled to reduce computational fingerprint. Short filter lengths are implemented in the direct space, while longer filters are implemented in frequency space, using a Fast Fourier Transform (FFT). Package: r-cran-sgpr Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-sgpr_0.1.2-1.ca2404.1_amd64.deb Size: 138724 MD5sum: 55578315ba366b40448ef0f350bfa04c SHA1: 0cd766fb707c3feb8e4b0f9cf11f3d0b1448967e SHA256: 5ae6b2fcefa428830b3b45c74b3a8e3897526da0e6e9bafe1b09348bf1f0891b SHA512: 9e1cc7b4583df6ec429413d95933bce24ffada53b7ac24a251c608a0b228de2ec26f037264e0766a4d02ba7f9004812c08c3c48f64d5b605410fd84e8a14d2c7 Homepage: https://cran.r-project.org/package=SGPR Description: CRAN Package 'SGPR' (Sparse Group Penalized Regression for Bi-Level VariableSelection) Fits the regularization path of regression models (linear and logistic) with additively combined penalty terms. All possible combinations with Least Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP) and Exponential Penalty (EP) are supported. This includes Sparse Group LASSO (SGL), Sparse Group SCAD (SGS), Sparse Group MCP (SGM) and Sparse Group EP (SGE). For more information, see Buch, G., Schulz, A., Schmidtmann, I., Strauch, K., & Wild, P. S. (2024) . Package: r-cran-sgs Architecture: amd64 Version: 0.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-caret, r-cran-slope, r-cran-rlab, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-sgl, r-cran-gglasso, r-cran-glmnet, r-cran-testthat, r-cran-knitr, r-cran-grpslope, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sgs_0.3.9-1.ca2404.1_amd64.deb Size: 364084 MD5sum: 4897d64e618e536cf6d282ae4371fa8a SHA1: e0915c00b963d4b7f5a2eaecb536348714502150 SHA256: 3911050368e6d425a551567c4d1674ea373ad980ead0cd2da6271d4e3cd21a9a SHA512: 0a77b295337a9b71c94462e72bcb9626a0a167602aa34c116d4f1b9f5223ee54afa43327706661c3f171ef150a9873ed886f315025300b6316889fdad339f505 Homepage: https://cran.r-project.org/package=sgs Description: CRAN Package 'sgs' (Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control) Implementation of Sparse-group SLOPE (SGS) (Feser and Evangelou (2023) ) models. Linear and logistic regression models are supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported. In addition, a general Adaptive Three Operator Splitting (ATOS) (Pedregosa and Gidel (2018) ) implementation is provided. Group SLOPE (gSLOPE) (Brzyski et al. (2019) ) and group-based OSCAR models (Feser and Evangelou (2024) ) are also implemented. All models are available with strong screening rules (Feser and Evangelou (2024) ) for computational speed-up. Package: r-cran-shapr Architecture: amd64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4762 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rcpp, r-cran-matrix, r-cran-future.apply, r-cran-cli, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-ranger, r-cran-xgboost, r-cran-mgcv, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-ggplot2, r-cran-gbm, r-cran-party, r-cran-partykit, r-cran-waldo, r-cran-progressr, r-cran-future, r-cran-ggbeeswarm, r-cran-vdiffr, r-cran-forecast, r-cran-torch, r-cran-ggally, r-cran-coro, r-cran-parsnip, r-cran-recipes, r-cran-workflows, r-cran-tune, r-cran-dials, r-cran-yardstick, r-cran-hardhat, r-cran-rsample Filename: pool/dists/noble/main/r-cran-shapr_1.0.8-1.ca2404.1_amd64.deb Size: 2771706 MD5sum: 5a5dac5853c1e8ae2c2ff99956fcc0c6 SHA1: 6b94af1276d925fee24e71d999826782dcf867c5 SHA256: edd1ff499a62f97ca8ac2739cc792101c8a6d30484ad2e8f6b3666f2dce9331c SHA512: ffbaa08b8aebe891a26a216411a4fba7ed1c082d00e83dae0491e4b54d6be869b1bf82d6e9a1c642da16e240b97d2b931471ebcd3f630ada4861ef7c93ac7a2d Homepage: https://cran.r-project.org/package=shapr Description: CRAN Package 'shapr' (Prediction Explanation with Dependence-Aware Shapley Values) Complex machine learning models are often hard to interpret. However, in many situations it is crucial to understand and explain why a model made a specific prediction. Shapley values is the only method for such prediction explanation framework with a solid theoretical foundation. Previously known methods for estimating the Shapley values do, however, assume feature independence. This package implements methods which accounts for any feature dependence, and thereby produces more accurate estimates of the true Shapley values. An accompanying 'Python' wrapper ('shaprpy') is available through PyPI. Package: r-cran-shard Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1006 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-testthat, r-cran-ps, r-cran-jsonlite, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-shard_0.1.1-1.ca2404.1_amd64.deb Size: 793250 MD5sum: f9c476a8c42d86086618ad0661ba2cfb SHA1: 3b84b38749765455aaaac611a0bd2cd04c5f9e67 SHA256: 15ba4d351f988008313d0dd9f82975f7b6b37eb018a2fff3168431164991c56a SHA512: 325ecf28703fd9738862dc8c4be58ae0820877c749d67ba00b642c05e326f20331533e5fce84d12eea3187ce6b7604b71ee803b5c269c62bfadac297d7cb9847 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. 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Package: r-cran-sharpdata Architecture: amd64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernsmooth, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-sharpdata_1.4-1.ca2404.1_amd64.deb Size: 44888 MD5sum: fa54b20f06f21455860830170a5fb4b7 SHA1: 938b1f3464becf084058264c2bb71cebc372ad4b SHA256: f4f56ec85f776826c82c9dd4560c90491142dd61bc8af848c5db6d0e0889b6af SHA512: 4d93b8de3dabefb1555fa6c3dcf79fec71343103dc0cedb5f126ab42f0bcbe841463e9f67a2e65ea772005b6406a6a9ac22ae3887974ba5f1c10dab02133cdae Homepage: https://cran.r-project.org/package=sharpData Description: CRAN Package 'sharpData' (Data Sharpening) Functions and data sets inspired by data sharpening - data perturbation to achieve improved performance in nonparametric estimation, as described in Choi, E., Hall, P. and Rousson, V. (2000). Capabilities for enhanced local linear regression function and derivative estimation are included, as well as an asymptotically correct iterated data sharpening estimator for any degree of local polynomial regression estimation. A cross-validation-based bandwidth selector is included which, in concert with the iterated sharpener, will often provide superior performance, according to a median integrated squared error criterion. Sample data sets are provided to illustrate function usage. Package: r-cran-sharperratio Architecture: amd64 Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ghyp, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-sharperratio_1.4.3-1.ca2404.1_amd64.deb Size: 89550 MD5sum: 0af3e4ff9e22ace403ae9bc0b8ebd6b3 SHA1: cb528f98ad151aa62029e2d035ba4eb5816bfc06 SHA256: 44dc2b57225758b4965617706194998613990af73d1d41ae959d51c8abb89353 SHA512: 729c344a2ad042d44b1f2846310f7b716520ba2ff0f802d2e1aae58f013006aaec0fa35883f97665c94358717ee433b2defd34b28eaca6eb69bd573f85da9a34 Homepage: https://cran.r-project.org/package=sharpeRratio Description: CRAN Package 'sharpeRratio' (Moment-Free Estimation of Sharpe Ratios) An efficient moment-free estimator of the Sharpe ratio, or signal-to-noise ratio, for heavy-tailed data (see ). 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Package: r-cran-shrinkdsm Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-stochvol, r-cran-coda, r-cran-shrinktvp, r-cran-survival, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-shrinkdsm_1.0.2-1.ca2404.1_amd64.deb Size: 239238 MD5sum: e81047a02493c7c70c60a9ecacb5e81b SHA1: 4370f2a3275f46b63377b94fec98934bb621b56e SHA256: e1287d26694e5794fa6764c029bc628986cd3401f798d64df26694e9841a5f98 SHA512: 22e4d9fc7e6af1e695513356c3add40bc1f5be152109768abd2eb5e0f9842c561fe2372daef4334b4525e3a233f9d28b63644740354c8a7f4de716ca45712c25 Homepage: https://cran.r-project.org/package=shrinkDSM Description: CRAN Package 'shrinkDSM' (Efficient Bayesian Inference for Dynamic Survival Models withShrinkage) Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of dynamic survival models with shrinkage priors. Details on the algorithms used are provided in Wagner (2011) , Bitto and Frühwirth-Schnatter (2019) and Cadonna et al. (2020) . Package: r-cran-shrinktvp Architecture: amd64 Version: 3.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1713 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-gigrvg, r-cran-stochvol, r-cran-coda, r-cran-zoo, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-rcppgsl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-shrinktvp_3.1.1-1.ca2404.1_amd64.deb Size: 1112914 MD5sum: f1025147251ee2f4ebada3215b000cad SHA1: fc4a626a14876fa7a502afc5dc03a3942111b08c SHA256: 1237af312509c5eac260312b0b9f774e9bb336d812f84490b04fc0f424f4ab6c SHA512: 99eee7c5afa59c548497543706ad554d2898d156a98fce80c4d3bbb79ce81808cdbc525cd6571497a8a6c4b3ba5b2daf27ae9fdedbd94957118ac8227406cc6e Homepage: https://cran.r-project.org/package=shrinkTVP Description: CRAN Package 'shrinkTVP' (Efficient Bayesian Inference for Time-Varying Parameter Modelswith Shrinkage) Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter models with shrinkage priors, both dynamic and static. Details on the algorithms used are provided in Bitto and Frühwirth-Schnatter (2019) and Cadonna et al. (2020) and Knaus and Frühwirth-Schnatter (2023) . For details on the package, please see Knaus et al. (2021) . For the multivariate extension, see the 'shrinkTVPVAR' package. Package: r-cran-shrinktvpvar Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 611 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-shrinktvp, r-cran-stochvol, r-cran-coda, r-cran-rcolorbrewer, r-cran-lattice, r-cran-zoo, r-cran-mvtnorm, r-cran-rcppprogress, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-shrinktvpvar_1.0.1-1.ca2404.1_amd64.deb Size: 313504 MD5sum: 75ee6d9395b90c397753f1484fbc58c9 SHA1: fd0b826e52e6549a0c3b5c4a22b90d721de9567d SHA256: 9c1dca42639d730857d11bccd0d30323f9e9ddb10b8682d99cb26932e0a24aa8 SHA512: 4967dd39a377829bb5b0f98875653cdb510566c5e7daed7a31d5a9bc39698a4959d6985b555492866dd1da8b1a72e9fbc8ce8d4185dbba675deb04b089a8b0c2 Homepage: https://cran.r-project.org/package=shrinkTVPVAR Description: CRAN Package 'shrinkTVPVAR' (Efficient Bayesian Inference for TVP-VAR-SV Models withShrinkage) Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter vector autoregressive models with stochastic volatility (TVP-VAR-SV) under shrinkage priors and dynamic shrinkage processes. Details on the TVP-VAR-SV model and the shrinkage priors can be found in Cadonna et al. (2020) , details on the software can be found in Knaus et al. (2021) , while details on the dynamic shrinkage process can be found in Knaus and Frühwirth-Schnatter (2023) . Package: r-cran-sht Architecture: amd64 Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 651 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-pracma, r-cran-flare, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-sht_0.1.9-1.ca2404.1_amd64.deb Size: 471490 MD5sum: 6e47a0fdcc2893387bd2f5d1b9353490 SHA1: 103d97d9f9d2a2ace925ce407d6db8a6376d759e SHA256: c6d65cafcdd0b3640d3458763d2488d9f5ab84500642635d82d11fa9e13fd3b8 SHA512: 70e3bb8519e0ee9f3ada978cce5e31ff8ca0bf3597bc77327f808cf669487293c744093558333c7c64148a76d8307d74f0cf6d9a4b510b19e884c0f010ad415e Homepage: https://cran.r-project.org/package=SHT Description: CRAN Package 'SHT' (Statistical Hypothesis Testing Toolbox) We provide a collection of statistical hypothesis testing procedures ranging from classical to modern methods for non-trivial settings such as high-dimensional scenario. For the general treatment of statistical hypothesis testing, see the book by Lehmann and Romano (2005) . Package: r-cran-siber Architecture: amd64 Version: 2.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2708 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hdrcde, r-cran-mnormt, r-cran-rjags, r-cran-spatstat.utils, r-cran-tidyr, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-coda, r-cran-ellipse, r-cran-ggplot2, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-viridis Filename: pool/dists/noble/main/r-cran-siber_2.1.10-1.ca2404.1_amd64.deb Size: 1632298 MD5sum: 9542c7f5dfe45d8a636e1a9b1fdd05b6 SHA1: d6b0bc8974d50ca538ebe72df1d273cb020dc172 SHA256: 8c3b38670d7db8bc7cc69960bef70c276ca4953a837142b28e945ec9af2fed26 SHA512: a381f24cae93b9c726c673875ba6e1fc3e5f08957e2d04dad38281001dbc93bb52c9967f54aeb1ce08f45e6f9722bfbf054d19e85590de064ecb9bbe1fe9ddde Homepage: https://cran.r-project.org/package=SIBER Description: CRAN Package 'SIBER' (Stable Isotope Bayesian Ellipses in R) Fits bi-variate ellipses to stable isotope data using Bayesian inference with the aim being to describe and compare their isotopic niche. Package: r-cran-sieve Architecture: amd64 Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-combinat, r-cran-glmnet, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-sieve_2.1-1.ca2404.1_amd64.deb Size: 152550 MD5sum: cce627333c5b4cee14ff300831961410 SHA1: 408fb64ff56b019d56cbf65d7980bf1990cdf33b SHA256: b4425f63b124febcca981d70140ee4c03f32f21a2d94212beb918bef79689f50 SHA512: e29de9706326aa062723858b6caf2feb782c24c3a6250a03069d0f33f756a6fe2367919aa3df7aadfbef8c80b3eb2ae945c63d513ab3a521c96d4e7546f50cf9 Homepage: https://cran.r-project.org/package=Sieve Description: CRAN Package 'Sieve' (Nonparametric Estimation by the Method of Sieves) Performs multivariate nonparametric regression/classification by the method of sieves (using orthogonal basis). The method is suitable for moderate high-dimensional features (dimension < 100). The l1-penalized sieve estimator, a nonparametric generalization of Lasso, is adaptive to the feature dimension with provable theoretical guarantees. We also include a nonparametric stochastic gradient descent estimator, Sieve-SGD, for online or large scale batch problems. Details of the methods can be found in: . Package: r-cran-sieveph Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-ggpubr, r-cran-scales, r-cran-plyr, r-cran-np, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-sieveph_1.1-1.ca2404.1_amd64.deb Size: 362518 MD5sum: 6f9656bc02831377c6d661b1b256edaf SHA1: 7d03493b261ca49bd194e5bebe2bf774b24b4a4c SHA256: 6dec0b9762a879530343ea4c944cdf73770d4a1dd3e23906e8796d95b2def917 SHA512: cbf43966de961ef4adcf3063c30a18e464b7496ea1dc2a99e01f4874bc97f498d6a08efd293e405dda808c82895389a25879b5b1b04019cef8d1f86ba139669c Homepage: https://cran.r-project.org/package=sievePH Description: CRAN Package 'sievePH' (Sieve Analysis Methods for Proportional Hazards Models) Implements a suite of semiparametric and nonparametric kernel-smoothed estimation and testing procedures for continuous mark-specific stratified hazard ratio (treatment/placebo) models in a randomized treatment efficacy trial with a time-to-event endpoint. Semiparametric methods, allowing multivariate marks, are described in Juraska M and Gilbert PB (2013), Mark-specific hazard ratio model with multivariate continuous marks: an application to vaccine efficacy. Biometrics 69(2):328-337 , and in Juraska M and Gilbert PB (2016), Mark-specific hazard ratio model with missing multivariate marks. Lifetime Data Analysis 22(4):606-25 . Nonparametric kernel-smoothed methods, allowing univariate marks only, are described in Sun Y and Gilbert PB (2012), Estimation of stratified mark‐specific proportional hazards models with missing marks. Scandinavian Journal of Statistics}, 39(1):34-52 , and in Gilbert PB and Sun Y (2015), Inferences on relative failure rates in stratified mark-specific proportional hazards models with missing marks, with application to human immunodeficiency virus vaccine efficacy trials. Journal of the Royal Statistical Society Series C: Applied Statistics, 64(1):49-73 . Both semiparametric and nonparametric approaches consider two scenarios: (1) the mark is fully observed in all subjects who experience the event of interest, and (2) the mark is subject to missingness-at-random in subjects who experience the event of interest. For models with missing marks, estimators are implemented based on (i) inverse probability weighting (IPW) of complete cases (for the semiparametric framework), and (ii) augmentation of the IPW estimating functions by leveraging correlations between the mark and auxiliary data to 'impute' the augmentation term for subjects with missing marks (for both the semiparametric and nonparametric framework). The augmented IPW estimators are doubly robust and recommended for use with incomplete mark data. The semiparametric methods make two key assumptions: (i) the time-to-event is assumed to be conditionally independent of the mark given treatment, and (ii) the weight function in the semiparametric density ratio/biased sampling model is assumed to be exponential. Diagnostic testing procedures for evaluating validity of both assumptions are implemented. Summary and plotting functions are provided for estimation and inferential results. Package: r-cran-sifinet Architecture: amd64 Version: 1.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-quantreg, r-cran-igraph, r-cran-matrix, r-cran-ggraph, r-cran-ggplot2, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-sifinet_1.13-1.ca2404.1_amd64.deb Size: 191758 MD5sum: c80fec1a9d96066584d546cc06a3448a SHA1: d3fe1a198203829f44ca9c0ec317cc852b42cb18 SHA256: ec4b55bfe180ca8d95e0b2fc4cd4ee1fe153dc393bf1cd966df57125648f7863 SHA512: 503611a7cad39a64597b0231ce5bdd1a02be51fd054df0f71cf46302f739f026a8ee4ff61f7dae4ccdf688544386abf20aa2e792f9709b739552b2aac4a7f8c3 Homepage: https://cran.r-project.org/package=SiFINeT Description: CRAN Package 'SiFINeT' (Single Cell Feature Identification with Network Topology) Cluster-independent method based on topology structure of gene co-expression network for identifying feature gene sets, extracting cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Without prior cell clustering, SifiNet circumvents potential inaccuracies in clustering that may influence subsequent analyses. This method is introduced in Qi Gao, Zhicheng Ji, Liuyang Wang, Kouros Owzar, Qi-Jing Li, Cliburn Chan, Jichun Xie "SifiNet: a robust and accurate method to identify feature gene sets and annotate cells" (2024) . Package: r-cran-sift Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1321 Depends: libc6 (>= 2.11), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.1.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-pastecs, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-tibble, r-cran-purrr, r-cran-glue, r-cran-tidyselect, r-cran-cpp11 Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-testthat, r-cran-rmarkdown, r-cran-mopac, r-cran-hms, r-cran-stringr, r-cran-readr Filename: pool/dists/noble/main/r-cran-sift_0.1.0-1.ca2404.1_amd64.deb Size: 831126 MD5sum: 8b500ed4a35bcd35c8e9dd86f844c20e SHA1: 9eccdd8b73247093461217aa84952b60c4d2c582 SHA256: 7b7678551e9472c23ea98cc2a6839f49897cea1be650a00b5dc177d6432cc5e9 SHA512: b73bc839a898312ab336d7e9037a7dbf0185c2d2d804019a27538b666a62b396db5b2a9987c34186b343096ec1c453ab5ab1cd11f5505dc578a99fcef367dab9 Homepage: https://cran.r-project.org/package=sift Description: CRAN Package 'sift' (Intelligently Peruse Data) Facilitate extraction of key information from common datasets. Package: r-cran-sigminer Architecture: amd64 Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5208 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-cowplot, r-cran-data.table, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-ggpubr, r-bioc-maftools, r-cran-magrittr, r-cran-nmf, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-tidyr Suggests: r-bioc-biobase, r-bioc-biostrings, r-bioc-bsgenome, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-cran-circlize, r-cran-cluster, r-cran-covr, r-cran-digest, r-bioc-genomicranges, r-cran-gensa, r-cran-ggalluvial, r-cran-ggcorrplot, r-cran-ggfittext, r-cran-ggplotify, r-cran-ggrepel, r-bioc-iranges, r-cran-knitr, r-cran-lpsolve, r-cran-markdown, r-cran-matrixstats, r-cran-nnls, r-cran-patchwork, r-cran-pheatmap, r-cran-quadprog, r-cran-r.utils, r-cran-rcolorbrewer, r-cran-reticulate, r-cran-rmarkdown, r-cran-roxygen2, r-cran-scales, r-cran-synchronicity, r-cran-testthat, r-cran-tibble, r-cran-ucscxenatools Filename: pool/dists/noble/main/r-cran-sigminer_2.3.1-1.ca2404.1_amd64.deb Size: 4686312 MD5sum: fe711f8ced20529f744a2fffb666ee20 SHA1: 3b8846e59ab8688d4aaf19e7d4ca6915d4dc98fa SHA256: c8af13201ffaf1a9af727b36c47078cb17acc9d01685de25cfcd9bfeee69ecbc SHA512: 3c989ca00fcaf4c878653b03b51fe4fbdfbccc63d9fb55e98c000084d2fcd6a3ef4a148d3b69ffab676feffecd06754f91a8fb23ec75149a19c01ecb92de23a3 Homepage: https://cran.r-project.org/package=sigminer Description: CRAN Package 'sigminer' (Extract, Analyze and Visualize Mutational Signatures for GenomicVariations) Genomic alterations including single nucleotide substitution, copy number alteration, etc. are the major force for cancer initialization and development. Due to the specificity of molecular lesions caused by genomic alterations, we can generate characteristic alteration spectra, called 'signature' (Wang, Shixiang, et al. (2021) & Alexandrov, Ludmil B., et al. (2020) & Steele Christopher D., et al. (2022) ). This package helps users to extract, analyze and visualize signatures from genomic alteration records, thus providing new insight into cancer study. Package: r-cran-sign Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-survcomp, r-cran-survival, r-bioc-gsva Filename: pool/dists/noble/main/r-cran-sign_0.1.0-1.ca2404.1_amd64.deb Size: 65028 MD5sum: 39af75092fdf1da49d28a306e6ed7a0b SHA1: 0b1ecea6c19919389b34ba0e276ced70de2e8d3d SHA256: 270849696483a30b06e61916d39e89cd99b0169dde3fe883956c6a17ca29b15e SHA512: be0a51d80813ba071eb7c26c9ca905e458f6269fc65202609e1ae3db0739f66adf79eeaa289e6d21a2ba6895c3f4209c156672cfa89c7906ccecaaaacc568664 Homepage: https://cran.r-project.org/package=SIGN Description: CRAN Package 'SIGN' (Similarity Identification in Gene Expression) Provides a classification framework to use expression patterns of pathways as features to identify similarity between biological samples. It provides a new measure for quantifying similarity between expression patterns of pathways. Package: r-cran-signac Architecture: amd64 Version: 1.17.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12471 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-iranges, r-cran-matrix, r-bioc-rsamtools, r-bioc-s4vectors, r-cran-seuratobject, r-cran-data.table, r-cran-dplyr, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-rlang, r-cran-pbapply, r-cran-tidyr, r-cran-patchwork, r-bioc-biocgenerics, r-cran-stringi, r-cran-fastmatch, r-cran-rcpproll, r-cran-scales, r-cran-rcpp, r-cran-tidyselect, r-cran-vctrs, r-cran-lifecycle, r-bioc-sparsematrixstats, r-cran-rspectra Suggests: r-cran-seurat, r-cran-ggforce, r-cran-ggrepel, r-cran-ggseqlogo, r-cran-testthat, r-bioc-summarizedexperiment, r-bioc-tfbstools, r-bioc-motifmatchr, r-bioc-bsgenome, r-cran-shiny, r-cran-miniui, r-bioc-rtracklayer, r-bioc-biovizbase, r-bioc-biostrings, r-cran-lsa, r-cran-mass, r-cran-wrswor, r-bioc-fgsea Filename: pool/dists/noble/main/r-cran-signac_1.17.1-1.ca2404.1_amd64.deb Size: 4566398 MD5sum: 8c1caf85bdf2ef354be8075c8ac21c61 SHA1: 640103afe1ae269c6791c45f22d39cf17ad7a1e6 SHA256: a7c26ce23cec954451f6425d5e00b97145ccc753f3ccef6271e4ab9111e3d1b4 SHA512: c4ba8373bfa76ae86a497104d2796faca2cf2691eaae70adcba5bbd827d1ae13b052d4dbd4bdb3e3f04fa5d3185eae685adb1d64648942f2ed31259381ff35b2 Homepage: https://cran.r-project.org/package=Signac Description: CRAN Package 'Signac' (Analysis of Single-Cell Chromatin Data) A framework for the analysis and exploration of single-cell chromatin data. The 'Signac' package contains functions for quantifying single-cell chromatin data, computing per-cell quality control metrics, dimension reduction and normalization, visualization, and DNA sequence motif analysis. Reference: Stuart et al. (2021) . Package: r-cran-signal Architecture: amd64 Version: 1.8-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-pracma Filename: pool/dists/noble/main/r-cran-signal_1.8-1-1.ca2404.1_amd64.deb Size: 334452 MD5sum: 963f64f11575ed195656c8185f1c6df0 SHA1: 5df105d4d8ec1e37d7e194a87df1ce882fbcd9a9 SHA256: be7df160d0a9dfd726276cc51237f058e64c9527c6f20608615f4528f79a4d0f SHA512: 4c31c3ead902ee748e958f7479edf38ba441e4211a2185eff0d2daaa25f7dd8ec901a41ff441ce28cbe5dadf5d407623b6e34ade6f4d2473b7d61cf4bc9db1a0 Homepage: https://cran.r-project.org/package=signal Description: CRAN Package 'signal' (Signal Processing) A set of signal processing functions originally written for 'Matlab' and 'Octave'. Includes filter generation utilities, filtering functions, resampling routines, and visualization of filter models. It also includes interpolation functions. 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The implemented algorithm can be accessed from both the command line and GUI. 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This includes several measures for structural balance as introduced by Cartwright and Harary (1956) , blockmodeling algorithms from Doreian (2008) , various centrality indices, and projections of signed two-mode networks introduced by Schoch (2020) . 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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. Package: r-cran-sim.diffproc Architecture: amd64 Version: 4.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2244 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-deriv, r-cran-mass Suggests: r-cran-desolve, r-cran-knitr, r-cran-rgl, r-cran-rmarkdown, r-cran-scatterplot3d, r-cran-sm Filename: pool/dists/noble/main/r-cran-sim.diffproc_4.9-1.ca2404.1_amd64.deb Size: 1510324 MD5sum: abc06c88d16123ceef5d464274b3c999 SHA1: 71e1de7a3c4cf7a4c58553ac530385600cad0a04 SHA256: ca2e6d02eccd8e02a8954f838fc9fff54bd8543baa5f039c340c9e72a404770a SHA512: 9c25065bfc13c27cf9cc602af12eb8921de3c63c0bb61d57469d4c82ec3b490c126f842387b530fc8c98ff038b392bb19e4a364ff57f89661bdac2e568b04095 Homepage: https://cran.r-project.org/package=Sim.DiffProc Description: CRAN Package 'Sim.DiffProc' (Simulation of Diffusion Processes) It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of stochastic differential systems in both forms Ito and Stratonovich. Statistical analysis with parallel Monte Carlo and moment equations methods of SDEs . Enabled many searchers in different domains to use these equations to modeling practical problems in financial and actuarial modeling and other areas of application, e.g., modeling and simulate of first passage time problem in shallow water using the attractive center (Boukhetala K, 1996) ISBN:1-56252-342-2. 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The input and output parameters of the simulated cartel opportunities can be visualized by three-dimensional projections. A description of the model is given in Moritz et al. (2018) . 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Package: r-cran-simexboost Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-simexboost_0.2.0-1.ca2404.1_amd64.deb Size: 50610 MD5sum: 4a45d2aae8093f7ba22be52f5cc749c3 SHA1: ee5fd292aaef6988d97aea476128072ae6594316 SHA256: f44e79928c568be046725b356b483667b8191e8be40a71a3eabe8007727e4c08 SHA512: a6e6081d7b89c1b24605fb4fc0ebb2a875f86fab2608913ac1aeef95adabccc05798f7dd3667f3409d11a138c96128ba8ee0451baa60cd0f0d7e28ee7800be6e Homepage: https://cran.r-project.org/package=SIMEXBoost Description: CRAN Package 'SIMEXBoost' (Boosting Method for High-Dimensional Error-Prone Data) Implementation of the boosting procedure with the simulation and extrapolation approach to address variable selection and estimation for high-dimensional data subject to measurement error in predictors. It can be used to address generalized linear models (GLM) in Chen (2023) and the accelerated failure time (AFT) model in Chen and Qiu (2023) . Some relevant references include Chen and Yi (2021) and Hastie, Tibshirani, and Friedman (2008, ISBN:978-0387848570). Package: r-cran-simfam Architecture: amd64 Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1765 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-tibble Suggests: r-cran-testthat, r-cran-popkin, r-cran-bnpsd, r-cran-kinship2, r-cran-rcolorbrewer, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-simfam_1.1.6-1.ca2404.1_amd64.deb Size: 1291564 MD5sum: 62a76a2fb050cd50dd94832b970196f9 SHA1: 987e12831ff7343d733d73bb3a371e92a144e1c6 SHA256: b4c548d4da2e3a56a6eccb3bb2ee54fa6e1bce9e9fc6c7840038bc1f001de5fb SHA512: 9ddde5d0274fe4411167c847a35d48b3d7c50cbfb4a5caa48708d0d6b0c5d58b7d1690433b1fdf6e70acb01504a9b4b32df293c3b3ba5cb3b04cd88813916679 Homepage: https://cran.r-project.org/package=simfam Description: CRAN Package 'simfam' (Simulate and Model Family Pedigrees with Structured Founders) The focus is on simulating and modeling families with founders drawn from a structured population (for example, with different ancestries or other potentially non-family relatedness), in contrast to traditional pedigree analysis that treats all founders as equally unrelated. Main function simulates a random pedigree for many generations, avoiding close relatives, pairing closest individuals according to a 1D geography and their randomly-drawn sex, and with variable children sizes to result in a target population size per generation. Auxiliary functions calculate kinship matrices, admixture matrices, and draw random genotypes across arbitrary pedigree structures starting from the corresponding founder values. The code is built around the plink FAM table format for pedigrees. Described in Yao and Ochoa (2022) . Package: r-cran-simframe Architecture: amd64 Version: 0.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2074 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-lattice Filename: pool/dists/noble/main/r-cran-simframe_0.5.4-1.ca2404.1_amd64.deb Size: 1678602 MD5sum: 83cb6ee63543bc4a3da8bacc346683d0 SHA1: da1959f582bbdf27966c6f92c841d443e1299b6e SHA256: 531aa82257f178884613a63ff112df147f000488c9bbacfe896edf1c9e6f8bc7 SHA512: 74865b763fc68df8be64e2139a414882a602e9f887a0994e3ab59be50b8f96ce51a91bc6a91615a048a4f136d1d2668c892cfd1e0555fa68e677ba3c6e9b93c4 Homepage: https://cran.r-project.org/package=simFrame Description: CRAN Package 'simFrame' (Simulation Framework) A general framework for statistical simulation, which allows researchers to make use of a wide range of simulation designs with minimal programming effort. The package provides functionality for drawing samples from a distribution or a finite population, for adding outliers and missing values, as well as for visualization of the simulation results. It follows a clear object-oriented design and supports parallel computing to increase computational performance. Package: r-cran-similar Architecture: amd64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 456 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-stringi, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-similar_1.0.8-1.ca2404.1_amd64.deb Size: 167378 MD5sum: 93d521b89d7d004d6f9e4d9b7d7dc255 SHA1: 9ceeb16c1bdc523833c8c9034946d2ef2db6e774 SHA256: bf7c466607e9f01a37b07d435186d8c8409dbe3bef74d3c249c793c0dccc600b SHA512: 86a22b8939a49c1516d99c48c60ed2132afcdfbb925fe34f3abfea4cc6f75180e465d3a96fe05a12a27a743138a1a16e0e7ba641f7c1f150df5dc0c4e0e5cbe5 Homepage: https://cran.r-project.org/package=SimilaR Description: CRAN Package 'SimilaR' (R Source Code Similarity Evaluation) An implementation of a novel method to quantify the similarity of the code-base of R functions by means of program dependence graphs. Possible use cases include detection of code clones for improving software quality and of plagiarism amongst students' assignments. Package: r-cran-siminf Architecture: amd64 Version: 10.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4041 Depends: libc6 (>= 2.38), libgomp1 (>= 4.9), libgsl27 (>= 2.7.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-mass, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-siminf_10.1.0-1.ca2404.1_amd64.deb Size: 3342668 MD5sum: d19208920e28d057cd028251584fa03e SHA1: 9aa630ba4cafcbeb40f51d2ab735647d5689c780 SHA256: 166d44d455e3ac127fbdd645c5e794d8aa444ea2b0f7c08edd2560cc60b2c0ad SHA512: d7685933e63908724b36748e2b99ffe25fb4b2cf2a64c34855500cee05da6547e44658dd2cdabbb184423cca70c163d3e26e8f25aeba27897d93498349491728 Homepage: https://cran.r-project.org/package=SimInf Description: CRAN Package 'SimInf' (A Framework for Data-Driven Stochastic Disease SpreadSimulations) Provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations. The framework integrates infection dynamics in subpopulations as continuous-time Markov chains using the Gillespie stochastic simulation algorithm and incorporates available data such as births, deaths and movements as scheduled events at predefined time-points. Using C code for the numerical solvers and 'OpenMP' (if available) to divide work over multiple processors ensures high performance when simulating a sample outcome. One of our design goals was to make the package extendable and enable usage of the numerical solvers from other R extension packages in order to facilitate complex epidemiological research. The package contains template models and can be extended with user-defined models. For more details see the paper by Widgren, Bauer, Eriksson and Engblom (2019) . The package also provides functionality to fit models to time series data using the Approximate Bayesian Computation Sequential Monte Carlo ('ABC-SMC') algorithm of Toni and others (2009) or the Particle Markov Chain Monte Carlo ('PMCMC') algorithm of 'Andrieu' and others (2010) . Package: r-cran-simjoint Architecture: amd64 Version: 0.3.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 811 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-simjoint_0.3.12-1.ca2404.1_amd64.deb Size: 419978 MD5sum: ee776d44df5a2626951fad53dd053042 SHA1: 9022a1092bae72c157981d53843791acf85a2d96 SHA256: 4b8a5e8e5d377d49bbe6b9fdfda7f092f88e5166006095a7d8e0f766a43c98ae SHA512: 2ca1fd1be201eb1d4c5de7c13a7f3a16e8a37c9642ac6a050ba620e8aca7a75a2e77a9e1947188620c9ffd9690d4a0db8177e8df98262a626ecac72820654182 Homepage: https://cran.r-project.org/package=SimJoint Description: CRAN Package 'SimJoint' (Simulate Joint Distribution) Simulate multivariate correlated data given nonparametric marginals and their joint structure characterized by a Pearson or Spearman correlation matrix. The simulator engages the problem from a purely computational perspective. It assumes no statistical models such as copulas or parametric distributions, and can approximate the target correlations regardless of theoretical feasibility. The algorithm integrates and advances the Iman-Conover (1982) approach and the Ruscio-Kaczetow iteration (2008) . Package functions are carefully implemented in C++ for squeezing computing speed, suitable for large input in a manycore environment. Precision of the approximation and computing speed both substantially outperform various CRAN packages to date. Benchmarks are detailed in function examples. A simple heuristic algorithm is additionally designed to optimize the joint distribution in the post-simulation stage. The heuristic demonstrated good potential of achieving the same level of precision of approximation without the enhanced Iman-Conover-Ruscio-Kaczetow. The package contains a copy of Permuted Congruential Generator. Package: r-cran-simmer Architecture: amd64 Version: 4.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3012 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-magrittr, r-cran-codetools Suggests: r-cran-simmer.plot, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rticles Filename: pool/dists/noble/main/r-cran-simmer_4.4.7-1.ca2404.1_amd64.deb Size: 1208210 MD5sum: 992e88354306c698623587df289f2dd0 SHA1: d00ea7d5dc9941aa1087388d8a77614b7018b33c SHA256: 8c0f41163c10ee584dde94832bc77aa456946498a31aae998abd9f7319b6929a SHA512: f0dcc2493720b1e3778c05be23178fc0330885ab8a9491227411d4f96d7b4db47763e9f0326660e03a8879259ffa35ef80b83bc8753c973d7912566ab69aa069 Homepage: https://cran.r-project.org/package=simmer Description: CRAN Package 'simmer' (Discrete-Event Simulation for R) A process-oriented and trajectory-based Discrete-Event Simulation (DES) package for R. It is designed as a generic yet powerful framework. The architecture encloses a robust and fast simulation core written in 'C++' with automatic monitoring capabilities. It provides a rich and flexible R API that revolves around the concept of trajectory, a common path in the simulation model for entities of the same type. Documentation about 'simmer' is provided by several vignettes included in this package, via the paper by Ucar, Smeets & Azcorra (2019, ), and the paper by Ucar, Hernández, Serrano & Azcorra (2018, ); see 'citation("simmer")' for details. Package: r-cran-simmr Architecture: amd64 Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2150 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-r2jags, r-cran-ggplot2, r-cran-compositions, r-cran-boot, r-cran-reshape2, r-cran-viridis, r-cran-bayesplot, r-cran-checkmate, r-cran-rcpp, r-cran-ggally, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-readxl, r-cran-testthat, r-cran-covr, r-cran-vdiffr, r-cran-tibble, r-cran-ggnewscale Filename: pool/dists/noble/main/r-cran-simmr_0.5.2-1.ca2404.1_amd64.deb Size: 1275106 MD5sum: 7df6e53b562e5921208911ea91eb5fad SHA1: 71982281ddf05f86dac485019af90121823c6399 SHA256: 1686a2f3b6774d5d82a4fecb95dfcb2bb9515a902dfa3ab167bb193e6a659b70 SHA512: f7efe4a6eda19111a83dc7fb481cd41ae584dc0dcd3e013736e48baa6755e7f1edff4c46d65a52e9cb6cb7778e8e7fc8cc6bde06910ad1e6cfca0773d25b8dfc Homepage: https://cran.r-project.org/package=simmr Description: CRAN Package 'simmr' (A Stable Isotope Mixing Model) Fits Stable Isotope Mixing Models (SIMMs) and is meant as a longer term replacement to the previous widely-used package SIAR. SIMMs are used to infer dietary proportions of organisms consuming various food sources from observations on the stable isotope values taken from the organisms' tissue samples. However SIMMs can also be used in other scenarios, such as in sediment mixing or the composition of fatty acids. The main functions are simmr_load() and simmr_mcmc(). The two vignettes contain a quick start and a full listing of all the features. The methods used are detailed in the papers Parnell et al 2010 , and Parnell et al 2013 . Package: r-cran-simplexreg Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 4.1.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-plotrix Filename: pool/dists/noble/main/r-cran-simplexreg_1.3-1.ca2404.1_amd64.deb Size: 154078 MD5sum: fd6cf2dde1cf21f51e59ea290e03abac SHA1: ec625e1e198870cb2ccea7984a1dabf93f887581 SHA256: 876d5d1f663fa945bbf96b6c65c1c01700174bfb5a3451c8f4a4ab0196d91ddf SHA512: e5bbc1e3ac660629c87239392f4614d7ec0bb1774349891178d24ecacc016ca07a2642567ca2c92ad306991c58024f2a6235b3fee351f6e08d1ab6679cfe369f Homepage: https://cran.r-project.org/package=simplexreg Description: CRAN Package 'simplexreg' (Regression Analysis of Proportional Data Using SimplexDistribution) Simplex density, distribution, quantile functions as well as random variable generation of the simplex distribution are given. Regression analysis of proportional data using various kinds of simplex models is available. In addition, GEE method can be applied to longitudinal data to model the correlation. Residual analysis is also involved. Some subroutines are written in C with GNU Scientific Library (GSL) so as to facilitate the computation. Package: r-cran-simplextree Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1411 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-simplextree_1.0.1-1.ca2404.1_amd64.deb Size: 723588 MD5sum: 66c81f8addad7deac2310e75c090cf50 SHA1: f054011347742ba7c90c094e2c6339cb4c188297 SHA256: 1b6ba1d597eb7334ab4bdaacd9355b70aee9c119271dd62b1adfa3e9fbb117d8 SHA512: ef693243f603b351e956d178a46080652a948d57019d6b922f7b0bdcae572fa306c790d4be408285d9383973b9d6885bdcedcd9b3a0b26335819c3e87bef1659 Homepage: https://cran.r-project.org/package=simplextree Description: CRAN Package 'simplextree' (Provides Tools for Working with General Simplicial Complexes) Provides an interface to a Simplex Tree data structure, which is a data structure aimed at enabling efficient manipulation of simplicial complexes of any dimension. The Simplex Tree data structure was originally introduced by Jean-Daniel Boissonnat and Clément Maria (2014) . Package: r-cran-simplybee Architecture: amd64 Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3155 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-extradistr, r-cran-rann, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-ggplot2, r-cran-testthat, r-cran-matrix Filename: pool/dists/noble/main/r-cran-simplybee_0.4.1-1.ca2404.1_amd64.deb Size: 2070086 MD5sum: 2bdcd00cf645dadbd208dd051075f055 SHA1: 92b77adbc8aab330f509f0bb65a482162c8c0f26 SHA256: dc6e3c678514bc3fe77eca36d19a880e812ccf07b719ba610bd496ce67956c5c SHA512: f69a2f9e44df048042a9540e860aa29290140869bcb0162a033b9735ad3c48de90ddf5d90891eedf1fe7ae70280ed8b505c07a4c75f6fd2fd907fd763a4c93e7 Homepage: https://cran.r-project.org/package=SIMplyBee Description: CRAN Package 'SIMplyBee' ('AlphaSimR' Extension for Simulating Honeybee Populations andBreeding Programmes) An extension of the 'AlphaSimR' package () for stochastic simulations of honeybee populations and breeding programmes. 'SIMplyBee' enables simulation of individual bees that form a colony, which includes a queen, fathers (drones the queen mated with), virgin queens, workers, and drones. Multiple colony can be merged into a population of colonies, such as an apiary or a whole country of colonies. Functions enable operations on castes, colony, or colonies, to ease 'R' scripting of whole populations. All 'AlphaSimR' functionality with respect to genomes and genetic and phenotype values is available and further extended for honeybees, including haplo-diploidy, complementary sex determiner locus, colony events (swarming, supersedure, etc.), and colony phenotype values. Package: r-cran-simpop Architecture: amd64 Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3173 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-vcd, r-cran-data.table, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-e1071, r-cran-nnet, r-cran-doparallel, r-cran-foreach, r-cran-colorspace, r-cran-vim, r-cran-envstats, r-cran-fitdistrplus, r-cran-ranger, r-cran-wrswor, r-cran-matrixstats, r-cran-xgboost, r-cran-partykit Suggests: r-cran-haven, r-cran-microbenchmark, r-cran-stringr, r-cran-tinytest, r-cran-sampling, r-cran-covr Filename: pool/dists/noble/main/r-cran-simpop_2.1.3-1.ca2404.1_amd64.deb Size: 2934332 MD5sum: 7bd2ddaef5600eaf599eac23b1b2c5d7 SHA1: ac79f7c770fd5231410d2f76d8ffe4c253a09d52 SHA256: 70c4f8a33763d8c45755ec9c632677478f2b3e512a00b39e248ea1dfb8ef1b24 SHA512: 3f61492e4370423e1cec5f8c933b5555c29ffa3b871c05bcbae8d00f4eac9ac45bd13a646d2dfb1c43e74bbcf5fd02e06e51041c07f35535b6c092b2adc00ae0 Homepage: https://cran.r-project.org/package=simPop Description: CRAN Package 'simPop' (Simulation of Complex Synthetic Data Information) Tools and methods to simulate populations for surveys based on auxiliary data. The tools include model-based methods, calibration and combinatorial optimization algorithms, see Templ, Kowarik and Meindl (2017) ) and Templ (2017) . The package was developed with support of the International Household Survey Network, DFID Trust Fund TF011722 and funds from the World bank. Package: r-cran-simreg Architecture: amd64 Version: 3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ontologyindex, r-cran-ontologysimilarity, r-cran-ontologyplot Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-simreg_3.4-1.ca2404.1_amd64.deb Size: 171404 MD5sum: c768339cf9cf6f5fd0ebec714b3497d1 SHA1: 0293637d3d8edd4d5915df1ea70b01cf36172ec0 SHA256: 61b31d997e68f5c998d691301c2c03da97a71c0731e860dbeeb5fdf17fd643f7 SHA512: ec1e417bd75457f57c8fb8a70395f4cb921d35d211db38e796c8df3c3d6b43c9f84d858c688a04596a59886dcb0b75ffca238adc80db62c35b8153ef782635b0 Homepage: https://cran.r-project.org/package=SimReg Description: CRAN Package 'SimReg' (Similarity Regression) Similarity regression, evaluating the probability of association between sets of ontological terms and binary response vector. A no-association model is compared with one in which the log odds of a true response is linked to the semantic similarity between terms and a latent characteristic ontological profile - 'Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases', Greene et al 2016 . Package: r-cran-simrestore Architecture: amd64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 906 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-shiny, r-cran-subplex, r-cran-tibble Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-shinybs, r-cran-shinythemes, r-cran-shinywidgets, r-cran-testthat, r-cran-tidyr, r-cran-egg Filename: pool/dists/noble/main/r-cran-simrestore_1.1.5-1.ca2404.1_amd64.deb Size: 490942 MD5sum: 0b930d36b9fe64bf3933e1b4c78a2f8b SHA1: 63e484ce2cdfd9a9efd6910cf5d75f2d093519cc SHA256: 10cccf6ab1066db899cb301ad6c7454298de7df7cbd235f2b46a8f5328817738 SHA512: 27ff633512900522261a611746e252685f77c9b1bb776aa5600d03a13f2d64fa01d0c52acf81d0bc264bace8a606a45b19e39741d4294591cde692b4f037e905 Homepage: https://cran.r-project.org/package=simRestore Description: CRAN Package 'simRestore' (Simulate the Effect of Management Policies on RestorationEfforts) Simulation methods to study the effect of management policies on efforts to restore populations back to their original genetic composition. Allows for single-scenario simulation and for optimization of specific chosen scenarios. Further information can be found in Hernandez, Janzen and Lavretsky (2023) . Package: r-cran-simriv Architecture: amd64 Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2444 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra, r-cran-mco Suggests: r-cran-adehabitatlt, r-cran-movehmm, r-cran-testthat, r-cran-sf Filename: pool/dists/noble/main/r-cran-simriv_1.0.7-1.ca2404.1_amd64.deb Size: 1405782 MD5sum: e4110bfc9eaac5f6e9ac236263fa6e42 SHA1: 03d64a8c110665dcf760897da8281481417dc09c SHA256: d47c019e659a3d55c989979f1c58588924437ad3f5b416012d3a93dc9b373a7b SHA512: 7a7dd344d244ed59c7be8d67f7aaa4d98eaa241a41dee839bbadaf5f3839fc775c3376f5b005081c4f46795f4b2c90655ae883462fe8c88e6fb07ef9c056cfb6 Homepage: https://cran.r-project.org/package=SiMRiv Description: CRAN Package 'SiMRiv' (Simulating Multistate Movements in River/HeterogeneousLandscapes) Provides functions to generate and analyze spatially-explicit individual-based multistate movements in rivers, heterogeneous and homogeneous spaces. This is done by incorporating landscape bias on local behaviour, based on resistance rasters. Although originally conceived and designed to simulate trajectories of species constrained to linear habitats/dendritic ecological networks (e.g. river networks), the simulation algorithm is built to be highly flexible and can be applied to any (aquatic, semi-aquatic or terrestrial) organism, independently on the landscape in which it moves. Thus, the user will be able to use the package to simulate movements either in homogeneous landscapes, heterogeneous landscapes (e.g. semi-aquatic animal moving mainly along rivers but also using the matrix), or even in highly contrasted landscapes (e.g. fish in a river network). The algorithm and its input parameters are the same for all cases, so that results are comparable. Simulated trajectories can then be used as mechanistic null models (Potts & Lewis 2014, ) to test a variety of 'Movement Ecology' hypotheses (Nathan et al. 2008, ), including landscape effects (e.g. resources, infrastructures) on animal movement and species site fidelity, or for predictive purposes (e.g. road mortality risk, dispersal/connectivity). The package should be relevant to explore a broad spectrum of ecological phenomena, such as those at the interface of animal behaviour, management, landscape and movement ecology, disease and invasive species spread, and population dynamics. Package: r-cran-simstatespace Architecture: amd64 Version: 1.2.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 905 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-expm, r-cran-dynr Filename: pool/dists/noble/main/r-cran-simstatespace_1.2.16-1.ca2404.1_amd64.deb Size: 541598 MD5sum: 81b0da94381a7e1d47864a2bdb48f818 SHA1: f11444b8d7527bfffcb12ac2cc0f61ba89d99807 SHA256: ae11e499000b1467e8623889611982ed7eb55442aa5e24094b72201a48164a65 SHA512: ac41fc755038a8ee0586458cddada9d0afe2cc3f3c6ad868b8f81204f47df39eed6d23518c378e69fc26cfd45d5fb7b0fe3a3ea7a80cb1653a322665a3d9d8cf Homepage: https://cran.r-project.org/package=simStateSpace Description: CRAN Package 'simStateSpace' (Simulate Data from State Space Models) Provides a streamlined and user-friendly framework for simulating data in state space models, particularly when the number of subjects/units (n) exceeds one, a scenario commonly encountered in social and behavioral sciences. This package was designed to generate data for the simulations performed in Pesigan, Russell, and Chow (2025) . Package: r-cran-simstudy Architecture: amd64 Version: 0.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2977 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-glue, r-cran-mvnfast, r-cran-rcpp, r-cran-backports, r-cran-fastglm, r-cran-pbv Suggests: r-cran-covr, r-cran-dplyr, r-cran-formatr, r-cran-gee, r-cran-ggplot2, r-cran-gridextra, r-cran-hedgehog, r-cran-knitr, r-cran-magrittr, r-cran-matrix, r-cran-mgcv, r-cran-ordinal, r-cran-pracma, r-cran-rmarkdown, r-cran-scales, r-cran-survival, r-cran-testthat, r-cran-gtsummary, r-cran-broom.helpers, r-cran-survminer, r-cran-katex, r-cran-dirmult, r-cran-rms, r-cran-lmertest Filename: pool/dists/noble/main/r-cran-simstudy_0.9.2-1.ca2404.1_amd64.deb Size: 1531606 MD5sum: 156c5ccdf0c23174760f03ed95702af4 SHA1: 559f689f972675851bc82c66f527b4c912612bf3 SHA256: 055e4fd56a51b88397578be804f95ef267f5f5bc4a73f2b674f5d740321de9fb SHA512: a07114da04eabee1643dafde8b09c2ab92cfdf4684771512d5466c7b6a7ea859a0a1ff4a1ee5f95c2ef1e760c1dd38606de2bb438f778b9c5b4928d9ab0ff739 Homepage: https://cran.r-project.org/package=simstudy Description: CRAN Package 'simstudy' (Simulation of Study Data) Simulates data sets in order to explore modeling techniques or better understand data generating processes. The user specifies a set of relationships between covariates, and generates data based on these specifications. The final data sets can represent data from randomized control trials, repeated measure (longitudinal) designs, and cluster randomized trials. Missingness can be generated using various mechanisms (MCAR, MAR, NMAR). Package: r-cran-simsurvnmarker Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2374 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-r.rsp, r-cran-matrix Filename: pool/dists/noble/main/r-cran-simsurvnmarker_0.1.3-1.ca2404.1_amd64.deb Size: 1483252 MD5sum: 2b73e39ce148cedb1cebee12364b402d SHA1: 37ceec98c237a07517261a1fe1a92203ef7968e6 SHA256: 7ecd16c88c274f4eaebc01ec17601ebbd074cc6ad38abe3be2f57149ef7ca72e SHA512: 6d056930e7baa40a720704c3ef16f0db75ea76f563281055578e0913155fde840ab9430ba8627f61393181ac1d73358029c4ca15435a115359cc8e46c5847a21 Homepage: https://cran.r-project.org/package=SimSurvNMarker Description: CRAN Package 'SimSurvNMarker' (Simulate Survival Time and Markers) Provides functions to simulate from joint survival and marker models. The user can specific all basis functions of time, random or deterministic covariates, random or deterministic left-truncation and right-censoring times, and model parameters. Package: r-cran-simtost Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 981 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-data.table, r-cran-matrixcalc, r-cran-rcpparmadillo Suggests: r-cran-powertost, r-cran-ggplot2, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-simtost_1.0.2-1.ca2404.1_amd64.deb Size: 397588 MD5sum: 0d54f162dc1ea8b628755dba6646dba5 SHA1: 18a052233c76eeb9b21694c9bc9e7dc067165400 SHA256: f6289bfa6a00d28d375160cce84af727c2b7d494e4009982e1f7e38569a21321 SHA512: c4184941c78034f9b5bbcec6c9ddca8e5a39f945443d53ec730c1cfcf099b4a1cfadbcee65d46f21572f4b87bf2ede5c6706f95ae563aabb5cba29440b8bd509 Homepage: https://cran.r-project.org/package=SimTOST Description: CRAN Package 'SimTOST' (Sample Size Estimation for Bio-Equivalence Trials ThroughSimulation) Sample size estimation for bio-equivalence trials is supported through a simulation-based approach that extends the Two One-Sided Tests (TOST) procedure. The methodology provides flexibility in hypothesis testing, accommodates multiple treatment comparisons, and accounts for correlated endpoints. Users can model complex trial scenarios, including parallel and crossover designs, intra-subject variability, and different equivalence margins. Monte Carlo simulations enable accurate estimation of power and type I error rates, ensuring well-calibrated study designs. The statistical framework builds on established methods for equivalence testing and multiple hypothesis testing in bio-equivalence studies, as described in Schuirmann (1987) , Mielke et al. (2018) , Shieh (2022) , and Sozu et al. (2015) . Comprehensive documentation and vignettes guide users through implementation and interpretation of results. Package: r-cran-simtrial Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3070 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-dofuture, r-cran-foreach, r-cran-future, r-cran-mvtnorm, r-cran-survival Suggests: r-cran-matrix, r-cran-covr, r-cran-dplyr, r-cran-ggplot2, r-cran-gsdesign, r-cran-gsdesign2, r-cran-gt, r-cran-knitr, r-cran-rmarkdown, r-cran-survmisc, r-cran-survrm2, r-cran-testthat, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-simtrial_1.0.2-1.ca2404.1_amd64.deb Size: 918194 MD5sum: 9b4d53b49aa46954c631b6ee921f77c7 SHA1: e9ecdf8c75b08064821b33d5c8ba9e94b3f95e50 SHA256: bac18cd56f7db35cacb4273575dd5c57d3a439be945cfa88082816d5771adeea SHA512: aa08455f6fec9e1e7f4794e98ddda0ae4e1ebf6441014498cc94e6136df8953ce2a4321069f3b4c21fb85e29ecf6814f44c8de1d870861629b4b9f8d70cc9299 Homepage: https://cran.r-project.org/package=simtrial Description: CRAN Package 'simtrial' (Clinical Trial Simulation) Provides some basic routines for simulating a clinical trial. The primary intent is to provide some tools to generate trial simulations for trials with time to event outcomes. Piecewise exponential failure rates and piecewise constant enrollment rates are the underlying mechanism used to simulate a broad range of scenarios such as those presented in Lin et al. (2020) . However, the basic generation of data is done using pipes to allow maximum flexibility for users to meet different needs. Package: r-cran-simts Architecture: amd64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3748 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-scales, r-cran-broom, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-tidyr, r-cran-robcor, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-simts_0.2.4-1.ca2404.1_amd64.deb Size: 2304606 MD5sum: e559d09380a52b4e17b73fbd8152745c SHA1: 95c7d6c9b68e76441a84f67ff5234c9ea67d5186 SHA256: aee8efaf6b17de0dec27bd95f354bd665a22b805dd732d20324e13870dec5f27 SHA512: f5b3333dd9569eb5702881424fa031a95e02fd9173b290fb0a763b79437bd3559d384ebe35ea5dd8d93a7199e5ddbf562657ee1f21c85b2367dfefae3214728f Homepage: https://cran.r-project.org/package=simts Description: CRAN Package 'simts' (Time Series Analysis Tools) A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) . More details can also be found in the paper linked to via the URL below. Package: r-cran-singr Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2840 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-clue, r-cran-gam, r-cran-ictest, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-covr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-singr_0.1.3-1.ca2404.1_amd64.deb Size: 2681502 MD5sum: e15b727a776bc97ae5890a9a7264580e SHA1: 5d4f2b4cce5789f1c98ecce5b76e6252584768ae SHA256: 22bf0bc3bee2258ef9da888159c120edebfc785beb36b4c9b82588664fe5f61b SHA512: 0d6f819d01ad8f13f20f52c3b401bd6589286f80f57723c35ac08160961aa1edd299fcc1aab1d7ea4d8a90d88a3abcdec81ec62db594ce7d88953196eede05c0 Homepage: https://cran.r-project.org/package=singR Description: CRAN Package 'singR' (Simultaneous Non-Gaussian Component Analysis) Implementation of SING algorithm to extract joint and individual non-Gaussian components from two datasets. SING uses an objective function that maximizes the skewness and kurtosis of latent components with a penalty to enhance the similarity between subject scores. Unlike other existing methods, SING does not use PCA for dimension reduction, but rather uses non-Gaussianity, which can improve feature extraction. Benjamin B.Risk, Irina Gaynanova (2021) . Package: r-cran-siphynetwork Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 840 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-rcpp, r-cran-rstackdeque, r-cran-lifecycle Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-siphynetwork_1.1.0-1.ca2404.1_amd64.deb Size: 542464 MD5sum: d3a7f0f9f8b3181b9c28bb6a6933515a SHA1: f7327fcf62e9894be160f98ecf0cc02e0b25cdbd SHA256: 3154d32cb3ca0768eb4c286ede3bde8e6d731788229a73e499e1fbbc0e023778 SHA512: 509e2a77dd5a055ac056b9e5190d86f6a694d2cf7c9c09f0dcb855a748f6ab1c5f5706baeb645d1f09e1141707ba1c817aca12fcccf2bded6915828dcc20b934 Homepage: https://cran.r-project.org/package=SiPhyNetwork Description: CRAN Package 'SiPhyNetwork' (A Phylogenetic Simulator for Reticulate Evolution) A simulator for reticulate evolution under a birth-death-hybridization process. Here the birth-death process is extended to consider reticulate Evolution by allowing hybridization events to occur. The general purpose simulator allows the modeling of three different reticulate patterns: lineage generative hybridization, lineage neutral hybridization, and lineage degenerative hybridization. Users can also specify hybridization events to be dependent on a trait value or genetic distance. We also extend some phylogenetic tree utility and plotting functions for networks. We allow two different stopping conditions: simulated to a fixed time or number of taxa. When simulating to a fixed number of taxa, the user can simulate under the Generalized Sampling Approach that properly simulates phylogenies when assuming a uniform prior on the root age. Package: r-cran-sipmg Architecture: amd64 Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 641 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-bioc-deseq2, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-glue, r-cran-lazyeval, r-cran-magrittr, r-cran-mass, r-bioc-phyloseq, r-cran-plyr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-biocmanager, r-bioc-ebimage, r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-sipmg_3.0-1.ca2404.1_amd64.deb Size: 391266 MD5sum: 01744f881794283c12adf93d987e4fcc SHA1: 47c5cce2a892f33080df3f477acf6162b511feab SHA256: 1091f3c1c9055b3b0a17f36e7a389975357b12d53121fc759578f338db8907c2 SHA512: 0f64c3f094108dcd6a7c3265858e29e7a6da79e8394485775cf49ff1da8cec308868a59f7514156ea2f933b83f7b1b203b2e1d05d6a430aede3cffa5338a8e11 Homepage: https://cran.r-project.org/package=SIPmg Description: CRAN Package 'SIPmg' (Statistical Analysis to Identify Isotope IncorporatingMetagenomic Features) Statistical analysis as part of a quantitative stable isotope probing (SIP) metagenomics study to identify isotope incorporating metagenomic features. Helpful reading and a vignette in bookdown format is provided on the package site . Package: r-cran-sirmcmc Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-sirmcmc_1.1.1-1.ca2404.1_amd64.deb Size: 100058 MD5sum: 130af409361b77125bd40129caf0b175 SHA1: baff728cab7fd2d9311dd97c23c22a2f7c76bb48 SHA256: aa932189dd5870258fd09fa832178c3fcb3f3d0b646d94f7ff133f1bd212e89b SHA512: f26a16f58b89a355c7cde0fc2fc114b5de42917d1fee1e7e85e793aa62336216f4ced749d4efbe41073deae627a5ec0144669233dc13838ace3ab5e06a0fdc13 Homepage: https://cran.r-project.org/package=SIRmcmc Description: CRAN Package 'SIRmcmc' (Compartmental Susceptible-Infectious-Recovered (SIR) Model ofCommunity and Household Infection) We build an Susceptible-Infectious-Recovered (SIR) model where the rate of infection is the sum of the household rate and the community rate. We estimate the posterior distribution of the parameters using the Metropolis algorithm. Further details may be found in: F Scott Dahlgren, Ivo M Foppa, Melissa S Stockwell, Celibell Y Vargas, Philip LaRussa, Carrie Reed (2021) "Household transmission of influenza A and B within a prospective cohort during the 2013-2014 and 2014-2015 seasons" . Package: r-cran-sirt Architecture: amd64 Version: 4.2-133-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9372 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cdm, r-cran-pbapply, r-cran-rcpp, r-cran-tam, r-cran-pbv, r-cran-rcpparmadillo Suggests: r-cran-coda, r-cran-igraph, r-cran-lavaan, r-cran-mass, r-cran-matrix, r-cran-miceadds, r-cran-minqa, r-cran-mirt, r-cran-mvtnorm, r-cran-nloptr, r-cran-optimx, r-cran-pbivnorm, r-cran-psych, r-cran-sfsmisc, r-cran-sm, r-cran-survey Filename: pool/dists/noble/main/r-cran-sirt_4.2-133-1.ca2404.1_amd64.deb Size: 8527888 MD5sum: e91a7f183732743f6caa7d37047b2e38 SHA1: 1afe96f9a7127580475641ddae51ba83d1b4bf71 SHA256: 48cc1c07180318e767e4ffbcf73aef0095cc1240873922a1fabc61b9ecf2a53a SHA512: 4ff352c27969c97dd7475a2936cbb8b3b880ea282cfc3827f2086965e89d6cff641ee76b87cbfb0620123303e9fe8ae9988b7d229306ca1e0e97ef75951e4d27 Homepage: https://cran.r-project.org/package=sirt Description: CRAN Package 'sirt' (Supplementary Item Response Theory Models) Supplementary functions for item response models aiming to complement existing R packages. The functionality includes among others multidimensional compensatory and noncompensatory IRT models (Reckase, 2009, ), MCMC for hierarchical IRT models and testlet models (Fox, 2010, ), NOHARM (McDonald, 1982, ), Rasch copula model (Braeken, 2011, ; Schroeders, Robitzsch & Schipolowski, 2014, ), faceted and hierarchical rater models (DeCarlo, Kim & Johnson, 2011, ), ordinal IRT model (ISOP; Scheiblechner, 1995, ), DETECT statistic (Stout, Habing, Douglas & Kim, 1996, ), local structural equation modeling (LSEM; Hildebrandt, Luedtke, Robitzsch, Sommer & Wilhelm, 2016, ). Package: r-cran-sirus Architecture: amd64 Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 832 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rocr, r-cran-ggplot2, r-cran-glmnet, r-cran-rcppeigen Suggests: r-cran-survival, r-cran-testthat, r-cran-ranger Filename: pool/dists/noble/main/r-cran-sirus_0.3.3-1.ca2404.1_amd64.deb Size: 408648 MD5sum: 0a8b433db0c433dde8fa295d25375f5d SHA1: a8c022093c11e7b4e98cb859725c93486c75a620 SHA256: db8983adf3617cebcb1b34b37b7aec8590c7e31e88c03bcb5821f5793d161501 SHA512: 7c885f52282ab462b696292361fa9fff6147f0fb25ef1d65ec83f1776a302396f4b60f37681bd5d99783053ecfd368385833db533bff9a619f4a5b9757fec530 Homepage: https://cran.r-project.org/package=sirus Description: CRAN Package 'sirus' (Stable and Interpretable RUle Set) A regression and classification algorithm based on random forests, which takes the form of a short list of rules. SIRUS combines the simplicity of decision trees with a predictivity close to random forests. The core aggregation principle of random forests is kept, but instead of aggregating predictions, SIRUS aggregates the forest structure: the most frequent nodes of the forest are selected to form a stable rule ensemble model. The algorithm is fully described in the following articles: Benard C., Biau G., da Veiga S., Scornet E. (2021), Electron. J. Statist., 15:427-505 for classification, and Benard C., Biau G., da Veiga S., Scornet E. (2021), AISTATS, PMLR 130:937-945 , for regression. This R package is a fork from the project ranger (). Package: r-cran-sis Architecture: amd64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4020 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-ncvreg, r-cran-survival, r-cran-nnet, r-cran-doparallel, r-cran-gcdnet, r-cran-msaenet, r-cran-foreach, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-formatr, r-cran-proc Filename: pool/dists/noble/main/r-cran-sis_1.5-1.ca2404.1_amd64.deb Size: 3846466 MD5sum: 6610ae2a81034845c2b84dd6e40ee4f4 SHA1: 87f87e726637f2c662261d53a40edf88c940b390 SHA256: d84b64f24b5bc391b4e02db1fa5c34b2df5d0bf9341035b17383116ce70dfff2 SHA512: e82062478f37b820c9f7130e2fa7fd7b6d8491aec83972e0d52459d5385513684f63fe735154cce91363db2d8dda9fdff95cfbb2a2890990d9d9770407b734bf Homepage: https://cran.r-project.org/package=SIS Description: CRAN Package 'SIS' (Sure Independence Screening) Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Through this publicly available package, we provide a unified environment to carry out variable selection using iterative sure independence screening (SIS) (Fan and Lv (2008)) and all of its variants in generalized linear models (Fan and Song (2009)) and the Cox proportional hazards model (Fan, Feng and Wu (2010)). Package: r-cran-sisireg Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-reticulate Filename: pool/dists/noble/main/r-cran-sisireg_1.2.1-1.ca2404.1_amd64.deb Size: 195210 MD5sum: d36752b5b19fc34500d9d1cec757fc6e SHA1: 4da5d1936a7ae38220502ba559f53041d5083446 SHA256: 3c8d7ce4e8733d5eca1b5fb3564914d6624c2452d02dce88f3c63e4708580deb SHA512: 4ec90673a84691fab944b6e123faba6f8dbd75516e1aa43c335abf0686dc22558fe37608531a7fb5bda9240b3440bdefb23bc9d90fe02d29cd23919dc03b9526 Homepage: https://cran.r-project.org/package=sisireg Description: CRAN Package 'sisireg' (Sign-Simplicity-Regression-Solver) Implementation of the SSR-Algorithm. The Sign-Simplicity-Regression model is a nonparametric statistical model which is based on residual signs and simplicity assumptions on the regression function. Goal is to calculate the most parsimonious regression function satisfying the statistical adequacy requirements. Theory and functions are specified in Metzner (2020, ISBN: 979-8-68239-420-3, "Trendbasierte Prognostik") and Metzner (2021, ISBN: 979-8-59347-027-0, "Adäquates Maschinelles Lernen"). Package: r-cran-sit Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-psychtools Filename: pool/dists/noble/main/r-cran-sit_0.1.1-1.ca2404.1_amd64.deb Size: 51032 MD5sum: f4e7af6e5648cce1755edfead170fc1b SHA1: a10a4766ad527b806eb265e070dca5f0649bffee SHA256: b5bc58cca83dbe6b0682e8ae088cf6d5fac905a254c965032c6bbc3e1913ab46 SHA512: 665c3313be96a0bfab27eef9c26aba4be979965a689f648dc67228ae1e2429c07e02f3e22594ff6679d86e92ca64f844b4fd6949b0a9b167400d5ff50ce23d4f Homepage: https://cran.r-project.org/package=SIT Description: CRAN Package 'SIT' (Association Measurement Through Sliced Independence Test (SIT)) Computes the sit coefficient between two vectors x and y, possibly all paired coefficients for a matrix. The reference for the methods implemented here is Zhang, Yilin, Canyi Chen, and Liping Zhu. 2022. "Sliced Independence Test." Statistica Sinica. . This package incorporates the Galton peas example. Package: r-cran-sith Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 962 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-scatterplot3d Suggests: r-cran-rgl, r-cran-igraph, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sith_1.1.0-1.ca2404.1_amd64.deb Size: 590046 MD5sum: 72e20b24fc25513a07f5ed4c1b0fcd81 SHA1: 5811cc746723073701de07c1a37993ed7f80c90f SHA256: 446d691a1c39ea095abeeec2cc484e9038f30cc1b733aa2c5e7eabe01c513ba4 SHA512: 8335d9b4bf8bf4c15927c9201b16342d23cdd1043c3a6e6127908f5b4ec39897da520e03de1a95e68b64930058f94fe5a9c554c8bec7c5f8ca074e2ce2d409ac Homepage: https://cran.r-project.org/package=SITH Description: CRAN Package 'SITH' (A Spatial Model of Intra-Tumor Heterogeneity) Implements a three-dimensional stochastic model of cancer growth and mutation similar to the one described in Waclaw et al. (2015) . Allows for interactive 3D visualizations of the simulated tumor. Provides a comprehensive summary of the spatial distribution of mutants within the tumor. Contains functions which create synthetic sequencing datasets from the generated tumor. Package: r-cran-sitmo Architecture: amd64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 924 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-sitmo_2.0.2-1.ca2404.1_amd64.deb Size: 132526 MD5sum: a3f6b0fb1e04503276bcd04e83e188e5 SHA1: 618ab8fd27e6f46ec28977fb9172544896aa4768 SHA256: d9e6a20af9a4ff5a314a778859bd688c0b371601ae8d94e202faffbc3c41be7e SHA512: 4cf64b5c75f694d11ea2e1d134b7595cb0d2a57c3175d39559252470ed2bd263d6bdb9c14e2383a8a2ba8a83e967183e61cd896abd746761c9437ca8489c72dd Homepage: https://cran.r-project.org/package=sitmo Description: CRAN Package 'sitmo' (Parallel Pseudo Random Number Generator (PPRNG) 'sitmo' HeaderFiles) Provided within are two high quality and fast PPRNGs that may be used in an 'OpenMP' parallel environment. In addition, there is a generator for one dimensional low-discrepancy sequence. The objective of this library to consolidate the distribution of the 'sitmo' (C++98 & C++11), 'threefry' and 'vandercorput' (C++11-only) engines on CRAN by enabling others to link to the header files inside of 'sitmo' instead of including a copy of each engine within their individual package. Lastly, the package contains example implementations using the 'sitmo' package and three accompanying vignette that provide additional information. Package: r-cran-sits Architecture: amd64 Version: 1.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4134 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-yaml, r-cran-dplyr, r-cran-httr2, r-cran-leafgl, r-cran-leaflet, r-cran-lubridate, r-cran-luz, r-cran-purrr, r-cran-randomforest, r-cran-rcpp, r-cran-rstac, r-cran-sf, r-cran-slider, r-cran-terra, r-cran-tibble, r-cran-tidyr, r-cran-tmap, r-cran-torch, r-cran-units, r-cran-rcpparmadillo Suggests: r-cran-aws.s3, r-cran-caret, r-cran-cli, r-cran-cols4all, r-cran-covr, r-cran-dendextend, r-cran-dtwclust, r-cran-digest, r-cran-e1071, r-cran-exactextractr, r-cran-fnn, r-cran-gdalcubes, r-cran-geojsonsf, r-cran-ggplot2, r-cran-jsonlite, r-cran-kohonen, r-cran-lightgbm, r-cran-mgcv, r-cran-nnet, r-cran-openxlsx, r-cran-parallelly, r-cran-proxy, r-cran-randomforestexplainer, r-cran-rcolorbrewer, r-cran-scales, r-cran-snic, r-cran-spdep, r-cran-stars, r-cran-stringr, r-cran-supercells, r-cran-testthat, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-sits_1.5.4-1.ca2404.1_amd64.deb Size: 2982642 MD5sum: 5c8d3fda73b39393f8932742e2f62876 SHA1: fb25e28f07dfa3b51cd066bad39a3cae693e92c2 SHA256: d9658726b71c6ca25630e58f5dd0b8fb67261bad0ba3876512b028ead4bf866e SHA512: fd5e47fa0c9074e51229807c3e100f46198b609f5227a4823147830c66cdd0194d6e1ab0ac0ae6e1fb73c6444a812d1a0cc164635426399ee0d0c3c1be316423 Homepage: https://cran.r-project.org/package=sits Description: CRAN Package 'sits' (Satellite Image Time Series Analysis for Earth Observation DataCubes) An end-to-end toolkit for land use and land cover classification using big Earth observation data. Builds satellite image data cubes from cloud collections. Supports visualization methods for images and time series and smoothing filters for dealing with noisy time series. Enables merging of multi-source imagery (SAR, optical, DEM). Includes functions for quality assessment of training samples using self-organized maps and to reduce training samples imbalance. Provides machine learning algorithms including support vector machines, random forests, extreme gradient boosting, multi-layer perceptrons, temporal convolution neural networks, and temporal attention encoders. Performs efficient classification of big Earth observation data cubes and includes functions for post-classification smoothing based on Bayesian inference. Enables best practices for estimating area and assessing accuracy of land change. Includes object-based spatio-temporal segmentation for space-time OBIA. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core. Package: r-cran-sk4fga Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 970 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-sk4fga_0.1.1-1.ca2404.1_amd64.deb Size: 904264 MD5sum: e12f205d561c95812cf9d344824449df SHA1: f8083d67241d0cba53d57a99410bacb67298ff50 SHA256: d3b5c8d5151de24c3bb0bcb589874b22818144438f36d45d9aa35ed3f1fb922a SHA512: 1f485c1c1bb13ed299b8210de5df14c46e52b92bc1bc64a4a7713a2db01fec214202333e62e9c68c507af6ce4352e67ab8f3ddfbeb8c7e97ee2e2b9e1d6ce9a7 Homepage: https://cran.r-project.org/package=SK4FGA Description: CRAN Package 'SK4FGA' (Scott-Knott for Forensic Glass Analysis) In forensics, it is common and effective practice to analyse glass fragments from the scene and suspects to gain evidence of placing a suspect at the crime scene. This kind of analysis involves comparing the physical and chemical attributes of glass fragments that exist on both the person and at the crime scene, and assessing the significance in a likeness that they share. The package implements the Scott-Knott Modification 2 algorithm (SKM2) (Christopher M. Triggs and James M. Curran and John S. Buckleton and Kevan A.J. Walsh (1997) "The grouping problem in forensic glass analysis: a divisive approach", Forensic Science International, 85(1), 1--14) for small sample glass fragment analysis using the refractive index (ri) of a set of glass samples. It also includes an experimental multivariate analog to the Scott-Knott algorithm for similar analysis on glass samples with multiple chemical concentration variables and multiple samples of the same item; testing against the Hotellings T^2 distribution (J.M. Curran and C.M. Triggs and J.R. Almirall and J.S. Buckleton and K.A.J. Walsh (1997) "The interpretation of elemental composition measurements from forensic glass evidence", Science & Justice, 37(4), 241--244). Package: r-cran-skat Architecture: amd64 Version: 2.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1475 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-spatest, r-cran-rspectra Filename: pool/dists/noble/main/r-cran-skat_2.2.5-1.ca2404.1_amd64.deb Size: 1318418 MD5sum: 19bd7cc015fa0e515f5680c9229f3713 SHA1: a870854ae5ce55601d4f34e92bd2358cbff40329 SHA256: 6be4c925e55eac82bde266c4ae4bfb2adfbc71c34a7091a4430ce9022c4ad56f SHA512: 10b7c39443fc586b8127057d45a9ad090e17d82a1bae054c9c75537568851562d7ae574f677c21432adb0890ef6431cd0c067ea6d72372c0e6b143a38d70437d 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: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2365 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-phangorn Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lmtest, r-cran-ivreg, r-cran-sandwich Filename: pool/dists/noble/main/r-cran-sketching_0.1.2-1.ca2404.1_amd64.deb Size: 1801256 MD5sum: 842b86dae42ed12cce8655f69022400b SHA1: e1ce6c5971a4d723478fa3cde43b6d3ab9686db4 SHA256: 12005d3c72cb73819b4382978772f2517e09090e997fd790aba4a90a80942095 SHA512: d33a0a31eb01b522917c739ccd4d689aa0b36af941cbaa3dc7b3550dce1c7c92552679f054e5db1ba4b0181979c7bb3ab33a12f8b1998638b82f78816a3a0c48 Homepage: https://cran.r-project.org/package=sketching Description: CRAN Package 'sketching' (Sketching of Data via Random Subspace Embeddings) Construct sketches of data via random subspace embeddings. For more details, see the following papers. Lee, S. and Ng, S. (2022). "Least Squares Estimation Using Sketched Data with Heteroskedastic Errors," Proceedings of the 39th International Conference on Machine Learning (ICML22), 162:12498-12520. Lee, S. and Ng, S. (2020). "An Econometric Perspective on Algorithmic Subsampling," Annual Review of Economics, 12(1): 45–80. Package: r-cran-skfcpd Architecture: amd64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-ggplot2, r-cran-ggpubr, r-cran-reshape2, r-cran-fastgasp, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-skfcpd_0.2.4-1.ca2404.1_amd64.deb Size: 241910 MD5sum: 456b43dc45bc85cc7857ec3afa3ebf06 SHA1: 02986ace6524ff7f8882fe524073a58e73bbd2f4 SHA256: 6dc05223768dc8c3c7eadf137e5a26a679c16fe7356af2af50888a23b3be843f SHA512: 003681957d33c1042f0fe364388804faf983504a7a483241565214ce91726aea9650ae0a4b481d90cb358b324e5de8b48ed770da71618586e3d070f5a2f5af10 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: amd64 Version: 3.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 575 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-extradistr, r-cran-hash, r-cran-laplacesdemon, r-cran-matrix, r-cran-mcmcse, r-cran-numderiv, r-cran-spam, r-cran-dfoptim Suggests: r-cran-lattice, r-cran-pbapply Filename: pool/dists/noble/main/r-cran-sklarsomega_3.0-3-1.ca2404.1_amd64.deb Size: 532010 MD5sum: 7633d7197fd6532afa22dbacd9f69309 SHA1: 62a0597a7407dc1077bab7208d55314b020bfbe6 SHA256: 91b24ad0218b608fbd54bf7ae5d690388efb1c5620661dac06e48902e8943280 SHA512: 05346be81fb44c202d7404fea2737b8894ffb0a66163e8005e458f443157aa271f52ef88fd94297d7740c82755f5c25df97208973afeb2e5bb48454418c697f1 Homepage: https://cran.r-project.org/package=sklarsomega Description: CRAN Package 'sklarsomega' (Measuring Agreement Using Sklar's Omega Coefficient) Provides tools for applying Sklar's Omega (Hughes, 2022) methodology to nominal scores, ordinal scores, percentages, counts, amounts (i.e., non-negative real numbers), and balances (i.e., any real number). The framework can accommodate any number of units, any number of coders, and missingness; and can be used to measure agreement with a gold standard, intra-coder agreement, and/or inter-coder agreement. Frequentist inference is supported for all levels of measurement. Bayesian inference is supported for continuous scores only. Package: r-cran-skm Architecture: amd64 Version: 0.1.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2478 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-data.table, r-cran-plyr, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-skm_0.1.5.4-1.ca2404.1_amd64.deb Size: 1388038 MD5sum: ae1e56df2db33c75b6ffefbb3c509f25 SHA1: 4741b24dc4848ffe305471536eeea8796b23750a SHA256: 7ebfc6f194124bf98d18d51f51a2cf9749e351e9b8443447806a36b648095407 SHA512: c5954886129bfb7019a66e8067ad0e3eaca22d9c2377c03a0884d7e6884ad71a8d704b9c740e0b8a7ba3bba1e21d1c19c24b40a8e42969ac900894166a08ed54 Homepage: https://cran.r-project.org/package=skm Description: CRAN Package 'skm' (Selective k-Means) Algorithms for solving selective k-means problem, which is defined as finding k rows in an m x n matrix such that the sum of each column minimal is minimized. In the scenario when m == n and each cell value in matrix is a valid distance metric, this is equivalent to a k-means problem. The selective k-means extends the k-means problem in the sense that it is possible to have m != n, often the case m < n which implies the search is limited within a small subset of rows. Also, the selective k-means extends the k-means problem in the sense that the instance in row set can be instance not seen in the column set, e.g., select 2 from 3 internet service provider (row) for 5 houses (column) such that minimize the overall cost (cell value) - overall cost is the sum of the column minimal of the selected 2 service provider. 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The model is a translation of the Fortran code by Janiczek and DeYoung (1987) . Package: r-cran-slam Architecture: amd64 Version: 0.1-55-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-slam_0.1-55-1.ca2404.1_amd64.deb Size: 186004 MD5sum: bf2429f8c231c23673135291fc377203 SHA1: 46aff6421fdd7887331aa46996a61ed4ffb5aeb8 SHA256: 473ae3981c84b3cc93acf66773ee97cb65cf7c804693419bdbe8b9fa2f1c9582 SHA512: dccd77457921682c100995f0b362f886da1dba55d0d1210847c48185aafd0c50e0d8e0de188d18222f0b84ed119958710b9e5e7282e030e722e7fd616148c37e Homepage: https://cran.r-project.org/package=slam Description: CRAN Package 'slam' (Sparse Lightweight Arrays and Matrices) Data structures and algorithms for sparse arrays and matrices, based on index arrays and simple triplet representations, respectively. 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Package: r-cran-sobol4r Architecture: amd64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2695 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp, r-cran-rlang, r-cran-sensitivity Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-simmer, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sobol4r_0.4.0-1.ca2404.1_amd64.deb Size: 1400650 MD5sum: 13aeefdcf3c38ec78ed3da74acf26764 SHA1: 48911bd78c6226d4d275015c3d987d17f0002c2d SHA256: 8513805107876954712aa2fedeaed91a8fff257feb67bd0111a3ee5d323c7b6a SHA512: 723bb95331f42d6dfb57a84539a36b92bb8c81b4e67dc90c97f11cefcf876d00638a0b9a1c0b876eabdd51b661dc6871ce2ecb59d065c5d2e0eb92e13e0fcfe2 Homepage: https://cran.r-project.org/package=Sobol4R Description: CRAN Package 'Sobol4R' (Sobol Indices for Models with Fixed and Stochastic Parameters) Tools to design experiments, compute Sobol sensitivity indices, and summarise stochastic responses inspired by the strategy described by Zhu and Sudret (2021) . Includes helpers to optimise toy models implemented in C++, visualise indices with uncertainty quantification, and derive reliability-oriented sensitivity measures based on failure probabilities. It is further detailed in Logosha, Maumy and Bertrand (2022) and (2023) or in Bertrand, Logosha and Maumy (2024) , and . Package: r-cran-sobol Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 884 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-checkmate Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-sobol_1.0.0-1.ca2404.1_amd64.deb Size: 289714 MD5sum: 11f947ad30d8eece9079841b881bd0c7 SHA1: dc84dfc5af118743128fd317bee90c35cbb6f4ae SHA256: 8155f4ea93f6a82d4323a85ed5a1a5de7e63ecc6864e4583fc94303bec0aecfd SHA512: c6060f1d597187b3d94c821550c5e09c6e66be1d5bf87309bbe2b1e67082afc59e6c15685b07914748682f92c9bf0b8bc2b0e232170db50e3f79256a3010db08 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-sobolsequence Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1983 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sobolsequence_1.0.1-1.ca2404.1_amd64.deb Size: 673808 MD5sum: 2e52780070aefb15f9de452a74cd20c4 SHA1: 4245d333dcb4808180a2b3aa1479d521652c476b SHA256: 2cb0b01b533ed0363f1d9d60bc59e457c600d2525efb08b8cd2cdeb55669d3d5 SHA512: b04ac7bd2bda856d54d03a4e47e2ea27c7907ec2c5cfdcbc95abe6a86231e59e2919371a3e85dbe5f6d402f7e1739f08154915846486a0de49cb18047986b082 Homepage: https://cran.r-project.org/package=SobolSequence Description: CRAN Package 'SobolSequence' (Sobol Sequences with Better Two-Dimensional Projections) R implementation of S. Joe and F. Y. Kuo (2008) . The implementation is based on the data file new-joe-kuo-6.21201 . Package: r-cran-soccer Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-soccer_0.1.1-1.ca2404.1_amd64.deb Size: 57372 MD5sum: af7e7bfb6efbf37915a476b60116e8ab SHA1: 42b2f2a07f8e5ce252bc751e74db584aff5a0538 SHA256: d20be74ec14f5bd784e11545ff0dcdfb487f8d5b6c35be8ebfb2ad3794a8e135 SHA512: c0c76b2eeaca2e8eab948551af794ca628639173e52e756187a06f36356541ec67edaf73419330c533f88920041e3840e72eabb3ae6f5eb1eadcd04ea62b8ee2 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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For usability, the package maintains the same style as the 'BayesTree' package. Package: r-cran-softimpute Architecture: amd64 Version: 1.4-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1098 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-softimpute_1.4-3-1.ca2404.1_amd64.deb Size: 483468 MD5sum: 43b165866713697d57746442c1a51ca9 SHA1: 54f9ae9012ecc6254143214298b9a4de4d466516 SHA256: 32658aa326af33c820d1a7bc36ab52c72513e24c74d2d02a937503218fe9b9b6 SHA512: a2129a78823a8cbbe63a41a012cb25a7bb77258f6d9fc7be40012bbe535b975b3bc45eb3734e52076bc7b0b03378c548a33ef02ac2a681dc2a2bd63cc350ce67 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). 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Package: r-cran-sommd Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1130 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bio3d, r-cran-kohonen, r-cran-abind, r-cran-cluster, r-cran-igraph Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sommd_0.1.2-1.ca2404.1_amd64.deb Size: 848746 MD5sum: 80128e6282c4283f95599d2db85c6af7 SHA1: 45d79540e54d0a5c916dab4815f92d302bec3ef6 SHA256: 871cb511721b6797d01b0e9ff178a05bb3f0997a2efd7d9005cbea00441df31a SHA512: 785cbc91eeeedb7e5ecdd40ab379c4383acc3e68bfa44d82a8a538352ebfc9c8705f3a2359da2740d53bc72e0e653165332ad15a6e334036f4810e29c5277eda Homepage: https://cran.r-project.org/package=SOMMD Description: CRAN Package 'SOMMD' (Self Organising Maps for the Analysis of Molecular Dynamics Data) Processes data from Molecular Dynamics simulations using Self Organising Maps. Features include the ability to read different input formats. 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REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the Henderson-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available. 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Package: r-cran-soynam Architecture: amd64 Version: 1.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5065 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nam, r-cran-lme4, r-cran-reshape2, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-soynam_1.6.2-1.ca2404.1_amd64.deb Size: 5042412 MD5sum: 9757bd3cb68a43a3b5b1551e610ccbac SHA1: 2eba017fc84c6739152e54f08193d6c710d89251 SHA256: 7be7053637d3718ae456aa011ae5352e9d1f81ce232235def48405051c491600 SHA512: 0d57a306078e74a7fffa0d53d563c8605e79698db85fbd9a63a43a87334a95fe832ce5a5d1219ce1d0b9c792437f5f15bdcb743944eb51d44a191e97930d44c4 Homepage: https://cran.r-project.org/package=SoyNAM Description: CRAN Package 'SoyNAM' (Soybean Nested Association Mapping Dataset) Genomic and multi-environmental soybean data. Soybean Nested Association Mapping (SoyNAM) project dataset funded by the United Soybean Board (USB). BLUP function formats data for genome-wide prediction and association analysis. Package: r-cran-sp Architecture: amd64 Version: 1:2.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9264 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Suggests: r-cran-rcolorbrewer, r-cran-gstat, r-cran-deldir, r-cran-knitr, r-cran-maps, r-cran-mapview, r-cran-rmarkdown, r-cran-sf, r-cran-terra, r-cran-raster Filename: pool/dists/noble/main/r-cran-sp_2.2-1-1.ca2404.1_amd64.deb Size: 4562546 MD5sum: 682f39b7bc54aa4c5da18a696a293218 SHA1: bd92819bcf0587072db6d21b5a18280c2b7dfbc1 SHA256: 724338aa7715bd4eba3042d8469c39c7d6f24b3cacc2b87d29df2efa1c3adcf8 SHA512: 7c149da1037ef3862ce9c65603c122e474428b30d2e1c609fa8a42f29516619d98079015d56264394875bc59f8febf2b8f544e3c933a74f9278ffb22cbc67e5d Homepage: https://cran.r-project.org/package=sp Description: CRAN Package 'sp' (Classes and Methods for Spatial Data) Classes and methods for spatial data; the classes document where the spatial location information resides, for 2D or 3D data. Utility functions are provided, e.g. for plotting data as maps, spatial selection, as well as methods for retrieving coordinates, for subsetting, print, summary, etc. From this version, 'rgdal', 'maptools', and 'rgeos' are no longer used at all, see for details. Package: r-cran-spabundance Architecture: amd64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2628 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-abind, r-cran-rann, r-cran-lme4, r-cran-foreach, r-cran-doparallel Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-spabundance_0.2.1-1.ca2404.1_amd64.deb Size: 2213922 MD5sum: 672ebb7a62614fc57e7525a498c035fa SHA1: e9771cb1f1cdd8f9f63e64eb4e91c02a0d5f5df8 SHA256: 71830e97f2a869413e2e514b9319883afb5db5587a6dae1bb6ef651315ed330c SHA512: 3616c1abbcca4d95d3c76633806de0156958b04cfb5f1624cf772e3d2edf18f67ddbb8b7b226193117e28ef5b792c5a0ecf1e141c989504da6d39dfd8d839bdb Homepage: https://cran.r-project.org/package=spAbundance Description: CRAN Package 'spAbundance' (Univariate and Multivariate Spatial Modeling of SpeciesAbundance) Fits single-species (univariate) and multi-species (multivariate) non-spatial and spatial abundance models in a Bayesian framework using Markov Chain Monte Carlo (MCMC). Spatial models are fit using Nearest Neighbor Gaussian Processes (NNGPs). Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) and Finley, Datta, and Banerjee (2022) . Fits single-species and multi-species spatial and non-spatial versions of generalized linear mixed models (Gaussian, Poisson, Negative Binomial), N-mixture models (Royle 2004 ) and hierarchical distance sampling models (Royle, Dawson, Bates (2004) ). Multi-species spatial models are fit using a spatial factor modeling approach with NNGPs for computational efficiency. Package: r-cran-spacci Architecture: amd64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6559 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-seurat, r-cran-nnls, r-cran-ggrepel, r-cran-pheatmap, r-cran-circlize, r-cran-matrix, r-cran-dplyr, r-cran-patchwork, r-cran-reshape2, r-cran-ggplot2, r-cran-fnn, r-cran-rlang, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-spacci_1.0.5-1.ca2404.1_amd64.deb Size: 5121392 MD5sum: f498c44c339bc2bf2db3e0b756e7ce7a SHA1: 9d1751283bacaf02ab765944fc573b3128dea506 SHA256: c364ec43d54e9c68dfc6f90bf263eb06824c1a41a2bcff93531a471921010ff0 SHA512: 2b9eb99877072989d79337287d0b41b79474afca891d71cf623ba49e8f6120ea09fcd142c299ffaea2713c13b4747e4bfd074bfe37e6af93cb393feb5e3dc0d5 Homepage: https://cran.r-project.org/package=SpaCCI Description: CRAN Package 'SpaCCI' (Spatially Aware Cell-Cell Interaction Analysis) Provides tools for analyzing spatial cell-cell interactions based on ligand-receptor pairs, including functions for local, regional, and global analysis using spatial transcriptomics data. Integrates with databases like 'CellChat' , 'CellPhoneDB' , 'Cellinker' , 'ICELLNET' , and 'ConnectomeDB' to identify ligand-receptor pairs, visualize interactions through heatmaps, chord diagrams, and infer interactions on different spatial scales. Package: r-cran-spacefillr Architecture: amd64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14705 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-spacefillr_0.4.0-1.ca2404.1_amd64.deb Size: 4717504 MD5sum: 1d55f1278757d40da3171cdff1a028fd SHA1: 3f4a25152117ff9a443d5826200da4676baed1c1 SHA256: 818846ef8ade558dc893d5b6dc7e49b211528ca3a382c1afb29080caf64b4908 SHA512: d3a00c202cda128c779319bc67e5ebccc06a5b061ea422d499bc96324c616fb83b355b96d575855c5b37a175c10986b38172501556e6e0c7f8a9ed5df444a15c Homepage: https://cran.r-project.org/package=spacefillr Description: CRAN Package 'spacefillr' (Space-Filling Random and Quasi-Random Sequences) Generates random and quasi-random space-filling sequences. Supports the following sequences: 'Halton', 'Sobol', 'Owen'-scrambled 'Sobol', 'Owen'-scrambled 'Sobol' with errors distributed as blue noise, progressive jittered, progressive multi-jittered ('PMJ'), 'PMJ' with blue noise, 'PMJ02', and 'PMJ02' with blue noise. Includes a 'C++' 'API'. Methods derived from "Constructing Sobol sequences with better two-dimensional projections" (2012) S. Joe and F. Y. Kuo, "Progressive Multi-Jittered Sample Sequences" (2018) Christensen, P., Kensler, A. and Kilpatrick, C., and "A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space" (2019) E. Heitz, B. Laurent, O. Victor, C. David and I. Jean-Claude, . Package: r-cran-spacetimebss Architecture: amd64 Version: 0.4-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2754 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-jade, r-cran-matrix, r-cran-rcpparmadillo Suggests: r-cran-sftime, r-cran-sf, r-cran-spacetime, r-cran-xts, r-cran-zoo Filename: pool/dists/noble/main/r-cran-spacetimebss_0.4-0-1.ca2404.1_amd64.deb Size: 2635848 MD5sum: 47558c8e054a47cf86f652212db1cc78 SHA1: 475a19281d29c34b770c37a0c9d265aa34576a2c SHA256: e15b9ab7c859d9b91f821e699730bb0e4a51be09aa929d49df8e727c6208c228 SHA512: 1d1e1f90441fc43448af09e34b2bb035268e24c78a2f074de545d509db761888110b9c639295faa54ed03880a8fa5e2acb7ce7a9e7bd76a8861157c3b8852208 Homepage: https://cran.r-project.org/package=SpaceTimeBSS Description: CRAN Package 'SpaceTimeBSS' (Blind Source Separation for Multivariate Spatio-Temporal Data) Simultaneous/joint diagonalization of local autocovariance matrices to estimate spatio-temporally uncorrelated random fields. Package: r-cran-spaco Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-seurat, r-cran-tibble, r-cran-ggforce, r-cran-rarpack, r-cran-tidyr, r-cran-mgcv, r-cran-scales, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-spaco_1.0.1-1.ca2404.1_amd64.deb Size: 119880 MD5sum: 49dc1f79889c6eba42d18dbc65aabc90 SHA1: 44e8e386b73f1fef0f71a90983f614d66b7f092d SHA256: dc18717e55afdc9ed7188cc365c029d0971f90f57afe7a714e2d657874ec6406 SHA512: 834de3642b0424d07a45c780deeef1de9ec387c2c7beac383ea29437d83aa7b26b7824f2fd53b895d856373ce59414e3231f2c487f08356eabd25f6ddf8c887e Homepage: https://cran.r-project.org/package=SPACO Description: CRAN Package 'SPACO' (Spatial Component Analysis for Spatial Sequencing Data) Spatial components offer tools for dimension reduction and spatially variable gene detection for high dimensional spatial transcriptomics data. Construction of a projection onto low-dimensional feature space of spatially dependent metagenes offers pre-processing to clustering, testing for spatial variability and denoising of spatial expression patterns. For more details, see Koehler et al. (2026) . Package: r-cran-spacoap Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-irlba, r-cran-laplacesdemon, r-cran-matrix, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spacoap_1.3-1.ca2404.1_amd64.deb Size: 198330 MD5sum: 578a31143c66556a08ddc410447e96f3 SHA1: 731a20945d3009b83ec6670ec286325e72ca4a32 SHA256: 4914da029da96314393bfba87216a161f90c1237242a37fb255cff12e9c6d5dc SHA512: 7d0c88c9dc896e6108123a9ce469ab5f56e7a52d87c45e6dc301d0b13384eb592146e8c6332873dfc5e4ce63261fe51e14e0fa65e026593323dfd5fc7bbbf45e Homepage: https://cran.r-project.org/package=SpaCOAP Description: CRAN Package 'SpaCOAP' (High-Dimensional Spatial Covariate-Augmented OverdispersedPoisson Factor Model) A spatial covariate-augmented overdispersed Poisson factor model is proposed to perform efficient latent representation learning method for high-dimensional large-scale spatial count data with additional covariates. Package: r-cran-spades.tools Architecture: amd64 Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1491 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-data.table, r-cran-fpcompare, r-cran-rcpp, r-cran-reproducible, r-cran-terra Suggests: r-cran-animation, r-cran-bit, r-cran-covr, r-cran-deoptim, r-cran-dqrng, r-cran-fastmatch, r-cran-knitr, r-cran-quickplot, r-cran-raster, r-cran-rmarkdown, r-cran-sf, r-cran-snow, r-cran-sp, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-spades.tools_2.1.1-1.ca2404.1_amd64.deb Size: 1325938 MD5sum: 3e12ec0ea7b4be8de565e1fe964ed6f2 SHA1: b8899ff805d2c5bebc03da6cd8c5e512715301bf SHA256: a5764c0b2ca04a9b3098a11bdf91c7fe58bffde3f104c1ddff6edf3056f95bb9 SHA512: ff1a4a28932b33577a43eb2fb9d41f60079d0d90b0a6f2240387449f15014e57d78bb2a4180345b036bad2bcedbee7911e755599c77c2793ffb8d528d9d9dd5b Homepage: https://cran.r-project.org/package=SpaDES.tools Description: CRAN Package 'SpaDES.tools' (Additional Tools for Developing Spatially Explicit DiscreteEvent Simulation (SpaDES) Models) Provides GIS and map utilities, plus additional modeling tools for developing cellular automata, dynamic raster models, and agent based models in 'SpaDES'. Included are various methods for spatial spreading, spatial agents, GIS operations, random map generation, and others. See '?SpaDES.tools' for an categorized overview of these additional tools. The suggested package 'NLMR' can be installed from the following repository: (). Package: r-cran-spam64 Architecture: amd64 Version: 2.10-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spam Filename: pool/dists/noble/main/r-cran-spam64_2.10-0-1.ca2404.1_amd64.deb Size: 76582 MD5sum: ffac577b2ac6c3dd0924185c0b455942 SHA1: 88429d1d03e62c330387b4b2bb5332299d75ba79 SHA256: 2aef4358ff92e394eb0d48de18445a518a252d2c066b3a02138b9fb180bf1dca SHA512: f1715b36ab75b29673c09c78aa774fe3969864d470172877226d29f70dab83f61220f4697bfbd44940aeeccbbb917a8a7bb4a312db61d22b6195e7ed5aa0be1f Homepage: https://cran.r-project.org/package=spam64 Description: CRAN Package 'spam64' (64-Bit Extension of the SPArse Matrix R Package 'spam') Provides the Fortran code of the R package 'spam' with 64-bit integers. Loading this package together with the R package spam enables the sparse matrix class spam to handle huge sparse matrices with more than 2^31-1 non-zero elements. Documentation is provided in Gerber, Moesinger and Furrer (2017) . Package: r-cran-spam Architecture: amd64 Version: 2.11-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2460 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dotcall64, r-cran-rcpp Suggests: r-cran-spam64, r-cran-fields, r-cran-matrix, r-cran-testthat, r-cran-r.rsp, r-cran-truncdist, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spam_2.11-3-1.ca2404.1_amd64.deb Size: 1855318 MD5sum: 563a181e50aa5341d2bc0a07a1b5bd51 SHA1: 5c764026ac1cadc13ead678eba4bd822b1661edc SHA256: 7c957dabafe1d8085ee426f3341fc7bef03102671b49f8a356eee0ad8cf7aa1e SHA512: 836b298b7a2a2bcecc4509bd29fe7e49a25d086a93a6e16da65ceb0f57274cc636c503061adca1651b840e7272769f478bfa780fef85a42f9b0239bf76e6ea48 Homepage: https://cran.r-project.org/package=spam Description: CRAN Package 'spam' (SPArse Matrix) Set of functions for sparse matrix algebra. Differences with other sparse matrix packages are: (1) we only support (essentially) one sparse matrix format, (2) based on transparent and simple structure(s), (3) tailored for MCMC calculations within G(M)RF. (4) and it is fast and scalable (with the extension package spam64). Documentation about 'spam' is provided by vignettes included in this package, see also Furrer and Sain (2010) ; see 'citation("spam")' for details. Package: r-cran-spamm Architecture: amd64 Version: 4.6.65-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5217 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-proxy, r-cran-rcpp, r-cran-nlme, r-cran-nloptr, r-cran-minqa, r-cran-pbapply, r-cran-cli, r-cran-gmp, r-cran-roi, r-cran-boot, r-cran-geometry, r-cran-numderiv, r-cran-backports, r-cran-reformulas, r-cran-rcppeigen Suggests: r-cran-maps, r-cran-testthat, r-cran-rcdd, r-cran-foreach, r-cran-future, r-cran-future.apply, r-cran-rann, r-cran-infusion, r-cran-isorix, r-cran-blackbox, r-cran-rspectra, r-cran-roi.plugin.glpk, r-cran-lme4, r-cran-rsae, r-cran-multilevel, r-cran-agridat, r-cran-fmesher Filename: pool/dists/noble/main/r-cran-spamm_4.6.65-1.ca2404.1_amd64.deb Size: 4442784 MD5sum: 50820abd75738df19cec8e45135340c9 SHA1: 136f2139dc91192dac38fd6e3bb2c664f45ea687 SHA256: d1c037a4cf62d6c6c73e4a73ad89d3d8edbd5544c662cd60353836a38e7ac104 SHA512: 27b5e8edd8f143dde90ebe01b3d0fe8833e0d62943caf738912f58fe9b31d3b77991f7f78480a486f44dcf9d0898b6a5c01bd35d934b5da6016cdd6210783ec0 Homepage: https://cran.r-project.org/package=spaMM Description: CRAN Package 'spaMM' (Mixed-Effect Models, with or without Spatial Random Effects) Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 ), and Markov random field models on irregular grids (as considered in the 'INLA' package, ), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 ) are also implemented. Package: r-cran-spanner Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9601 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-conicfit, r-cran-fnn, r-cran-rann, r-cran-cpprouting, r-cran-sf, r-cran-terra, r-cran-sfheaders, r-cran-rfast, r-cran-geometry, r-cran-dplyr, r-cran-mathjaxr, r-cran-data.table, r-cran-lidr, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-testthat, r-cran-magick, r-cran-rgl, r-cran-rstac Filename: pool/dists/noble/main/r-cran-spanner_1.0.4-1.ca2404.1_amd64.deb Size: 9227840 MD5sum: 494a317cd75d5a256698854b3d05c9ff SHA1: 73ee3b3a3215c14dfa429f8c15b29355be4bc2c0 SHA256: fbaca47968153c0b684af701d0636b12e69b1a3439f26dd0965bb0fcce95f47d SHA512: cf61f0c71a79adf8648ab2e87119bc16cb94a66a03f2ea6908b43c4d46d6dcb46b6a46084a28e3bc701bb1f2b5429160c37489c320392df535f22b1facac655f Homepage: https://cran.r-project.org/package=spanner Description: CRAN Package 'spanner' (Utilities to Support Lidar Applications at the Landscape,Forest, and Tree Scale) Implements algorithms for terrestrial, mobile, and airborne lidar processing, tree detection, segmentation, and attribute estimation (Donager et al., 2021) , and a hierarchical patch delineation algorithm 'PatchMorph' (Girvetz & Greco, 2007) . Tree detection uses rasterized point cloud metrics (relative neighborhood density and verticality) combined with RANSAC cylinder fitting to locate tree boles and estimate diameter at breast height. Tree segmentation applies graph-theory approaches inspired by Tao et al. (2015) with cylinder fitting methods from de Conto et al. (2017) . PatchMorph delineates habitat patches across spatial scales using organism-specific thresholds. Built on 'lidR' (Roussel et al., 2020) . Package: r-cran-spant Architecture: amd64 Version: 4.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3739 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-abind, r-cran-plyr, r-cran-pbapply, r-cran-pracma, r-cran-stringr, r-cran-expm, r-cran-signal, r-cran-minpack.lm, r-cran-ptw, r-cran-mmand, r-cran-rnifti, r-cran-rniftyreg, r-cran-fields, r-cran-numderiv, r-cran-nloptr, r-cran-irlba, r-cran-jsonlite Suggests: r-cran-viridislite, r-cran-shiny, r-cran-shinyfiles, r-cran-ggplot2, r-cran-miniui, r-cran-knitr, r-cran-kableextra, r-cran-rmarkdown, r-cran-testthat, r-cran-ragg, r-cran-doparallel, r-cran-digest, r-cran-readxl, r-cran-fslr, r-cran-car, r-cran-divest, r-cran-rpyants Filename: pool/dists/noble/main/r-cran-spant_4.1.0-1.ca2404.1_amd64.deb Size: 2802694 MD5sum: e0c0474c8a35d77ffae59b86b40f1fb3 SHA1: 5237973a16a2dc67b19b4044c8cef98cf71e5d47 SHA256: 0e58d44825296b821129159ef4da98e246ad0f0075e46d0fb20a21fc82d3e6eb SHA512: 01d6402efe10310b955b5e562623faf380e38d103623e93f4f409d39cfbb5cba00e2206f94cdc6549318bf1c11c6fd0959d082a663dafa02dd1b18f124717d3e Homepage: https://cran.r-project.org/package=spant Description: CRAN Package 'spant' (MR Spectroscopy Analysis Tools) Tools for reading, visualising and processing Magnetic Resonance Spectroscopy data. The package includes methods for spectral fitting: Wilson (2021) , Wilson (2025) and spectral alignment: Wilson (2018) . Package: r-cran-sparcl Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sparcl_1.0.4-1.ca2404.1_amd64.deb Size: 83012 MD5sum: 9a454e7784f794fdc36ff74200e700fd SHA1: a3f09f70d6732b193deb67ffbc5dc2f1b507933c SHA256: 7e5b6380d55e0db18d8117dabc6564c45c8aaf6accf34559fc0e9eea67f00701 SHA512: b95273cce6b1b6b4ff930e3e1adf447331def632d4887bc9f90f59524febf7283728fa4efbfcf25397aa1367d36eb513213cb1547961f84f46ff0164f202140a Homepage: https://cran.r-project.org/package=sparcl Description: CRAN Package 'sparcl' (Perform Sparse Hierarchical Clustering and Sparse K-MeansClustering) Implements the sparse clustering methods of Witten and Tibshirani (2010): "A framework for feature selection in clustering"; published in Journal of the American Statistical Association 105(490): 713-726. Package: r-cran-sparkwarc Architecture: amd64 Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1062 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-sparklyr, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-sparkwarc_0.1.6-1.ca2404.1_amd64.deb Size: 368134 MD5sum: af5506ef4ece5625467a8467e562ed58 SHA1: f8847d41b2edc33bebbd101ef8f6eda8322a24d1 SHA256: e4ddbfa7d4d08e6cfe4402881560587009104aac2ba9e36c119fe993c85e252b SHA512: 3ed4c25af3d60ad31620d9271b2c29f46369bd06258e41855ef66a82d1fcf8dd42b2f983856283f7eeefb1027c8ea2f35d870db4fbb945ba8e31635ee1c30f0f Homepage: https://cran.r-project.org/package=sparkwarc Description: CRAN Package 'sparkwarc' (Load WARC Files into Apache Spark) Load WARC (Web ARChive) files into Apache Spark using 'sparklyr'. This allows to read files from the Common Crawl project . Package: r-cran-sparsechol Architecture: amd64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparsechol_0.3.2-1.ca2404.1_amd64.deb Size: 92418 MD5sum: 1e3d2cfbd0a2cb25512d38816c50d5bc SHA1: ceadfa814d889c9fef23b5672d33a9aae6a5a794 SHA256: 828516a0960853c3cde48ee7a1a1c88f32a535127f13403c49a99b88cf8d9476 SHA512: 1720033367af926984555adb48a59e099e4bbc54f8f9fad96391fec5e89adf94d1723c5eee8f60310742982ae89a2e797ac3218256aef2179d4029cff4e71b85 Homepage: https://cran.r-project.org/package=SparseChol Description: CRAN Package 'SparseChol' (Sparse Matrix C++ Classes Including Sparse Cholesky LDLDecomposition of Symmetric Matrices) 'C++' classes for sparse matrix methods including implementation of sparse LDL decomposition of symmetric matrices and solvers described by Timothy A. Davis (2016) . Provides a set of C++ classes for basic sparse matrix specification and linear algebra, and a class to implement sparse LDL decomposition and solvers. See for details. Package: r-cran-sparsedfm Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1059 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-ggplot2, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-sparsedfm_1.0-1.ca2404.1_amd64.deb Size: 662740 MD5sum: 595a80622d2e4cf088502ac35adbad3b SHA1: 9e2d4012e359d6d68ac97d8ff3e6d49fe9754b4e SHA256: 0e6a421f1683c963432a97894fa75cb34e8844fda774e9fa857b0b42ef284955 SHA512: f12b2f10ad607f6b6f01706a3b6dbff007912876a7b900c450715524b6713ac068adb247336241d503cf3a8ea104398eac9298cd38d143d1bd94b906831edb58 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-sparsegam Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-pracma, r-cran-grpreg Filename: pool/dists/noble/main/r-cran-sparsegam_1.0-1.ca2404.1_amd64.deb Size: 163792 MD5sum: 77bacfaa556b13ef0ecedfc95a7a89c3 SHA1: ddfd853c26e166b16c19f82e0b9af4b8129573a6 SHA256: 506b29e558c113f6cd1182c237e67c89691e9e30376fe1e2fa185c2b0088cdb3 SHA512: 4a09776643e00fb6488e85fa94a844f6f34d41e43420ddb3f6f7ecd1ccf2e061c84bccd0a51b14a75908cb65b35d6a0e51fce88d6b84f0b1b9cbf584bc4e3d13 Homepage: https://cran.r-project.org/package=sparseGAM Description: CRAN Package 'sparseGAM' (Sparse Generalized Additive Models) Fits sparse frequentist GAMs (SF-GAM) for continuous and discrete responses in the exponential dispersion family with the group lasso, group smoothly clipped absolute deviation (SCAD), and group minimax concave (MCP) penalties . Also fits sparse Bayesian generalized additive models (SB-GAM) with the spike-and-slab group lasso (SSGL) penalty of Bai et al. (2021) . B-spline basis functions are used to model the sparse additive functions. Stand-alone functions for group-regularized negative binomial regression, group-regularized gamma regression, and group-regularized regression in the exponential dispersion family with the SSGL penalty are also provided. Package: r-cran-sparsegl Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1045 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dotcall64, r-cran-ggplot2, r-cran-magrittr, r-cran-matrix, r-cran-rlang, r-cran-rspectra, r-cran-tidyr Suggests: r-cran-dplyr, r-cran-gglasso, r-cran-glmnet, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparsegl_1.1.1-1.ca2404.1_amd64.deb Size: 733016 MD5sum: 40f95c5dcabf2b2414020f159b928e19 SHA1: 66d5d7b0b1daa49ffe644820b733d15498d895c4 SHA256: ac7f8348d7eed0a7bef66d68462d53b4f241b207781cd717ff25515b0668e734 SHA512: 48e2e8dcee476bc6343f025d18b78294ad6041b6a1e1cc511e8b28fd2ddc419bd1213f370e20f06b0b5a94cf227752f23ecdeb4287cddc22035bcc2af162065d Homepage: https://cran.r-project.org/package=sparsegl Description: CRAN Package 'sparsegl' (Sparse Group Lasso) Efficient implementation of sparse group lasso with optional bound constraints on the coefficients; see . It supports the use of a sparse design matrix as well as returning coefficient estimates in a sparse matrix. Furthermore, it correctly calculates the degrees of freedom to allow for information criteria rather than cross-validation with very large data. Finally, the interface to compiled code avoids unnecessary copies and allows for the use of long integers. Package: r-cran-sparsehessianfd Architecture: amd64 Version: 0.3.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 679 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-numderiv, r-cran-scales, r-cran-knitr, r-cran-xtable, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-sparsehessianfd_0.3.3.7-1.ca2404.1_amd64.deb Size: 510480 MD5sum: 0fb906d720ebbf5e0d113533c046d1ee SHA1: c2fdcf94fd891e7417742c7d01ee2c78ae126b43 SHA256: 4fb3c093ea468656db9905ec0b910c778eda88b9b376aa04c63097a011f4cc6b SHA512: eb712bd35fb77dea326dd36555f689aceb52264cc801fa48c5fad4c95708e18654baa5ea397a6c4ded538e63097c566953f15ef41d7a3d4a497ca05aa1eb61c4 Homepage: https://cran.r-project.org/package=sparseHessianFD Description: CRAN Package 'sparseHessianFD' (Numerical Estimation of Sparse Hessians) Estimates Hessian of a scalar-valued function, and returns it in a sparse Matrix format. The sparsity pattern must be known in advance. The algorithm is especially efficient for hierarchical models with a large number of heterogeneous units. See Braun, M. (2017) . Package: r-cran-sparseica Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 672 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-irlba, r-cran-clue, r-cran-ciftitools, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-sparseica_0.1.4-1.ca2404.1_amd64.deb Size: 555080 MD5sum: 6438d0c89a1842e362c3d7f6b261b3fc SHA1: f4a76210a3c148e80ba1f11708aeae855c08cf55 SHA256: 034f610b9ff0b2bd6317692ef60092809c1d9c5021e8e59fc5d5b7f4d2054d01 SHA512: 7ff39b4830ed4a6715ff4d281104ff580e0dae34ec3f029d2bbd0e5355b07362c5e148d1107a60cda936b227da24bc166864f6c6323887f4cd09b3061b06f3e8 Homepage: https://cran.r-project.org/package=SparseICA Description: CRAN Package 'SparseICA' (Sparse Independent Component Analysis) Provides an implementation of the Sparse ICA method in Wang et al. (2024) for estimating sparse independent source components of cortical surface functional MRI data, by addressing a non-smooth, non-convex optimization problem through the relax-and-split framework. This method effectively balances statistical independence and sparsity while maintaining computational efficiency. Package: r-cran-sparseinv Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-spam Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparseinv_0.1.3-1.ca2404.1_amd64.deb Size: 74174 MD5sum: 997fc0a5813d5b5f48bcaf2c20d97237 SHA1: aa25ecff03e86cdf3c1afdf5726c4f4c03b849e5 SHA256: ad7866d844656d0a8da5296ce9791c4055374a2a88affd73ea0a956452952ca4 SHA512: 74f7600cde29c9322c632b258f0575ff19281ff7fb17f5b074f5c950e3eb72c7d0844c83676fccb0bd766104ae6ef26aa2ece346f09d3e4a9e1d3a90b14a72b4 Homepage: https://cran.r-project.org/package=sparseinv Description: CRAN Package 'sparseinv' (Computation of the Sparse Inverse Subset) Creates a wrapper for the 'SuiteSparse' routines that execute the Takahashi equations. These equations compute the elements of the inverse of a sparse matrix at locations where the its Cholesky factor is structurally non-zero. The resulting matrix is known as a sparse inverse subset. Some helper functions are also implemented. Support for spam matrices is currently limited and will be implemented in the future. See Rue and Martino (2007) and Zammit-Mangion and Rougier (2018) for the application of these equations to statistics. Package: r-cran-sparselm Architecture: amd64 Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-sparselm_0.5-1.ca2404.1_amd64.deb Size: 59028 MD5sum: 899557ec31d397e64701f7e91865b2cd SHA1: ac596d9f4448452002b8004cbeec83d2bb973220 SHA256: 2875ea48ca2171d121b61d3265427ba08acc74b324eebbbadf09757957d87171 SHA512: a5dd9b0c83b10d9d2b3aa49f53cbb7cc44493bb9786836946b3bd3b3247cb03f2ce8e813eb80c4484df899a8eaaafebba51ba93141fcfde679586e9690ba031a Homepage: https://cran.r-project.org/package=sparseLM Description: CRAN Package 'sparseLM' (Interface to the 'sparseLM' Levenberg-Marquardt Library) Provides an R interface to the 'sparseLM' C library for large-scale nonlinear least squares problems with arbitrarily sparse Jacobians. The underlying solver implements a sparse variant of the Levenberg-Marquardt algorithm for minimizing sum-of-squares objective functions, supports user-supplied analytic Jacobians or finite-difference approximation, and is designed to exploit sparsity for improved memory use and performance. This package exposes the solver in R and uses sparse matrix classes and the 'CHOLMOD' sparse Cholesky factorization routines through the 'Matrix' package interface. Methods from the C library are described in Lourakis (2010) . Package: r-cran-sparselpm Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-gtools, r-cran-vegan, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-sparselpm_1.0-1.ca2404.1_amd64.deb Size: 93818 MD5sum: f13a71e79bb897d15a4460ce063dd9cd SHA1: 94a6b768a7192ff81b11bef83ce8af17a84f82d9 SHA256: 396138662f817a8842666bb79f50f57b4346261caa04b0900ba9c88f95807546 SHA512: 8fdded328d9d435d4044ada693f96b8d1474c3cff62eb3d49956928ea6596085eb19bdd942f40de09d880392a09e85e89397c8054a60fab113fc8906e4b3d1f6 Homepage: https://cran.r-project.org/package=SparseLPM Description: CRAN Package 'SparseLPM' (The Sparse Latent Position Model for Nonnegative InteractionData) Models the nonnegative entries of a rectangular adjacency matrix using a sparse latent position model, as illustrated in Rastelli, R. (2018) "The Sparse Latent Position Model for nonnegative weighted networks" . Package: r-cran-sparseltseigen Architecture: amd64 Version: 0.2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-robusthd, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-sparseltseigen_0.2.0.1-1.ca2404.1_amd64.deb Size: 112340 MD5sum: 114e8e9929ef0c11fb324c312c0809dd SHA1: 76920f57faa05f66ef01929073925963bd3507a2 SHA256: b2f15821437f1929102536c6b552c5490db38be3c289a6c6252e5d516d02be75 SHA512: 81e1c5ee267bd6c984474ed51c962af212f920312eec89a6147081d3caccdc66778ed6043673a36092b23864d6f91d00a3669f4de7d099379d8b0ef9443eb2c6 Homepage: https://cran.r-project.org/package=sparseLTSEigen Description: CRAN Package 'sparseLTSEigen' (RcppEigen back end for sparse least trimmed squares regression) Use RcppEigen to fit least trimmed squares regression models with an L1 penalty in order to obtain sparse models. Package: r-cran-sparselu Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), libumfpack6 (>= 1:7.0.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-matrix, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparselu_0.3.0-1.ca2404.1_amd64.deb Size: 45928 MD5sum: 541bcc69f04d03b5380a27aef020c376 SHA1: cb2e6bfc0b850c71d0bc86b6788db083b136c805 SHA256: ff611e4038d2b1ba775a6472a711dd24f7b8305f4c66d1003ea735497e94d412 SHA512: e6cb8f3751aee16838faaa4aedc6da45421321e7d53de3d31bf58fca84aef84488295811c1df5349d0173fee10b039ecdbebbd3d03c76fe952621a076d750982 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. 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Package: r-cran-sparsenet Architecture: amd64 Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-shape Filename: pool/dists/noble/main/r-cran-sparsenet_1.7-1.ca2404.1_amd64.deb Size: 92958 MD5sum: d3431b4746a12e33a721b2237e04c3a8 SHA1: 3decd0661bad9d6fd55e845e51e9613b7ccb83d4 SHA256: 04c6bf834a8e58ac571faea72c5819e346bc067716324ae05755268c865d0c12 SHA512: 1097381faab3523702a430d11dd07925c30606c21512dbbcbe8410c0b5fd53a58860069cacff99351ec0da2b993f4a3af44c6571870eddfdbbb8c76519683fe0 Homepage: https://cran.r-project.org/package=sparsenet Description: CRAN Package 'sparsenet' (Fit Sparse Linear Regression Models via Nonconvex Optimization) Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010). 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Package: r-cran-sparsesem Architecture: amd64 Version: 4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2134 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-plot.matrix Filename: pool/dists/noble/main/r-cran-sparsesem_4.1-1.ca2404.1_amd64.deb Size: 1863148 MD5sum: ff2d4339378625b50602dd0fa2d03b09 SHA1: cd6aab3a2b3cfdce441010f255ee148da8796fb0 SHA256: 83af157e3c7cd0680812aab6bada157dd74213f03c9cc9e77ce2a01f7e2c708f SHA512: 068dea4e2ca41f9c8efb1365bbb63a090a9ec3dec1a1abe626857226c890a294d481b913c0ed79314938b7a37d3131d143bc6192da6e9d99106c5b97f56906e8 Homepage: https://cran.r-project.org/package=sparseSEM Description: CRAN Package 'sparseSEM' (Elastic Net Penalized Maximum Likelihood for Structural EquationModels with Network GPT Framework) Provides elastic net penalized maximum likelihood estimator for structural equation models (SEM). The package implements `lasso` and `elastic net` (l1/l2) penalized SEM and estimates the model parameters with an efficient block coordinate ascent algorithm that maximizes the penalized likelihood of the SEM. Hyperparameters are inferred from cross-validation (CV). A Stability Selection (STS) function is also available to provide accurate causal effect selection. The software achieves high accuracy performance through a `Network Generative Pre-trained Transformer` (Network GPT) Framework with two steps: 1) pre-trains the model to generate a complete (fully connected) graph; and 2) uses the complete graph as the initial state to fit the `elastic net` penalized SEM. Package: r-cran-sparsesvd Architecture: amd64 Version: 0.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-sparsesvd_0.2-3-1.ca2404.1_amd64.deb Size: 32214 MD5sum: 3ed75448439295a945785aae57f28b43 SHA1: 6cc987f4def43982944fcda5fe07eced6178ca0f SHA256: 9da78ae4f434fe733ec90365c0f7acb67938126de3dec660abad465ea7331949 SHA512: 04e6142fbcadfdd35c9e45e54c559aec963fe71f0d6fe86609c268e1d3cbf708eadf8dbe6805868b0d6dffb2bf477faaf96fad5c7d65c244a31babb4e38ca63b Homepage: https://cran.r-project.org/package=sparsesvd Description: CRAN Package 'sparsesvd' (Sparse Truncated Singular Value Decomposition (from 'SVDLIBC')) Wrapper around the 'SVDLIBC' library for (truncated) singular value decomposition of a sparse matrix. Currently, only sparse real matrices in Matrix package format are supported. Package: r-cran-sparsesvm Architecture: amd64 Version: 1.1-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sparsesvm_1.1-7-1.ca2404.1_amd64.deb Size: 64210 MD5sum: ab1dadac4da736fb69e65811d8b23178 SHA1: 60018ea9b41a7ce689e98f6307ffb124d0b14dba SHA256: 8ac72659ced8069c93eaf8c9e953f35f32087682b13107addb1229cd301b7dd8 SHA512: 867aae83d9677aa35d16122d76677f9993c0942eac62b52438f8980b147bc0476a6f15aeccf02d7259bb8f761f22e66e618254db3deb3045595e811a4cda04e4 Homepage: https://cran.r-project.org/package=sparseSVM Description: CRAN Package 'sparseSVM' (Solution Paths of Sparse High-Dimensional Support Vector Machinewith Lasso or Elastic-Net Regularization) Offers a fast algorithm for fitting solution paths of sparse SVM models with lasso or elastic-net regularization. Reference: Congrui Yi and Jian Huang (2017) . Package: r-cran-sparsetscgm Architecture: amd64 Version: 5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-glasso, r-cran-longitudinal, r-cran-huge, r-cran-mass, r-cran-mvtnorm, r-cran-network, r-cran-abind Filename: pool/dists/noble/main/r-cran-sparsetscgm_5.0-1.ca2404.1_amd64.deb Size: 80452 MD5sum: dcbee2e5f852b76a03b862b90b396498 SHA1: 55ce8dc394fac854671c229c1e34d68a8f791f0d SHA256: 01179e11b6699bb99a92e01cd13db7cb6818a14c4bffd4e89da1584e9521581e SHA512: 47ec2577c00a727a1a4d4a64bd1b04dbe667a84a7dc0bfcca3a7de63647b03fc7abde1358c623a7a70288da4448abd594124efbe0085be6de84ae250a5ea3c80 Homepage: https://cran.r-project.org/package=SparseTSCGM Description: CRAN Package 'SparseTSCGM' (Sparse Time Series Chain Graphical Models) Computes sparse vector autoregressive coefficients and sparse precision matrices for time series chain graphical models. Methods are described in Abegaz and Wit (2013) . Package: r-cran-sparsevb Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-selectiveinference, r-cran-glmnet, r-cran-rcpparmadillo, r-cran-rcppensmallen Filename: pool/dists/noble/main/r-cran-sparsevb_0.1.1-1.ca2404.1_amd64.deb Size: 87822 MD5sum: 74cfc285de846c8ea98437354d43e9b7 SHA1: 21caa0cbe451255ef450c9cc1bb29d88f9cb8126 SHA256: 691289c950b3fe7c419d0f61c6ffbf777bb231a7cd29486a29bac00e1d336fe0 SHA512: 9cba2b9662b4de6f28a3e4ede98b241698fe2d2916cdc0cf4d35c7b92304a31a6c72072a8c96a8bd2caf2d5ab49ee0afe34a15fdf7ec76eb31c03a88fce93eeb Homepage: https://cran.r-project.org/package=sparsevb Description: CRAN Package 'sparsevb' (Spike-and-Slab Variational Bayes for Linear and LogisticRegression) Implements variational Bayesian algorithms to perform scalable variable selection for sparse, high-dimensional linear and logistic regression models. 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Package: r-cran-sparsevcbart Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 573 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-sparsevcbart_1.0.0-1.ca2404.1_amd64.deb Size: 234902 MD5sum: 7e169148c63a39c1720da06372b0a633 SHA1: 76b6eff282ba78db170729784dc1be8463f50f32 SHA256: da1cd99b8db66ff3611f21877371fdd6a84ee09151e4dfc143ff7b89736dace9 SHA512: ddaccd3c274edb411f11ee99b9d822dd1091f239b7a9af988f0449db22a67eb9f643e79d08b46fa9f5dfa91fbef7e5a4f9284830068b8452546a793c9c58f245 Homepage: https://cran.r-project.org/package=sparseVCBART Description: CRAN Package 'sparseVCBART' (Sparse Varying Coefficient BART with Global-Local Priors") Fits sparse linear varying coefficient models (VCMs), which assert a linear relationship between an outcome and several covariates that is allowed to change as functions of additional variables known as effect modifiers. Designed for high-dimensional settings where the number of covariates (i.e., number of slopes) is comparable to or larger than the number of observations. Approximates the coefficient functions using a version of Bayesian Additive Regression Trees that can perform global-local shrinkage. For more details see Ghosh, Bhogale, and Deshpande (2026+) . Package: r-cran-sparsevctrs Architecture: amd64 Version: 0.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-vctrs Suggests: r-cran-knitr, r-cran-matrix, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-sparsevctrs_0.3.6-1.ca2404.1_amd64.deb Size: 192696 MD5sum: e8ca3ebf92d1965f69e8164fb786cd7b SHA1: be2889ed9382ae993de6c3b0d2290d8e5054404a SHA256: 8a65325972b5da7719ce9785a30a1fc5667c8b171c0be0431f5f0725f92c12c7 SHA512: a18b33bfd45a4a8b483dcbbc3b8b40353f95be57f45518cda021a011a695b545452999577761af06937f032b30226906368ff11e6179d12e93f6bf74720baaec 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. 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Package: r-cran-sparvaride Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sparvaride_1.0.0-1.ca2404.1_amd64.deb Size: 47760 MD5sum: 1102796be087d6106585e24e28a42c3b SHA1: 8c10281f35122fa4134d3bd39cd00df8ccb15b73 SHA256: 7f4763db492c10fdd06694b757ac623059b531eb8f7e62c835e7a50296701f48 SHA512: 0472b1c1730285406995c9d9883370dd07c31a38741da4eb8e80d8b9c74fdd6bd311235e02ed7a9cbaa2f8c98eb821f39c40f7bd0ea3fcbf7979c3fc08858278 Homepage: https://cran.r-project.org/package=sparvaride Description: CRAN Package 'sparvaride' (Variance Identification in Sparse Factor Analysis) This is an implementation of the algorithm described in Section 3 of Hosszejni and Frühwirth-Schnatter (2026) . The algorithm is used to verify that the counting rule CR(r,1) holds for the sparsity pattern of the transpose of a factor loading matrix. As detailed in Section 2 of the same paper, if CR(r,1) holds, then the idiosyncratic variances are generically identified. If CR(r,1) does not hold, then we do not know whether the idiosyncratic variances are identified or not. Package: r-cran-spas Architecture: amd64 Version: 2026.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1666 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-mass, r-cran-matrix, r-cran-msm, r-cran-numderiv, r-cran-plyr, r-cran-reshape2, r-cran-tmb, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spas_2026.4.1-1.ca2404.1_amd64.deb Size: 452264 MD5sum: d7f12447b56f2a4217af17e72a18b683 SHA1: 7440f1fcb81dc53f837cb7138202eda7bbc58b09 SHA256: 3e208ea61ad929b69392e154535f6778b2e3d0780b2afdf787c122f6b3a71fc9 SHA512: 06841d80f0e04fd6939db47442a97194dad5d3ba2a7b55a803f912f60dd52739566e101d303dd1ab38f6e2146bbd27772e3e70d4ed265c078315768442334642 Homepage: https://cran.r-project.org/package=SPAS Description: CRAN Package 'SPAS' (Stratified-Petersen Analysis System) The Stratified-Petersen Analysis System (SPAS) is designed to estimate abundance in two-sample capture-recapture experiments where the capture and recaptures are stratified. This is a generalization of the simple Lincoln-Petersen estimator. Strata may be defined in time or in space or both, and the s strata in which marking takes place may differ from the t strata in which recoveries take place. When s=t, SPAS reduces to the method described by Darroch (1961) . When s. Schwarz and Taylor (1998) describe the use of SPAS in estimating return of salmon stratified by time and geography. A related package, BTSPAS, deals with temporal stratification where a spline is used to model the distribution of the population over time as it passes the second capture location. This is the R-version of the (now obsolete) standalone Windows program of the same name. Package: r-cran-spass Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 426 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-multcomp, r-cran-mass, r-cran-geepack Filename: pool/dists/noble/main/r-cran-spass_1.3-1.ca2404.1_amd64.deb Size: 270506 MD5sum: 55b37eae9bbe799567af9ba6f4e02313 SHA1: 0097a1e5f29e99a4605a0dc57fd62b381f6eea82 SHA256: 30b0e023a2d999f82b0f2a125f37b09d256558259e153e10c3a90b96e1865c1e SHA512: f38be01a5431c9b2a964f7b53bbf8d86bd61a37a47d5c0f8a3e1dcc26ea18efb0e8f2fd15c773c66a21764c986254e3fbe094da76ce9e4ffea4a83bd2155de50 Homepage: https://cran.r-project.org/package=spass Description: CRAN Package 'spass' (Study Planning and Adaptation of Sample Size) Sample size estimation and blinded sample size reestimation in Adaptive Study Design. Package: r-cran-spate Architecture: amd64 Version: 1.7.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1492 Depends: libc6 (>= 2.4), libfftw3-double3 (>= 3.3.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-truncnorm Filename: pool/dists/noble/main/r-cran-spate_1.7.5-1.ca2404.1_amd64.deb Size: 1340956 MD5sum: d6570ad27b98dcf34f5162790ed55566 SHA1: 1c76aa7decb73eee5c783ae694bc494030e26b33 SHA256: 2f1f5127c6592472b161bd0d156614fcd4474e0094d67ffe0c4b2ca9f178cf8e SHA512: 164e04690a3b27273c599dad20936d758ec20d2111255bf2c1df6fd0845ea3c3f4d51930426af71f3571232a3d8080bed7da0103f52dfa7ea4e7188ecaf2e009 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: amd64 Version: 3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Suggests: r-cran-rgl Filename: pool/dists/noble/main/r-cran-spatgraphs_3.4-1.ca2404.1_amd64.deb Size: 156532 MD5sum: 9cb2b331ff9ded29831f0ab8b4601f7b SHA1: 61dfff769638065b8b17186a07b021014f64663a SHA256: 0f083ca1dd06890051f89de88552c7a21827e85a0737a576af3ee3e2047d5403 SHA512: 98b226f08782841715e692ccfa51037d7b701991e7b927112ea7c51d6f2b600b14fac7572485a44ca7bf81e5660750421c385e582ca1b59c7fc471fa1af4512b Homepage: https://cran.r-project.org/package=spatgraphs Description: CRAN Package 'spatgraphs' (Graph Edge Computations for Spatial Point Patterns) Graphs (or networks) and graph component calculations for spatial locations in 1D, 2D, 3D etc. Package: r-cran-spaths Architecture: amd64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1982 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-data.table Suggests: r-cran-terra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spaths_1.2.0-1.ca2404.1_amd64.deb Size: 517468 MD5sum: e0ae1be6783281990b3f79271fbebe92 SHA1: 63380e0ae74e8f50cdb3abf6e4e56474d194d035 SHA256: 27b51dd7fa769c1ed27885822e08a85715cbebbc175f05f6019b068b87ad5fbb SHA512: 1fdc3754a6b8cf35fb6a46749818e6bcfdc9bce217cbd6e9213ca9117685597396fbc2dfd013c4b0bbbf68a87bbba72c0d347ddd169f5146fa2ca70a1ed2bca6 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: amd64 Version: 7.3-18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-spatial_7.3-18-1.ca2404.1_amd64.deb Size: 132186 MD5sum: 0c58aebca86b97e7078ce78c42480531 SHA1: a3777a53d2f002d4088417c5889d3e852f745f70 SHA256: ee9f6f97cf04b829542b900af8e15504e0771b73c19c4879ad24a6f516ed85d3 SHA512: 28ed9bfdf3547837b5eb9cdce2d5e43792db3ed983717ab3ce5dba77fe05890505e0970fb8ba8dd9eb31035cd3a523dc61a507a58664f20d1ace30cd0b093fab 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: amd64 Version: 0.16-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1028 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-jade, r-cran-sp, r-cran-spatialnp, r-cran-distances, r-cran-robustbase, r-cran-rcpparmadillo Suggests: r-cran-sf, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-gstat Filename: pool/dists/noble/main/r-cran-spatialbss_0.16-0-1.ca2404.1_amd64.deb Size: 810558 MD5sum: bffffada6b8b84e18f55c3b5054c4806 SHA1: 34f967a436225254e7b22dffe7feca41cfcce1de SHA256: a20f1baa47701ef7b8f493c7ff5337eeedc81bb431c37b9d7ddda7e1b5ece356 SHA512: cb52064c242ad8fba4276e0447dcec9aae877e3be19f023c9b1edf82e04071343d0a642b92b25b02193280fe90ce02d83df3de2dd28ccd5d8c5eeda32414caac Homepage: https://cran.r-project.org/package=SpatialBSS Description: CRAN Package 'SpatialBSS' (Blind Source Separation for Multivariate Spatial Data) Blind source separation for multivariate spatial data based on simultaneous/joint diagonalization of (robust) local covariance matrices. This package is an implementation of the methods described in Bachoc, Genton, Nordhausen, Ruiz-Gazen and Virta (2020) . Package: r-cran-spatialepi Architecture: amd64 Version: 1.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 553 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-rcpp, r-cran-mass, r-cran-spdep, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-markdown, r-cran-knitr, r-cran-testthat, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-spatialepi_1.2.8-1.ca2404.1_amd64.deb Size: 383962 MD5sum: 940744c09cfc157c0ffbe9015ecbe97f SHA1: 5b48bc1673fca4fb4f8e300eb277d4aa1b8e1507 SHA256: 4afe96393e6e27da9c0531b3c3f92dc5275fa0cfb441d5e567ad9c4dbe2997f9 SHA512: 2d3ae8d487d42831502426cb8d37e14e01fd8279f3d3e57fa7d8c39b20565ea84618487669fc9ff9ad00f46157b3865a3f7df0321b45abb374706db51176bf6e Homepage: https://cran.r-project.org/package=SpatialEpi Description: CRAN Package 'SpatialEpi' (Methods and Data for Spatial Epidemiology) Methods and data for cluster detection and disease mapping. Package: r-cran-spatialextremes Architecture: amd64 Version: 2.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2290 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-maps, r-cran-fields Filename: pool/dists/noble/main/r-cran-spatialextremes_2.1-0-1.ca2404.1_amd64.deb Size: 1850872 MD5sum: 7a4dda424919f247686bdecebcd662aa SHA1: efaf5c0d1b6e7ab6bd018e60b52cdd464de31f49 SHA256: eaf64af9fcfbed935792e12edeae38f86539e5963359339053090f1e9f6d87dd SHA512: 8be226a214209a0bc50f100adef9e116345466525b45700efa5c94858ced4d52f7bcaa0f25005f71b6ff83712a66e4b0f3332d88a870893336bbcd7a42978335 Homepage: https://cran.r-project.org/package=SpatialExtremes Description: CRAN Package 'SpatialExtremes' (Modelling Spatial Extremes) Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models. More precisely, this package allows (conditional) simulations from various parametric max-stable models, analysis of the extremal spatial dependence, the fitting of such processes using composite likelihoods or least square (simple max-stable processes only), model checking and selection and prediction. Other approaches (although not completely in agreement with the extreme value theory) are available such as the use of (spatial) copula and Bayesian hierarchical models assuming the so-called conditional assumptions. The latter approaches is handled through an (efficient) Gibbs sampler. Some key references: Davison et al. (2012) , Padoan et al. (2010) , Dombry et al. (2013) . Package: r-cran-spatialge Architecture: amd64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1033 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-bioc-biocparallel, r-cran-concaveman, r-bioc-complexheatmap, r-cran-data.table, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-cran-dynamictreecut, r-cran-dplyr, r-bioc-ebimage, r-cran-ggforce, r-cran-ggplot2, r-cran-ggpolypath, r-cran-ggrepel, r-cran-gstat, r-bioc-gsva, r-cran-hdf5r, r-cran-jpeg, r-cran-jsonlite, r-cran-khroma, r-cran-magrittr, r-cran-matrix, r-cran-mass, r-cran-png, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-scales, r-cran-sctransform, r-cran-sfsmisc, r-cran-sf, r-cran-sp, r-cran-spamm, r-cran-spdep, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-uwot, r-cran-wordspace, r-cran-rcppeigen, r-cran-rcppprogress Suggests: r-cran-curl, r-cran-geor, r-cran-ggpubr, r-cran-httr, r-cran-janitor, r-cran-kableextra, r-cran-knitr, r-cran-msigdbr, r-cran-progress, r-cran-rmarkdown, r-cran-rpart, r-cran-r.utils, r-cran-scspatialsim, r-cran-spatstat, r-cran-seuratobject, r-cran-tidyverse, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spatialge_1.2.2-1.ca2404.1_amd64.deb Size: 760592 MD5sum: 85d16973e98df2712c771a18828c0fda SHA1: e4c07d9f11eeb60b8e576439369bbef17bcecff0 SHA256: 8de6b3821f37f411e6aa9043437f8c7988c3f7382bfbb6ea5ccd3c335a49a033 SHA512: 63315061c90a61101d0b960da05ddbb30877522c0e6efc51163b9fa3b2a9a919eb5e30024336084572587c98a6ae2bd585e4327c045d97ec1bfda89f11aa5973 Homepage: https://cran.r-project.org/package=spatialGE Description: CRAN Package 'spatialGE' (Visualization and Analysis of Spatial Heterogeneity inSpatially-Resolved Gene Expression) Visualization and analysis of spatially resolved transcriptomics data. The 'spatialGE' R package provides methods for visualizing and analyzing spatially resolved transcriptomics data, such as 10X Visium, CosMx, or csv/tsv gene expression matrices. It includes tools for spatial interpolation, autocorrelation analysis, tissue domain detection, gene set enrichment, and differential expression analysis using spatial mixed models. Package: r-cran-spatialgev Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2966 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tmb, r-cran-mvtnorm, r-cran-evd, r-cran-matrix, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spatialgev_1.0.1-1.ca2404.1_amd64.deb Size: 1345634 MD5sum: 924afbed66929300cb5697a36d5f8123 SHA1: d8f54684b3110e0147fafc4d25b33580962c4c74 SHA256: fed1256cc924d0a7e1ba344a4f2cfff7b5778f58de465a449cc3bfdd56b40a56 SHA512: 6c8c6f8e81951f00704594126778a25b828cd7a3f7299f895aac8c116e377116020033c69f0807bb315b52084020a1ce0eb700845751933412df1e55de75d911 Homepage: https://cran.r-project.org/package=SpatialGEV Description: CRAN Package 'SpatialGEV' (Fit Spatial Generalized Extreme Value Models) Fit latent variable models with the GEV distribution as the data likelihood and the GEV parameters following latent Gaussian processes. The models in this package are built using the template model builder 'TMB' in R, which has the fast ability to integrate out the latent variables using Laplace approximation. This package allows the users to choose in the fit function which GEV parameter(s) is considered as a spatially varying random effect following a Gaussian process, so the users can fit spatial GEV models with different complexities to their dataset without having to write the models in 'TMB' by themselves. This package also offers methods to sample from both fixed and random effects posteriors as well as the posterior predictive distributions at different spatial locations. Methods for fitting this class of models are described in Chen, Ramezan, and Lysy (2024) . Package: r-cran-spatialinference Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1576 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-sf, r-cran-data.table, r-cran-magrittr, r-cran-tibble, r-cran-rcpparmadillo Suggests: r-cran-lfe, r-cran-fixest, r-cran-dplyr, r-cran-stringr, r-cran-spdep, r-cran-ncf, r-cran-gstat, r-cran-sandwich, r-cran-ggplot2, r-cran-modelsummary, r-cran-knitr, r-cran-rmarkdown, r-cran-geosphere, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spatialinference_0.1.0-1.ca2404.1_amd64.deb Size: 1206142 MD5sum: 576a3c947beec948f2c799af7984f83b SHA1: ea539715134e33a48156b490c85160dbd4beb843 SHA256: b3b242dacb641054bb2bf9093c1acbc069523c38beed8e9af400a80e2504012a SHA512: ed3132150a1f8f12821e893e780d52e9d5a12a04455d74203c659336f14320f254faa14194512bfe24013467833a3839552a2132854248e799c40fd176da7850 Homepage: https://cran.r-project.org/package=SpatialInference Description: CRAN Package 'SpatialInference' (Tools for Statistical Inference with Geo-Coded Data) Fast computation of Conley (1999) spatial heteroskedasticity and autocorrelation consistent (HAC) standard errors for linear regression models with geo-coded data, with a fast C++ implementation by Christensen, Hartman, and Samii (2021) . Performance-critical distance calculations, kernel weighting, and variance component accumulation are implemented in C++ via 'Rcpp' and 'RcppArmadillo'. Includes tools for estimating the spatial correlation range from covariograms and correlograms following the bandwidth selection method proposed in Lehner (2026) , and diagnostic visualizations for bandwidth selection. Package: r-cran-spatialising Architecture: amd64 Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-comat, r-cran-rcpp, r-cran-terra Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-optimization, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spatialising_0.6.2-1.ca2404.1_amd64.deb Size: 193628 MD5sum: ca2ab6e323bfa89902a68fb6b4a25d66 SHA1: 3d6801c92d6363a03f0eb5a3913d4225cff3a506 SHA256: 0b8ad6abed592c88a0eba4e31ef41b287584e272d764cd5780fda5a1222d8f89 SHA512: 9bbc0701ed658ec341839f469ccb8ca9d390f3b7c80c8fee22951ebb1153ef39392b94032e856266d8c8082aa6ba9be8f5d4bd9ac300d3ff87f3f9ff6c6270f6 Homepage: https://cran.r-project.org/package=spatialising Description: CRAN Package 'spatialising' (Ising Model for Spatial Data) Performs simulations of binary spatial raster data using the Ising model (Ising (1925) ; Onsager (1944) ). It allows to set a few parameters that represent internal and external pressures, and the number of simulations (Stepinski and Nowosad (2023) ). Package: r-cran-spatialkde Architecture: amd64 Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-rlang, r-cran-vctrs, r-cran-raster, r-cran-cpp11, r-cran-progress Suggests: r-cran-tmap, r-cran-sp, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-spatialkde_0.8.2-1.ca2404.1_amd64.deb Size: 129720 MD5sum: 76144ac22208e8332794b5fe4656b79b SHA1: b46b7c523b2e0d00c1bfea0f3cd432609ae07955 SHA256: 4c0c7daed492447aeb79710010966db9d039632c4e716372d7b339560a640080 SHA512: 296163994317c6e7a98d18129b3f6b54bd759f124518e8d5890c189f0456a5c0111cac1a42b2225472c2b3ec176e43c7e6846752c6b77943b05a01d696bbf6be Homepage: https://cran.r-project.org/package=SpatialKDE Description: CRAN Package 'SpatialKDE' (Kernel Density Estimation for Spatial Data) Calculate Kernel Density Estimation (KDE) for spatial data. The algorithm is inspired by the tool 'Heatmap' from 'QGIS'. The method is described by: Hart, T., Zandbergen, P. (2014) , Nelson, T. A., Boots, B. (2008) , Chainey, S., Tompson, L., Uhlig, S.(2008) . Package: r-cran-spatialkwd Architecture: amd64 Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 899 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-spatialkwd_0.4.1-1.ca2404.1_amd64.deb Size: 514668 MD5sum: e758b10b553e0e3a19377bfd04819084 SHA1: 156cb6e53594e6b3723167489c15ef620ba67430 SHA256: 93913eb6fbe1251841cda4edf5ad49d11f65ccab25bb69a96e9877b6ac59f945 SHA512: da477f10e69bc82380f9e7116763aaa211cbff0ffc03032d72cb153d948a8936efa4591300b597e73325603631091054e58fb33dcf615ebd21a228c7976405a2 Homepage: https://cran.r-project.org/package=SpatialKWD Description: CRAN Package 'SpatialKWD' (Spatial KWD for Large Spatial Maps) Contains efficient implementations of Discrete Optimal Transport algorithms for the computation of Kantorovich-Wasserstein distances between pairs of large spatial maps (Bassetti, Gualandi, Veneroni (2020), ). All the algorithms are based on an ad-hoc implementation of the Network Simplex algorithm. The package has four main helper functions: compareOneToOne() (to compare two spatial maps), compareOneToMany() (to compare a reference map with a list of other maps), compareAll() (to compute a matrix of distances between a list of maps), and focusArea() (to compute the KWD distance within a focus area). In non-convex maps, the helper functions first build the convex-hull of the input bins and pad the weights with zeros. Package: r-cran-spatialnp Architecture: amd64 Version: 1.1-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: libc6 (>= 2.2.5), libstdc++6 (>= 4.3), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-icsnp, r-cran-mnm Filename: pool/dists/noble/main/r-cran-spatialnp_1.1-6-1.ca2404.1_amd64.deb Size: 148846 MD5sum: a797f05f04284f7aba427c04e930ee7b SHA1: 8641a1652b39decd9c36969f5112071bb2dddcdf SHA256: 182be463468f6d4328d3371921949cd2776a56314ab4bcde2f1eeb193496c3ea SHA512: 22ccfafef9a01e9f11d6d7dc3d06a15dfb42da8c73adc7347c55b9e733eb3a9b57653d403f32a515ccc79cb2156d1c052a944e790a81b953cafa171e6e1ee883 Homepage: https://cran.r-project.org/package=SpatialNP Description: CRAN Package 'SpatialNP' (Multivariate Nonparametric Methods Based on Spatial Signs andRanks) Test and estimates of location, tests of independence, tests of sphericity and several estimates of shape all based on spatial signs, symmetrized signs, ranks and signed ranks. For details, see Oja and Randles (2004) and Oja (2010) . Package: r-cran-spatialpack Architecture: amd64 Version: 0.4-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 650 Depends: libc6 (>= 2.35), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastmatrix Filename: pool/dists/noble/main/r-cran-spatialpack_0.4-1-1.ca2404.1_amd64.deb Size: 585302 MD5sum: 7cee42e8af64fdcba7f0ba3110d0bf48 SHA1: b0ce4b85002efb30a46f70eff9d349c595a99ac5 SHA256: c90313ef356273b7dd579636ab46a3ddd4f836206b72f9a77396d67bf1d1684a SHA512: 5760e1f253b152d83deaeee161d0087f15ea03a22888e000a78ff14eea639daabd705611b6060cd771e05464ca01792b14d05cca5c3401a60967c3b7cafd5c0e Homepage: https://cran.r-project.org/package=SpatialPack Description: CRAN Package 'SpatialPack' (Tools for Assessment the Association Between Two SpatialProcesses) Tools to assess the association between two spatial processes. Currently, several methodologies are implemented: A modified t-test to perform hypothesis testing about the independence between the processes, a suitable nonparametric correlation coefficient, the codispersion coefficient, and an F test for assessing the multiple correlation between one spatial process and several others. Functions for image processing and computing the spatial association between images are also provided. Functions contained in the package are intended to accompany Vallejos, R., Osorio, F., Bevilacqua, M. (2020). Spatial Relationships Between Two Georeferenced Variables: With Applications in R. Springer, Cham . Package: r-cran-spatialreg Architecture: amd64 Version: 1.4-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4331 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spdata, r-cran-matrix, r-cran-sf, r-cran-spdep, r-cran-coda, r-cran-mvtnorm, r-cran-boot, r-cran-learnbayes, r-cran-nlme, r-cran-multcomp, r-cran-marginaleffects Suggests: r-cran-rspectra, r-cran-tmap, r-cran-foreign, r-cran-spam, r-cran-knitr, r-cran-lmtest, r-cran-expm, r-cran-sandwich, r-cran-rmarkdown, r-cran-igraph, r-cran-tinytest, r-cran-codingmatrices Filename: pool/dists/noble/main/r-cran-spatialreg_1.4-3-1.ca2404.1_amd64.deb Size: 1552724 MD5sum: f48dd3fef49bdfd13ec46329e431618b SHA1: 36a3cae1d77a93d6f1f50ec202a3e34404ebaa2c SHA256: 0baf6470f7f04e5c51d36737bca2a0966b4762a401c7905ca449d2e9f9171a23 SHA512: c9a803617c21295453c4c884b3e14beac366e74402993a0ae6b9e817223f0596749c50debd50f4e3f10f6ef9e7be18c34ef1baac4c5e00aebe7c521bbe4f2557 Homepage: https://cran.r-project.org/package=spatialreg Description: CRAN Package 'spatialreg' (Spatial Regression Analysis) A collection of all the estimation functions for spatial cross-sectional models (on lattice/areal data using spatial weights matrices) contained up to now in 'spdep'. These model fitting functions include maximum likelihood methods for cross-sectional models proposed by 'Cliff' and 'Ord' (1973, ISBN:0850860369) and (1981, ISBN:0850860814), fitting methods initially described by 'Ord' (1975) . The models are further described by 'Anselin' (1988) . Spatial two stage least squares and spatial general method of moment models initially proposed by 'Kelejian' and 'Prucha' (1998) and (1999) are provided. Impact methods and MCMC fitting methods proposed by 'LeSage' and 'Pace' (2009) are implemented for the family of cross-sectional spatial regression models. Methods for fitting the log determinant term in maximum likelihood and MCMC fitting are compared by 'Bivand et al.' (2013) , and model fitting methods by 'Bivand' and 'Piras' (2015) ; both of these articles include extensive lists of references. A recent review is provided by 'Bivand', 'Millo' and 'Piras' (2021) . 'spatialreg' >= 1.1-* corresponded to 'spdep' >= 1.1-1, in which the model fitting functions were deprecated and passed through to 'spatialreg', but masked those in 'spatialreg'. From versions 1.2-*, the functions have been made defunct in 'spdep'. From version 1.3-6, add Anselin-Kelejian (1997) test to `stsls` for residual spatial autocorrelation . Package: r-cran-spatialrisk Architecture: amd64 Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5683 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-fs, r-cran-lifecycle, r-cran-rcpp, r-cran-rcppprogress, r-cran-rlang, r-cran-sf, r-cran-terra, r-cran-units Suggests: r-cran-classint, r-cran-colourvalues, r-cran-gensa, r-cran-geohashtools, r-cran-ggplot2, r-cran-knitr, r-cran-leafem, r-cran-leafgl, r-cran-leaflet, r-cran-mapview, r-cran-mgcv, r-cran-rmarkdown, r-cran-testthat, r-cran-tmap, r-cran-vroom Filename: pool/dists/noble/main/r-cran-spatialrisk_0.8.0-1.ca2404.1_amd64.deb Size: 4536902 MD5sum: 5ab679c1768283da4d531807befa3de8 SHA1: 42a798b50e2050b9270aff1a1261ed05023f9ff2 SHA256: 5a9718a3d9e084dabde495adf173c13d5a6af45aba7961162b490227d63b3051 SHA512: 6b67736fddf63819c77566b437e4d03aebe07055783716260eba1b436acd13468984349e93f9780bd9b1237fdfd7a6463b5c39305cae0418e8b0dfcbe3ba1346 Homepage: https://cran.r-project.org/package=spatialrisk Description: CRAN Package 'spatialrisk' (Spatial Concentration and Radius-Based Risk Calculations) Provides methods for spatial concentration and radius-based risk calculations. The package focuses on efficient determination of the sum of observations within a given radius, identifying local concentration hotspots, and aggregating point data to polygon geometries. These methods are useful for applications such as insurance, urban analytics, environmental exposure analysis, and other spatial point pattern workflows. The underlying maximum covering problem is described by Church (1974) . Package: r-cran-spatialsample Architecture: amd64 Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1969 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-rsample, r-cran-sf, r-cran-tibble, r-cran-tidyselect, r-cran-units, r-cran-vctrs, r-cran-cpp11 Suggests: r-cran-covr, r-cran-gifski, r-cran-knitr, r-cran-lwgeom, r-cran-modeldata, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-vdiffr, r-cran-whisker, r-cran-withr, r-cran-yardstick Filename: pool/dists/noble/main/r-cran-spatialsample_0.6.1-1.ca2404.1_amd64.deb Size: 1593212 MD5sum: cbd840c988b3678af866114f1ed51ce4 SHA1: f625e0fdfb7af68c22519bef9db28ae08dff996f SHA256: 70fcce515510b0c9a9f20f90832d6d48790b5fbd74b0f816f106fe25b5988f51 SHA512: 2276b5ded9d40834e260f07bad93483a0462e577c2f6995f702241489d398baee8064f4372b47bf17a391a6d7ea856c1118aefc9c4bc489118057559facf5c1c Homepage: https://cran.r-project.org/package=spatialsample Description: CRAN Package 'spatialsample' (Spatial Resampling Infrastructure) Functions and classes for spatial resampling to use with the 'rsample' package, such as spatial cross-validation (Brenning, 2012) . The scope of 'rsample' and 'spatialsample' is to provide the basic building blocks for creating and analyzing resamples of a spatial data set, but neither package includes functions for modeling or computing statistics. The resampled spatial data sets created by 'spatialsample' do not contain much overhead in memory. Package: r-cran-spatialtools Architecture: amd64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 561 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-spbayes, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-spatialtools_1.0.5-1.ca2404.1_amd64.deb Size: 319728 MD5sum: eeea1b1bc289e4bd7f47bfde894cfa60 SHA1: ce220ce1e2d053b3b0c3879d68d9c1bba1826bae SHA256: ae2e6aa07ffea8e353b673c738938277b8dfaeb9715315581319784f0ff97087 SHA512: 6fe0fbbb4e4c02208724de57f391ef7b689bcaa0ebca621ce43ce5cbe5b2ec9bfba9ebf3cd43868f9085aa5c24e264dbceaefcc73dbf673ac12c426641a903a2 Homepage: https://cran.r-project.org/package=SpatialTools Description: CRAN Package 'SpatialTools' (Tools for Spatial Data Analysis) Tools for spatial data analysis. Emphasis on kriging. Provides functions for prediction and simulation. Intended to be relatively straightforward, fast, and flexible. Package: r-cran-spatialwarnings Architecture: amd64 Version: 3.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1584 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-rcpp, r-cran-ggplot2, r-cran-plyr, r-cran-future.apply, r-cran-gsl, r-cran-segmented, r-cran-rcpparmadillo Suggests: r-cran-moments, r-cran-powerlaw, r-cran-reshape2, r-cran-testthat, r-cran-covr, r-cran-acss, r-cran-acss.data, r-cran-mgcv, r-cran-gstat, r-cran-sp, r-cran-raster Filename: pool/dists/noble/main/r-cran-spatialwarnings_3.1.1-1.ca2404.1_amd64.deb Size: 1402776 MD5sum: f04fac6d725b5c0a454c1b4152874655 SHA1: 82746bd99753c7191acfacf215335e2e5cc5991e SHA256: 6660450011afb81504b2bf46c64a5f0da538c2d0b7e811c2afd8b31a2150e1cb SHA512: 2349568a7c846d0f4b60bd7802cda138f426a2dc6acbc1deefc6d7debe70137532c911e0059ab2e00556c588ada2ea256582514c6de491066f954edd62ba0456 Homepage: https://cran.r-project.org/package=spatialwarnings Description: CRAN Package 'spatialwarnings' (Spatial Early Warning Signals of Ecosystem Degradation) Tools to compute and assess significance of early-warnings signals (EWS) of ecosystem degradation. EWS are spatial metrics derived from raster data -- e.g. spatial autocorrelation -- that increase before an ecosystem undergoes a non-linear transition (Genin et al. (2018) ). Package: r-cran-spatialwidget Architecture: amd64 Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3322 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh, r-cran-colourvalues, r-cran-geojsonsf, r-cran-geometries, r-cran-interleave, r-cran-jsonify, r-cran-rapidjsonr, r-cran-sfheaders Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spatialwidget_0.2.6-1.ca2404.1_amd64.deb Size: 823404 MD5sum: d0e8aaf8ea9a935127ae76200557754e SHA1: 1de3cbae18fe6f3dc7d2374cc0679ec4581f4d43 SHA256: bbf9c50a257c859c4b4ca4302469da1699383f994f5c046619192cf1864f3096 SHA512: afcbd240196d22abeb3667d17c90935e105bb5904f238321ac75e5b45029385a23948acf9eb35eadf6219e1a7249c723b6d77814b6cdd5a754b723f9f3252ad9 Homepage: https://cran.r-project.org/package=spatialwidget Description: CRAN Package 'spatialwidget' (Formats Spatial Data for Use in Htmlwidgets) Many packages use 'htmlwidgets' for interactive plotting of spatial data. This package provides functions for converting R objects, such as simple features, into structures suitable for use in 'htmlwidgets' mapping libraries. Package: r-cran-spatmca Architecture: amd64 Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-ggplot2, r-cran-scales, r-cran-rcpparmadillo, r-cran-rcppparallel Suggests: r-cran-testthat, r-cran-rcolorbrewer, r-cran-plot3d, r-cran-pracma, r-cran-sptimer, r-cran-fields, r-cran-maps, r-cran-covr, r-cran-v8 Filename: pool/dists/noble/main/r-cran-spatmca_1.0.7-1.ca2404.1_amd64.deb Size: 188922 MD5sum: 496507c105b4f0db06a814b094bba03b SHA1: b29c70e62815d681395b8d0b077d1b4b66e0f80f SHA256: a7778f3e161b1d563ba8f3cf4dc80a64ec18414afa2bcc7e719a05857d651c08 SHA512: cacc68d3d1fd4083b32c6cdc266f5dffb5eae3797e0f2484f3c12d85bd6a0b17fe65cb169ad0543f471860e51fbd4581b006db7b7fb174ed11c0ced19d7da71d Homepage: https://cran.r-project.org/package=SpatMCA Description: CRAN Package 'SpatMCA' (Regularized Spatial Maximum Covariance Analysis) Provide regularized maximum covariance analysis incorporating smoothness, sparseness and orthogonality of couple patterns by using the alternating direction method of multipliers algorithm. The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D (Wang and Huang, 2018 ). Package: r-cran-spatopic Architecture: amd64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 908 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rann, r-cran-sf, r-cran-foreach, r-cran-iterators, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-seuratobject, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-spatopic_1.2.0-1.ca2404.1_amd64.deb Size: 616898 MD5sum: b371cc582e6671a961f08edcd4694c6e SHA1: dcc638e9f5e0eab9e2fd42dec385c2c6805b18c5 SHA256: 4395bb5c988de2443b85cf09cc4d10d07f063ade51f8344a8bdf33d7aee537aa SHA512: 8179032a4f487698756f6ef2b8d546d2808189f41e695894b0461707a22dad4a05910c0d8edb85c6c0d6b3030936fb2875218f1bb8da86862742eb6867b3c427 Homepage: https://cran.r-project.org/package=SpaTopic Description: CRAN Package 'SpaTopic' (Topic Inference to Identify Tissue Architecture in MultiplexedImages) A novel spatial topic model to integrate both cell type and spatial information to identify the complex spatial tissue architecture on multiplexed tissue images without human intervention. The Package implements a collapsed Gibbs sampling algorithm for inference. 'SpaTopic' is scalable to large-scale image datasets without extracting neighborhood information for every single cell. For more details on the methodology, see . Package: r-cran-spatpca Architecture: amd64 Version: 1.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 787 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr, r-cran-tidyr, r-cran-fields, r-cran-scico, r-cran-plot3d, r-cran-pracma, r-cran-rcolorbrewer, r-cran-maps, r-cran-covr, r-cran-styler, r-cran-v8 Filename: pool/dists/noble/main/r-cran-spatpca_1.3.8-1.ca2404.1_amd64.deb Size: 410382 MD5sum: 1579920a3bfc998a04708d4783e470fe SHA1: 53646e886e18d2cc065b8b1933f598117d45e31b SHA256: c2ebeaa1946accd53c7f4687a0e501407f7b6abe2912cc7e13ff2d0f58ebaf56 SHA512: 975ae1b294b0e3471983b8b9fd4fe570b0d7f5305e892af52e21e8003364f1c6c1f047d491d6f67cdab4b7e7f3f0bc504eff55d239e0449b3d139bcc87c87518 Homepage: https://cran.r-project.org/package=SpatPCA Description: CRAN Package 'SpatPCA' (Regularized Principal Component Analysis for Spatial Data) Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, ). The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D. Package: r-cran-spatpomp Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2245 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pomp, r-cran-foreach, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-abind, r-cran-rlang, r-cran-ggplot2 Suggests: r-cran-doparallel, r-cran-dorng Filename: pool/dists/noble/main/r-cran-spatpomp_1.1.0-1.ca2404.1_amd64.deb Size: 1982178 MD5sum: 5e99544f9f51aaa955c52bc4a4a0c2f2 SHA1: 4bdb84f6f6e0d3947aa9687bac1a1c5a96f77f94 SHA256: d76c0d5f6be2000a9e59243d7a1c83803d4d4100c18f1cf612ec2c69eb660616 SHA512: 601a47d97c582dc95f9ae98f805b3555fac8b50b8a066c7f12dcf1eb7fddac7f93c18fe9bb70d70f0d2629f8589a6bdd79a64cfd871b9a8d04638e46470b1dbe Homepage: https://cran.r-project.org/package=spatPomp Description: CRAN Package 'spatPomp' (Inference for Spatiotemporal Partially Observed Markov Processes) Inference on panel data using spatiotemporal partially-observed Markov process (SpatPOMP) models. The 'spatPomp' package extends 'pomp' to include algorithms taking advantage of the spatial structure in order to assist with handling high dimensional processes. See Asfaw et al. (2024) for further description of the package. Package: r-cran-spatstat.explore Architecture: amd64 Version: 3.8-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3820 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-nlme, r-cran-spatstat.utils, r-cran-spatstat.sparse, r-cran-goftest, r-cran-matrix, r-cran-abind Suggests: r-cran-sm, r-cran-gsl, r-cran-locfit, r-cran-spatial, r-cran-fftwtools, r-cran-spatstat.linnet, r-cran-spatstat.model, r-cran-spatstat Filename: pool/dists/noble/main/r-cran-spatstat.explore_3.8-0-1.ca2404.1_amd64.deb Size: 3551230 MD5sum: d9d0b198d1bb830114ff56e05263fbde SHA1: 242b07912ffe78b80cc06323433226bba25fc4c2 SHA256: 6446767f42081f682c2215f4454666494830aee003b2f009c22290d65ac84b8e SHA512: 780613dc546c2717c94d74bfe55ac94b3b2fd6d60306793057daa74863f0e437279f10bafd21d80c0635ccc98a42d811890051f1d64a8353a82363dd860d9997 Homepage: https://cran.r-project.org/package=spatstat.explore Description: CRAN Package 'spatstat.explore' (Exploratory Data Analysis for the 'spatstat' Family) Functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Package: r-cran-spatstat.geom Architecture: amd64 Version: 3.7-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4627 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.utils, r-cran-deldir, r-cran-polyclip Suggests: r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.linnet, r-cran-spatial, r-cran-fftwtools, r-cran-spatstat Filename: pool/dists/noble/main/r-cran-spatstat.geom_3.7-3-1.ca2404.1_amd64.deb Size: 4119830 MD5sum: 7725ce9d19fcf665ecacc88d576d8d65 SHA1: 0e4be3bbe6329d1778dee6b4431767cceb47c5df SHA256: d189ec659c9e10a4035b71fa39a0838c423c870bdfc4bae6afb471b23e084567 SHA512: 14639d32a1e96139a21cd888abd65770c69c25e12d203dd32701858aaec76a823e641931bfa2af6ec4aeb0d29cfb85f2ff7b3e5b69170c58a665305f278b75fb Homepage: https://cran.r-project.org/package=spatstat.geom Description: CRAN Package 'spatstat.geom' (Geometrical Functionality of the 'spatstat' Family) Defines spatial data types and supports geometrical operations on them. Data types include point patterns, windows (domains), pixel images, line segment patterns, tessellations and hyperframes. Capabilities include creation and manipulation of data (using command line or graphical interaction), plotting, geometrical operations (rotation, shift, rescale, affine transformation), convex hull, discretisation and pixellation, Dirichlet tessellation, Delaunay triangulation, pairwise distances, nearest-neighbour distances, distance transform, morphological operations (erosion, dilation, closing, opening), quadrat counting, geometrical measurement, geometrical covariance, colour maps, calculus on spatial domains, Gaussian blur, level sets of images, transects of images, intersections between objects, minimum distance matching. (Excludes spatial data on a network, which are supported by the package 'spatstat.linnet'.) Package: r-cran-spatstat.knet Architecture: amd64 Version: 3.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2247 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.sparse, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.linnet, r-cran-spatstat, r-cran-spatstat.utils, r-cran-matrix Filename: pool/dists/noble/main/r-cran-spatstat.knet_3.1-3-1.ca2404.1_amd64.deb Size: 2229430 MD5sum: 8a3b7fd06a35ab577ca565a5c9bf7d8f SHA1: 122e38c055aa92058a5884d250a1b38050a071ef SHA256: ffe41ac37750a623bf32fbc0ad55c22677eb4a41dedfe3c74eaaf414e2fa85cf SHA512: 3393ddce418c33684941a245128d629986be095e7afa7895139382bb4efad5d53774fb5918eea376ccd6bcdf5764f88ee39c965eeb640a996418f937f2c6b8be Homepage: https://cran.r-project.org/package=spatstat.Knet Description: CRAN Package 'spatstat.Knet' (Extension to 'spatstat' for Large Datasets on a Linear Network) Extension to the 'spatstat' family of packages, for analysing large datasets of spatial points on a network. The geometrically- corrected K function is computed using a memory-efficient tree-based algorithm described by Rakshit, Baddeley and Nair (2019). Package: r-cran-spatstat.linnet Architecture: amd64 Version: 3.5-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1923 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-matrix, r-cran-spatstat.utils, r-cran-spatstat.sparse Suggests: r-cran-goftest, r-cran-locfit, r-cran-spatstat Filename: pool/dists/noble/main/r-cran-spatstat.linnet_3.5-0-1.ca2404.1_amd64.deb Size: 1767232 MD5sum: 8e53b7e38a15635e55af474db4511134 SHA1: 78077140d62fae136f610f5e5f98f45290a40672 SHA256: b58cf8bbcd45c1e76f8a882922663e1f84c2f4d1e2cb17a36a3bf876062d6f3a SHA512: d5b2ecab94331a9dd016cb62a54c850a2f3d32eebc4fb23288ee185b463466d420e14c64a9efc2f605e970f5bc38824e637ade8f9a30f4e1d4553d208450d59a Homepage: https://cran.r-project.org/package=spatstat.linnet Description: CRAN Package 'spatstat.linnet' (Linear Networks Functionality of the 'spatstat' Family) Defines types of spatial data on a linear network and provides functionality for geometrical operations, data analysis and modelling of data on a linear network, in the 'spatstat' family of packages. Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks. Data types defined on a network include point patterns, pixel images, functions, and tessellations. Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. Random point patterns on a network can be generated using a variety of models. Package: r-cran-spatstat.model Architecture: amd64 Version: 3.7-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3816 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-nlme, r-cran-rpart, r-cran-spatstat.utils, r-cran-spatstat.sparse, r-cran-mgcv, r-cran-matrix, r-cran-abind, r-cran-tensor, r-cran-goftest Suggests: r-cran-sm, r-cran-gsl, r-cran-locfit, r-cran-spatial, r-cran-fftwtools, r-cran-nleqslv, r-cran-glmnet, r-cran-spatstat.linnet, r-cran-spatstat Filename: pool/dists/noble/main/r-cran-spatstat.model_3.7-0-1.ca2404.1_amd64.deb Size: 3535918 MD5sum: 4a6971ccf567659d2dfaf776ce42901b SHA1: fbebc779330acd3024cb315a95ae4e7040a4fc6a SHA256: 6f4154169203a9b66ad95824156190e53666a52ec9ab60872da6f4e36926c802 SHA512: 8cc684aec1318e562e6c7e24b883045a8823f7775e24fadeb3b759b8d353f6601945c981bd107d48a5e55eccc4ef12d1e16bd903baa620975eaa84f8b21b7568 Homepage: https://cran.r-project.org/package=spatstat.model Description: CRAN Package 'spatstat.model' (Parametric Statistical Modelling and Inference for the'spatstat' Family) Functionality for parametric statistical modelling and inference for spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Supports parametric modelling, formal statistical inference, and model validation. Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots. Package: r-cran-spatstat.random Architecture: amd64 Version: 3.4-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1456 Depends: libc6 (>= 2.35), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.utils Suggests: r-cran-spatial, r-cran-spatstat.linnet, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat, r-cran-gsl Filename: pool/dists/noble/main/r-cran-spatstat.random_3.4-5-1.ca2404.1_amd64.deb Size: 1240098 MD5sum: d5f55a9d5a31de5850efc2b75616d4a0 SHA1: 6f8b682fd8dd0d363a565aec81ca2931b6304f4f SHA256: c20d97d841f8ed2a059a3129d678b20756b28413e0cd5a4566186e3eae884488 SHA512: d3f42b3979808c7fd6f8df6fed0b7a50963cba89c5d40590d14da822cc5236a4f85d22fce5899ecb32ee1c9b2284e263a9d3598b452f1d379c9e63f69ce7ab68 Homepage: https://cran.r-project.org/package=spatstat.random Description: CRAN Package 'spatstat.random' (Random Generation Functionality for the 'spatstat' Family) Functionality for random generation of spatial data in the 'spatstat' family of packages. Generates random spatial patterns of points according to many simple rules (complete spatial randomness, Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns (thinning, random shift, jittering), simulated realisations of random point processes including simple sequential inhibition, Matern inhibition models, Neyman-Scott cluster processes (using direct, Brix-Kendall, or hybrid algorithms), log-Gaussian Cox processes, product shot noise cluster processes and Gibbs point processes (using Metropolis-Hastings birth-death-shift algorithm, alternating Gibbs sampler, or coupling-from-the-past perfect simulation). Also generates random spatial patterns of line segments, random tessellations, and random images (random noise, random mosaics). Excludes random generation on a linear network, which is covered by the separate package 'spatstat.linnet'. Package: r-cran-spatstat.sparse Architecture: amd64 Version: 3.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-abind, r-cran-tensor, r-cran-spatstat.utils Filename: pool/dists/noble/main/r-cran-spatstat.sparse_3.2-0-1.ca2404.1_amd64.deb Size: 238302 MD5sum: 1a90c60d0e84c060b28b327b8bca7cb6 SHA1: bb3771f54ebec0b2ae9853028594b186db1d4ec1 SHA256: e458a5be401eb8e7cfded0d835e8fa13fcd0fe707eaeccd7875fe4373fa6ec3d SHA512: fc55d5f795103e1e75a4d89accd680d552d8ee908d20c9f9ec55b0ae837f111e853e4afa035ab0b5dd483433a81cb5b9f8ad371eb20432982d618a9e24d2e9f7 Homepage: https://cran.r-project.org/package=spatstat.sparse Description: CRAN Package 'spatstat.sparse' (Sparse Three-Dimensional Arrays and Linear Algebra Utilities) Defines sparse three-dimensional arrays and supports standard operations on them. The package also includes utility functions for matrix calculations that are common in statistics, such as quadratic forms. Package: r-cran-spatstat.univar Architecture: amd64 Version: 3.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.6.0), r-api-4.0, r-cran-spatstat.utils Filename: pool/dists/noble/main/r-cran-spatstat.univar_3.2-0-1.ca2404.1_amd64.deb Size: 356602 MD5sum: 6b64480c25bb63489022cdb6f9c7f4a4 SHA1: 6b29c2d6ab0756e8d25ee993235f69b1263e2ea5 SHA256: c590627a0071d2fa075cc67efb0cead3d131ef3d113fad2c93cef755b6375243 SHA512: 78209fc271481f236783494bf144e5092cb7c4bf81ee2a89333cb671b1da995fa163193fe92e5fff6f04b08286e9227e2bbe7efa3ae80311d6a01a939cb905e9 Homepage: https://cran.r-project.org/package=spatstat.univar Description: CRAN Package 'spatstat.univar' (One-Dimensional Probability Distribution Support for the'spatstat' Family) Estimation of one-dimensional probability distributions including kernel density estimation, weighted empirical cumulative distribution functions, Kaplan-Meier and reduced-sample estimators for right-censored data, heat kernels, special distributions, kernel properties, quantiles and integration. Package: r-cran-spatstat.utils Architecture: amd64 Version: 3.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-spatstat.model Filename: pool/dists/noble/main/r-cran-spatstat.utils_3.2-3-1.ca2404.1_amd64.deb Size: 402602 MD5sum: 042ca4ab1e191c88841a1c1efbeef5c2 SHA1: a100f789c254e4a042f2da80f332433e6dcbedd9 SHA256: 8dce548c067bd06953d52e448925e960057ec3126b4122b17ada83218503f83d SHA512: 9bc0b424d9e8fc54f019c40758336050d465b987d9510d46c0296adf5cea741b5e77c3cf06caca9418b5386476e725a759741b94b4bdfab3b2fc164e84da5075 Homepage: https://cran.r-project.org/package=spatstat.utils Description: CRAN Package 'spatstat.utils' (Utility Functions for 'spatstat') Contains utility functions for the 'spatstat' family of packages which may also be useful for other purposes. Package: r-cran-spatstat Architecture: amd64 Version: 3.3-2-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5296 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.linnet, r-cran-spatstat.utils Filename: pool/dists/noble/main/r-cran-spatstat_3.3-2-1.ca2404.2_amd64.deb Size: 4204660 MD5sum: d66c87b8eff390344e891e9f3806e077 SHA1: e2d2b34e21d45edda3c4b242ca10dbb48d2104d5 SHA256: d41aba2692c19680728fc8c47810a127243047e37889cf18f3d0bbafb6b4c53f SHA512: 3c2d875e12582753721f9351377b27f382800e8493bab621f6e14d3d5b5ccd3490f1c779e5b8443e7724c77c2be19c6474badb51c2e84ec0453a8aceceeaf8a3 Homepage: https://cran.r-project.org/package=spatstat Description: CRAN Package 'spatstat' (Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests) Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 3000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. Package: r-cran-spbal Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1157 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-units, r-cran-sf, r-cran-rcpp, r-cran-rcppthread Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-bookdown, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-spbal_1.0.1-1.ca2404.1_amd64.deb Size: 660130 MD5sum: 44b4338faa130a614d2d81bcd4bf4409 SHA1: 91d8670a14ec633dc7151414bda5c4fe8401967c SHA256: dbdeb1c1edcccfdba3907bd1bc0cc2430da6ba2eda8c55e76ba9e7109f22ab57 SHA512: 03b647cb8e4755b578d567f38cba288ed5bd0610ebe3754e1026081c61f7f0c34efa08a817cae3514c1090aa37cc883a71ff415168f7edc34960424f86111d6b Homepage: https://cran.r-project.org/package=spbal Description: CRAN Package 'spbal' (Spatially Balanced Sampling Algorithms) Encapsulates a number of spatially balanced sampling algorithms, namely, Balanced Acceptance Sampling (equal, unequal, seed point, panels), Halton frames (for discretizing a continuous resource), Halton Iterative Partitioning (equal probability) and Simple Random Sampling. Robertson, B. L., Brown, J. A., McDonald, T. and Jaksons, P. (2013) . Robertson, B. L., McDonald, T., Price, C. J. and Brown, J. A. (2017) . Robertson, B. L., McDonald, T., Price, C. J. and Brown, J. A. (2018) . Robertson, B. L., van Dam-Bates, P. and Gansell, O. (2021a) . Robertson, B. L., Davies, P., Gansell, O., van Dam-Bates, P., McDonald, T. (2025) . Package: r-cran-spbayes Architecture: amd64 Version: 0.4-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1389 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-sp, r-cran-magic, r-cran-formula, r-cran-matrix Suggests: r-cran-mba Filename: pool/dists/noble/main/r-cran-spbayes_0.4-8-1.ca2404.1_amd64.deb Size: 1130646 MD5sum: 69abc1359148268fbe68c81675e9eb85 SHA1: 21abca6dca70318161d4dcd67b4bb0022649df0b SHA256: 16515629c1a8f59654850e3f8b13e32478e2993bffd3f2e8066693bbbc444f59 SHA512: b27cf0b407a0bd033a661edb9e5516ad831a479ac5ec770c456887adaa4537683fdc9ad2e600d12b1a65753b4ff313e25c1f1abca525c9971f1a02ccb934c968 Homepage: https://cran.r-project.org/package=spBayes Description: CRAN Package 'spBayes' (Univariate and Multivariate Spatial-Temporal Modeling) Fits univariate and multivariate spatio-temporal random effects models for point-referenced data using Markov chain Monte Carlo (MCMC). Details are given in Finley, Banerjee, and Gelfand (2015) and Finley and Banerjee . Package: r-cran-spbayessurv Architecture: amd64 Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2836 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-coda, r-cran-mass, r-cran-fields, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-spbayessurv_1.1.9-1.ca2404.1_amd64.deb Size: 1014262 MD5sum: 079774d65dc9c6f5015535284a11fbc8 SHA1: 8ed68ba98b192baa51b77a99092b8ab785ae13d3 SHA256: b5feae0bf9f9f6e567402bd0d5b04f62ca50af77ee723e95eca4522821fe1ec5 SHA512: 16d54aa8db7f55d37360a6cef58c2b7981af38bcb1e45bda8d18bfacb347e9f97b604d4dac6eb3f423a76830296754a463e6f6624c66d9abd319c456436e4212 Homepage: https://cran.r-project.org/package=spBayesSurv Description: CRAN Package 'spBayesSurv' (Bayesian Modeling and Analysis of Spatially Correlated SurvivalData) Provides several Bayesian survival models for spatial/non-spatial survival data: proportional hazards (PH), accelerated failure time (AFT), proportional odds (PO), and accelerated hazards (AH), a super model that includes PH, AFT, PO and AH as special cases, Bayesian nonparametric nonproportional hazards (LDDPM), generalized accelerated failure time (GAFT), and spatially smoothed Polya tree density estimation. The spatial dependence is modeled via frailties under PH, AFT, PO, AH and GAFT, and via copulas under LDDPM and PH. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou, Hanson and Zhang (2020) . Package: r-cran-spbfa Architecture: amd64 Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3524 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-msm, r-cran-mvtnorm, r-cran-pgdraw, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-coda, r-cran-classint, r-cran-knitr, r-cran-rmarkdown, r-cran-womblr Filename: pool/dists/noble/main/r-cran-spbfa_1.5.0-1.ca2404.1_amd64.deb Size: 3013412 MD5sum: bbf84d1af11ed18830127af254242752 SHA1: aef36db1bcb16df9397a35613f63714b7d824164 SHA256: 06d35a1e7ecbfcd00de755a43716422b83d6e3f49fd6fb682d75ae169c41c21a SHA512: 64cfe2cf855d871972bcd5375e338bae3105b143155f7d0e2e677908d8b16dedd9731816dc55101d83bb2bf812685719c708ea382936aeab7d946714ebddef4b Homepage: https://cran.r-project.org/package=spBFA Description: CRAN Package 'spBFA' (Spatial Bayesian Factor Analysis) Implements a spatial Bayesian non-parametric factor analysis model with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC). Spatial correlation is introduced in the columns of the factor loadings matrix using a Bayesian non-parametric prior, the probit stick-breaking process. Areal spatial data is modeled using a conditional autoregressive (CAR) prior and point-referenced spatial data is treated using a Gaussian process. The response variable can be modeled as Gaussian, probit, Tobit, or Binomial (using Polya-Gamma augmentation). Temporal correlation is introduced for the latent factors through a hierarchical structure and can be specified as exponential or first-order autoregressive. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in "Bayesian Non-Parametric Factor Analysis for Longitudinal Spatial Surfaces", by Berchuck et al (2019), in Bayesian Analysis. Package: r-cran-spbps Architecture: amd64 Version: 2.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1106 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-cvxr, r-cran-mniw, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-abind, r-cran-mvnfast, r-cran-ecosolver, r-cran-foreach, r-cran-doparallel, r-cran-tictoc, r-cran-mba, r-cran-rcolorbrewer, r-cran-classint, r-cran-sp, r-cran-fields, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spbps_2.0-1-1.ca2404.1_amd64.deb Size: 558162 MD5sum: ec2beeffcc107cacc538f8f0f5ec9ca6 SHA1: 02e144a616b39c0b42530e1d8b9d5df7198899a2 SHA256: 7202259c18a2e7697bc2c212993595fb1ef8f4fcd22236290122c0188d8c2766 SHA512: b88a44cb16693f0c3e7e94464c2b9a44613c4141a0d6e5a2f29326992859d9835cc8195ad74082816ff27265a46385895a4e5ff9f8e36d782c381a01741ff940 Homepage: https://cran.r-project.org/package=spBPS Description: CRAN Package 'spBPS' (Bayesian Predictive Stacking for Scalable Geospatial TransferLearning) Provides functions for Bayesian Predictive Stacking within the Bayesian transfer learning framework for geospatial artificial systems, as introduced in "Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach" (Presicce and Banerjee, 2025) . This methodology enables efficient Bayesian geostatistical modeling, utilizing predictive stacking to improve inference across spatial datasets. The core functions leverage 'C++' for high-performance computation, making the framework well-suited for large-scale spatial data analysis in parallel and distributed computing environments. Designed for scalability, it allows seamless application in computationally demanding scenarios. Package: r-cran-spbsampling Architecture: amd64 Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 608 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-spbsampling_1.3.5-1.ca2404.1_amd64.deb Size: 451216 MD5sum: 62072f5e4d4e1408fad9607c271c698a SHA1: 58e3547eeeffcc22cc540859f1b7fb2f50eb422f SHA256: 1f51f31010abacdbb89da94cb491445afb35c58b01dac5be82f136ea0975c64c SHA512: 5a999a305c089822b557888b74e832608d79fafa69d5ba2bd914bdaccf6bad960745b88ef5d5085a653097eba111b20a233c448d39ac688dff7b31732db642e7 Homepage: https://cran.r-project.org/package=Spbsampling Description: CRAN Package 'Spbsampling' (Spatially Balanced Sampling) Selection of spatially balanced samples. In particular, the implemented sampling designs allow to select probability samples well spread over the population of interest, in any dimension and using any distance function (e.g. Euclidean distance, Manhattan distance). For more details, Pantalone F, Benedetti R, and Piersimoni F (2022) , Benedetti R and Piersimoni F (2017) , and Benedetti R and Piersimoni F (2017) . The implementation has been done in C++ through the use of 'Rcpp' and 'RcppArmadillo'. Package: r-cran-spc Architecture: amd64 Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1434 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spc_0.7.2-1.ca2404.1_amd64.deb Size: 854120 MD5sum: 1edab7e13c93a3553a48a00fbb94bbd4 SHA1: baef4e14e04bbcd7c8138dbe24cc6bf89edb5646 SHA256: 6731c5b5c837f4dfe5734b1e5f75ad065724d0f5aacb85d03caf7bdfcbe1e161 SHA512: 92cc8700ffda7dee927605014924fb5154dedce396437cdf34fdd4e93c405f2c51bb7b9bd30efd44d0d43a04e76f7f796102bf572095de0e8755342ee4f083da Homepage: https://cran.r-project.org/package=spc Description: CRAN Package 'spc' (Statistical Process Control -- Calculation of ARL and OtherControl Chart Performance Measures) Evaluation of control charts by means of the zero-state, steady-state ARL (Average Run Length) and RL quantiles. Setting up control charts for given in-control ARL. The control charts under consideration are one- and two-sided EWMA, CUSUM, and Shiryaev-Roberts schemes for monitoring the mean or variance of normally distributed independent data. ARL calculation of the same set of schemes under drift (in the mean) are added. Eventually, all ARL measures for the multivariate EWMA (MEWMA) are provided. Package: r-cran-spcf Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1524 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn, r-cran-fields, r-cran-nloptr, r-cran-dbscan, r-cran-ranger, r-cran-withr, r-cran-rcpp Suggests: r-cran-sp, r-cran-sf, r-cran-knitr, r-cran-rmarkdown, r-cran-carbayesdata Filename: pool/dists/noble/main/r-cran-spcf_0.1.1-1.ca2404.1_amd64.deb Size: 1034500 MD5sum: 158a8fb6214e102b6e94a60b8542d083 SHA1: 3ffc81cc57aa863ca27ec0e3759633d03a62f86e SHA256: c923d2eed144aa6663113fe26561104ba6109efdde3092aaf9f64c70db1e2863 SHA512: 3782ff4c4fb8191d2aad6f3f8657f6b487f0087d08033dcea1bf95849e9c63350c02d51adf61b7fc224fc0c85e04dbf71cd389e629019b04261ebffdbe5a1f6b Homepage: https://cran.r-project.org/package=spCF Description: CRAN Package 'spCF' (Coarse-to-Fine Spatial Modeling) Provides functions for coarse-to-fine spatial modeling (CFSM), enabling fast spatial prediction, regression, and uncertainty quantification. This method is suitable for moderate to large samples. For further details, see Murakami et al. (2026) . Package: r-cran-spcp Architecture: amd64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1174 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-msm, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-coda, r-cran-classint, r-cran-knitr, r-cran-rmarkdown, r-cran-womblr Filename: pool/dists/noble/main/r-cran-spcp_1.4.0-1.ca2404.1_amd64.deb Size: 616218 MD5sum: ab0c8ac8513270469e6370d7a329a445 SHA1: b705a1e7f03a323cac4a52404280f0fa486041c3 SHA256: 0e029d4d558ea2dfff2f26abfb1b868db753d502ef8206c05333cf898122a0d6 SHA512: cec1eeb7551722ecf225841a68766c898481b427093cde41f93cb70f66a5a13687ea4ba69c2f59db79941ec237184579fb3d083e7c986891ca88fbdf9fb5083a Homepage: https://cran.r-project.org/package=spCP Description: CRAN Package 'spCP' (Spatially Varying Change Points) Implements a spatially varying change point model with unique intercepts, slopes, variance intercepts and slopes, and change points at each location. Inference is within the Bayesian setting using Markov chain Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget), probit or Tobit link and the five spatially varying parameter are modeled jointly using a multivariate conditional autoregressive (MCAR) prior. The MCAR is a unique process that allows for a dissimilarity metric to dictate the local spatial dependencies. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in the corresponding paper published in Spatial Statistics by Berchuck et al (2019): "A spatially varying change points model for monitoring glaucoma progression using visual field data", . Package: r-cran-spcr Architecture: amd64 Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spcr_2.1.1-1.ca2404.1_amd64.deb Size: 97440 MD5sum: ca1f14e1c4bece9b0c505351159e9ef0 SHA1: 7042efd2b9eb18d2cedbe8fad291d36f93fdb711 SHA256: 81e87fd7fe15fc7c2c8b2478df60895903410838ced8c68a43c47f0cdaa6cfd6 SHA512: 21ab01689558f18389b372662a5da4a8d289f13a2127305bf5e77c2ddd633c21a9fcd2bd3a3b31bb63bf100b524c491d2bd21a61fc656959fb6be4aef3c67658 Homepage: https://cran.r-project.org/package=spcr Description: CRAN Package 'spcr' (Sparse Principal Component Regression) The sparse principal component regression is computed. The regularization parameters are optimized by cross-validation. Package: r-cran-spdep Architecture: amd64 Version: 1.4-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9736 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spdata, r-cran-sf, r-cran-deldir, r-cran-boot, r-cran-units, r-cran-s2, r-cran-e1071, r-cran-sp Suggests: r-cran-spatialreg, r-cran-matrix, r-cran-dbscan, r-cran-rcolorbrewer, r-cran-lattice, r-cran-xtable, r-cran-foreign, r-cran-igraph, r-cran-rspectra, r-cran-knitr, r-cran-classint, r-cran-tmap, r-cran-spam, r-cran-ggplot2, r-cran-rmarkdown, r-cran-tinytest, r-cran-rgeoda, r-cran-mipfp, r-cran-guerry, r-cran-codingmatrices Filename: pool/dists/noble/main/r-cran-spdep_1.4-2-1.ca2404.1_amd64.deb Size: 4141160 MD5sum: e12bf84715189e58a9fcad632ec63ef0 SHA1: 1e25ea9ea9ede12e2db5a9d9c177cc462883e521 SHA256: e76ef8d4600fa145bbe4c856c07be65f76a15f77c69dbb95adac589e455f3a8a SHA512: c92c9a3189a4514f43fc910052af4d78ad592089f6d508f63ddf9aea4a9f37a8011be93dae65eafd1ce720332a277818990df75e51b782ce58577cc2da6538eb Homepage: https://cran.r-project.org/package=spdep Description: CRAN Package 'spdep' (Spatial Dependence: Weighting Schemes, Statistics) A collection of functions to create spatial weights matrix objects from polygon 'contiguities', from point patterns by distance and tessellations, for summarizing these objects, and for permitting their use in spatial data analysis, including regional aggregation by minimum spanning tree; a collection of tests for spatial 'autocorrelation', including global 'Morans I' and 'Gearys C' proposed by 'Cliff' and 'Ord' (1973, ISBN: 0850860369) and (1981, ISBN: 0850860814), 'Hubert/Mantel' general cross product statistic, Empirical Bayes estimates and 'Assunção/Reis' (1999) Index, 'Getis/Ord' G ('Getis' and 'Ord' 1992) and multicoloured join count statistics, 'APLE' ('Li et al.' ) , local 'Moran's I', 'Gearys C' ('Anselin' 1995) and 'Getis/Ord' G ('Ord' and 'Getis' 1995) , 'saddlepoint' approximations ('Tiefelsdorf' 2002) and exact tests for global and local 'Moran's I' ('Bivand et al.' 2009) and 'LOSH' local indicators of spatial heteroscedasticity ('Ord' and 'Getis') . The implementation of most of these measures is described in 'Bivand' and 'Wong' (2018) , with further extensions in 'Bivand' (2022) . 'Lagrange' multiplier tests for spatial dependence in linear models are provided ('Anselin et al'. 1996) , as are 'Rao' score tests for hypothesised spatial 'Durbin' models based on linear models ('Koley' and 'Bera' 2023) . Additions in 2024 include Local Indicators for Categorical Data based on 'Carrer et al.' (2021) and 'Bivand et al.' (2017) ; also Weighted Multivariate Spatial Autocorrelation Measures ('Bavaud' 2024) . . A local indicators for categorical data (LICD) implementation based on 'Carrer et al.' (2021) and 'Bivand et al.' (2017) was added in 1.3-7. Multivariate 'spatialdelta' ('Bavaud' 2024) was added in 1.3-13 ('Bivand' 2025 ). From 'spdep' and 'spatialreg' versions >= 1.2-1, the model fitting functions previously present in this package are defunct in 'spdep' and may be found in 'spatialreg'. Package: r-cran-spduration Architecture: amd64 Version: 0.17.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 744 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor, r-cran-forecast, r-cran-mass, r-cran-rcpp, r-cran-separationplot, r-cran-xtable, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-devtools, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/noble/main/r-cran-spduration_0.17.3-1.ca2404.1_amd64.deb Size: 473766 MD5sum: dc4a37637447ca9f0d31d3b75fbe077f SHA1: dd9242ade1f302bef8c4b50eb121e40ae67257d0 SHA256: 7c0c263374d23b4effb36519c01bbdfac1a2660edb205cb9e731ecbac45253f2 SHA512: b5b83e52c613c1b92c25f8d3a5b90513c454d5fd7e42a09edcdcffcd666bb8a536066a9becdb7624b6388678b85b6dc50d2364c9f2ed3d0f846850c1bb3ea827 Homepage: https://cran.r-project.org/package=spduration Description: CRAN Package 'spduration' (Split-Population Duration (Cure) Regression) An implementation of split-population duration regression models. Unlike regular duration models, split-population duration models are mixture models that accommodate the presence of a sub-population that is not at risk for failure, e.g. cancer patients who have been cured by treatment. This package implements Weibull and Loglogistic forms for the duration component, and focuses on data with time-varying covariates. These models were originally formulated in Boag (1949) and Berkson and Gage (1952), and extended in Schmidt and Witte (1989). Package: r-cran-speakeasyr Architecture: amd64 Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 708 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-igraph, r-bioc-scrnaseq, r-bioc-summarizedexperiment, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-speakeasyr_0.1.8-1.ca2404.1_amd64.deb Size: 285402 MD5sum: bfec143ebc8b8c257a506453d08b1360 SHA1: b554031eb516ec422b1a492164ff17accde2f2af SHA256: 14f63f18e45bb2f165432f48e48f1736b204dca72b1c57f75998244dcb215f24 SHA512: 822f32ccfaada0856faf9efc13d1a166b52765b1e3d830a25e081064534abafead25fc3cc4c781ab7fee754a0c9633c84c0985b3c4d121b0a808e18075ef3d47 Homepage: https://cran.r-project.org/package=speakeasyR Description: CRAN Package 'speakeasyR' (Fast and Robust Multi-Scale Graph Clustering) A graph community detection algorithm that aims to be performant on large graphs and robust, returning consistent results across runs. SpeakEasy 2 (SE2), the underlying algorithm, is described in Chris Gaiteri, David R. Connell & Faraz A. Sultan et al. (2023) . The core algorithm is written in 'C', providing speed and keeping the memory requirements low. This implementation can take advantage of multiple computing cores without increasing memory usage. SE2 can detect community structure across scales, making it a good choice for biological data, which often has hierarchical structure. Graphs can be passed to the algorithm as adjacency matrices using base 'R' matrices, the 'Matrix' library, 'igraph' graphs, or any data that can be coerced into a matrix. Package: r-cran-species Architecture: amd64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-species_1.2.0-1.ca2404.1_amd64.deb Size: 125906 MD5sum: b47db6760a6362093bf2fba4ce105095 SHA1: 147a7c73ced785f22b65fdf74ba1956e15e65010 SHA256: 099d8e71c75a464f511c1d875234d139556c822b4ee39c8b4ee49d03ddf5f69c SHA512: c7f0c39b0bf98d65eea997e1bfeb6e8ffeba91e56deeea085f21aadeb371c71b1bed64384d90fd3a4ac3444b983dada27c6681764a4d5c3157e949d89454eb1c Homepage: https://cran.r-project.org/package=SPECIES Description: CRAN Package 'SPECIES' (Statistical Package for Species Richness Estimation) Implementation of various methods in estimation of species richness or diversity in Wang (2011). Package: r-cran-specklestar Architecture: amd64 Version: 0.0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1787 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-imager, r-cran-tidyverse, r-cran-rgl, r-cran-fftw, r-cran-mrbsizer, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-specklestar_0.0.1.7-1.ca2404.1_amd64.deb Size: 1039710 MD5sum: 6bdc184417cb31ec74dc52cce3da460e SHA1: af06b474f166caeb41e14aa72cf31d09eb8260a2 SHA256: e264e66d0cbe0a1a5c8d4f659bae90c4c494ddf30537f183db00eb9ff60b1723 SHA512: 77122abd66e79fdeb3f6bed7aee4e3ebf428c1b5d1f1a2c719d5c74bb125c5013ef548826686dced93c9dc86632c80183c496d018706ef024a952e4b43a88462 Homepage: https://cran.r-project.org/package=specklestar Description: CRAN Package 'specklestar' (Reduction of Speckle Data from BTA 6-m Telescope) A set of functions for obtaining positional parameters and magnitude difference between components of binary and multiple stellar systems from series of speckle images. Package: r-cran-specs Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-specs_1.0.1-1.ca2404.1_amd64.deb Size: 169608 MD5sum: d9be4ca03a9386060e55cf728dbb4960 SHA1: 72e39aa9ef59e844b539496ebef0b6d451966d66 SHA256: c78b2dc412111504f7a5c294b42a7cb7970fa9a5abc8c104156a1bb34a6635ce SHA512: 009982db1204b3ee432d4607c627c24b23e9e86243c35bf23a299885967ea540babea5fe4a41ac9d0c12a4357905c94b1d1ea9a5d6b4c659b29ff7845507f57e Homepage: https://cran.r-project.org/package=specs Description: CRAN Package 'specs' (Single-Equation Penalized Error-Correction Selector (SPECS)) Implementation of SPECS, your favourite Single-Equation Penalized Error-Correction Selector developed in Smeekes and Wijler (2021) . SPECS provides a fully automated estimation procedure for large and potentially (co)integrated datasets. The dataset in levels is converted to a conditional error-correction model, either by the user or by means of the functions included in this package, and various specialised forms of penalized regression can be applied to the model. Automated options for initializing and selecting a sequence of penalties, as well as the construction of penalty weights via an initial estimator, are available. Moreover, the user may choose from a number of pre-specified deterministic configurations to further simplify the model building process. Package: r-cran-specsverification Architecture: amd64 Version: 0.5-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 369 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-specsverification_0.5-3-1.ca2404.1_amd64.deb Size: 234346 MD5sum: 231aa1166b9e8c675a4426dab341948e SHA1: 98efba268f1870c6245fde37c659cf614bcb1cd0 SHA256: 4ddee2f083d2750f27dd7a46d2eaef1d26a55d5628baa04e51cc44aa1351a7ad SHA512: 516e654773b44858167677f4a027d80363d1fb693f6afb2be6213b0724ed8e59ac80e959dd12fff344064aaed731f00ba4ca5bb5772ebd1925e998d7e4561c78 Homepage: https://cran.r-project.org/package=SpecsVerification Description: CRAN Package 'SpecsVerification' (Forecast Verification Routines for Ensemble Forecasts of Weatherand Climate) A collection of forecast verification routines developed for the SPECS FP7 project. The emphasis is on comparative verification of ensemble forecasts of weather and climate. Package: r-cran-spectralgraphtopology Architecture: amd64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3673 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-matrix, r-cran-progress, r-cran-rlist, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-cvxr, r-cran-bookdown, r-cran-knitr, r-cran-prettydoc, r-cran-rmarkdown, r-cran-r.rsp, r-cran-testthat, r-cran-patrick, r-cran-corrplot, r-cran-igraph, r-cran-kernlab, r-cran-pals, r-cran-clustersim, r-cran-viridis, r-cran-quadprog, r-cran-matrixcalc Filename: pool/dists/noble/main/r-cran-spectralgraphtopology_0.2.3-1.ca2404.1_amd64.deb Size: 2561712 MD5sum: df8db445e333aed0d18d904fa0124638 SHA1: e18d6892347dd2ae5b80d4093158422105dbc61c SHA256: 73941a3555c6d9bf7fb6875f4f19ea20368ca8d56185682879f84a5c3e87ce03 SHA512: af21c9bd7b139a0e525f16f7462f693a8e7f46299795843483e4d17ca9963ba756e608755f9764de5b5532d6f11f11d3971c4b5c78098b51bf0a74603ea5ab9d Homepage: https://cran.r-project.org/package=spectralGraphTopology Description: CRAN Package 'spectralGraphTopology' (Learning Graphs from Data via Spectral Constraints) In the era of big data and hyperconnectivity, learning high-dimensional structures such as graphs from data has become a prominent task in machine learning and has found applications in many fields such as finance, health care, and networks. 'spectralGraphTopology' is an open source, documented, and well-tested R package for learning graphs from data. It provides implementations of state of the art algorithms such as Combinatorial Graph Laplacian Learning (CGL), Spectral Graph Learning (SGL), Graph Estimation based on Majorization-Minimization (GLE-MM), and Graph Estimation based on Alternating Direction Method of Multipliers (GLE-ADMM). In addition, graph learning has been widely employed for clustering, where specific algorithms are available in the literature. To this end, we provide an implementation of the Constrained Laplacian Rank (CLR) algorithm. Package: r-cran-spectre Architecture: amd64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 893 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp, r-cran-rcppprogress, r-cran-testthat Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-spectre_1.0.5-1.ca2404.1_amd64.deb Size: 380532 MD5sum: 821e3a44af90bc473b59c67b739081be SHA1: e5657fea999a4ce62fd73ddd0ec2718ef03944a5 SHA256: d488d3c1470b5d33a6eb26f6bc42878c1909c039e33670bf42c33a6ee155511b SHA512: ed525c5b08a624ec5d2a806e028271570caea1d38990e75b3ed3856df5593abcd3ae2f34dadec58fe1a6668747dfd32dedfd67f9ffffd01341fe43e396af4261 Homepage: https://cran.r-project.org/package=spectre Description: CRAN Package 'spectre' (Predict Regional Community Composition) Predict regional community composition at a fine spatial resolution using only sparse biological and environmental data. See Simpkins et al. (2022) for full details on the algorithm underlying the package. Package: r-cran-sped Architecture: amd64 Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-pooh Filename: pool/dists/noble/main/r-cran-sped_0.3-1.ca2404.1_amd64.deb Size: 354354 MD5sum: 5126685599dd0d69802f14c42da7c8df SHA1: d95cdae2b3b4cb01b39c7cc5aed071b0ae54274e SHA256: 540208349221ab73f77a0f3a2bac499dcc5707f0b969b2bfbddc4b8722f91f8d SHA512: 49e18fcf32c69f826f0fbd92da68d65f72410dd5931d9358676719f1107c33f43dc097fa8bc76947658c9b9a752a2f849da75d9b9cc8f103c989ad3b34852a6d Homepage: https://cran.r-project.org/package=sped Description: CRAN Package 'sped' (Multi-Gene Descent Probabilities) Do multi-gene descent probabilities (Thompson, 1983, ) and special cases thereof (Thompson, 1986, ) including inbreeding and kinship coefficients. But does much more: probabilities of any set of genes descending from any other set of genes. Package: r-cran-spedm Architecture: amd64 Version: 1.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4486 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-sdsfun, r-cran-sf, r-cran-terra, r-cran-rcpp, r-cran-rcppthread, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-readr, r-cran-plot3d Filename: pool/dists/noble/main/r-cran-spedm_1.12-1.ca2404.1_amd64.deb Size: 2369676 MD5sum: 4122e1933759aec8e5bf76735a1a0319 SHA1: 910499a90b4a09ef590fe8f89401e3d099a0fcdd SHA256: 16e520eb4b94221903785cf518ce619d09297d93df683052ddd1a58d6fec6794 SHA512: 4423fcd8d3369f9e33b59627aa43674bdc53e2bc07a586de8c22733c49beff250068bd2be95e16dfdd1d2eec0d6a9b546270820c57d4d36ca08f161f621ae62a Homepage: https://cran.r-project.org/package=spEDM Description: CRAN Package 'spEDM' (Spatial Empirical Dynamic Modeling) Inferring causation from spatial cross-sectional data through empirical dynamic modeling (EDM), with methodological extensions including geographical convergent cross mapping from Gao et al. (2023) , as well as the spatial causality test following the approach of Herrera et al. (2016) , together with geographical pattern causality proposed in Zhang & Wang (2025) . Package: r-cran-speedytax Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-bioc-phyloseq, r-cran-rcpp, r-cran-stringr, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-speedytax_1.0.4-1.ca2404.1_amd64.deb Size: 62806 MD5sum: ce1960da91a19ca3236dbb4f8925add2 SHA1: 1024c1774bfb748e178ff5ceff9048ae87506315 SHA256: c74d770e529d049d05cceaac89d1e1d603f3b5ca60dc9358c1e9bf2afa23620a SHA512: 6a1a05859d7f8e730b565127b055a46ec8eadb7707ba09ae2adc427524d6b31c323dc27d9681bebd9efadef8825946e2b49a1411a35c23dfe0f0dcec21c67b6d Homepage: https://cran.r-project.org/package=speedytax Description: CRAN Package 'speedytax' (Rapidly Import Classifier Results into 'phyloseq') Import classification results from the 'RDP Classifier' (Ribosomal Database Project),' 'USEARCH sintax,' 'vsearch sintax' and the 'QIIME2' (Quantitative Insights into Microbial Ecology) classifiers into 'phyloseq' tax_table objects. Package: r-cran-spef Architecture: amd64 Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 532 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bb, r-cran-squarem, r-cran-ggplot2, r-cran-sm, r-cran-survival, r-cran-plyr, r-cran-nleqslv Filename: pool/dists/noble/main/r-cran-spef_1.0.9-1.ca2404.1_amd64.deb Size: 302400 MD5sum: db5f8155af1b9164b53328d1fe3b4da2 SHA1: 51dbad4811cf6a980958bcc23c8abcecd3c7f07f SHA256: e5d6392d75581fcc55eb61d0067588ff3a1e18af68bbd7a9c3a8220d603a387b SHA512: 7fa867a0f52faba0ee5db3de72c23589e56553da49dc903cb498d291e134d46ecda12589b3d9a0bf3e60f24d331c4dc486f60549557b0377edacec62360033d6 Homepage: https://cran.r-project.org/package=spef Description: CRAN Package 'spef' (Semiparametric Estimating Functions) Functions for fitting semiparametric regression models for panel count survival data. An overview of the package can be found in Wang and Yan (2011) and Chiou et al. (2018) . Package: r-cran-spetestnp Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-aer Filename: pool/dists/noble/main/r-cran-spetestnp_1.1.0-1.ca2404.1_amd64.deb Size: 159272 MD5sum: c26a817de4964d9e092226dd51fb6ede SHA1: 55ec286104135962d32a20c9e1b8af732ab3225e SHA256: 73086f088b71f2ebf34c54bfa81bec38b0ddd14d8c4cbfc38acc9d1e4d9a3af8 SHA512: aa60d447c1dafb27b047ef2c9f1bf998404ff78d3bf3a6791ee792fbd7a563df01d7be26482aa91c1d5e17e024f6f300fc8fe6942303af2cac8d20691d487ad0 Homepage: https://cran.r-project.org/package=SpeTestNP Description: CRAN Package 'SpeTestNP' (Non-Parametric Tests of Parametric Specifications) Performs non-parametric tests of parametric specifications. Five tests are available. Specific bandwidth and kernel methods can be chosen along with many other options. Allows parallel computing to quickly compute p-values based on the bootstrap. Methods implemented in the package are H.J. Bierens (1982) , J.C. Escanciano (2006) , P.L. Gozalo (1997) , P. Lavergne and V. Patilea (2008) , P. Lavergne and V. Patilea (2012) , J.H. Stock and M.W. Watson (2006) , C.F.J. Wu (1986) , J. Yin, Z. Geng, R. Li, H. Wang (2010) and J.X. Zheng (1996) . Package: r-cran-spexvb Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-caret, r-cran-foreach, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-spexvb_0.1.0-1.ca2404.1_amd64.deb Size: 140416 MD5sum: adb5686793da5cb6131abb1595f14874 SHA1: 7874f00ad1d0e8ca310a581174e65418abac2315 SHA256: 5d16111868a5a78109b27e2909646463339d969d93bd5f2adab8056ee6fdb916 SHA512: 8cb2e247799a15cf6afaaa991a7bad358a2fb0da8c903044adf8a4f2978986b764e1004577fdf92dc4f3a6171df9e4653441147f4b509717930ba8a00c0701cd Homepage: https://cran.r-project.org/package=spexvb Description: CRAN Package 'spexvb' (Parameter Expanded Variational Bayes for High-Dimensional LinearRegression) Implements a parameter expanded variational Bayes algorithm for linear regression models with high-dimensional variable selection. The methodology utilizes spike-and-slab priors to perform simultaneous estimation and selection. Details can be found in Olejua et al. (2024) . Package: r-cran-spfa Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 776 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-spfa_1.0-1.ca2404.1_amd64.deb Size: 399468 MD5sum: 229fb77dba28c7bbea370cfe8eb35c3e SHA1: 11094be12619280264fcc0115204fb792992e593 SHA256: b296363fab418b88d7264aa1ccb2a38f1874a8d08ed9edc117709aa13e38395f SHA512: 90344d18c07ce7cdecf46b34a3792aef7bf8f48e05b3b0cb7540bf81dd1a94036a73f1a0d95f4ea85e8536139aa924062233c7b6fe8478415c145c8ed07b453d Homepage: https://cran.r-project.org/package=spfa Description: CRAN Package 'spfa' (Semi-Parametric Factor Analysis) Estimation, scoring, and plotting functions for the semi-parametric factor model proposed by Liu & Wang (2022) and Liu & Wang (2023) . Both the conditional densities of observed responses given the latent factors and the joint density of latent factors are estimated non-parametrically. Functional parameters are approximated by smoothing splines, whose coefficients are estimated by penalized maximum likelihood using an expectation-maximization (EM) algorithm. E- and M-steps can be parallelized on multi-thread computing platforms that support 'OpenMP'. Both continuous and unordered categorical response variables are supported. Package: r-cran-spffbs Architecture: amd64 Version: 0.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 864 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-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/noble/main/r-cran-spffbs_0.0-2-1.ca2404.1_amd64.deb Size: 482856 MD5sum: f36be93ebc58891951bf2da342161cef SHA1: 718cc1e6a003a1a1443cb65e0f33705cee12e8d6 SHA256: d89c93526acb0867d0e7f230a619a30dd0da2a64750073fadb1612e0c7c17a3d SHA512: 125443b3428319e9294ffa3c5a2ca6be52773f471c9ee1aa13a1744c0389cc36da9aa4fc2774cdbf2f04ec4e0bce45bb9ad12feb29573a02b492905c7d5d51d3 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-spfw Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-spfw_0.1.0-1.ca2404.1_amd64.deb Size: 202490 MD5sum: d5eeb4a8077488997877d1ebb9f9b57a SHA1: c4d598bfe35f461d840e39868ab72263097078f2 SHA256: 80ac620f7eb2bc7b8ee4754080c94a20d5beb372ccdc69f69b3d4e76b5afd99c SHA512: 7d28e4c84fa91992dddcff7e60944dafc1990cdbd1184092820a3ced1359f15f499ded3e2853c4744c23937407741c2e74355f89d4323ce6cc2b331949c6f3b9 Homepage: https://cran.r-project.org/package=spFW Description: CRAN Package 'spFW' (Hierarchical Spatial Finlay-Wilkinson Model) Estimation and Prediction Functions Using Bayesian Hierarchical Spatial Finlay-Wilkinson Model for Analysis of Multi-Environment Field Trials. Package: r-cran-spgarch Architecture: amd64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 674 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-truncnorm, r-cran-rsolnp, r-cran-spdep, r-cran-matrix, r-cran-nleqslv, r-cran-crayon, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-spgarch_0.2.3-1.ca2404.1_amd64.deb Size: 418302 MD5sum: 6a0fc869b85ddbcd3c9652da2c9ab6c7 SHA1: b082b000e1f5f3b34c65bd150aa0e44cb4b11076 SHA256: 48a258b3a46dbef62854c75c9601eae17fe184ac6d8bd63f419e6dab8d2522d5 SHA512: c8b809e899aea0c2bcfb66dfae995cf97e43e094718f4cbf76d5e1c20dc0c72723b8dc887f06980bff0e5d119e65263e3e8a4916dca487907d6aaaa9732972c0 Homepage: https://cran.r-project.org/package=spGARCH Description: CRAN Package 'spGARCH' (Spatial ARCH and GARCH Models (spGARCH)) A collection of functions to deal with spatial and spatiotemporal autoregressive conditional heteroscedasticity (spatial ARCH and GARCH models) by Otto, Schmid, Garthoff (2018, Spatial Statistics) : simulation of spatial ARCH-type processes (spARCH, log/exponential-spARCH, complex-spARCH); quasi-maximum-likelihood estimation of the parameters of spARCH models and spatial autoregressive models with spARCH disturbances, diagnostic checks, visualizations. Package: r-cran-spgs Architecture: amd64 Version: 1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 548 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spgs_1.0-4-1.ca2404.1_amd64.deb Size: 496154 MD5sum: 1fb072365340da377e66f4d7fea3a6f0 SHA1: 3ff146d4dfe722636027ca520d3fc470ba1602dd SHA256: 2c9a3fd4e7fe52b1d666faae211c2d78dc881b6ff175daee3aa987bf59ccc6ee SHA512: 6634c10c63904c9f1712eae11bfc85d650de6511c37e3aea60501460ee85ae0155654f3c2b8e0f6bfd635216fa48a526f4c440dd8e9a527bd1c136c756052fe2 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. In particular, it provides tests and graphical methods for determining whether or not DNA sequences comply with Chargaff's second parity rule or exhibit purine-pyrimidine parity. In addition, there are functions for efficiently simulating discrete state space Markov chains and testing arbitrary symbolic sequences of symbols for the presence of first-order Markovianness. Also, it has functions for counting words/k-mers (and cylinder patterns) in arbitrary symbolic sequences. Functions which take a DNA sequence as input can handle sequences stored as SeqFastadna objects from the 'seqinr' package. Package: r-cran-spgwr Architecture: amd64 Version: 0.6-37-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3502 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-spdata Suggests: r-cran-spdep, r-cran-sf, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-spgwr_0.6-37-1.ca2404.1_amd64.deb Size: 2155204 MD5sum: fbb4f054fb78b8606d3a9cd6fa643f3e SHA1: be97195c12056f01be4c3b37d591e733a04f276b SHA256: 9ad1b95049167810218b834f3c2bfc3121165951f95ccaefc082e304878e28f0 SHA512: 10dc4576b30a84b49eccc69f834cba9ad2cc7b27849947de43cd22adfbcec9eeac2a764c25018efbed2fcbab1072b9b1ae6ab9bad2c903f6a00f22254c467bbf Homepage: https://cran.r-project.org/package=spgwr Description: CRAN Package 'spgwr' (Geographically Weighted Regression) Functions for computing geographically weighted regressions are provided, based on work by Chris Brunsdon, Martin Charlton and Stewart Fotheringham. Package: r-cran-spheretessellation Architecture: amd64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorsgen, r-cran-polychrome, r-cran-rcpp, r-cran-rgl, r-cran-bh, r-cran-rcppcgal, r-cran-rcppeigen Suggests: r-cran-uniformly Filename: pool/dists/noble/main/r-cran-spheretessellation_1.2.0-1.ca2404.1_amd64.deb Size: 162908 MD5sum: 617ca6aad3df506c6fa1f0c112b5a82f SHA1: 6a17ceb00a4ad1788fc98e6c224a8ae1e416eb3c SHA256: 370df8625d19a7ccc77c8f1a0c7803c24f74d7037ceb22e919b3f50188177871 SHA512: aaa435dad841d359bd5895d48a63ad087d2b4fdd156a8d357cf125d3638ace7c44f09bbc6ed1e13d52a53c5c42e17ba115018c3e852d4cb9494abf40cc0e4e3a Homepage: https://cran.r-project.org/package=sphereTessellation Description: CRAN Package 'sphereTessellation' (Delaunay and Voronoï Tessellations on the Sphere) Performs Delaunay and Voronoï tessellations on spheres and provides some functions to plot them. The algorithms are mainly performed by the 'C++' library 'CGAL' (). Package: r-cran-sphunif Architecture: amd64 Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2132 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-dofuture, r-cran-dorng, r-cran-foreach, r-cran-future, r-cran-gsl, r-cran-rotasym, r-cran-rcpparmadillo Suggests: r-cran-compquadform, r-cran-goftest, r-cran-knitr, r-cran-markdown, r-cran-mvtnorm, r-cran-numderiv, r-cran-progress, r-cran-progressr, r-cran-rmarkdown, r-cran-scatterplot3d, r-cran-testthat, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-sphunif_1.4.3-1.ca2404.1_amd64.deb Size: 1306476 MD5sum: eb50ecedf4922f4b95d77b836161994f SHA1: 64cae6bfa2d847456e8ccbef979881b6a6acf40c SHA256: ed01e37bf187521b38a3935fdc6e4e19c4f72ea5db847759c3f0d50e114359ec SHA512: 992dc47f2494a3b5c262998709e53db85769e18f3fc33bd41c9b57eb10085749730179444d16a5a88384715dd2bea9120ddb124748b44a8da3da3072da2b6a09 Homepage: https://cran.r-project.org/package=sphunif Description: CRAN Package 'sphunif' (Uniformity Tests on the Circle, Sphere, and Hypersphere) Implementation of uniformity tests on the circle and (hyper)sphere. The main function of the package is unif_test(), which conveniently collects more than 35 tests for assessing uniformity on S^{p-1} = {x in R^p : ||x|| = 1}, p >= 2. The test statistics are implemented in the unif_stat() function, which allows computing several statistics for different samples within a single call, thus facilitating Monte Carlo experiments. Furthermore, the unif_stat_MC() function allows parallelizing them in a simple way. The asymptotic null distributions of the statistics are available through the function unif_stat_distr(). The core of 'sphunif' is coded in C++ by relying on the 'Rcpp' package. The package also provides several novel datasets and gives the replicability for the data applications/simulations in García-Portugués et al. (2021) , García-Portugués et al. (2023) , Fernández-de-Marcos and García-Portugués (2024) , and García-Portugués et al. (2025) . Package: r-cran-spiderbar Architecture: amd64 Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 361 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-robotstxt, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-spiderbar_0.2.5-1.ca2404.1_amd64.deb Size: 117086 MD5sum: 70eedab363d1bbeddb72f1e157c85bae SHA1: 3cde136a045e7542ed2f9ee40dbbebbb37f039f6 SHA256: e31ebb1e2cc1f1c1e5e95da3c8dbeba847d0e63e4774969d6aa36a79f3dc5cab SHA512: 1d4b97a5f5eee2153a8410ef5da00eee26b5279a47d92e4d72f8deacf2d3fcad111102f36c3b76e4dca1cb04e7f9f00a2d6979c7d2dcc3cccd214e9f5d017edb Homepage: https://cran.r-project.org/package=spiderbar Description: CRAN Package 'spiderbar' (Parse and Test Robots Exclusion Protocol Files and Rules) The 'Robots Exclusion Protocol' documents a set of standards for allowing or excluding robot/spider crawling of different areas of site content. 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Package: r-cran-spinbayes Architecture: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1035 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-ggplot2, r-cran-rcpparmadillo, r-cran-testthat Suggests: r-cran-covr Filename: pool/dists/noble/main/r-cran-spinbayes_0.2.2-1.ca2404.1_amd64.deb Size: 673492 MD5sum: c010da8d9baff6bcd1cc3fc85dbdd9f0 SHA1: b2b527274d8067bc4d5e5dbfedb9c9e8dbc5d1cf SHA256: 22c0a201f7e5a1092466dcd4ed86972fd8189eff15e91df5e09b2c14e29ed7e0 SHA512: 72253daa267d83a453dd18fe09f42d71b121e7d54a8fe22dd69a05a3fbf4a86eccdc7997033c96cea3315dbc2886dd9c59d96c5345c816698cc98b1b07f729a7 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++. Package: r-cran-splancs Architecture: amd64 Version: 2.01-45-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp Filename: pool/dists/noble/main/r-cran-splancs_2.01-45-1.ca2404.1_amd64.deb Size: 310028 MD5sum: 6ccbc20daaf93d89002a809645dbe836 SHA1: d19418806bf8dc4dae2799e43ecbe1d57eebd945 SHA256: d8343ec70eaa5a963d79f763d366caceab5bfa4333833b3c544a1e21a59d6fa7 SHA512: 745062e48efe8860f04df6be851ab0394de3dc347812af8828ccfbf09c7e30b46f8c46ab6413892d0baaaf72fd08c9430821dcdb572b53364ed03e92ecbfab42 Homepage: https://cran.r-project.org/package=splancs Description: CRAN Package 'splancs' (Spatial and Space-Time Point Pattern Analysis) The Splancs package was written as an enhancement to S-Plus for display and analysis of spatial point pattern data; it has been ported to R and is in "maintenance mode". Package: r-cran-splikit Architecture: amd64 Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9759 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-data.table, r-cran-rcpp, r-cran-r6, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-splikit_2.3.1-1.ca2404.1_amd64.deb Size: 6343870 MD5sum: 93da489718b4080a8322909dfce9d14e SHA1: 1730176a78b875cc99cb252196c1603f49c6a2d4 SHA256: 5174af03a9e2caa699fb509717db6df3bfc2dc5be7e118bdfcbcf0027323a665 SHA512: ff9fcd5d05fd21974ef5c2f62b31c3f0a08de54f6f32a6a6b122c798b4c97b2a505bb15af6a834cc1da878c953fd6e8216132f12729bcfa39415f74d4f1a7c4a Homepage: https://cran.r-project.org/package=splikit Description: CRAN Package 'splikit' (Analysing RNA Splicing in Single-Cell RNA Sequencing Data) Provides analysis of high-dimensional single-cell splicing data. 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Package: r-cran-splines2 Architecture: amd64 Version: 0.5.4-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1972 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-splines2_0.5.4-1.ca2404.2_amd64.deb Size: 1078488 MD5sum: d7f44472dfa5c64a1f16670768e85063 SHA1: 3b6a12bb2e1b414f9921b68d4227e3b9b8d831bb SHA256: 353e9844353a40eca33ea98949439e0b709d12416db350bf78648e3d6cd02244 SHA512: 92a010afc66cc73ce2b2e37d0368570ae064dbc03573a3ef74542cdbb9503efc30f42537943196e8d794322037a8758228a269c3cf23fb41af92a6e1d7617ba1 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: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-split_1.3-1.ca2404.1_amd64.deb Size: 159530 MD5sum: 636f53b047d5766044ea97a5d3c085ed SHA1: 71e003a18ed9063f4df9d479853107721fb50df9 SHA256: 708d3a03342299749950a058946f54f0957012231d1abb4ff073cef747832b5b SHA512: 485fb9250a793d8c72503fb50ab9e4d5d922aa6bebbaf50919ff9bb840a5476f40d0d88c71e746990d1009c20a428698f28fb48e14e658e241882dcd42ed4e3b 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. 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The approach fits generalized linear models that split the covariates into groups. The optimal split of the variables into groups and the regularized estimation of the coefficients are performed by minimizing an objective function that encourages sparsity within each group and diversity among them. Example applications can be found in Christidis et al. (2021) . Package: r-cran-splithalf Architecture: amd64 Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 712 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-rcpp, r-cran-robustbase, r-cran-ggplot2, r-cran-plyr, r-cran-patchwork, r-cran-psych, r-cran-lme4 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-splithalf_0.8.2-1.ca2404.1_amd64.deb Size: 389332 MD5sum: 514f1d9825081c494b3ffeb7b35a7d40 SHA1: 5828b0067cdd2d002c50b23ed9cabd5870a9ba71 SHA256: a196415033b7d9cb6be01d67bd8564f3639a73e340621bae3db9557bf20393f2 SHA512: cee73db82af44318f130123e1bf70f7c6c98ecb48f02a4709aef34a793d27f0768fb917157076a0db7b70f7840002b2b9f73fbb9e7512a866c81a802d1bb7054 Homepage: https://cran.r-project.org/package=splithalf Description: CRAN Package 'splithalf' (Calculate Task Split Half Reliability Estimates) Estimate the internal consistency of your tasks with a permutation based split-half reliability approach. Unofficial release name: "I eat stickers all the time, dude!". Package: r-cran-splitreg Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-glmnet, r-cran-mass Filename: pool/dists/noble/main/r-cran-splitreg_1.0.3-1.ca2404.1_amd64.deb Size: 100888 MD5sum: d994408cc4e50037d747138c5ee2d3e9 SHA1: f7e08276dcfa9d4cc5215a3392a19877590e112f SHA256: 6111e67b5eedc767ab3274a5033bd5ad5e100999b7f861d372a1dfe0eb8aa58a SHA512: f2d080efbc3b5ba1e6b1c5a6e42e16ef039a2a8c2f2577e36237c81fc4c9626ddcf3ab395b60e08d1851acd3cf72973e39997cb6764bae6905af956aeda9644d Homepage: https://cran.r-project.org/package=SplitReg Description: CRAN Package 'SplitReg' (Split Regularized Regression) Functions for computing split regularized estimators defined in Christidis, Lakshmanan, Smucler and Zamar (2019) . The approach fits linear regression models that split the set of covariates into groups. The optimal split of the variables into groups and the regularized estimation of the regression coefficients are performed by minimizing an objective function that encourages sparsity within each group and diversity among them. The estimated coefficients are then pooled together to form the final fit. Package: r-cran-splitsoftening Architecture: amd64 Version: 2.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-gsl Filename: pool/dists/noble/main/r-cran-splitsoftening_2.1-1-1.ca2404.1_amd64.deb Size: 56146 MD5sum: 2ee2e52f4d762fe37c7421f068acb684 SHA1: 9d937d08d658ab4b8c817d58229437100d4dc01a SHA256: 227d12b4e32bf5c139d7c8c07c50828c4521a278bddf4db08271bc8f2e1d64d3 SHA512: 17e0bbd0c6752b46bab77721425652fdd5db52879a9a3ac94714ec4835f59da8c91f4510411d6dceabc6d4341b3aec9b1b55059898fb191808c13f6752f4ad51 Homepage: https://cran.r-project.org/package=SplitSoftening Description: CRAN Package 'SplitSoftening' (Softening Splits in Decision Trees) Allows to produce and use classification trees with soft (probability) splits, as described in: Dvořák, J. (2019), . Package: r-cran-splmm Architecture: amd64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-emulator, r-cran-misctools, r-cran-penalized, r-cran-ggplot2, r-cran-gridextra, r-cran-plot3d, r-cran-mass, r-cran-progress, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-splmm_1.2.0-1.ca2404.1_amd64.deb Size: 223334 MD5sum: 4ce6b597ba5df264a125f9919767c5cb SHA1: ea0af38c4851a43ca50c4fa4d4adccf8e1fee542 SHA256: 3a051fcc5a79ebe1a3b24cc21cdc192563574746013f5a767a560b2e4f15f3b0 SHA512: a65c8579c04728f240b65e671cf65b210bc45b7621b374d5e57822812701be32153bcce16ca7662d7f47af459971c295c29314c071ae6e90e0790223636b0a88 Homepage: https://cran.r-project.org/package=splmm Description: CRAN Package 'splmm' (Simultaneous Penalized Linear Mixed Effects Models) Contains functions that fit linear mixed-effects models for high-dimensional data (p>>n) with penalty for both the fixed effects and random effects for variable selection. The details of the algorithm can be found in Luoying Yang PhD thesis (Yang and Wu 2020). The algorithm implementation is based on the R package 'lmmlasso'. Reference: Yang L, Wu TT (2020). Model-Based Clustering of Longitudinal Data in High-Dimensionality. Unpublished thesis. Package: r-cran-splus2r Architecture: amd64 Version: 1.3-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-splus2r_1.3-5-1.ca2404.1_amd64.deb Size: 328510 MD5sum: aa5c24828ecddd53be5415c303ded25c SHA1: c50469f68887bbfdd4aa0059c20bcca3dc54a041 SHA256: 78c51f4bd7ceac155fb775188542780ec849ac1b016304640013801f49879b79 SHA512: c263e26bab52dde8be477f2c304793e400429397af2c2f74ba596d8adb7938e5e2dcf503cf7771d5ce4f8259b6512664770aded118cc0f11393ce738d8604058 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. 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The code was originally available in 'S-PLUS'. Package: r-cran-spmc Architecture: amd64 Version: 0.3.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 563 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spmc_0.3.15-1.ca2404.1_amd64.deb Size: 421302 MD5sum: 1c3e3cf67702358c4be615703274b56d SHA1: 45cedb2cc044d15fefe152bff8cbbae34a515e90 SHA256: 5d8c68bfde0de3811c18ad0d85293182facccd1cb4b1ed1dc3785c8330d858e8 SHA512: 19da0bf44b1820739c28cf6537c099db20928a794b3742f94709a96dcfa9c0805ac8a4a0c99ee89cb4b0e3f2aa3b139ee2d203ec4cf32df0f547cc750cde6dbc Homepage: https://cran.r-project.org/package=spMC Description: CRAN Package 'spMC' (Continuous-Lag Spatial Markov Chains) A set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields. More details on the methodology are discussed in Sartore (2013) and Sartore et al. (2016) . Package: r-cran-spnetwork Architecture: amd64 Version: 0.4.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6089 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spdep, r-cran-igraph, r-cran-cubature, r-cran-future.apply, r-cran-ggplot2, r-cran-progressr, r-cran-data.table, r-cran-rcpp, r-cran-rdpack, r-cran-dbscan, r-cran-sf, r-cran-abind, r-cran-sfheaders, r-cran-cpprouting, r-cran-rcppprogress, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-future, r-cran-testthat, r-cran-kableextra, r-cran-rcolorbrewer, r-cran-classint, r-cran-reshape2, r-cran-rlang, r-cran-rgl, r-cran-tmap, r-cran-smoothr, r-cran-tibble, r-cran-concaveman, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spnetwork_0.4.4.7-1.ca2404.1_amd64.deb Size: 4962180 MD5sum: 8f9417bced21a0b6effd1b194b689c78 SHA1: bbc1faa1a4917573291ee033559bfcd1bc9c6d89 SHA256: 6b7ecfdbe2bf1abc9b3db79ee07a1d009747c5340aa8454b53ebd206e772b00a SHA512: 1861aec72019bd5eefb81561b857fbf4c14e98a0d57ee89bf33913d9c5fc2bb62c342a79601a1150cb6dc6070bf6adf7bdca71ac81b469dcde135754e0117e04 Homepage: https://cran.r-project.org/package=spNetwork Description: CRAN Package 'spNetwork' (Spatial Analysis on Network) Perform spatial analysis on network. Implement several methods for spatial analysis on network: Network Kernel Density estimation, building of spatial matrices based on network distance ('listw' objects from 'spdep' package), K functions estimation for point pattern analysis on network, k nearest neighbours on network, reachable area calculation, and graph generation References: Okabe et al (2019) ; Okabe et al (2012, ISBN:978-0470770818);Baddeley et al (2015, ISBN:9781482210200). Package: r-cran-spnn Architecture: amd64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-spnn_1.3.0-1.ca2404.1_amd64.deb Size: 70348 MD5sum: d734847f15340fd8f1e1ba9fe1327168 SHA1: 5b5085bad59bf7bb92c624b8fe87f091990ece22 SHA256: 4f43276a00148689f5fdf7bc9b8de7f94800e0847900fcffe1639cf8aebffcb2 SHA512: 477d77df520e6aaaeeb8136ee952bc5cf91c8afbbd8fa26df6af266a3c2f279f81a00aef6ff3ccc9a725b7652f5716ea3bc13d5bd1872ae2aff3e1f144ea81bd Homepage: https://cran.r-project.org/package=spnn Description: CRAN Package 'spnn' (Scale Invariant Probabilistic Neural Networks) Scale invariant version of the original PNN proposed by Specht (1990) with the added functionality of allowing for smoothing along multiple dimensions while accounting for covariances within the data set. It is written in the R statistical programming language. Given a data set with categorical variables, we use this algorithm to estimate the probabilities of a new observation vector belonging to a specific category. This type of neural network provides the benefits of fast training time relative to backpropagation and statistical generalization with only a small set of known observations. Package: r-cran-spnngp Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3419 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-formula, r-cran-rann Filename: pool/dists/noble/main/r-cran-spnngp_1.0.1-1.ca2404.1_amd64.deb Size: 3396618 MD5sum: 2f8557e1d04c8aebfa22245357f1984d SHA1: b36f91c62dd9e733a234465773f642fa142c791e SHA256: 9840209c63b4ee4af2262ea0e3b6c94658dd6c795b27b3decb7694d91eabda1d SHA512: 0af8668737353a5d17c632889ac9d9fae75ff84e9f3da9a77292bfa9d6f81112a2faf2dafdf814b1d6c8bcc5f65d4c311b4a852ce135ed5ed9b6543ce817788c Homepage: https://cran.r-project.org/package=spNNGP Description: CRAN Package 'spNNGP' (Spatial Regression Models for Large Datasets using NearestNeighbor Gaussian Processes) Fits univariate Bayesian spatial regression models for large datasets using Nearest Neighbor Gaussian Processes (NNGP) detailed in Finley, Datta, Banerjee (2022) , Finley, Datta, Cook, Morton, Andersen, and Banerjee (2019) , and Datta, Banerjee, Finley, and Gelfand (2016) . Package: r-cran-spoccupancy Architecture: amd64 Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4014 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-abind, r-cran-rann, r-cran-lme4, r-cran-foreach, r-cran-doparallel, r-cran-spabundance Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-spoccupancy_0.8.0-1.ca2404.1_amd64.deb Size: 3468852 MD5sum: 41c7095c329e5ab7ca4b3e5a89beba4d SHA1: 17712e38f0ecfd2dc23b07e364402a3eab171340 SHA256: a1f8fa756fa39ad4dbafc0d4d0262d81f86113b778b4a407f15ba4d5ec3336d0 SHA512: 1aa1feafea044abed3b167faeeb80d755902ca03ef04c48b91dfe15e8994fa5a89641d8e4209202c268beb91b33e3e967653d953e432cd96f77c99097aed8e8c 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. 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Facility location problems also accept user-supplied network travel-time matrices. Uses a 'Rust' backend via 'extendr' for graph and routing algorithms, and the 'HiGHS' solver via the 'highs' package for facility location mixed-integer programs. Method-level references are provided in the documentation of the individual functions. Package: r-cran-sport Architecture: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 785 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-dplyr, r-cran-knitr, r-cran-lobstr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-sport_0.2.2-1.ca2404.1_amd64.deb Size: 480994 MD5sum: 8fd02a2bfbb7cd3f0a6850a6e779a79f SHA1: 038387716f395904aa86e5f680e8503bf3291253 SHA256: fda2380a9901600266ce407efc929c818cb6ad713ef94c57435ffd97447a2544 SHA512: 739e8bb2ad20a915f61c6cb4e536cff0ae644d01a03c3fadd2590e9dafc22f9e2fca37a9f502be0b430cef0fbe2187bbd38b0547d31b524bfd64c34b3d2f00f2 Homepage: https://cran.r-project.org/package=sport Description: CRAN Package 'sport' (Sequential Pairwise Online Rating Techniques) Calculates ratings for two-player or multi-player challenges. Methods included in package such as are able to estimate ratings (players strengths) and their evolution in time, also able to predict output of challenge. Algorithms are based on Bayesian Approximation Method, and they don't involve any matrix inversions nor likelihood estimation. Parameters are updated sequentially, and computation doesn't require any additional RAM to make estimation feasible. Additionally, base of the package is written in C++ what makes sport computation even faster. Methods used in the package refer to Mark E. Glickman (1999) ; Mark E. Glickman (2001) ; Ruby C. Weng, Chih-Jen Lin (2011) ; W. Penny, Stephen J. Roberts (1999) . Package: r-cran-spotr Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 863 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv, r-cran-rcpp Suggests: r-cran-testthat, r-cran-brms, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-spotr_0.1.0-1.ca2404.1_amd64.deb Size: 576554 MD5sum: aa18a48d9b993f52e1118f8dd342c83b SHA1: 782c6ea6b553b59576e115241b762d9119d835f0 SHA256: c163f105c406b735c27fa58c5a24e84ec8dc260c5804bb3f1c37ff6ac69e9161 SHA512: 0dd0d2f6bb3dacd22ade5ae681f0a531c19fff7d279baf8aef79e02fe6a52c7d7dafc38099cefb29233b7cf6d7191910d43f0212856c24589dc8d2dfb8f0324a Homepage: https://cran.r-project.org/package=spotr Description: CRAN Package 'spotr' (Estimate Spatial Population Indices from Ecological AbundanceData) Compute relative or absolute population trends across space and time using predictions from models fitted to ecological population abundance data, as described in Knape (2025) . The package supports models fitted by 'mgcv' or 'brms', and draws from posterior predictive distributions. Package: r-cran-spray Architecture: amd64 Version: 1.0-27-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 538 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-partitions, r-cran-magic, r-cran-disordr, r-cran-stringr Suggests: r-cran-polynom, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-spray_1.0-27-1.ca2404.1_amd64.deb Size: 346054 MD5sum: e9716e8d7b78ca04660fcb57b9eecba1 SHA1: 9f11b433a36042aba559223831f0e9d2567ed31e SHA256: 2bbf60b3f085242b36717eb761edcbc467a214ae47f8f513de5fc1822db7ed31 SHA512: 3ab2b4c3d305c08828e614a671491cb87e2ff6f927febb63fa78f4e252b3a7ec988d73defd346d3fbf5c01f8729bc995a5e8495f8c42ce8849830420a7b019a2 Homepage: https://cran.r-project.org/package=spray Description: CRAN Package 'spray' (Sparse Arrays and Multivariate Polynomials) Sparse arrays interpreted as multivariate polynomials. Uses 'disordR' discipline (Hankin, 2022, ). To cite the package in publications please use Hankin (2022) . Package: r-cran-spreadr Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1850 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-assertthat, r-cran-igraph, r-cran-extrafont, r-cran-ggplot2 Suggests: r-cran-dplyr, r-cran-fs, r-cran-gganimate, r-cran-ggraph, r-cran-gifski, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spreadr_0.2.0-1.ca2404.1_amd64.deb Size: 844474 MD5sum: f44bc1b464c71cfa1ab66d436c3cd7f9 SHA1: 588b645b1921a45d75fdf9ea5bb717e7023f7788 SHA256: 7b67a476b0ac6b545fb97e69fd08f2a3919c3adc6ee55824d5277f4418e30aa1 SHA512: f124a2d090d625dbe7d6ea1fee5de61e0a33cf85f8e2302aa4fd238325fbec628744f80cd7331f4eae5a0dc20b7a7c1a108ab7b4320ccc870e49a21cb3807dc0 Homepage: https://cran.r-project.org/package=spreadr Description: CRAN Package 'spreadr' (Simulating Spreading Activation in a Network) The notion of spreading activation is a prevalent metaphor in the cognitive sciences. This package provides the tools for cognitive scientists and psychologists to conduct computer simulations that implement spreading activation in a network representation. The algorithmic method implemented in 'spreadr' subroutines follows the approach described in Vitevitch, Ercal, and Adagarla (2011, Frontiers), who viewed activation as a fixed cognitive resource that could spread among nodes that were connected to each other via edges or connections (i.e., a network). See Vitevitch, M. S., Ercal, G., & Adagarla, B. (2011). Simulating retrieval from a highly clustered network: Implications for spoken word recognition. Frontiers in Psychology, 2, 369. and Siew, C. S. Q. (2019). spreadr: A R package to simulate spreading activation in a network. Behavior Research Methods, 51, 910-929. . Package: r-cran-springer Architecture: amd64 Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 321 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-springer_0.1.9-1.ca2404.1_amd64.deb Size: 157772 MD5sum: c8000458624ea1d694dcecf2c56d96fb SHA1: 1f5021bab02a0ab24cddab08ae3281805b6fca36 SHA256: 7c77280aabe7b6fc65614b2c2c2e50a5d251acf2876b27ba254673c8bfb0c09d SHA512: d4a15d710e35a5f00a5ecc8a14a66bf9da244ceaba100e3e8ecf42ff7e6a5dd03cc1efbf0936a72f936178f555737481ff3885db20600d9e4ee3b5da5a01917b Homepage: https://cran.r-project.org/package=springer Description: CRAN Package 'springer' (Sparse Group Variable Selection for Gene-EnvironmentInteractions in the Longitudinal Study) Recently, regularized variable selection has emerged as a powerful tool to identify and dissect gene-environment interactions. Nevertheless, in longitudinal studies with high dimensional genetic factors, regularization methods for G×E interactions have not been systematically developed. In this package, we provide the implementation of sparse group variable selection, based on both the quadratic inference function (QIF) and generalized estimating equation (GEE), to accommodate the bi-level selection for longitudinal G×E studies with high dimensional genomic features. Alternative methods conducting only the group or individual level selection have also been included. The core modules of the package have been developed in C++. Package: r-cran-sprintr Architecture: amd64 Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sprintr_0.9.0-1.ca2404.1_amd64.deb Size: 73416 MD5sum: da4720d6a49422cb29eab46cfcad7faf SHA1: a322ff078e2e65f6deae947bc4fa1ffdfb983cc9 SHA256: 10bb4d86bc40b5e4e4d5d5b92a4283d9957478ab8f80b2c368a7500638c5e2b7 SHA512: 00170d72a8b9ecf57bc359f6099e5d141e0ee126eb254c3b131a8dbeaf24283273234ae9a0a83345b3d7818a59cc66c9a50f70cb7e5b6a26eb53ba3f5c52f7e8 Homepage: https://cran.r-project.org/package=sprintr Description: CRAN Package 'sprintr' (Sparse Reluctant Interaction Modeling) An implementation of a computationally efficient method to fit large-scale interaction models based on the reluctant interaction selection principle. The method and its properties are described in greater depth in Yu, G., Bien, J., and Tibshirani, R.J. (2019) "Reluctant interaction modeling", which is available at . Package: r-cran-spsp Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 871 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-ncvreg, r-cran-matrix, r-cran-lars Suggests: r-cran-testthat, r-cran-mass Filename: pool/dists/noble/main/r-cran-spsp_0.2.0-1.ca2404.1_amd64.deb Size: 819176 MD5sum: a2f67505fbe5b60ae0684a7fdcba06e5 SHA1: d7e19d8d84bc6fcd6d824880a08bc21c5c161219 SHA256: 68c2ef701bd7e3e5c6b0f04307fc32484354a57928313e47b2323a7e01168d25 SHA512: c908257a35f04f1f199a86d66ff21c50582eb283a296cc0c537892c111aecc257337848bc24d3683e572ea7faa9b22b48c735c8271947ada368702c9e655a0df Homepage: https://cran.r-project.org/package=SPSP Description: CRAN Package 'SPSP' (Selection by Partitioning the Solution Paths) An implementation of the feature Selection procedure by Partitioning the entire Solution Paths (namely SPSP) to identify the relevant features rather than using a single tuning parameter. By utilizing the entire solution paths, this procedure can obtain better selection accuracy than the commonly used approach of selecting only one tuning parameter based on existing criteria, cross-validation (CV), generalized CV, AIC, BIC, and extended BIC (Liu, Y., & Wang, P. (2018) ). It is more stable and accurate (low false positive and false negative rates) than other variable selection approaches. In addition, it can be flexibly coupled with the solution paths of Lasso, adaptive Lasso, ridge regression, and other penalized estimators. Package: r-cran-spstack Architecture: amd64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1987 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cvxr, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-loo, r-cran-mba, r-cran-rstudioapi Suggests: r-cran-dplyr, r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-spstack_1.1.3-1.ca2404.1_amd64.deb Size: 1243412 MD5sum: 1e0f2018252754d269920fd7c4a0f3fc SHA1: 5794014f4aa6a58e6ec48ef97e9ef15fbb3de61a SHA256: 317a1168b97c7c2505bddfff80b259603e2c9b0f405b7fe2922e0043d31fb17a SHA512: 516a7aa75df16915bb6a1fd57ec851342aced07df8117d0c1d74484aed1a71bab65ca63fd1df454427df9c103ca82a3b25d8ed0d714f0893f6d2bed9b2ac045c Homepage: https://cran.r-project.org/package=spStack Description: CRAN Package 'spStack' (Bayesian Geostatistics Using Predictive Stacking) Fits Bayesian hierarchical spatial and spatial-temporal process models for point-referenced Gaussian, Poisson, binomial, and binary data using stacking of predictive densities. It involves sampling from analytically available posterior distributions conditional upon candidate values of the spatial process parameters and, subsequently assimilate inference from these individual posterior distributions using Bayesian predictive stacking. Our algorithm is highly parallelizable and hence, much faster than traditional Markov chain Monte Carlo algorithms while delivering competitive predictive performance. See Zhang, Tang, and Banerjee (2025) , and, Pan, Zhang, Bradley, and Banerjee (2025) for details. Package: r-cran-spsurv Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4442 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-loo, r-cran-coda, r-cran-mass, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-devtools, r-cran-roxygen2, r-cran-testthat, r-cran-kmsurv Filename: pool/dists/noble/main/r-cran-spsurv_1.0.0-1.ca2404.1_amd64.deb Size: 1603640 MD5sum: d0f2126b495bb70631d501e18ff5c17d SHA1: f54ae96544b776432348048af19efb46aafcf516 SHA256: 997c819751db43bbc48dcc077fb65ad9e2a6b02a165a9884dc6cb9aff625ce90 SHA512: 3a8c86e80a94c36f62ca7697cdedc0d8eacab677379667d0aa8f8fe2d44e3565ce4350a3611b0f121d783f9f62b8a68cc4d0d901cafe178e1d9946a1adada5db Homepage: https://cran.r-project.org/package=spsurv Description: CRAN Package 'spsurv' (Bernstein Polynomial Based Semiparametric Survival Analysis) A set of reliable routines to ease semiparametric survival regression modeling based on Bernstein polynomials. 'spsurv' includes proportional hazards, proportional odds and accelerated failure time frameworks for right-censored data. RV Panaro (2020) . Package: r-cran-spt Architecture: amd64 Version: 2.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spt_2.5.1-1.ca2404.1_amd64.deb Size: 42008 MD5sum: 6990ff0a54bddb9ed548553aac75322e SHA1: 55e00fa9a01c140d7c8c0ddcd08197028ac99448 SHA256: 709e6815a50715e2c7cf23cbaa76606ee549a7f97bb6dd5d63b81c531ec9c8e8 SHA512: e700533a4000d28dc25cb67e1188ab2bf81ee5a6702337b2ad8cfd588041798444d041b2f5f2dc3d1ad49322d768e2d73ebf03eee0588f6c57e4bcd74126ee5f Homepage: https://cran.r-project.org/package=spt Description: CRAN Package 'spt' (Sierpinski Pedal Triangle) A collection of algorithms related to Sierpinski pedal triangle (SPT). 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Bakar et al., (2016). Bakar et al., (2015). Package: r-cran-spte2m Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1801 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-mass, r-cran-ggplot2, r-cran-maps, r-cran-mapproj, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spte2m_1.0.3-1.ca2404.1_amd64.deb Size: 1544512 MD5sum: 7c22b663f05bf6c28a9f442d65f26b67 SHA1: 50a2807d99d42bf4f58d7ba79a0bd901092a75c0 SHA256: 4d6458a8fee4c1f1e628462d88acc14f5ef3872a2603e73a02b6133a1e145403 SHA512: db6982b2228c4fddaea8362d7e43f9f790319ccb3f6e52efebc64f44eb1fc927d411f9f16a998dc2df4ea7ec06c5b36c70e83b720711f9a17a5f2eea72b15082 Homepage: https://cran.r-project.org/package=SpTe2M Description: CRAN Package 'SpTe2M' (Nonparametric Modeling and Monitoring of Spatio-Temporal Data) Spatio-temporal data have become increasingly popular in many research fields. Such data often have complex structures that are difficult to describe and estimate. This package provides reliable tools for modeling complicated spatio-temporal data. It also includes tools of online process monitoring to detect possible change-points in a spatio-temporal process over time. More specifically, the package implements the spatio-temporal mean estimation procedure described in Yang and Qiu (2018) , the spatio-temporal covariance estimation procedure discussed in Yang and Qiu (2019) , the three-step method for the joint estimation of spatio-temporal mean and covariance functions suggested by Yang and Qiu (2022) , the spatio-temporal disease surveillance method discussed in Qiu and Yang (2021) that can accommodate the covariate effect, the spatial-LASSO-based process monitoring method proposed by Qiu and Yang (2023) , and the online spatio-temporal disease surveillance method described in Yang and Qiu (2020) . Package: r-cran-sptimer Architecture: amd64 Version: 3.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 815 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-sp, r-cran-spacetime, r-cran-extradistr Filename: pool/dists/noble/main/r-cran-sptimer_3.3.4-1.ca2404.1_amd64.deb Size: 679790 MD5sum: c65c6bb9b1ea72e34c92cb5206b56541 SHA1: e6907cf2b559983d0a19583408f005ae96a3056d SHA256: 9d946f63996e471a5369175c640b073aa317a8b91ed7ad3adecf451efa4c09d9 SHA512: 068acf8b8d65328ac8852c023638ac62f5cbebe227e307ac1475f028170f54e30649941dfaff57a6d28c23cfb9a641d7f24cb7d3d4825591ba151e5b90825be8 Homepage: https://cran.r-project.org/package=spTimer Description: CRAN Package 'spTimer' (Spatio-Temporal Bayesian Modelling) Fits, spatially predicts and temporally forecasts large amounts of space-time data using [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian Auto-Regressive (AR) Models, and [3] Bayesian Gaussian Predictive Processes (GPP) based AR Models for spatio-temporal big-n problems. Bakar and Sahu (2015) . Package: r-cran-sqlformatter Architecture: amd64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1955 Depends: libc6 (>= 2.39), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat Suggests: r-cran-covr, r-cran-docopt, r-cran-git2r, r-cran-jsonlite, r-cran-lintr, r-cran-optparse, r-cran-precommit, r-cran-rextendr, r-cran-roxygen2, r-cran-styler, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sqlformatter_0.0.2-1.ca2404.1_amd64.deb Size: 664086 MD5sum: a1f0b717c44321a5e245984175f64918 SHA1: c9948cd55fcacb0cf34b4b27c6c222c8f9a02520 SHA256: 4222d481c3faaa7642a713916e9e6621cb4f99ad655a998592409658cfae971b SHA512: 6fe206ccf29d32708efdeac0040da1f550f7367b5e5eb441855f841ec176001f63bf4cb459544c8d4a00f448f655570c88b29ab28f57e9a859d31d9451048a76 Homepage: https://cran.r-project.org/package=SQLFormatteR Description: CRAN Package 'SQLFormatteR' (Format SQL Queries) A convenient interface for formatting 'SQL' queries directly within 'R'. It acts as a wrapper around the 'sql_format' Rust crate. The package allows you to format 'SQL' code with customizable options, including indentation, case formatting, and more, ensuring your 'SQL' queries are clean, readable, and consistent. Package: r-cran-squat Architecture: amd64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2821 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dbscan, r-cran-dtw, r-cran-fdacluster, r-cran-fdasrvf, r-cran-fundata, r-cran-future.apply, r-cran-ggplot2, r-cran-ggrepel, r-cran-mfpca, r-cran-progressr, r-cran-rcpp, r-cran-rlang, r-cran-roahd, r-cran-scales, r-cran-tibble, r-cran-tidyr, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-covr, r-cran-future, r-cran-gganimate, r-cran-gghighlight, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-squat_0.5.0-1.ca2404.1_amd64.deb Size: 2362160 MD5sum: 9abdd3f4b1c0a30d1cdd34ba4f83a824 SHA1: 8ed32e6002821c5620ba1b4c04a123a502e95570 SHA256: 94a75367f515f14dad97303b95b63f779170208ebb3829a53b8f280d52047dd7 SHA512: 5bd348df9ac75f6cc886eb427cd2379d6312a5df0c81a35a1dbf2cc4418b0f560fbc3ce348bec0489097be75de01ead5f163b304fa95936175d28341bbde95d7 Homepage: https://cran.r-project.org/package=squat Description: CRAN Package 'squat' (Statistics for Quaternion Temporal Data) An implementation of statistical tools for the analysis of rotation-valued time series and functional data. 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Package: r-cran-srm Architecture: amd64 Version: 0.4-26-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 603 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-srm_0.4-26-1.ca2404.1_amd64.deb Size: 397744 MD5sum: dc6dde8c50477d35a164ec23483ba81c SHA1: 36084595b897049b4812589c1c45a8555ff6ad5b SHA256: 636e1e50fe459fd7afd1dd717e76c90e13c9374711219f8d86289c49872bac01 SHA512: 1a85f493265014bab0fcfe26602157c6bd5653ed46e5e0e3866173cfe3e900fdc7005e241e031abf881ca6d7bce941e6b59a4fb2285178e2eb24f49a086b35cc 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, ). 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Package: r-cran-ssdtools Architecture: amd64 Version: 2.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2777 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-chk, r-cran-furrr, r-cran-generics, r-cran-ggplot2, r-cran-ggtext, r-cran-glue, r-cran-goftest, r-cran-lifecycle, r-cran-plyr, r-cran-purrr, r-cran-rcpp, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-ssddata, r-cran-stringr, r-cran-tibble, r-cran-tmb, r-cran-universals, r-cran-rcppeigen Suggests: r-cran-actuar, r-cran-covr, r-cran-dplyr, r-cran-envstats, r-cran-extradistr, r-cran-fitdistrplus, r-cran-knitr, r-cran-latex2exp, r-cran-magrittr, r-cran-mle.tools, r-cran-patchwork, r-cran-reshape2, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-tidyselect, r-cran-tinytex, r-cran-vgam, r-cran-withr Filename: pool/dists/noble/main/r-cran-ssdtools_2.6.0-1.ca2404.1_amd64.deb Size: 1770272 MD5sum: b2567cc8fb2d223d95236a393a568218 SHA1: cbc5495074e9d44f999d87b5f676d6cdf54b7555 SHA256: 620701a49654afe6086211383c17f57cb6c68a78524453635eb8ba1787a38076 SHA512: d7e02ecdf49b0a9956ddadf8bef9a5773650f54457e237e54e09d4592092c7ca1d512fe6ac6186fdced199198b67b21c9cde76c2bbe7d25d872ca43cd8b49206 Homepage: https://cran.r-project.org/package=ssdtools Description: CRAN Package 'ssdtools' (Species Sensitivity Distributions) Species sensitivity distributions are cumulative probability distributions which are fitted to toxicity concentrations for different species as described by Posthuma et al.(2001) . The ssdtools package uses Maximum Likelihood to fit distributions such as the gamma, log-logistic, log-normal and log-normal log-normal mixture. Multiple distributions can be averaged using Akaike Information Criteria. Confidence intervals on hazard concentrations and proportions are produced by bootstrapping. Package: r-cran-ssgl Architecture: amd64 Version: 2.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-grpreg, r-cran-caret, r-cran-doparallel, r-cran-dorng, r-cran-foreach, r-cran-mass, r-cran-matrix, r-cran-gigrvg, r-cran-bayeslogit Filename: pool/dists/noble/main/r-cran-ssgl_2.0-1.ca2404.2_amd64.deb Size: 68662 MD5sum: fa9b455f1e45b952d39d72bbfda1061f SHA1: 6acf20eade0cf46273a9243e3ea55927e79bbb77 SHA256: b27dbee82836d9e43d1be5888c0c00e4715b0d5cdf66de50593ed6db265eeef8 SHA512: d91ba0ab3015bddbbcb81397d5ee03152961896461d393080d1aa60561c1ede7edd7b9f5dda39ce6d56647b779b5a27dcdd3a54099de8c603183626b6ee104cf Homepage: https://cran.r-project.org/package=SSGL Description: CRAN Package 'SSGL' (Spike-and-Slab Group Lasso for Group-Regularized GeneralizedLinear Models) Fits group-regularized generalized linear models (GLMs) using the spike-and-slab group lasso (SSGL) prior of Bai et al. 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Package: r-cran-ssgraph Architecture: amd64 Version: 1.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bdgraph Suggests: r-cran-skimr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ssgraph_1.16-1.ca2404.1_amd64.deb Size: 246260 MD5sum: ab8d8c3707d82c97d8c0e0349d3901e2 SHA1: 8adcc0e2346dcb63747c6d64104509c8621d42e2 SHA256: 1cce0d2678233b564e2bb18261f6a999515d8ea925aa0468b833c378afa05e47 SHA512: ae68bdbc667f0f57586f897da39c07c5c8ddd475e55d298a0ca9b3aa58d8fc583a5947c07faf4cdf1f704405ce6075db53b482618dbabe0f15f27b1e66eba55c 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-sshicm Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1496 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-sdsfun, r-cran-sf, r-cran-rcpp, r-cran-rcppthread Suggests: r-cran-gdverse, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sshicm_0.1.0-1.ca2404.1_amd64.deb Size: 705018 MD5sum: 54cdaa0d876fd6d35a5f5f3da8120d07 SHA1: 4202a7c347eb52197a03556dfd33952d40677959 SHA256: a532aaae11fc0dbfdc6476335398fd351dca47c2523ee38c1ff357be7618be2f SHA512: b7a4d00ff39a73e37288df31648baf1194494c1dd49921e2dafaabbd2c3e36ef9d1ac1dbd32eebc283199a647eb253553dffb4dfdd5ea2f79da8ccbf28eaa2ae Homepage: https://cran.r-project.org/package=sshicm Description: CRAN Package 'sshicm' (Information Consistency-Based Measures for Spatial StratifiedHeterogeneity) Spatial stratified heterogeneity (SSH) denotes the coexistence of within-strata homogeneity and between-strata heterogeneity. Information consistency-based methods provide a rigorous approach to quantify SSH and evaluate its role in spatial processes, grounded in principles of geographical stratification and information theory (Bai, H. et al. (2023) ; Wang, J. et al. (2024) ). Package: r-cran-sshist Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sshist_0.1.3-1.ca2404.1_amd64.deb Size: 166610 MD5sum: 14aa9427feb775a7b7ed76f99fdc9b5a SHA1: 876eefc6e0ef8de96f3feb2b5dd22e8a94dbfe99 SHA256: a5724680230ddb896f678b365017a16262e1ac1a652fc0a458f835a0de080f13 SHA512: db51df91728b61615cbd6c663879ea40511948dd3300287042ff4db45f1eef076799bae0ba4e8958de32f539fa8271b4459497f3e022557fda0f2c6b0e443ae0 Homepage: https://cran.r-project.org/package=sshist Description: CRAN Package 'sshist' (Optimal Histogram Binning Using Shimazaki-Shinomoto Method) Implements the Shimazaki-Shinomoto method for optimizing the bin width of a histogram. This method minimizes the mean integrated squared error (MISE) and features a 'C++' backend for high performance and shift-averaging to remove edge-position bias. Ideally suits for time-dependent rate estimation and identifying intrinsic data structures. Supports both 1D and 2D data distributions. For more details see Shimazaki and Shinomoto (2007) "A Method for Selecting the Bin Size of a Time Histogram" . Package: r-cran-sslasso Architecture: amd64 Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sslasso_1.2.3-1.ca2404.1_amd64.deb Size: 33798 MD5sum: d3aa0f78b3042068358770c05654df9a SHA1: 144aae3ae55362baa06b1f95d6c3dc7080c0c181 SHA256: f8141e3eeb622624c37720bacd977cd819695cf8174bb53651a4001f84ce8fc1 SHA512: 83d1a58f74f2f38ac762856cf2546809fe241007d8485428b9b7639ad39e093f8205f55190f72cc3b9bf8f0e0993d68eb89a64ae68e35b386e9744f42389ea15 Homepage: https://cran.r-project.org/package=SSLASSO Description: CRAN Package 'SSLASSO' (The Spike-and-Slab LASSO) Efficient coordinate ascent algorithm for fitting regularization paths for linear models penalized by Spike-and-Slab LASSO of Rockova and George (2018) . Package: r-cran-sslr Architecture: amd64 Version: 0.9.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1988 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-parsnip, r-cran-plyr, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-proxy, r-cran-generics, r-cran-rann, r-cran-foreach, r-cran-rssl, r-cran-conclust, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-caret, r-cran-tidymodels, r-cran-e1071, r-cran-c50, r-cran-kernlab, r-cran-testthat, r-cran-doparallel, r-cran-tidyverse, r-cran-factoextra, r-cran-survival, r-cran-covr, r-cran-kknn, r-cran-randomforest, r-cran-ranger, r-cran-mass, r-cran-nlme, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sslr_0.9.3.3-1.ca2404.1_amd64.deb Size: 1063512 MD5sum: 3d63d6429eb1cb89466500a0b5b2e6ce SHA1: dc10034db35bc3d1daabdd55b9daae0305cd93fc SHA256: d046b96b679177fb64ecb118526b612dc399291862e6858c3bd930a395f47322 SHA512: 43cb29492feec873d8cea2ecd556e87ef441e5fc111702d7b721ac96feb35a4a5cb59221dc6f797dbee37bca53aa67916fb4b81482fa9e257bc7e8942772fe39 Homepage: https://cran.r-project.org/package=SSLR Description: CRAN Package 'SSLR' (Semi-Supervised Classification, Regression and ClusteringMethods) Providing a collection of techniques for semi-supervised classification, regression and clustering. In semi-supervised problem, both labeled and unlabeled data are used to train a classifier. The package includes a collection of semi-supervised learning techniques: self-training, co-training, democratic, decision tree, random forest, 'S3VM' ... etc, with a fairly intuitive interface that is easy to use. Package: r-cran-ssmousetrack Architecture: amd64 Version: 1.1.7-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6341 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-circstats, r-cran-dtw, r-cran-ggplot2, r-cran-cowplot, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/noble/main/r-cran-ssmousetrack_1.1.7-1.ca2404.2_amd64.deb Size: 1467822 MD5sum: e1fde4842430f5b1dbf62d761a766cf4 SHA1: fe7bc87a76f18aa4b5cd41df90661c5c764a7a0f SHA256: 43e70d0eb8957ec96db00e44dffda92f8a142cd7685d24b0fd586e83d5be756b SHA512: b4d8a8e90b2c977be8185ee459a43b595bb6838a248a3e576698c48cf731923782505e0cf01569386a22701dec43db9a0e5742442fa85ee0f1a70a38488e2b0a Homepage: https://cran.r-project.org/package=ssMousetrack Description: CRAN Package 'ssMousetrack' (Bayesian State-Space Modeling of Mouse-Tracking Experiments viaStan) Estimates previously compiled state-space modeling for mouse-tracking experiments using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Package: r-cran-ssmrcd Architecture: amd64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1806 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase, r-cran-scales, r-cran-ellipse, r-cran-dbscan, r-cran-ggplot2, r-cran-expm, r-cran-rrcov, r-cran-desctools, r-cran-rootsolve, r-cran-matrix, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-cellwise Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-dplyr, r-cran-tidyr, r-cran-ggridges Filename: pool/dists/noble/main/r-cran-ssmrcd_2.0.1-1.ca2404.1_amd64.deb Size: 1309322 MD5sum: 4d3b0923d0b92246050e5e373c2809b5 SHA1: b1858cfa2f5969891f6c5f38b680cafad9ba452b SHA256: f0bd5ba064f07656cf468a69a02738b797cabb574abea7f67471ff44852df29f SHA512: 697aaf22a4e3f3c849c547dcf6bc5d915418b1d136b3dac91e26dd47009b2c6dc26956f4ef7cfe492ece1b16ddcd3a98fbada2381c9a9c47f206d473ab05c088 Homepage: https://cran.r-project.org/package=ssMRCD Description: CRAN Package 'ssMRCD' (Robust Estimators for Multi-Group and Spatial Data) Estimation of robust estimators for multi-group and spatial data including the casewise robust Spatially Smoothed Minimum Regularized Determinant (ssMRCD) estimator and its usage for local outlier detection as described in Puchhammer and Filzmoser (2023) as well as for sparse robust PCA for multi-source data described in Puchhammer, Wilms and Filzmoser (2024) . Moreover, a cellwise robust multi-group Gaussian mixture model (MG-GMM) is implemented as described in Puchhammer, Wilms and Filzmoser (2024) . Included are also complementary visualization and parameter tuning tools. Package: r-cran-ssn2 Architecture: amd64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2867 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-matrix, r-cran-generics, r-cran-tibble, r-cran-spmodel, r-cran-rsqlite, r-cran-withr, r-cran-doparallel, r-cran-filematrix, r-cran-foreach, r-cran-itertools, r-cran-iterators Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-ggplot2, r-cran-sp, r-cran-statmod, r-cran-proc, r-cran-emmeans, r-cran-estimability Filename: pool/dists/noble/main/r-cran-ssn2_0.4.0-1.ca2404.1_amd64.deb Size: 1719718 MD5sum: 599f68dde7cce257ce957bfdbf9bf08c SHA1: 49b2f57477aee1bccdec9738ccbd860b08791541 SHA256: 11527524d43cc169939e123544acd926a0ac2a3d705b86d9c760ae8b14a093ee SHA512: d8af5586fd07fe975bcb7bad8bd78e51794631c3034672fd4a9db35017d6a2236c251cade8a3ac1cda507d79252551997e7e8b75e5b8c2142444170e8e53b3b3 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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Package: r-cran-stima Architecture: amd64 Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rpart Filename: pool/dists/noble/main/r-cran-stima_1.2.4-1.ca2404.1_amd64.deb Size: 123470 MD5sum: 3863a844fd1bbaeb4c3c0e0e687209c6 SHA1: 45b72051119a64a2f87562b87aa2c9e21aec0d8b SHA256: 0979effa674c2ab6c319536d2ddbdf654de3f851b1e7a2bd1a225d00a0fc86f8 SHA512: c03ec724f5aaffc589a3c55c15f96b47c14d28f9dab9a3044625b9f5b35967ce4d08f535156ee84a4e1c0f2ba82b644727bbc9f7562055755fd1964e8b0f1b9b Homepage: https://cran.r-project.org/package=stima Description: CRAN Package 'stima' (Simultaneous Threshold Interaction Modeling Algorithm) Regression trunk model estimation proposed by Dusseldorp and Meulman (2004) and Dusseldorp, Conversano, Van Os (2010) , integrating a regression tree and a multiple regression model. Package: r-cran-stlplus Architecture: amd64 Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-yaimpute, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-stlplus_0.5.2-1.ca2404.1_amd64.deb Size: 114190 MD5sum: db51c6f1ae11f099f7d0a654c86bf015 SHA1: c223ca3bdb5d11b5975589e0cfe987647e129bb9 SHA256: 0bd65463eb8d663ec7b587987131f09b052afa9a813b56b761c2df7903bf02a3 SHA512: 4bb94304d8757a745f054c3109577976a70c426c8f9456c56239bed3c69dc7d29e0c7754e343e1233dbb9ca6d8a58defdfbcd7dc67c9ae315a9a05caa099c3b3 Homepage: https://cran.r-project.org/package=stlplus Description: CRAN Package 'stlplus' (Enhanced Seasonal Decomposition of Time Series by Loess) Decompose a time series into seasonal, trend, and remainder components using an implementation of Seasonal Decomposition of Time Series by Loess (STL) that provides several enhancements over the STL method in the stats package. 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Package: r-cran-stm Architecture: amd64 Version: 1.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2924 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-data.table, r-cran-glmnet, r-cran-lda, r-cran-matrix, r-cran-matrixstats, r-cran-quadprog, r-cran-quanteda, r-cran-slam, r-cran-stringr, r-cran-rcpparmadillo Suggests: r-cran-clue, r-cran-geometry, r-cran-huge, r-cran-hunspell, r-cran-igraph, r-cran-ldavis, r-cran-kernsmooth, r-cran-nlp, r-cran-rsvd, r-cran-rtsne, r-cran-snowballc, r-cran-spelling, r-cran-testthat, r-cran-tm, r-cran-wordcloud Filename: pool/dists/noble/main/r-cran-stm_1.3.8-1.ca2404.1_amd64.deb Size: 2702448 MD5sum: 576915ae427c278dd4eab4d6f1e52663 SHA1: a8b3e7f4d116403dd6aa217b73ff0ece5b939775 SHA256: c1094a64c8358a561e3194c6ab044f1ebc0b05362ab429a09e27fa636b8083d5 SHA512: d66581defba50a5969f0033afe0cb19d3ebcb667406ed431a7ae94fdea6d4f879c1588988a0d05093335b21be4d78ddf756c5f099a677bb33f2268ff1b9d5f2d Homepage: https://cran.r-project.org/package=stm Description: CRAN Package 'stm' (Estimation of the Structural Topic Model) The Structural Topic Model (STM) allows researchers to estimate topic models with document-level covariates. The package also includes tools for model selection, visualization, and estimation of topic-covariate regressions. Methods developed in Roberts et. al. (2014) and Roberts et. al. (2016) . Vignette is Roberts et. al. (2019) . Package: r-cran-stmosim Architecture: amd64 Version: 3.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcppparallel, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-stmosim_3.2.0-1.ca2404.1_amd64.deb Size: 50220 MD5sum: 53cfab9a0075d89884c299ce18a8bdbc SHA1: 8aefe34bdd691b57d714be1b3957f6f21e03e229 SHA256: 8ba8caa332aab2bedb02e3b09719fd48cbc68cb5b15cf4009984edd1e7391e24 SHA512: e9b37795df211e61e958f257692d837b93e5944302f7af5159074fd2cf144f92d0141dd5e6cd1da8fc39a77cdee680597c50095da0c5d1f2393ed0f7d0a9d1f6 Homepage: https://cran.r-project.org/package=StMoSim Description: CRAN Package 'StMoSim' (Quantile-Quantile Plot with Several Gaussian Simulations) Plots a QQ-Norm Plot with several Gaussian simulations. Package: r-cran-stochblock Architecture: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-blockmodeling, r-cran-doparallel, r-cran-dorng, r-cran-foreach, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-stochblock_0.1.5-1.ca2404.1_amd64.deb Size: 173526 MD5sum: e60c9512bea8c965a48d34542ad68e4a SHA1: 7b62db26315fed39b7035df1abfd365fbf3a7506 SHA256: 4e12cc3b2efcf2606617e1b41da443a32d04f97d7214edeb67111f13d165c69f SHA512: f47bb6a47f31072ff017bb73fa23f52db8dedbd003193be6aa107f197473626d69d5f2729b91f8702ce2f7c4eea87f1197298abab86ad642bf732a73a59797bd Homepage: https://cran.r-project.org/package=StochBlock Description: CRAN Package 'StochBlock' (Stochastic Blockmodeling of One-Mode and Linked Networks) Stochastic blockmodeling of one-mode and linked networks as presented in Škulj and Žiberna (2022) . The optimization is done via CEM (Classification Expectation Maximization) algorithm that can be initialized by random partitions or the results of k-means algorithm. The development of this package is financially supported by the Slovenian Research Agency () within the research programs P5-0168 and the research projects J7-8279 (Blockmodeling multilevel and temporal networks) and J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks). Package: r-cran-stochcorr Architecture: amd64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-nloptr, r-cran-progress, r-cran-foreach, r-cran-dosnow, r-cran-snow, r-cran-rcpparmadillo Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-stochcorr_0.0.1-1.ca2404.1_amd64.deb Size: 168202 MD5sum: 3d285057cff127a298d303e7b03d4802 SHA1: 1ef0d632a2c9a02ca2fffd1e1ad8a478acfb3361 SHA256: 8a0ca64305ac0aa45fc4ddc62bc15fc653955d55e210a4289a36f48517408fca SHA512: dab45dfb90c9b9c20df9c48209469f399064c9a52ad636e14beb05035da3f8007fceb2ee14cda30ceeb86e9f058c17d1f2baa7c095f284c8aa7d05f08988defb Homepage: https://cran.r-project.org/package=stochcorr Description: CRAN Package 'stochcorr' (Stochastic Correlation Modelling via Circular Diffusion) Performs simulation and inference of diffusion processes on circle. Stochastic correlation models based on circular diffusion models are provided. For details see Majumdar, S. and Laha, A.K. (2024) "Diffusion on the circle and a stochastic correlation model" . Package: r-cran-stochqn Architecture: amd64 Version: 0.1.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-stochqn_0.1.2-1-1.ca2404.1_amd64.deb Size: 170712 MD5sum: 9d5fef57e49d0a5bfd89630fd97af682 SHA1: 733b0d99145e4dda70cc087e7015e5d4c77e85e5 SHA256: fcf554a4d60a920e3837a38bbb66bb6c9bae1814d42c875992141e5259b89a21 SHA512: 669175423b7e2ff83a296cb4638da373a7f64618dacfb281487d84f209f5d1f281b79dee93c49883b538d9531166f7f13994d1ea7c292d4843255ff3135eb778 Homepage: https://cran.r-project.org/package=stochQN Description: CRAN Package 'stochQN' (Stochastic Limited Memory Quasi-Newton Optimizers) Implementations of stochastic, limited-memory quasi-Newton optimizers, similar in spirit to the LBFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) algorithm, for smooth stochastic optimization. Implements the following methods: oLBFGS (online LBFGS) (Schraudolph, N.N., Yu, J. and Guenter, S., 2007 ), SQN (stochastic quasi-Newton) (Byrd, R.H., Hansen, S.L., Nocedal, J. and Singer, Y., 2016 ), adaQN (adaptive quasi-Newton) (Keskar, N.S., Berahas, A.S., 2016, ). Provides functions for easily creating R objects with partial_fit/predict methods from some given objective/gradient/predict functions. Includes an example stochastic logistic regression using these optimizers. Provides header files and registered C routines for using it directly from C/C++. Package: r-cran-stochtree Architecture: amd64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2620 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-cpp11, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-stochtree_0.4.2-1.ca2404.1_amd64.deb Size: 1440808 MD5sum: 962bb3cf4490c0497a0b6aac6498234b SHA1: 924b01d4a3fb6e8130fbcce16ff6022ed62e58ba SHA256: c77bdd4ebd377c2ee301a84a30f7199565fdce15a1c62013ffd3937f8ec15c5f SHA512: 31d408f204c0c6f41d53c685437ab608f1ab361a48fe8160907a70c01fea7956cf974dd50a0839ef5c56f9998f40d9171fabda646141cb2724e0f799082dd273 Homepage: https://cran.r-project.org/package=stochtree Description: CRAN Package 'stochtree' (Stochastic Tree Ensembles (XBART and BART) for SupervisedLearning and Causal Inference) Flexible stochastic tree ensemble software. Robust implementations of Bayesian Additive Regression Trees (BART) (Chipman, George, McCulloch (2010) ) for supervised learning and Bayesian Causal Forests (BCF) (Hahn, Murray, Carvalho (2020) ) for causal inference. Enables model serialization and parallel sampling and provides a low-level interface for custom stochastic forest samplers. Includes the grow-from-root algorithm for accelerated forest sampling (He and Hahn (2021) ), a log-linear leaf model for forest-based heteroskedasticity (Murray (2020) ), and the cloglog BART model of Alam and Linero (2025) for ordinal outcomes. Package: r-cran-stochvol Architecture: amd64 Version: 3.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3156 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-coda, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-mvtnorm, r-cran-knitr Filename: pool/dists/noble/main/r-cran-stochvol_3.2.9-1.ca2404.1_amd64.deb Size: 2290998 MD5sum: 22bc43326d50bd719bd8c27fee4c8aad SHA1: 703f242628e77c6e2697ec240b00bb087d9f863f SHA256: b68cbc91d4e801267b40cff525857ca13ab31e330ab51490e0eb3114d6863bc7 SHA512: ac35afc7e277083666f6328059121dd1a9ae8c72e9849dd487238b9c683b86ccbac54d0f8f6fa339df9c78ad65352f714421da44bd9a8bad2cb5d47630e9609f Homepage: https://cran.r-project.org/package=stochvol Description: CRAN Package 'stochvol' (Efficient Bayesian Inference for Stochastic Volatility (SV)Models) Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models with and without asymmetry (leverage) via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Frühwirth-Schnatter (2014) and Hosszejni and Kastner (2019) ; the most common use cases are described in Hosszejni and Kastner (2021) and Kastner (2016) and the package examples. Package: r-cran-stochvoltmb Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7332 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tmb, r-cran-ggplot2, r-cran-sn, r-cran-data.table, r-cran-mass, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-shiny, r-cran-knitr, r-cran-rmarkdown, r-cran-stochvol Filename: pool/dists/noble/main/r-cran-stochvoltmb_0.3.0-1.ca2404.1_amd64.deb Size: 1923298 MD5sum: c6286693af89c3f54dc2164b318db96b SHA1: c23bd6d45edabc664cf077f3a44133623fa6e467 SHA256: ae2496a1492910e2196fce1d8c00b400ef9347d7dc4c85663ffd124ef052f48d SHA512: 257e920c0b561ad837c1d11dd7ed16371935dd3f662d9b59066e45a6b766b6677ec9cc02fa7e6d6601c95f6c98f67700345edc2dc98269feae9074be9120cecd Homepage: https://cran.r-project.org/package=stochvolTMB Description: CRAN Package 'stochvolTMB' (Likelihood Estimation of Stochastic Volatility Models) Parameter estimation for stochastic volatility models using maximum likelihood. The latent log-volatility is integrated out of the likelihood using the Laplace approximation. The models are fitted via 'TMB' (Template Model Builder) (Kristensen, Nielsen, Berg, Skaug, and Bell (2016) ). Package: r-cran-stockr Architecture: amd64 Version: 1.0.76-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 917 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-gtools, r-cran-rcolorbrewer Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-stockr_1.0.76-1.ca2404.1_amd64.deb Size: 861222 MD5sum: 9a3690b664d7441387bd3ea71843df91 SHA1: 585792672d9c565ea8b91eef2b673468a2596a0d SHA256: d55b6509c0f5981513fb29ffe5b6cae20ba20c8c58f7b245792a1eb2ff49b7f2 SHA512: ee65db04680d6ebc1639b57313c1421819cdfa080b48987f59567a26bc0809299d267c7d5dc7363e3854860287a3540e8310c7bf30d93ab87602c7e23c5bb8da Homepage: https://cran.r-project.org/package=stockR Description: CRAN Package 'stockR' (Identifying Stocks in Genetic Data) Provides a mixture model for clustering individuals (or sampling groups) into stocks based on their genetic profile. Here, sampling groups are individuals that are sure to come from the same stock (e.g. breeding adults or larvae). The mixture (log-)likelihood is maximised using the EM-algorithm after finding good starting values via a K-means clustering of the genetic data. Details can be found in: Foster, S. D.; Feutry, P.; Grewe, P. M.; Berry, O.; Hui, F. K. C. & Davies (2020) . 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Main features are loading and aligning historical data for ticker symbols, calculating performance metrics for individual funds or portfolios (e.g. annualized growth, maximum drawdown, Sharpe/Sortino ratio), and creating graphs. C++ code is used to improve processing speed where possible. Package: r-cran-stormr Architecture: amd64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2867 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-leaflet, r-cran-maps, r-cran-ncdf4, r-cran-rworldmap, r-cran-sf, r-cran-stringr, r-cran-terra, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rworldxtra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stormr_0.2.1-1.ca2404.1_amd64.deb Size: 1316058 MD5sum: b173937c8a9a3841d3d1469673af8ee0 SHA1: 4227580d05596ee6a34b3bbd5f1555f701cccaa1 SHA256: 2ef3a1a7141d00b537c383145119e2418921e7e1b95d4756fdcd11c732fd459e SHA512: 3de3cd4340dadcf61f92a662687ee4fe876717b5f39ef3d49b440ce2329f83f2279f99b6a8d9c05e091d229ebbd14da260b43332151dad2720190f96e0c9d308 Homepage: https://cran.r-project.org/package=StormR Description: CRAN Package 'StormR' (Analyzing the Behaviour of Wind Generated by Tropical Storms andCyclones) Set of functions to quantify and map the behaviour of winds generated by tropical storms and cyclones in space and time. 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Package: r-cran-strat Architecture: amd64 Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-strat_0.1-1.ca2404.1_amd64.deb Size: 220808 MD5sum: 490406af4264d1a8db7e656debb7baec SHA1: 130ebaa2abcbff096dff35f0584fda1ff24be1a2 SHA256: 1d5539ae37485d0eae9b4e18c5118e150e9971ca8e903f6492f8a922880bb0f1 SHA512: cd672ddca7856672a3534fc93f7eaf6ba87896a7b863ebd18480235dc044e2fbc9339ce30cdcdb811f1af2798f842e4029014f105ccd3010f6b02885a88e994a Homepage: https://cran.r-project.org/package=strat Description: CRAN Package 'strat' (An Implementation of the Stratification Index) An implementation of the stratification index proposed by Zhou (2012) . 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Package: r-cran-strathe2e2 Architecture: amd64 Version: 3.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1814 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-netindices Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-strathe2e2_3.3.0-1.ca2404.1_amd64.deb Size: 1456862 MD5sum: a0b6dd2fd1e4e23b716ea2e7a72003a5 SHA1: 9069f4b0a4fb9db2e20ac5f795aa875434654570 SHA256: 7e3035c58f6ba15cb788ac9bcd35eb21cd9e4ec2a2427e3b91d91a7e105a2bdf SHA512: 9d2d36aa78a063a9388384e283606d2ad09edf089ea08ddafa2a450ce416e7466dc5d28ce8941a4212ce6bec3b403cb812721585423b7765623fa95f21b6f316 Homepage: https://cran.r-project.org/package=StrathE2E2 Description: CRAN Package 'StrathE2E2' (End-to-End Marine Food Web Model) A dynamic model of the big-picture, whole ecosystem effects of hydrodynamics, temperature, nutrients, and fishing on continental shelf marine food webs. The package is described in: Heath, M.R., Speirs, D.C., Thurlbeck, I. and Wilson, R.J. (2020) StrathE2E2: An R package for modelling the dynamics of marine food webs and fisheries. 8pp. Package: r-cran-stratification Architecture: amd64 Version: 2.2-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 714 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-stratification_2.2-7-1.ca2404.1_amd64.deb Size: 625814 MD5sum: 46e83f61c3d152e7e1cad0ebf80fd5e7 SHA1: fd035e79a3c1421920335556199d1871f845e978 SHA256: 5c62c35979af86bc0b3d33aa5894a744c24a6e983d05d986aa49afcdd1c87a6a SHA512: 1d463b4184c0c31980d18fef48d2ba4c55c4fcf72cf4a8cd163ef71597207e69443b62460ebf022df9c2895812c012134462bed7e0be49ee81f00f0979719282 Homepage: https://cran.r-project.org/package=stratification Description: CRAN Package 'stratification' (Univariate Stratification of Survey Populations) Univariate stratification of survey populations with a generalization of the Lavallee-Hidiroglou method of stratum construction. The generalized method takes into account a discrepancy between the stratification variable and the survey variable. The determination of the optimal boundaries also incorporate, if desired, an anticipated non-response, a take-all stratum for large units, a take-none stratum for small units, and a certainty stratum to ensure that some specific units are in the sample. The well known cumulative root frequency rule of Dalenius and Hodges and the geometric rule of Gunning and Horgan are also implemented. Package: r-cran-stratifiedsampling Architecture: amd64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 679 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-transport, r-cran-proxy, r-cran-mass, r-cran-sampling, r-cran-rglpk, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-balancedsampling, r-cran-testthat, r-cran-statmatch, r-cran-laeken, r-cran-prettydoc, r-cran-ggplot2, r-cran-viridis, r-cran-geojsonio, r-cran-sf, r-cran-rmapshaper Filename: pool/dists/noble/main/r-cran-stratifiedsampling_0.4.2-1.ca2404.1_amd64.deb Size: 335496 MD5sum: 82b5584d01fc18873dee7dd4b0802b48 SHA1: 6f3859a3d67022a3027e7d023436f8a815741aba SHA256: d906f17a8cbde247e0e95322653c667645187456fabb81115365e4f97c1d4367 SHA512: f6fe4ac390eb7767bb503b5431c93941a5bb67956d750629a54e4ad3095c3a93f380459e63698eaf5269e369931b785bf8a408828cc5e9499153995b9cb161f8 Homepage: https://cran.r-project.org/package=StratifiedSampling Description: CRAN Package 'StratifiedSampling' (Different Methods for Stratified Sampling) Integrating a stratified structure in the population in a sampling design can considerably reduce the variance of the Horvitz-Thompson estimator. We propose in this package different methods to handle the selection of a balanced sample in stratified population. For more details see Raphaël Jauslin, Esther Eustache and Yves Tillé (2021) . The package propose also a method based on optimal transport and balanced sampling, see Raphaël Jauslin and Yves Tillé . Package: r-cran-stratifyr Architecture: amd64 Version: 1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1668 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fitdistrplus, r-cran-zipfr, r-cran-actuar, r-cran-triangle, r-cran-mc2d Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stratifyr_1.0-4-1.ca2404.1_amd64.deb Size: 842898 MD5sum: 50c06b1828d5f8b2ad9866d6931d7257 SHA1: dccd41603b307c862c363f4d28273dc4b3488aba SHA256: 73142fa8442db5ec3e226c53016edacdb7fe5a9b8b3c38e602ec0a20d21a09bf SHA512: bc3bc26df0de7804031a844604256c0bd63daac79c60462f5c25ea598ea533905e3bd3a0f35c5273e92d82245f906c051fbab0af62c81cf073c86940c8fadc86 Homepage: https://cran.r-project.org/package=stratifyR Description: CRAN Package 'stratifyR' (Optimal Stratification of Univariate Populations) The stratification of univariate populations under stratified sampling designs is implemented according to Khan et al. (2002) and Khan et al. (2015) in this library. It determines the Optimum Strata Boundaries (OSB) and Optimum Sample Sizes (OSS) for the study variable, y, using the best-fit frequency distribution of a survey variable (if data is available) or a hypothetical distribution (if data is not available). The method formulates the problem of determining the OSB as mathematical programming problem which is solved by using a dynamic programming technique. If a dataset of the population is available to the surveyor, the method estimates its best-fit distribution and determines the OSB and OSS under Neyman allocation directly. When the dataset is not available, stratification is made based on the assumption that the values of the study variable, y, are available as hypothetical realizations of proxy values of y from recent surveys. Thus, it requires certain distributional assumptions about the study variable. At present, it handles stratification for the populations where the study variable follows a continuous distribution, namely, Pareto, Triangular, Right-triangular, Weibull, Gamma, Exponential, Uniform, Normal, Log-normal and Cauchy distributions. 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The development of this package was supported in part by NSF IIS-0948893, NSF CMMI 1728612, and NIH R21HG005912. Hahsler et al (2017) . 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The model (version 1.0) is introduced in Schuwirth, N. and Reichert, P., (2013) . This package includes model extensions and the core functions introduced and used in Schuwirth, N. et al. (2016) , Kattwinkel, M. et al. (2016) , Mondy, C. P., and Schuwirth, N. (2017) , and Paillex, A. et al. (2017) . Package: r-cran-strex Architecture: amd64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-checkmate, r-cran-lifecycle, r-cran-magrittr, r-cran-rlang, r-cran-stringi Suggests: r-cran-bench, r-cran-covr, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-strex_2.0.1-1.ca2404.1_amd64.deb Size: 256904 MD5sum: 4fc43e224e1fc0d19e8387dc1390d2d4 SHA1: 85274aa9ebb0e34f19b0ada3dc418dff1a94b1db SHA256: 8bdf7508a00e2d3ea8c364b34b9122fe2eb590b238265e1cb439edbf74ea35a3 SHA512: 49acd065f48a148ec780b1b18a5f0243a8833b4e04cba3b807195192175a8c331b12de3b47dd6ca161c08322535360743e9d3873cf8753f2bd266f888936466f 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'. 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Package: r-cran-strucchange Architecture: amd64 Version: 1.5-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1053 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo, r-cran-sandwich Suggests: r-cran-car, r-cran-dynlm, r-cran-e1071, r-cran-foreach, r-cran-lmtest, r-cran-mvtnorm, r-cran-tseries Filename: pool/dists/noble/main/r-cran-strucchange_1.5-4-1.ca2404.1_amd64.deb Size: 942994 MD5sum: b6d1033c882cea4de6b4f684ff411ed6 SHA1: ce8ff1e18ecfc42052d645241c3c5973b5cc9ef2 SHA256: ead22b3e729b3209cb2e89589330c95102fac9d9130157ff2be52b821242cc85 SHA512: cc141590a4900944f94976e712e3cdcf5d1637c79469f42dcbf736ac738b7550da53779d0d5451c5f2a61a336190b58dd81b837eeac8d7eecd65aa9ad9e0388f Homepage: https://cran.r-project.org/package=strucchange Description: CRAN Package 'strucchange' (Testing, Monitoring, and Dating Structural Changes) Testing, monitoring and dating structural changes in (linear) regression models. strucchange features tests/methods from the generalized fluctuation test framework as well as from the F test (Chow test) framework. This includes methods to fit, plot and test fluctuation processes (e.g., CUSUM, MOSUM, recursive/moving estimates) and F statistics, respectively. It is possible to monitor incoming data online using fluctuation processes. Finally, the breakpoints in regression models with structural changes can be estimated together with confidence intervals. Emphasis is always given to methods for visualizing the data. 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'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. 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The 'R' package 'StrucDiv' provides methods to quantify spatial structural diversity in continuous remote sensing data, or in other data in raster format. Structure is based on the spatial arrangement of value pairs. The 'R' package 'StrucDiv' includes methods to combine information from different spatial scales, which allows to quantify multi-scale spatial structural diversity. 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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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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: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-superexacttest_1.1.0-1.ca2404.1_amd64.deb Size: 192694 MD5sum: c4a5280fec42e9128b7711c1600a7f4c SHA1: 83bd29289496f90624ce9cf65480df30d6f3a8c0 SHA256: b1b55e8fbf569a094b0a7920dd44abb4450d5011f4405d742c8e206ff3157d7d SHA512: 2b5db17292abe51c46832088ade03c156fc0f053cb3ebbc63e82a8df927500f7b6635618753d123565344ee2523d4833e03f02ea71b5ef3e7baa6d338d25290b 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-supergauss Architecture: amd64 Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1008 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rcpp, r-cran-fftw, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mvtnorm, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-supergauss_2.0.4-1.ca2404.1_amd64.deb Size: 563878 MD5sum: 5a563b3e42ef22a5a15f8ec76a14a070 SHA1: 9f1427b6976e1cbd4c756218d3f8e90fdb526037 SHA256: 63b9eb72a8b91a5089472eaf0370abc23ef38b4c5f0c5b72f72c0fe711e4510b SHA512: 05d49a1e4289e1bcb0e74d0be35a71fe05daeceee051250a6a73ff2f8a3183aa0e793f84fdefeed6f206a0b118f1bfaa6da4448e6d4072342164d5f8e6b3a694 Homepage: https://cran.r-project.org/package=SuperGauss Description: CRAN Package 'SuperGauss' (Superfast Likelihood Inference for Stationary Gaussian TimeSeries) Likelihood evaluations for stationary Gaussian time series are typically obtained via the Durbin-Levinson algorithm, which scales as O(n^2) in the number of time series observations. 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Package: r-cran-superml Architecture: amd64 Version: 0.5.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1300 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-data.table, r-cran-rcpp, r-cran-assertthat, r-cran-metrics, r-cran-bh, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rlang, r-cran-testthat, r-cran-rmarkdown, r-cran-naivebayes, r-cran-clusterr, r-cran-fnn, r-cran-ranger, r-cran-caret, r-cran-xgboost, r-cran-glmnet, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-superml_0.5.7-1.ca2404.1_amd64.deb Size: 756012 MD5sum: fa141f99e0fd1b9426a05ecc4f018762 SHA1: d608bf4d4b65cde8664e5ac688ac653b02cfb043 SHA256: c552ab5d09be715ab4b3434efd35fe34f6f11cbc0d228d8ccb314ca5b138c91d SHA512: b84b1aad01755d5982c3d57232a8ab79f60e37e11038ed0abacc790a85f93b62f686bd797b4300af96080e8b2bf7559ca55d9b29aacc1e7cabadefea8ef6dc98 Homepage: https://cran.r-project.org/package=superml Description: CRAN Package 'superml' (Build Machine Learning Models Like Using Python's Scikit-LearnLibrary in R) The idea is to provide a standard interface to users who use both R and Python for building machine learning models. 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The R code is based primarily on the article "Image Segmentation using SLIC Superpixels and Affinity Propagation Clustering, Bao Zhou, International Journal of Science and Research (IJSR), 2013" . Package: r-cran-superranker Architecture: amd64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-prodlim Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-superranker_1.2.1-1.ca2404.1_amd64.deb Size: 99726 MD5sum: 0219e9d3ac8d957d8eeeb312056177fb SHA1: 8e8c64611bab494f8e4bad5ebde63efd1e08fc9d SHA256: bbc9aa0b28e35fa1eaf7c196b87e6afa6a44fd54c1ecceb56d7dd42ee97b86eb SHA512: 11194b1539575df90a23e6684b33e59f5da4d9352b69c1bf4c3359b9fce8690e713d0527e1794c789df45f862ac2c561aaeceb371dfbdd45a50b7c7e05d731c0 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. 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Package: r-cran-suppdists Architecture: amd64 Version: 1.1-9.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 Depends: libc6 (>= 2.29), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rcppziggurat Filename: pool/dists/noble/main/r-cran-suppdists_1.1-9.9-1.ca2404.1_amd64.deb Size: 222076 MD5sum: bd7a068f80706b1c561155918b63dd59 SHA1: 8f543028fa6f87a3613fdc2ce7c8eb59bdc81e8f SHA256: f83c84be9b73a44147a89b91d777c2015a8b5a08bab5e5452662c21e06ee0ef6 SHA512: ce12f7c69e62db1ed51f77f9b22d2a475d1aa885f2f0278ed0267c97e9068e88a36f3daff62620edb68a22233f7adaaa4079cdaab196a78b139f00ec12c2dd84 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. 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Package: r-cran-surbayes Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rlist, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-surbayes_0.1.2-1.ca2404.1_amd64.deb Size: 143964 MD5sum: 5dd056707ad48e5dd36b4c7cf73c380c SHA1: 00b2a3dcb85c77c648ee484fab2525831c21585a SHA256: ea161d813c544a14e5b1382765a022d7811962cee3dab16f3922df6d8e2ff6e3 SHA512: af35b61b3acb8258b441aa9412d1b0e92efbd85500aa3bc3e395794218eefd06ff62b302e806c8c430acf058cb6e4e58c70a2ed311f54bb63aff696dd9f857d3 Homepage: https://cran.r-project.org/package=surbayes Description: CRAN Package 'surbayes' (Bayesian Analysis of Seemingly Unrelated Regression Models) Implementation of the direct Monte Carlo approach of Zellner and Ando (2010) to sample from posterior of Seemingly Unrelated Regression (SUR) models. 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See Ahuja et al (2020) JAMIA for details. Package: r-cran-surfrough Architecture: amd64 Version: 0.0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4635 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-surfrough_0.0.1.2-1.ca2404.1_amd64.deb Size: 4161466 MD5sum: 627292ced4c6ec000c25801fa82884b7 SHA1: 4f9331c4a2073e0e1d7d5656430858f97f3dd7bc SHA256: ff2d2eee0fcda58b05a6cedf4a4fc39d42da0d540dfc9ca23c801590962729ae SHA512: b64d14e6f83d4fee51d3b6c67c0fc6de7a96f51723219d48b683a1cf219803455432508575b01627568be680a543745ad0ea69121f5dbca6c5b134613b129c40 Homepage: https://cran.r-project.org/package=SurfRough Description: CRAN Package 'SurfRough' (Calculate Surface/Image Texture Indexes) Methods for the computation of surface/image texture indices using a geostatistical based approach (Trevisani et al. (2023) and Trevisani and Guth (2025) ). It provides various functions for the computation of surface texture indices (e.g., omnidirectional roughness and roughness anisotropy), including the ones based on the robust MAD estimator. The kernels included in the software permit also to calculate the surface/image texture indices directly from the input surface (i.e., without de-trending) using increments of order 2 and of order 4. It also provides the new radial roughness index (RRI), representing the improvement of the popular topographic roughness index (TRI). The framework can be easily extended with ad-hoc surface/image texture indices. Package: r-cran-surrogatebma Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mcmcpack, r-cran-mvtnorm, r-cran-rsurrogate, r-cran-rcppeigen, r-cran-rcppnumerical Filename: pool/dists/noble/main/r-cran-surrogatebma_1.0-1.ca2404.1_amd64.deb Size: 110194 MD5sum: 34a6c92ca5829e231b6b373ee72a6bc9 SHA1: 402b53762413c2f459a691eaebad09cc2dfa9a3f SHA256: 358148382efdedd26e8b9941b783c0b8625fb0668689ac5de5beccba4aa88d62 SHA512: e5f52809a9531447b27496f9ef7af453ea9ad0c5cc0a2116d5a1b4766daaa251cfb4609f489e318df698aa18c2aba00ce7943dd4e265d6b6429ead75b4da0a80 Homepage: https://cran.r-project.org/package=SurrogateBMA Description: CRAN Package 'SurrogateBMA' (Flexible Evaluation of Surrogate Markers with Bayesian ModelAveraging) Provides functions to estimate the proportion of treatment effect explained by the surrogate marker using a Bayesian Model Averaging approach. Duan and Parast (2023) . Package: r-cran-surrogateparadoxtest Architecture: amd64 Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-monotonicitytest, r-cran-mass, r-cran-ggplot2, r-cran-rcpp, r-cran-numderiv, r-cran-matrix, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-surrogateparadoxtest_2.2-1.ca2404.1_amd64.deb Size: 188608 MD5sum: 6cc76da3affdf6ea75ca5699273b816b SHA1: 35e76a1fa667ddfa9f6858aa50a14e875722a9ab SHA256: 8f2e9fdadf4297b7c7ab956527c65cda6b66ddcd6a10f7cb19c402da3bb1da82 SHA512: 7ed8bb6f2979847973966d94903c7bd211498e9fbe9ca2efae95cc49468f034e76552780a02345e1418ce80909112bad1b1d51159b8c866416c1051209c10289 Homepage: https://cran.r-project.org/package=SurrogateParadoxTest Description: CRAN Package 'SurrogateParadoxTest' (Empirical Testing of Surrogate Paradox Assumptions) Provides functions to nonparametrically assess assumptions sufficient to prevent the surrogate paradox through hypothesis tests of stochastic dominance, monotonicity of conditional mean functions, and non-negative residual treatment effect. Details are described in: Hsiao E, Tian L, and Parast L (2026). "Avoiding the surrogate paradox: an empirical framework for assessing assumptions." Journal of Nonparametric Statistics . There are also functions to assess resilience to the surrogate paradox via calculation of the resilience probability, the resilience bound, and the resilience set. Details will be available in Hsiao E, Tian L, and Parast L, "Resilience Measures for the Surrogate Paradox" (Under Review). Lastly, there is a function to assess resilience to the surrogate paradox in the met-analytic setting, described in Hsiao E and Parast L, "A Functional-Class Meta-Analytic Framework for Quantifying Surrogate Resilience" (Under Review). A tutorial for this package can be found at . Package: r-cran-surrogateregression Architecture: amd64 Version: 0.6.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 733 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/noble/main/r-cran-surrogateregression_0.6.0.1-1.ca2404.1_amd64.deb Size: 531298 MD5sum: 9125617a6d481299c665b05ded1114a2 SHA1: 3d5e3dcb82c6cae9e7e3df36dbab38f77ab9320d SHA256: b09cc80d92fcd3cbf66ab9d678b31e56a293fc87f20ff6dd00decaa9a05ed3c9 SHA512: 0c3f1996155d0be9858a60a69e346cbba60bc4a02cc753e1f0129902bde7f2f8d33b6033aa8a5bdef51a8dec94ea839df9854089bedcf9cde5c9e53b7ffdc281 Homepage: https://cran.r-project.org/package=SurrogateRegression Description: CRAN Package 'SurrogateRegression' (Surrogate Outcome Regression Analysis) Performs estimation and inference on a partially missing target outcome (e.g. gene expression in an inaccessible tissue) while borrowing information from a correlated surrogate outcome (e.g. gene expression in an accessible tissue). Rather than regarding the surrogate outcome as a proxy for the target outcome, this package jointly models the target and surrogate outcomes within a bivariate regression framework. Unobserved values of either outcome are treated as missing data. In contrast to imputation-based inference, no assumptions are required regarding the relationship between the target and surrogate outcomes. Estimation in the presence of bilateral outcome missingness is performed via an expectation conditional maximization either algorithm. In the case of unilateral target missingness, estimation is performed using an accelerated least squares procedure. A flexible association test is provided for evaluating hypotheses about the target regression parameters. For additional details, see: McCaw ZR, Gaynor SM, Sun R, Lin X: "Leveraging a surrogate outcome to improve inference on a partially missing target outcome" . Package: r-cran-surtvep Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1128 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-ggpubr, r-cran-tibble, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-surtvep_1.0.0-1.ca2404.1_amd64.deb Size: 776182 MD5sum: a5797379599d34a078985f3e5b8bab8a SHA1: dfbbc004b2b25f0f8243aee677df8c771a1a549f SHA256: 67b134561946d512529b0a48ffe6277a4347d5c9f60d225eae81e22b5753e497 SHA512: 43eb22aac21d39a7cc403d3a18f32386e5d950a2ee00d72ab583e500b7620a27dc54cc02a8b0e26d21860f5dbafc97d1ff43cf5fc0aacecb5f3078e1c877c562 Homepage: https://cran.r-project.org/package=surtvep Description: CRAN Package 'surtvep' (Cox Non-Proportional Hazards Model with Time-VaryingCoefficients) Fit Cox non-proportional hazards models with time-varying coefficients. Both unpenalized procedures (Newton and proximal Newton) and penalized procedures (P-splines and smoothing splines) are included using B-spline basis functions for estimating time-varying coefficients. For penalized procedures, cross validations, mAIC, TIC or GIC are implemented to select tuning parameters. Utilities for carrying out post-estimation visualization, summarization, point-wise confidence interval and hypothesis testing are also provided. For more information, see Wu et al. (2022) and Luo et al. (2023) . Package: r-cran-survauc Architecture: amd64 Version: 1.4-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rms Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-survauc_1.4-0-1.ca2404.1_amd64.deb Size: 185072 MD5sum: b4e382bb017192797d287891b3f79754 SHA1: 637f08f6adc80fabca29583b8a8cb941bfbc81d2 SHA256: 1b03901f967fd9f6b6a09f911f324d230f1e8bd0771116d4d8684717b0334f05 SHA512: e63771b56a3d3408b405b7450b445514bf927c51e1186e8c22a626ddbf63f0408297d4bfe13fc0b10df9d78b4f7125f2a42cedbac4a387affa734f0e73447dac Homepage: https://cran.r-project.org/package=survAUC Description: CRAN Package 'survAUC' (Estimators of Prediction Accuracy for Time-to-Event Data) Provides a variety of functions to estimate time-dependent true/false positive rates and AUC curves from a set of censored survival data. Package: r-cran-survc1 Architecture: amd64 Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-survc1_1.0-3-1.ca2404.1_amd64.deb Size: 53330 MD5sum: 7401dada7a79dc98c845abc3e23e6256 SHA1: fff223779b955e9911cd12a9bc06107a6f35e303 SHA256: 1e16a91f84ce4e9221efa027cd0dbbe3e6e085b3b2ca62cfdeb64270705df467 SHA512: 97df07ec40492d25ed20b7434a52d5b0e63d966624d095025424641a46ff9a290c91480351633af07514096053151c7a29964232b3be94552e6176f37e680a12 Homepage: https://cran.r-project.org/package=survC1 Description: CRAN Package 'survC1' (C-Statistics for Risk Prediction Models with Censored SurvivalData) Performs inference for C of risk prediction models with censored survival data, using the method proposed by Uno et al. (2011) . Inference for the difference in C between two competing prediction models is also implemented. Package: r-cran-survdistr Architecture: amd64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-r6, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-survdistr_0.0.3-1.ca2404.1_amd64.deb Size: 153866 MD5sum: 4603f9c2f0176f9ece86d7415cbb58de SHA1: 8408f0e5c3faf68077a397283f8cb0cfcdf98cfa SHA256: 98636fbe0dd229c40a26433dd6f85f25c9d9c54e43fbda9c3fbdb1276d2f5cf8 SHA512: 4d87d6d548dd192e92382bc7e42cdd9c3d12d29f226d4c56df1291125976218e69d70fbfb502ea9954a0d584e34e9e3f1e0182ee4d602084ad903de462cab7f6 Homepage: https://cran.r-project.org/package=survdistr Description: CRAN Package 'survdistr' (Survival Distribution Container with Flexible InterpolationMethods) Efficient containers for storing and managing prediction outputs from survival models, including Cox proportional hazards, random survival forests, and modern machine learning estimators. Provides fast C++ methods to evaluate survival probabilities, hazards, probability densities, and related quantities at arbitrary time points, with support for multiple interpolation methods via 'Rcpp'. Package: r-cran-surveil Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3353 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstantools, r-cran-rcpp, r-cran-rstan, r-cran-tidybayes, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-ggplot2, r-cran-gridextra, r-cran-scales, r-cran-ggdist, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-surveil_0.3.0-1.ca2404.1_amd64.deb Size: 1407878 MD5sum: 39564fb2dbfb9e0248d28b7cdd17c504 SHA1: d142bff3ccce221d2b4df6032960fa73c5f593f1 SHA256: f354620f8e6c234d120ff450dd9fc591005e1fca81d29e68ccf51474250942b4 SHA512: d8cdf1f6abdec789950a90ec285ef672c54546f0ea2659f6265ecefe04a06afde33b5efc747ad6a975743a8ff7e2bfddd7702ac7b64a33ba6550d1a8288ba5fc Homepage: https://cran.r-project.org/package=surveil Description: CRAN Package 'surveil' (Time Series Models for Disease Surveillance) Fits time trend models for routine disease surveillance tasks and returns probability distributions for a variety of quantities of interest, including age-standardized rates, period and cumulative percent change, and measures of health inequality. The models are appropriate for count data such as disease incidence and mortality data, employing a Poisson or binomial likelihood and the first-difference (random-walk) prior for unknown risk. Optionally add a covariance matrix for multiple, correlated time series models. Inference is completed using Markov chain Monte Carlo via the Stan modeling language. References: Donegan, Hughes, and Lee (2022) ; Stan Development Team (2021) ; Theil (1972, ISBN:0-444-10378-3). Package: r-cran-surveillance Architecture: amd64 Version: 1.25.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6351 Depends: libc6 (>= 2.35), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp, r-cran-xtable, r-cran-polycub, r-cran-mass, r-cran-matrix, r-cran-nlme, r-cran-spatstat.geom Suggests: r-cran-gridextra, r-cran-lattice, r-cran-colorspace, r-cran-scales, r-cran-animation, r-cran-msm, r-cran-spc, r-cran-coda, r-cran-runjags, r-cran-spdep, r-cran-numderiv, r-cran-maxlik, r-cran-gsl, r-cran-fanplot, r-cran-hhh4contacts, r-cran-quadprog, r-cran-memoise, r-cran-polyclip, r-cran-intervals, r-cran-splancs, r-cran-gamlss, r-cran-mglm, r-cran-sf, r-cran-tinytest, r-cran-knitr Filename: pool/dists/noble/main/r-cran-surveillance_1.25.0-1.ca2404.1_amd64.deb Size: 5467282 MD5sum: 657538432939167b571378e5fe50c996 SHA1: b92dc4e103006625f0b740ac18ed106a8bfe425d SHA256: 2c0153b568486770809c4f43051767f2b540e6bff2a0b7e3d2898d840785e22d SHA512: 82e6810afd4ad7542ac34c1113bc4f8e5b4799107895454028be8370907e462cf9ed337b8b0b2a63ca9d2a756a2315509404007755361e9e062365a115728917 Homepage: https://cran.r-project.org/package=surveillance Description: CRAN Package 'surveillance' (Temporal and Spatio-Temporal Modeling and Monitoring of EpidemicPhenomena) Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Hoehle and Paul (2008) . A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) . For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. hhh4() estimates models for (multivariate) count time series following Paul and Held (2011) and Meyer and Held (2014) . twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Hoehle (2009) . twinstim() estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) . A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) . Package: r-cran-surveval Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-surveval_1.1-1.ca2404.1_amd64.deb Size: 47452 MD5sum: 0760c4c1b4ce57d1ff6d5cf4cb049023 SHA1: 750de41c5a599ebce4d0eb0d6a46eb7df5d79ede SHA256: c7386710d3bcc8229cc4dd85f7b48968ca02e1564f6587e6b8295c62f62e36a0 SHA512: 680bd8b74ef96a3255ffc8398487fb69913b54890cadc1166171a601694096342daa0b3688aeb26a768b338058b3628cb2554f82d89c913aab60960a3a7793a2 Homepage: https://cran.r-project.org/package=SurvEval Description: CRAN Package 'SurvEval' (Methods for the Evaluation of Survival Models) Provides predictive accuracy tools to evaluate time-to-event survival models. This includes calculating the concordance probability estimate that incorporates the follow-up time for a particular study developed by Devlin, Gonen, Heller (2020). It also evaluates the concordance probability estimate for nested Cox proportional hazards models using a projection-based approach by Heller and Devlin (under review). Package: r-cran-survextrap Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6544 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-gridextra, r-cran-loo, r-cran-posterior, r-cran-rcpp, r-cran-rstan, r-cran-splines2, r-cran-tibble, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rstantools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-shelf, r-cran-flexsurv, r-cran-flexsurvcure, r-cran-dplyr, r-cran-viridis, r-cran-forcats, r-cran-fs, r-cran-purrr, r-cran-tidyr, r-cran-stringr, r-cran-survminer Filename: pool/dists/noble/main/r-cran-survextrap_1.0.1-1.ca2404.1_amd64.deb Size: 1976826 MD5sum: 6dcfa926b994f54bb63806664bd542ba SHA1: d245baf81d17497df250e345bb301480e26104bd SHA256: 2e93ee29c167d0fccbf4193378295f7dcf4a445a2065675e19162ad2af9152f2 SHA512: 5d6a24ed54acd3d0b6e26df720dcf6c359d0a464e8fcaf6460930092ebe2f53af34bbacd05f614a419ac211a14d1445a0b5afe53cbc8069d630f2310fcdcba54 Homepage: https://cran.r-project.org/package=survextrap Description: CRAN Package 'survextrap' (Bayesian Flexible Parametric Survival Modelling andExtrapolation) Survival analysis using a flexible Bayesian model for individual-level right-censored data, optionally combined with aggregate data on counts of survivors in different periods of time. An M-spline is used to describe the hazard function, with a prior on the coefficients that controls over-fitting. Proportional hazards or flexible non-proportional hazards models can be used to relate survival to predictors. Additive hazards (relative survival) models, waning treatment effects, and mixture cure models are also supported. Priors can be customised and calibrated to substantive beliefs. Posterior distributions are estimated using 'Stan', and outputs are arranged in a tidy format. See Jackson (2023) . Package: r-cran-survey Architecture: amd64 Version: 4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4184 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-survival, r-cran-lattice, r-cran-minqa, r-cran-numderiv, r-cran-mitools, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-foreign, r-cran-mass, r-cran-kernsmooth, r-cran-hexbin, r-cran-rsqlite, r-cran-quantreg, r-cran-compquadform, r-cran-dbi, r-cran-aer, r-cran-summer, r-cran-r.rsp, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-survey_4.5-1.ca2404.1_amd64.deb Size: 3445050 MD5sum: a567d7ba20e558feb4ca5f4fb49a1f61 SHA1: bc93ad150159b9d0490bdbedd3c309e7dc594f24 SHA256: 34c74e5fcfc5f74b0c6bf82d7baeaccd1ab6502e340e69071da0ddd6a35f16a3 SHA512: 9a36164e74b96f172beb08f7b4586df5dc21e5efe2f301158589b8199324d7f2d80b62f0184fb763409f0130e96c3c2fa049536854191db88d8606bf64cd8e91 Homepage: https://cran.r-project.org/package=survey Description: CRAN Package 'survey' (Analysis of Complex Survey Samples) Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase and multiphase subsampling designs. Graphics. PPS sampling without replacement. Small-area estimation. Dual-frame designs. Package: r-cran-surveybootstrap Architecture: amd64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-stringr, r-cran-dplyr, r-cran-plyr, r-cran-purrr, r-cran-functional, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-surveybootstrap_0.0.3-1.ca2404.1_amd64.deb Size: 163806 MD5sum: 65c2cd6273e8b56a87a248cb9deb31a6 SHA1: 25840a06205c537dcae8fd6bdb1eecd4574ed6ae SHA256: f4090a093105dee08c15aa1bb3dce746aee60fe4bfe7b086f41df0eef5cb3179 SHA512: d5528795e444989286f6fe73235b26709acb0d65402b967ec7b6bf34acaab7ddb5b3776fc11d24cb70f5e4b1beef4b26ce62c870edd6a454d31fd438b856971f Homepage: https://cran.r-project.org/package=surveybootstrap Description: CRAN Package 'surveybootstrap' (Bootstrap with Survey Data) Implements different kinds of bootstraps to estimate sampling variation from survey data with complex designs. Includes the rescaled bootstrap described in Rust and Rao (1996) and Rao and Wu (1988) . Package: r-cran-surveygraph Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-surveygraph_1.0.0-1.ca2404.1_amd64.deb Size: 439192 MD5sum: 34a326f8e029aad9f9d6c54534f3cc3f SHA1: befb133c8f01dba0dd76c3c61f7122f5ee2fb0ec SHA256: 1227f57a455bdf3afd453569b56494a32933a82f4ffd5df904cc255d1f0be177 SHA512: 83f0d98d6dfffb192408bc5b502e6de6c8a3306031f9b3e16bedce09315cb8886758cffee196da9ebd4288777f48d171e202ae15c224f7e914bc248a1b026b6f Homepage: https://cran.r-project.org/package=surveygraph Description: CRAN Package 'surveygraph' (Network Representations of Attitudes) A tool for computing network representations of attitudes, extracted from tabular data such as sociological surveys. Development of surveygraph software and training materials was initially funded by the European Union under the ERC Proof-of-concept programme (ERC, Attitude-Maps-4-All, project number: 101069264). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. Package: r-cran-surveyplanning Architecture: amd64 Version: 4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-laeken Filename: pool/dists/noble/main/r-cran-surveyplanning_4.0-1.ca2404.1_amd64.deb Size: 111008 MD5sum: 7ce74d3bf69a6924693efb90cdbe22e8 SHA1: afddb8e1cbee755913d7824448b65fbfa7d9e855 SHA256: 6e084baef557cee68a2b168eed87a69c0e91515fc816143dfdfd6bb4d173148f SHA512: 28ed75b2a5d682a50483fce4e899278fbf229f1963e76b77438c080cd270f30f9c90fc9e9458cc68cde5fc71a90d75432bad1b1f23ac12c717e452c11fe8f692 Homepage: https://cran.r-project.org/package=surveyplanning Description: CRAN Package 'surveyplanning' (Survey Planning Tools) Tools for sample survey planning, including sample size calculation, estimation of expected precision for the estimates of totals, and calculation of optimal sample size allocation. Package: r-cran-surveysd Architecture: amd64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 884 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-ggplot2, r-cran-laeken Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-surveysd_2.0.2-1.ca2404.1_amd64.deb Size: 515464 MD5sum: 170884342970053aef9327ac7e802163 SHA1: 5d0dd0dda9260919d05c0acd726ba4f5ab9fb62a SHA256: 6e7e4fe88a975d4736a179a18b0b49f2610d5ac9d604b9905288f96e9213b4cf SHA512: 977610d970b226f7655081764d1e59079fda5a82fc041363939c70e9479065383fd98a24f0a55ca0d3069d977cd7b0119491c8b4d0a324fb2df0832021d78239 Homepage: https://cran.r-project.org/package=surveysd Description: CRAN Package 'surveysd' (Survey Standard Error Estimation for Cumulated Estimates andtheir Differences in Complex Panel Designs) Calculate point estimates and their standard errors in complex household surveys using bootstrap replicates. Bootstrapping considers survey design with a rotating panel. A comprehensive description of the methodology can be found under . Package: r-cran-surveyvoi Architecture: amd64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1075 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libmpfr6 (>= 3.1.3), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-sf, r-cran-nloptr, r-cran-progress, r-cran-assertthat, r-cran-xgboost, r-cran-plyr, r-cran-withr, r-cran-tibble, r-cran-scales, r-cran-doparallel, r-cran-dplyr, r-cran-vegan, r-cran-rcppalgos, r-cran-groupdata2, r-cran-rcpp, r-cran-rsymphony, r-cran-rcppeigen, r-cran-poissonbinomial Suggests: r-cran-testthat, r-cran-knitr, r-cran-roxygen2, r-cran-rmarkdown, r-cran-tidyr, r-cran-ggplot2, r-cran-gridextra, r-cran-viridis, r-cran-rmpfr, r-cran-runjags Filename: pool/dists/noble/main/r-cran-surveyvoi_1.1.1-1.ca2404.1_amd64.deb Size: 690352 MD5sum: 8bfdd7328933ae03a05012340a834755 SHA1: 800029189f9308ba7523a63ef95642b6c15829db SHA256: 6624e0aeddfaf1fa52320b8d1d5796084b244e0ce234f32781d2c67b01c16408 SHA512: da23fac1d317edbdc0710b0eed62bf4c7b251c416405edc7721de3d852a39082ad23d9cb4b8d7653ff1dd716239ec6a3ace89e4af4262bab4669fed5c001311a Homepage: https://cran.r-project.org/package=surveyvoi Description: CRAN Package 'surveyvoi' (Survey Value of Information) Decision support tool for prioritizing sites for ecological surveys based on their potential to improve plans for conserving biodiversity (e.g. plans for establishing protected areas). Given a set of sites that could potentially be acquired for conservation management, it can be used to generate and evaluate plans for surveying additional sites. Specifically, plans for ecological surveys can be generated using various conventional approaches (e.g. maximizing expected species richness, geographic coverage, diversity of sampled environmental algorithms. After generating such survey plans, they can be evaluated using conditions) and maximizing value of information. Please note that several functions depend on the 'Gurobi' optimization software (available from ). Additionally, the 'JAGS' software (available from ) is required to fit hierarchical generalized linear models. For further details, see Hanson et al. (2023) . Package: r-cran-survhe Architecture: amd64 Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-flexsurv, r-cran-dplyr, r-cran-ggplot2, r-cran-rms, r-cran-xlsx, r-cran-tibble, r-cran-tidyr Suggests: r-cran-rstan, r-cran-testthat Filename: pool/dists/noble/main/r-cran-survhe_2.0.3-1.ca2404.1_amd64.deb Size: 290332 MD5sum: b7449fb454efabc9f28183d10927d35c SHA1: cead9b4afd1b6a3f9b074d83ac41a26282f5fa27 SHA256: 758193a4c938f0d9f0ee4b12f648a01644de986a32bea05a699b3963bb15fa07 SHA512: 280bc6c24978c7ad0971baeddc44d9783c1241b2c524ec648d5e9dda1a3d2e923a086bd612e75ea99b6aa5655f240bd4acef44db8aa23d9571e79c9e2ae4306e Homepage: https://cran.r-project.org/package=survHE Description: CRAN Package 'survHE' (Survival Analysis in Health Economic Evaluation) Contains a suite of functions for survival analysis in health economics. These can be used to run survival models under a frequentist (based on maximum likelihood) or a Bayesian approach (both based on Integrated Nested Laplace Approximation or Hamiltonian Monte Carlo). To run the Bayesian models, the user needs to install additional modules (packages), i.e. 'survHEinla' and 'survHEhmc'. These can be installed using 'remotes::install_github' from their GitHub repositories: ( and respectively). 'survHEinla' is based on the package INLA, which is available for download at . The user can specify a set of parametric models using a common notation and select the preferred mode of inference. The results can also be post-processed to produce probabilistic sensitivity analysis and can be used to export the output to an Excel file (e.g. for a Markov model, as often done by modellers and practitioners). . Package: r-cran-survidinri Architecture: amd64 Version: 1.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survc1, r-cran-survival Filename: pool/dists/noble/main/r-cran-survidinri_1.1-2-1.ca2404.1_amd64.deb Size: 48792 MD5sum: 61002d771775cf298858f08ac6db9661 SHA1: 3cdcdd9990b0df5125ba92a8a13c46ef0e615365 SHA256: 93e6e1dc399f4c2fd9c5c26f2ec0dc2fffed2f29df80671bd9e4cd8e7e515b37 SHA512: 20fc212b739115b0fb0acca06ae5b8de044082d4513a318e2c81f9e8f8ef76a078427170a6435459ca335b8587c09aa0f55df923e76c9750522ce3ebf9c30e8e Homepage: https://cran.r-project.org/package=survIDINRI Description: CRAN Package 'survIDINRI' (IDI and NRI for Comparing Competing Risk Prediction Models withCensored Survival Data) Performs inference for a class of measures to compare competing risk prediction models with censored survival data. The class includes the integrated discrimination improvement index (IDI) and category-less net reclassification index (NRI). Package: r-cran-survidm Architecture: amd64 Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernsmooth, r-cran-np, r-cran-survival, r-cran-doparallel, r-cran-dorng, r-cran-foreach, r-cran-tpmsm, r-cran-rcpp, r-cran-ggplot2, r-cran-gridextra, r-cran-plotly Filename: pool/dists/noble/main/r-cran-survidm_1.3.2-1.ca2404.1_amd64.deb Size: 362082 MD5sum: ce604ff54056693d3a59bb8a2bc3d486 SHA1: 1aabe9bb88f3e6d30ebccf6685f7f2b4113c92a8 SHA256: a36e686bdd7f4eaf8c8d1764a234d631add4db921bbf0caf8243004ad41facc2 SHA512: 3e9e47c5a9d6c74b236e79c1df3825896a77018ca22ac93a296cce40e0e7eff9b5f2ee54387cbf291da94322e1a7b743448db79d345ad9df84414438fe77f67b Homepage: https://cran.r-project.org/package=survidm Description: CRAN Package 'survidm' (Inference and Prediction in an Illness-Death Model) Newly developed methods for the estimation of several probabilities in an illness-death model. The package can be used to obtain nonparametric and semiparametric estimates for: transition probabilities, occupation probabilities, cumulative incidence function and the sojourn time distributions. Additionally, it is possible to fit proportional hazards regression models in each transition of the Illness-Death Model. Several auxiliary functions are also provided which can be used for marginal estimation of the survival functions. Package: r-cran-survival.svb Architecture: amd64 Version: 0.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-survival, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-survival.svb_0.0-2-1.ca2404.1_amd64.deb Size: 72340 MD5sum: c7a2933110aaf20e07886ce7ab3e147e SHA1: e76e220b045c70682b6b5a00060843459231ebdd SHA256: 3d2a860e2df380ae455d59b09a6172fa9a908333d72ba2ecdd4ef4a19d081a79 SHA512: 5b6aa5aa6d4f870f4c957539b0dacd9d61ae7a146d1eea05eed43bfeac822775e6bf7453bddf3db01afea2fbf005af7c254652cc1f5e02272f6044f9ab64b7fb Homepage: https://cran.r-project.org/package=survival.svb Description: CRAN Package 'survival.svb' (Fit High-Dimensional Proportional Hazards Models) Implementation of methodology designed to perform: (i) variable selection, (ii) effect estimation, and (iii) uncertainty quantification, for high-dimensional survival data. Our method uses a spike-and-slab prior with Laplace slab and Dirac spike and approximates the corresponding posterior using variational inference, a popular method in machine learning for scalable conditional inference. Although approximate, the variational posterior provides excellent point estimates and good control of the false discovery rate. For more information see Komodromos et al. (2021) . Package: r-cran-survival Architecture: amd64 Version: 3.8-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9587 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-survival_3.8-6-1.ca2404.1_amd64.deb Size: 8297858 MD5sum: 3fe23471442bc7172fe116c47566c901 SHA1: 2315278f098560b392f8a64df5e5937e9d5dbefa SHA256: a0277016bb70a42ab265d5d6eda10065334cb85b118c59adce31f2e005b06665 SHA512: 0c20c0fc39a9dbe4119572bce77038fc70f4511e55c05859f8e25dd9eb8d8212f0769b496fe46c10f3600329a59bce7cf64799a54593716acda6ebf351f94cb5 Homepage: https://cran.r-project.org/package=survival Description: CRAN Package 'survival' (Survival Analysis) Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models. Package: r-cran-survivalclusteringtree Architecture: amd64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-dplyr, r-cran-formula.tools, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-miceranger Filename: pool/dists/noble/main/r-cran-survivalclusteringtree_1.1.3-1.ca2404.1_amd64.deb Size: 276134 MD5sum: 4fd30c1ea4b4955dbde925d345a83202 SHA1: 42ed2a4301f1a1fe563c9b58adf9948200cca431 SHA256: 3b7349c3b5e9330aa05a773c4566be50497945348ccb42067d1192afc703f972 SHA512: 2266e1adb226ea85ad397f10c700cedfcd33bc3657be8d8da974a7c58f105e10e7ed4d8b7b63713140fa6db2c9a922992228141cf743300e78857d0f025b83d0 Homepage: https://cran.r-project.org/package=SurvivalClusteringTree Description: CRAN Package 'SurvivalClusteringTree' (Clustering Analysis Using Survival Tree and Forest Algorithms) An outcome-guided algorithm is developed to identify clusters of samples with similar characteristics and survival rate. The algorithm first builds a random forest and then defines distances between samples based on the fitted random forest. Given the distances, we can apply hierarchical clustering algorithms to define clusters. Details about this method is described in . Package: r-cran-survivalmodels Architecture: amd64 Version: 0.1.191-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-keras, r-cran-pseudo, r-cran-reticulate, r-cran-survival Filename: pool/dists/noble/main/r-cran-survivalmodels_0.1.191-1.ca2404.1_amd64.deb Size: 183206 MD5sum: c90f1bb3b6ef03b36942764a1e42fd59 SHA1: 6753e8de4ce591ce5fca1eac07b097612bca17d0 SHA256: 084d1d8e70dde99202883d994f5ad669b16e4b6a58da0fe57fb6be1e32192267 SHA512: e129f6108c48ff99a143e50fa1a5a50f8c939378127df455c45502fcc3fed34b913e197922c88b334760fe2a9d3332a6eee7112ef940627c65f93e47b262e2ba Homepage: https://cran.r-project.org/package=survivalmodels Description: CRAN Package 'survivalmodels' (Models for Survival Analysis) Implementations of classical and machine learning models for survival analysis, including deep neural networks via 'keras' and 'tensorflow'. Each model includes a separated fit and predict interface with consistent prediction types for predicting risk or survival probabilities. Models are either implemented from 'Python' via 'reticulate' , from code in GitHub packages, or novel implementations using 'Rcpp' . Neural networks are implemented from the 'Python' package 'pycox' . Package: r-cran-survivalrec Architecture: amd64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-kernsmooth Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-survivalrec_1.1-1.ca2404.1_amd64.deb Size: 135558 MD5sum: 0ae3791a2a34788e39e508343630566a SHA1: f9228472629dcb08fc831322082fe886e3be4bee SHA256: 187daa1f4d9b96ff4c9fa8ead0d368cc41e7b49e926aeac067cba3d7e01792b0 SHA512: ea77f33b506a9c02d1b1c42beedee0d1e6426e50d63525176b6191ed8607688935f7997d3327ce3310c5a1bcdcbe8124c89b3b10e30c8d0e2faeaa371aae8cb5 Homepage: https://cran.r-project.org/package=survivalREC Description: CRAN Package 'survivalREC' (Nonparametric Estimation of the Distribution of Gap Times forRecurrent Events) Provides estimates for the bivariate and trivariate distribution functions and bivariate and trivariate survival functions for censored gap times. Two approaches, using existing methodologies, are considered: (i) the Lin's estimator, which is based on the extension the Kaplan-Meier estimator of the distribution function for the first event time and the Inverse Probability of Censoring Weights for the second time (Lin DY, Sun W, Ying Z (1999) and (ii) another estimator based on Kaplan-Meier weights (Una-Alvarez J, Meira-Machado L (2008) ). The proposed methods are the landmark estimators based on subsampling approach, and the estimator based on weighted cumulative hazard estimator. The package also provides nonparametric estimator conditional to a given continuous covariate. All these methods have been submitted to be published. Package: r-cran-survivalroc Architecture: amd64 Version: 1.0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-survivalroc_1.0.3.1-1.ca2404.1_amd64.deb Size: 40680 MD5sum: 73232711989f5c7a9c59ed60de6ddb24 SHA1: e60654a42c1211aa89e4c153fa28840b8dd0f31d SHA256: ced8b767c38b8b6c0b688cf8efde0afe16798564ed3435cf1f2fef575513d6a9 SHA512: d46807bcdb14f2d37e8fb2f50d0aff5f859e5cccafe3723989f284c83eb41282b61714636569b1505342a6865cfdf2f030eba5110e3316d27a06ef8273ad0649 Homepage: https://cran.r-project.org/package=survivalROC Description: CRAN Package 'survivalROC' (Time-Dependent ROC Curve Estimation from Censored Survival Data) Compute time-dependent ROC curve from censored survival data using Kaplan-Meier (KM) or Nearest Neighbor Estimation (NNE) method of Heagerty, Lumley & Pepe (Biometrics, Vol 56 No 2, 2000, PP 337-344). Package: r-cran-survkl Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1977 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-cowplot, r-cran-matrix, r-cran-rlang, r-cran-rcpparmadillo, r-cran-rcppparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-survival Filename: pool/dists/noble/main/r-cran-survkl_1.0.0-1.ca2404.1_amd64.deb Size: 1681864 MD5sum: 1755571487619bb5de34296a253d8af4 SHA1: 7c5028c3a72db22f48927ab8129ae87c2861eb0d SHA256: 5e83eea5c2471191c2acd6b8240aacb55aff1eefc00e08715e9e87db74e6e4be SHA512: 5b7b3b284f4f27d15fd9bbceba07f533d6e95fbe55b484f0c04a05a437ef6cc2f34385ec8a9371e2a67a733c49ab941650cf37684a66c5a968b5686f81f0cfd8 Homepage: https://cran.r-project.org/package=survkl Description: CRAN Package 'survkl' (Estimate Survival Data with Data Integration) Provides flexible and efficient tools for integrating external risk scores into Cox proportional hazards models while accounting for population heterogeneity. Enables robust estimation, improved predictive accuracy, and user-friendly workflows for modern survival analysis. For more information, see Wang et al. (2023) . Package: r-cran-survpen Architecture: amd64 Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2257 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-statmod, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-survpen_2.0.4-1.ca2404.1_amd64.deb Size: 1018720 MD5sum: c6b45223e442b229a7d650cc923c3c66 SHA1: 8eac5c765ce9d9979b2be81a2e95e2ff2d5f430e SHA256: 16dbc9740ef5b9f62c17a17a6a1e143c017268ffb862c641a025703f6dc26df1 SHA512: cd95712a1e529f3451c323466c880104c0968388305a5c4b553233bca7a16be732c5c5f33f437578b84b4d6e6ab1aa12650e4559ce4c76141d8df6bab8795beb Homepage: https://cran.r-project.org/package=survPen Description: CRAN Package 'survPen' (Multidimensional Penalized Splines for (Excess) Hazard Models,Relative Mortality Ratio Models and Marginal Intensity Models) Fits (excess) hazard, relative mortality ratio or marginal intensity models with multidimensional penalized splines allowing for time-dependent effects, non-linear effects and interactions between several continuous covariates. In survival and net survival analysis, in addition to modelling the effect of time (via the baseline hazard), one has often to deal with several continuous covariates and model their functional forms, their time-dependent effects, and their interactions. Model specification becomes therefore a complex problem and penalized regression splines represent an appealing solution to that problem as splines offer the required flexibility while penalization limits overfitting issues. Current implementations of penalized survival models can be slow or unstable and sometimes lack some key features like taking into account expected mortality to provide net survival and excess hazard estimates. In contrast, survPen provides an automated, fast, and stable implementation (thanks to explicit calculation of the derivatives of the likelihood) and offers a unified framework for multidimensional penalized hazard and excess hazard models. Later versions (>2.0.0) include penalized models for relative mortality ratio, and marginal intensity in recurrent event setting. survPen may be of interest to those who 1) analyse any kind of time-to-event data: mortality, disease relapse, machinery breakdown, unemployment, etc 2) wish to describe the associated hazard and to understand which predictors impact its dynamics, 3) wish to model the relative mortality ratio between a cohort and a reference population, 4) wish to describe the marginal intensity for recurrent event data. See Fauvernier et al. (2019a) for an overview of the package and Fauvernier et al. (2019b) for the method. Package: r-cran-survpresmooth Architecture: amd64 Version: 1.1-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-survpresmooth_1.1-12-1.ca2404.1_amd64.deb Size: 89640 MD5sum: 32f5cf94c79616d34351156b187d9ba4 SHA1: 9a72e24cbbd569d1d6e9d7a81d11f556ab2861f3 SHA256: dca06053844e457d95c010f46708a8756aa1549ef4f0a9d87c0f09ba92836642 SHA512: c427416fd41543f635ab3a7d01b6234ea687ef28039f80387035d89bcd89a33dcd15bc2e8f2a796134bc20d45035dd7d6e5c2ec0ce8b593d879e7aee9c0bf513 Homepage: https://cran.r-project.org/package=survPresmooth Description: CRAN Package 'survPresmooth' (Presmoothed Estimation in Survival Analysis) Presmoothed estimators of survival, density, cumulative and non-cumulative hazard functions with right-censored survival data. For details, see Lopez-de-Ullibarri and Jacome (2013) . Package: r-cran-survsnp Architecture: amd64 Version: 0.26-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-lattice, r-cran-foreach, r-cran-xtable Suggests: r-cran-rcolorbrewer, r-cran-latticeextra, r-cran-knitr Filename: pool/dists/noble/main/r-cran-survsnp_0.26-1.ca2404.1_amd64.deb Size: 182824 MD5sum: a49f496719576866ae666408605fcefe SHA1: b75b5df90a5deddaa544d98a1e83955d0647cf77 SHA256: 11545462fbbd5f66c3bf52621c6c8a6a36f121d9bbb987fe3e76fff2fde29326 SHA512: 6042c1d8bc65848429406b4c9b6ccaf1d017a08986c87b2230cc772bc1470d74d527b89114b24a9c66a61046ec8fb8708b6da03dfd72208c8caba8e203db099c Homepage: https://cran.r-project.org/package=survSNP Description: CRAN Package 'survSNP' (Power Calculations for SNP Studies with Censored Outcomes) Conduct asymptotic and empirical power and sample size calculations for Single-Nucleotide Polymorphism (SNP) association studies with right censored time to event outcomes. Package: r-cran-survstan Architecture: amd64 Version: 0.0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2293 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-actuar, r-cran-broom, r-cran-dofuture, r-cran-dplyr, r-cran-extradistr, r-cran-foreach, r-cran-future, r-cran-generics, r-cran-ggplot2, r-cran-gridextra, r-cran-mass, r-cran-purrr, r-cran-rcpp, r-cran-rdpack, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-tibble, r-cran-tidyr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-emmeans, r-cran-estimability, r-cran-ggally, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-survstan_0.0.7.1-1.ca2404.1_amd64.deb Size: 915460 MD5sum: ac82468fc72c4d2394509acbdf319d70 SHA1: 9a2e5c83c9d619cb9e25b2a3562f753992906d81 SHA256: c835700ff9de35aeeeb39253562ad08880a788b14f94dd467a5cb2190697f8b9 SHA512: 15ef794787b836401a398a6065840e0daf1cae3f727847100897d92077f253aa2e6a3d6d3ee8503d23419076da194f06e66fc06827d6945ef2029325c588cdf7 Homepage: https://cran.r-project.org/package=survstan Description: CRAN Package 'survstan' (Fitting Survival Regression Models via 'Stan') Parametric survival regression models under the maximum likelihood approach via 'Stan'. Implemented regression models include accelerated failure time models, proportional hazards models, proportional odds models, accelerated hazard models, Yang and Prentice models, and extended hazard models. Available baseline survival distributions include exponential, Weibull, log-normal, log-logistic, gamma, generalized gamma, rayleigh, Gompertz and fatigue (Birnbaum-Saunders) distributions. References: Lawless (2002) ; Bennett (1982) ; Chen and Wang(2000) ; Demarqui and Mayrink (2021) . 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(2021) . Based on an existing VAR model object (provided by e.g. VAR() from the 'vars' package), the structural impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) ), patterns of GARCH (Normadin, M., Phaneuf, L. (2004) ), independent component analysis (Matteson, D. S, Tsay, R. S., (2013) ), least dependent innovations (Herwartz, H., Ploedt, M., (2016) ), smooth transition in variances (Luetkepohl, H., Netsunajev, A. (2017) ) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) )). Package: r-cran-svd Architecture: amd64 Version: 0.5.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-svd_0.5.8-1.ca2404.1_amd64.deb Size: 160934 MD5sum: cdbba2c3e401038ab96a232743978d9d SHA1: 047f067c9b073903c91ba7c911aebd95fe8a0898 SHA256: a6918e6526da1cad7a4cb4b20dbcfb5c3d505fcc769ec49214c6e3830206ef40 SHA512: 10fdc6edcbe91ec8a02212c47a8a7938aa9f1c566548a84359db5c8b29afac9166bc701dd142fb7704b99c53de30e67c96fd351b5637b0897e2adffb9d475a9b Homepage: https://cran.r-project.org/package=svd Description: CRAN Package 'svd' (Interfaces to Various State-of-Art SVD and Eigensolvers) R bindings to SVD and eigensolvers (PROPACK, nuTRLan). Package: r-cran-svdnf Architecture: amd64 Version: 0.1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1066 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-zoo, r-cran-xts Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-svdnf_0.1.11-1.ca2404.1_amd64.deb Size: 977532 MD5sum: 6892ef7e6833b14bcfc56c2bb93b2b65 SHA1: 98b097238b8b9b49aca6a9054be242d36c767c85 SHA256: 65e3571d6d1c4590f3ce6c119dd6c3bd342e6d2a4625217fe936d85c1c9c966c SHA512: 6bb009fa511918f860ffd94c94f33b0f55bf351850cf210e8da53e71093a25941b2155b91485cba0e6f3673dc047330df70680df4d53336383efb18f56490998 Homepage: https://cran.r-project.org/package=SVDNF Description: CRAN Package 'SVDNF' (Discrete Nonlinear Filtering for Stochastic Volatility Models) Implements the discrete nonlinear filter (DNF) of Kitagawa (1987) to a wide class of stochastic volatility (SV) models with return and volatility jumps following the work of Bégin and Boudreault (2021) to obtain likelihood evaluations and maximum likelihood parameter estimates. 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This package integrates multiple state-of-the-art SVG detection methods including 'MERINGUE' (Moran's I based spatial autocorrelation), 'Giotto' binSpect (binary spatial enrichment test), 'SPARK-X' (non-parametric kernel-based test), and 'nnSVG' (nearest-neighbor Gaussian processes). Each method is implemented with optimized performance through vectorization, parallelization, and 'C++' acceleration where applicable. Methods are described in Miller et al. (2021) , Dries et al. (2021) , Zhu et al. (2021) , and Weber et al. (2023) . 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Package: r-cran-swatches Architecture: amd64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-pack, r-cran-stringr, r-cran-xml2, r-cran-colorspace Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-swatches_0.5.0-1.ca2404.1_amd64.deb Size: 60832 MD5sum: d32f47f302a4bb8c94ea636248728aaf SHA1: e4d24cc77d160a05cb37ee5f291865df5f1ed7f1 SHA256: 234a37fdbd65744a6aed56e1a88e89cda44247a4cf0bfc495b4b5a98cc66266e SHA512: eaaee98ee84f09b90ebad3b432440de5e68705aaccb77f98404b067a7ca78942d51dd504c3e729096d309bd6f09b1337fd3eee894c1033cfaee4cff33b3ff277 Homepage: https://cran.r-project.org/package=swatches Description: CRAN Package 'swatches' (Read, Inspect, and Manipulate Color Swatch Files) There are numerous places to create and download color palettes. These are usually shared in 'Adobe' swatch file formats of some kind. There is also often the need to use standard palettes developed within an organization to ensure that aesthetics are carried over into all projects and output. Now there is a way to read these swatch files in R and avoid transcribing or converting color values by hand or or with other programs. This package provides functions to read and inspect 'Adobe Color' ('ACO'), 'Adobe Swatch Exchange' ('ASE'), 'GIMP Palette' ('GPL'), 'OpenOffice' palette ('SOC') files and 'KDE Palette' ('colors') files. Detailed descriptions of 'Adobe Color' and 'Swatch Exchange' file formats as well as other swatch file formats can be found at . Package: r-cran-swdpwr Architecture: amd64 Version: 1.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.6.0), r-api-4.0, r-cran-spatstat.random Filename: pool/dists/noble/main/r-cran-swdpwr_1.12-1.ca2404.1_amd64.deb Size: 105436 MD5sum: 2dd22ba4d3d941ea9b842abe15502095 SHA1: 2358f692448b18205cbf38ab87775fc76d7d5c88 SHA256: ef72f01bb7f9b26eaea577437460c5c205084bc04e41520e3c7cab1ef2141c7b SHA512: 476fb088d21b8f71080f627129eae7870797c6c28492126d183a23d9d4c5e709021f2072b2c0a40edfd3901091f569cb37fbfa2538e479b2b3741a7cebb58a8c Homepage: https://cran.r-project.org/package=swdpwr Description: CRAN Package 'swdpwr' (Power Calculation for Stepped Wedge Cluster Randomized Trials) To meet the needs of statistical power calculation for stepped wedge cluster randomized trials, we developed this software. Different parameters can be specified by users for different scenarios, including: cross-sectional and cohort designs, binary and continuous outcomes, marginal (GEE) and conditional models (mixed effects model), three link functions (identity, log, logit links), with and without time effects (the default specification assumes no-time-effect) under exchangeable, nested exchangeable and block exchangeable correlation structures. Unequal numbers of clusters per sequence are also allowed. The methods included in this package: Zhou et al. (2020) , Li et al. (2018) . Supplementary documents can be found at: . The Shiny app for swdpwr can be accessed at: . The package also includes functions that perform calculations for the intra-cluster correlation coefficients based on the random effects variances as input variables for continuous and binary outcomes, respectively. Package: r-cran-sweater Architecture: amd64 Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 970 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-purrr, r-cran-quanteda, r-cran-liblinear, r-cran-proxy, r-cran-data.table, r-cran-cli, r-cran-combinat Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sweater_0.1.8-1.ca2404.1_amd64.deb Size: 629426 MD5sum: 5f62b903eeac1cbc9fd8c7cdde6293c6 SHA1: 09a3c910d7b00bd56331b6d9801e180465420f4f SHA256: 7978f2fd219848ee0ba343a24202d361c7ac52cc819d19a119e5c8e15cc3b2b4 SHA512: 31ccb61eeb0952261e43f25c31b832b26d7caef2cb31d595831423ad935ba2c2afebfd3c93444c1b89c7e9aec081e8a092997c79b36b63937d7670e4b7c7223c Homepage: https://cran.r-project.org/package=sweater Description: CRAN Package 'sweater' (Speedy Word Embedding Association Test and Extras Using R) Conduct various tests for evaluating implicit biases in word embeddings: Word Embedding Association Test (Caliskan et al., 2017), , Relative Norm Distance (Garg et al., 2018), , Mean Average Cosine Similarity (Mazini et al., 2019) , SemAxis (An et al., 2018) , Relative Negative Sentiment Bias (Sweeney & Najafian, 2019) , and Embedding Coherence Test (Dev & Phillips, 2019) . Package: r-cran-swephr Architecture: amd64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1184 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-swephr_0.3.2-1.ca2404.1_amd64.deb Size: 505520 MD5sum: 300a13ce44651cc629e1a5ce88176d7d SHA1: baa22ccf23f91a3acba4ca2b2fb2b2939edfd872 SHA256: 34ce031722b98140b9948ee032bfb0e329d55ca159f812ffcefaa36d2931219a SHA512: 8ce570a8f683ae73d129d5ff0db0381c86d185ed79be038c0a9f044ec7b4fba71379a9699f5698aaa10a4a2cfb2a3ecc9c40938bc002586ea4ae8d2b966fb62a Homepage: https://cran.r-project.org/package=swephR Description: CRAN Package 'swephR' (High Precision Swiss Ephemeris) The Swiss Ephemeris (version 2.10.03) is a high precision ephemeris based upon the DE431 ephemerides from NASA's JPL. It covers the time range 13201 BCE to 17191 CE. This package uses the semi-analytic theory by Steve Moshier. For faster and more accurate calculations, the compressed Swiss Ephemeris data is available in the 'swephRdata' package. To access this data package, run 'install.packages("swephRdata", repos = "https://rstub.r-universe.dev", type = "source")'. The size of the 'swephRdata' package is approximately 115 MB. The user can also use the original JPL DE431 data. Package: r-cran-switchselection Architecture: amd64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1046 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-hpa, r-cran-mnorm, r-cran-gena, r-cran-dplyr, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-switchselection_2.1.0-1.ca2404.1_amd64.deb Size: 804012 MD5sum: b02b5d48346fe8d7ecd970103cbb40f9 SHA1: 7750210716027e0cde3211654f061ff71e0a8752 SHA256: d75cd4ba31ce6d810fee761d6e00411f0073348fe4770dc7f5b21cfe610ed2bf SHA512: 625dee4d017b8f88df2cd4910afbd35b189cd7d8d0cd10bbdf12f1ad503db2b4c38355ee793b95d51bfc38cd01d3ec8f1094bd73cede6687d2d568143e02da94 Homepage: https://cran.r-project.org/package=switchSelection Description: CRAN Package 'switchSelection' (Endogenous Switching and Sample Selection Regression Models) Estimate the parameters of multivariate endogenous switching and sample selection models using methods described in Newey (2009) , E. Kossova, B. Potanin (2018) , E. Kossova, L. Kupriianova, B. Potanin (2020) and E. Kossova, B. Potanin (2022) . Package: r-cran-swjm Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1658 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rereg, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-survival, r-cran-timeroc, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-swjm_0.1.0-1.ca2404.1_amd64.deb Size: 1111052 MD5sum: e2a7edbc709007b186e2edc07e84f8af SHA1: 63d03b86a90b312f541aabba5c0fba79560956ca SHA256: fad7fbd34d591778f347b781339675cbf20d6c51db6d9c834ccc63c8b13dfedb SHA512: c56c838f90e9f59f75df14e30257eb9cd2b584558edde8d8095bf702db2cf11cdc37161de5b1f61ca0340cb849332a4b350bbcd4f8320308bdaa1f30328452b6 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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Package: r-cran-sylcount Architecture: amd64 Version: 0.2-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6929 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sylcount_0.2-6-1.ca2404.1_amd64.deb Size: 294414 MD5sum: 8d2080f94e319eac58e0e4b00c6af453 SHA1: 19d9ace0ed3afc935b3287e8d4f8a20e6bc095bb SHA256: dcb3eb6f38a830bb1b38ff4af2fe31e8a3e688c98d1817819a96a81ef6536a03 SHA512: a43e01054f1cd079d3ff70aa4810719766fca7dfa1e10b091c72f13130ec817d15a704eb5c30f87d7f4493317e6d77de50aabbedfcb87f6465bee4edcb458ff0 Homepage: https://cran.r-project.org/package=sylcount Description: CRAN Package 'sylcount' (Syllable Counting and Readability Measurements) An English language syllable counter, plus readability score measure-er. For readability, we support 'Flesch' Reading Ease and 'Flesch-Kincaid' Grade Level ('Kincaid' 'et al'. 1975) , Automated Readability Index ('Senter' and Smith 1967) , Simple Measure of Gobbledygook (McLaughlin 1969), and 'Coleman-Liau' (Coleman and 'Liau' 1975) . The package has been carefully optimized and should be very efficient, both in terms of run time performance and memory consumption. The main methods are 'vectorized' by document, and scores for multiple documents are computed in parallel via 'OpenMP'. Package: r-cran-symbolicqspray Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1758 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-qspray, r-cran-ratioofqsprays, r-cran-gmp, r-cran-rcpp, r-cran-bh, r-cran-rcppcgal Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-symbolicqspray_1.1.0-1.ca2404.1_amd64.deb Size: 700106 MD5sum: 1c2f39c6a91d3f5645b8b93e009a16b4 SHA1: d3da90d8a3bba61b047ad6f1b1c2de5f92147ca2 SHA256: df72fa588fe0200ced2cf95c7ef1012081635a180f85c83266362398cab2d8f3 SHA512: fc47d616990e08dd77484cc785b1aed3b0643048da33af9b9ea60872e8d71572bfda802b5d8ad8262fdee653425bca31c8a718317cc8d0964c5f87985899119e Homepage: https://cran.r-project.org/package=symbolicQspray Description: CRAN Package 'symbolicQspray' (Multivariate Polynomials with Symbolic Parameters in theirCoefficients) Introduces the 'symbolicQspray' objects. 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The package has functionalities for symbolic computation like calculating exact mathematical expressions, solving systems of linear equations and code generation. Package: r-cran-symmcd Architecture: amd64 Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-symmcd_0.6-1.ca2404.1_amd64.deb Size: 107432 MD5sum: 94f3a5c106de423700f30f0f47ef3989 SHA1: 7ab5de37b99c102eaa50bd4f508df1c837e5cbaa SHA256: d78ef13a6a69819137a1b9ea3b9402c315d1c2f2cef8a761d828cf507bca88ca SHA512: 4ba0b0b0869f54a3adaa4bbc32372db5c1a9cb94e849f0ef8d9e683bc63b2dddd56d176fac582fad12303e49ee531c2f4f6d44362ca12468ba2363cefab5caa8 Homepage: https://cran.r-project.org/package=symMCD Description: CRAN Package 'symMCD' (Symmetrized MCD) Provides implementations of origin-based and symmetrized minimum covariance determinant (MCD) estimators, together with supporting utility functions. 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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: amd64 Version: 1.3.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bigmemory.sri, r-cran-rcpp, r-cran-uuid, r-cran-bh Filename: pool/dists/noble/main/r-cran-synchronicity_1.3.10-1.ca2404.1_amd64.deb Size: 92176 MD5sum: e7bdee98014bcff8a699a90b5790db93 SHA1: d766d80ef42025c3f8943c096d588fdf7c2c9162 SHA256: ef9bdb0cadae56787df25ff4f8ac1b95fb720f2afe84c0ce3576ec6ecdad1bd1 SHA512: b37863c2f1fee2004c864a2af6a569bdc06c8b2c3ce07a37e06b8b858141f19a2a316110ad8290431238e86bd2e39f20f95d2d2f338ad1d5a5b0a223744d397e Homepage: https://cran.r-project.org/package=synchronicity Description: CRAN Package 'synchronicity' (Boost Mutex Functionality in R) Boost mutex functionality in R. Package: r-cran-synchwave Architecture: amd64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields Filename: pool/dists/noble/main/r-cran-synchwave_1.1.2-1.ca2404.1_amd64.deb Size: 98528 MD5sum: 21200e8d7bb4667408caa2987c1a9af0 SHA1: 0646ec6711f1e3e1f02ba43208b4686680ddc776 SHA256: 4f05d2a1eb177e4ed27cb19485fd9fdb922f61c3ccda7605fc5409bae2e3b33b SHA512: 2b6ff58b954bd4a4b4ac632d55a6bfaed44d4d71248b2c995a14b77a3f48d0150a235839fe73b89bde644886c3aeb2524acedf66d1085d742645118557c87052 Homepage: https://cran.r-project.org/package=SynchWave Description: CRAN Package 'SynchWave' (Synchrosqueezed Wavelet Transform) The synchrosqueezed wavelet transform is implemented. The package is a translation of MATLAB Synchrosqueezing Toolbox, version 1.1 originally developed by Eugene Brevdo (2012). The C code for curve_ext was authored by Jianfeng Lu, and translated to Fortran by Dongik Jang. Synchrosqueezing is based on the papers: [1] Daubechies, I., Lu, J. and Wu, H. T. (2011) Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30. 243-261. [2] Thakur, G., Brevdo, E., Fukar, N. S. and Wu, H-T. (2013) The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing, 93, 1079-1094. Package: r-cran-syncrng Architecture: amd64 Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-syncrng_1.3.3-1.ca2404.1_amd64.deb Size: 124082 MD5sum: d2e29fb2c317b4afc2d29ccc81a0b4e5 SHA1: beef78df6d9223f428573fd155ba898b92b02c61 SHA256: 5b2d6170b364c1015ca025f155e23defae7719d8febd66b808ed04cf782f595e SHA512: 70b3b4ab160d58f498338c56164a0eff5439ecc7d962dd39ac25f040a4410afbc29cfb229ed61e1d764b22fc434054c4647eeb15683a661ee972e6bffc117570 Homepage: https://cran.r-project.org/package=SyncRNG Description: CRAN Package 'SyncRNG' (A Synchronized Tausworthe RNG for R and Python) Generate the same random numbers in R and Python. Package: r-cran-synlik Architecture: amd64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1342 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-markdown, r-cran-stabledist Filename: pool/dists/noble/main/r-cran-synlik_0.1.7-1.ca2404.1_amd64.deb Size: 1067332 MD5sum: 0bcc3c5fa23521fb217555654091321f SHA1: af0f1306ef3283ffbf59b537927ac6c4881d1e0e SHA256: 21b7e7d67a7668d8b1d9d0b11ac6a83d2def858edbd474ba059a22ecff33a177 SHA512: c1ae7302ad323024bce0802233c6974591ed27988baf8cbeb295c26e171977750f6935aa25ec4b105c3c7eb14f2688542c0dcce90dce75cee589356f0a24efaa Homepage: https://cran.r-project.org/package=synlik Description: CRAN Package 'synlik' (Synthetic Likelihood Methods for Intractable Likelihoods) Framework to perform synthetic likelihood inference for models where the likelihood function is unavailable or intractable. Package: r-cran-synmicrodata Architecture: amd64 Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 414 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-synmicrodata_2.1.3-1.ca2404.1_amd64.deb Size: 172206 MD5sum: 021b8defd20f5f3ff714aa7ea9603d6d SHA1: 8c137481920e975a40c9bf84e91f4c838e558994 SHA256: 31328dc95ced8043f2f89bfae4ffe4338d6ce06290d74333003daebdfb8c0f25 SHA512: e99aa99cb5027129165cdc6f10746186abe59b775dba759619b7c061af8df6523b0cee3004488997a22e70cfbc5e06c4a264ee4cbd0e37a932559f0f0faf9077 Homepage: https://cran.r-project.org/package=synMicrodata Description: CRAN Package 'synMicrodata' (Synthetic Microdata Generator) This tool fits a non-parametric Bayesian model called a "hierarchically coupled mixture model with local dependence (HCMM-LD)" to the original microdata in order to generate synthetic microdata for privacy protection. The non-parametric feature of the adopted model is useful for capturing the joint distribution of the original input data in a highly flexible manner, leading to the generation of synthetic data whose distributional features are similar to that of the input data. The package allows the original input data to have missing values and impute them with the posterior predictive distribution, so no missing values exist in the synthetic data output. The method builds on the work of Murray and Reiter (2016) . Package: r-cran-synthacs Architecture: amd64 Version: 1.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1434 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-rcpp, r-cran-acs, r-cran-retry Suggests: r-cran-testthat, r-cran-sp, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-synthacs_1.7.1-1.ca2404.1_amd64.deb Size: 1106096 MD5sum: 977ce97b7446e3e431994a5ba568083c SHA1: 7a8aabf86569690aee23f45218cd73701865c511 SHA256: 3a3cb3ed31a308b4fb933a3f555cc893a2aee82724994684dd7bbf5094ce898a SHA512: ca9402853126c79b7ccf017eabdb58b9dc51fea26d7ae9d531d5a759df79657c8680df6654c2ecb00c0dbda1235517837e0c5717e50fb11384454a23353bdb57 Homepage: https://cran.r-project.org/package=synthACS Description: CRAN Package 'synthACS' (Synthetic Microdata and Spatial MicroSimulation Modeling for ACSData) Provides access to curated American Community Survey (ACS) base tables via a wrapper to library(acs). Builds synthetic micro-datasets at any user-specified geographic level with ten default attributes; and, conducts spatial microsimulation modeling (SMSM) via simulated annealing. SMSM is conducted in parallel by default. Lastly, we provide functionality for data-extensibility of micro-datasets . Package: r-cran-sys Architecture: amd64 Version: 3.4.3-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: libc6 (>= 2.34), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-unix, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sys_3.4.3-1.ca2404.2_amd64.deb Size: 40880 MD5sum: 8774d0822ff6d7dc63af55e3e8146994 SHA1: cc8324160afe4dcdacd4dd5a6bed94f629b3d990 SHA256: dcd9e72aa9bcaf05164d06b01fb3b85208e322487c53167ff24bb0baab0c5c8f SHA512: e6cfbca515b1289ccfd215e918d1c6a50ff85424527db3e31bd5213506a906b94fc44a675940d510a85cd65e9c0c19885ffd6cea90746df7b0bd476d2e1f5519 Homepage: https://cran.r-project.org/package=sys Description: CRAN Package 'sys' (Powerful and Reliable Tools for Running System Commands in R) Drop-in replacements for the base system2() function with fine control and consistent behavior across platforms. Supports clean interruption, timeout, background tasks, and streaming STDIN / STDOUT / STDERR over binary or text connections. Arguments on Windows automatically get encoded and quoted to work on different locales. Package: r-cran-sysfonts Architecture: amd64 Version: 0.8.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1870 Depends: libc6 (>= 2.4), libfreetype6 (>= 2.2.1), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-curl, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-sysfonts_0.8.9-1.ca2404.1_amd64.deb Size: 953646 MD5sum: c64bc94bffb1bc46d195e1e203f836a2 SHA1: f3b247fa7b4ea16e594ae9aa5c0ea4f04e55f763 SHA256: c423aa8049fa8077b87d69e0a97829cf820389fa2b50e3fa5905cd81d88e3825 SHA512: de62c59a63a633bd31fd1f96497bf83a283f069af077ddae186d1eeed5ce8dbf647b82c155ff8b1338314dbae4f8cf8194129c99051da6ed6fbab8ed5358aa27 Homepage: https://cran.r-project.org/package=sysfonts Description: CRAN Package 'sysfonts' (Loading Fonts into R) Loading system fonts and Google Fonts into R, in order to support other packages such as 'R2SWF' and 'showtext'. 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As font handling varies between systems it is difficult to correctly locate installed fonts across different operating systems. The 'systemfonts' package provides bindings to the native libraries on Windows, macOS and Linux for finding font files that can then be used further by e.g. graphic devices. The main use is intended to be from compiled code but 'systemfonts' also provides access from R. Package: r-cran-systemicrisk Architecture: amd64 Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 510 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-rcpp Suggests: r-cran-coda, r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-systemicrisk_0.4.3-1.ca2404.1_amd64.deb Size: 324280 MD5sum: e44a0552c1b1d9decd4401b4092a5f29 SHA1: 5934cef26e1afcf517751079298551d95b4e5e80 SHA256: a7f6482051f90ddb0f8feb1fae141f1e5e1e2975e7cd9894da1c8f984780f325 SHA512: 5d2fdcdde59009c6f5ea83bc0968ac3a31d9b62a64911d2d9afa89a0a13ef98fb3f886fafc3111f53ea95eab8de04217fe85c4adc8f97e6358bcda7d671143c6 Homepage: https://cran.r-project.org/package=systemicrisk Description: CRAN Package 'systemicrisk' (Systemic Risk and Network Reconstruction) Analysis of risk through liability matrices. Contains a Gibbs sampler for network reconstruction, where only row and column sums of the liabilities matrix as well as some other fixed entries are observed, following the methodology of Gandy&Veraart (2016) . It also incorporates models that use a power law distribution on the degree distribution. Package: r-cran-t4cluster Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1468 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-rdimtools, r-cran-admm, r-cran-mass, r-cran-fda, r-cran-ggplot2, r-cran-lpsolve, r-cran-maotai, r-cran-mclustcomp, r-cran-rstiefel, r-cran-scatterplot3d, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-t4cluster_0.1.4-1.ca2404.1_amd64.deb Size: 789380 MD5sum: c29c3a5a135c10460d2946e9d6691d94 SHA1: be62bbdccadcb0b4303a0913a4d29bd390fcafcc SHA256: df1746da6ce12e7c3b5fa2e852f95858ffb74804e370a3018a86c691a182fb01 SHA512: 2838d725b3df82dafb1336d3e382163aede0c8dc3efee4e0ef086b613bab8dc46258a21a4dcec927dd5715983f2b87a8e9f1f7fd6a0ce8d2d8067650df296657 Homepage: https://cran.r-project.org/package=T4cluster Description: CRAN Package 'T4cluster' (Tools for Cluster Analysis) Cluster analysis is one of the most fundamental problems in data science. We provide a variety of algorithms from clustering to the learning on the space of partitions. See Hennig, Meila, and Rocci (2016, ISBN:9781466551886) for general exposition to cluster analysis. 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We provide a variety of algorithms to compute Wasserstein distance, barycenter, and others. See Peyré and Cuturi (2019) for the general exposition to the study of computational optimal transport. 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This package uses 'RcppArmadillo' to provide a fast implementation of the MLC to train and predict over tabular data (data.frame). The algorithms were based on Mather (1985) method. Package: r-cran-tabulate Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 666 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-magrittr, r-cran-rlang, r-cran-rcpp Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-emoji Filename: pool/dists/noble/main/r-cran-tabulate_0.1.0-1.ca2404.1_amd64.deb Size: 221240 MD5sum: 5efec93ab477af67793308c8ed51f62e SHA1: 0fc52cfeb832813c2323515c5f1f634f54b5d431 SHA256: ba95e60237f5888afba812dbd8ca8c2c7e55118311574d63a2eeeb8c0fde629b SHA512: ba4f35b72b55b473e7646f2f010863a0ef55b582a161f61f4479d6fd3f385c329e37679780dded7fa3abf0b4cc89b51240af399f45c1d0e8a7350626cfb05ca2 Homepage: https://cran.r-project.org/package=tabulate Description: CRAN Package 'tabulate' (Pretty Console Output for Tables) Generates pretty console output for tables allowing for full customization of cell colors, font type, borders and many others attributes. It also supports 'multibyte' characters and nested tables. Package: r-cran-tag Architecture: amd64 Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-dicekriging, r-cran-matrix, r-cran-mgcv, r-cran-fastgp, r-cran-mlegp, r-cran-randtoolbox, r-cran-foreach, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tag_0.7.1-1.ca2404.1_amd64.deb Size: 120972 MD5sum: 9a3048e2bf0f531ad891a7e2c44ad60d SHA1: b5a91eb9aaf8c7665b70f3d09c4b81a896ca5521 SHA256: 13c311b67ae1adb2ff6b7c11874c1402ad0fab7546e162bc8fdf1fcd97ea0c28 SHA512: a31709cb2dfaf5b80bc750522b1eea89045c7276d9a2bcbaff1b75c999b6539b90c42eba4aadcfe7105fcb300fe88c6b969e14371354b5ce2289c3418c02b3f1 Homepage: https://cran.r-project.org/package=TAG Description: CRAN Package 'TAG' (Transformed Additive Gaussian Processes) Implement the transformed additive Gaussian (TAG) process and the transformed approximately additive Gaussian (TAAG) process proposed in Lin and Joseph (2020) . These functions can be used to model deterministic computer experiments, obtain predictions at new inputs, and quantify the uncertainty of the predictions. This research is supported by a U.S. National Science Foundation grant DMS-1712642 and a U.S. Army Research Office grant W911NF-17-1-0007. Package: r-cran-tagcloud Architecture: amd64 Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcolorbrewer Suggests: r-cran-extrafont, r-cran-knitr Filename: pool/dists/noble/main/r-cran-tagcloud_0.7.0-1.ca2404.1_amd64.deb Size: 339246 MD5sum: c692b6fb2cdbdc43d778713a9c30d2cf SHA1: 856cfcf1750633b937eca37e6f98a230408fba1c SHA256: 1626f74adcb10815ef6636c0ed5770caba286c54681d37de99ed0ed1f5693414 SHA512: 40e30ec5a9191ebab8c0a786f0a3ebea69dc8f2226e878905be8009572778e11d2db206941dbb0c728eae1fec9f7364d87ef5c1064f980e2c13dbd1231aa5028 Homepage: https://cran.r-project.org/package=tagcloud Description: CRAN Package 'tagcloud' (Tag Clouds) Generating Tag and Word Clouds. Package: r-cran-tagtools Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1469 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-tagtools_0.3.0-1.ca2404.1_amd64.deb Size: 1319316 MD5sum: 82303a4e6f7c1db39862e770a55cace2 SHA1: a30a91dc04089f83cb168b7e99c64da5b717022f SHA256: fca16cc8366e8128d88178226051b12a42f12922c9865df4d854d2739a8ba53a SHA512: 6c5f01ed14927a2e113331656128f66c4a6a6a89dc9248243777a9156b145c3becc9718b4ece76e39fb702fdb402f364d31fc2162d5c2be81c859a4f3df58382 Homepage: https://cran.r-project.org/package=tagtools Description: CRAN Package 'tagtools' (Work with Data from High-Resolution Biologging Tags) High-resolution movement-sensor tags typically include accelerometers to measure body posture and sudden movements or changes in speed, magnetometers to measure direction of travel, and pressure sensors to measure dive depth in aquatic or marine animals. The sensors in these tags usually sample many times per second. Some tags include sensors for speed, turning rate (gyroscopes), and sound. This package provides software tools to facilitate calibration, processing, and analysis of such data. Tools are provided for: data import/export; calibration (from raw data to calibrated data in scientific units); visualization (for example, multi-panel time-series plots); data processing (such as event detection, calculation of derived metrics like jerk and dynamic acceleration, dive detection, and dive parameter calculation); and statistical analysis (for example, track reconstruction, a rotation test, and Mahalanobis distance analysis). Package: r-cran-taildepfun Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 504 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubature, r-cran-mvtnorm, r-cran-spatialextremes, r-cran-copula Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-taildepfun_1.0.1-1.ca2404.1_amd64.deb Size: 460272 MD5sum: 9b3cb29ec378787388e3ac8085004c28 SHA1: 36470d572c8f7951df8bcb2e42c17d31e34b9410 SHA256: 6a683304d12b00e1ffcd1fa78dd5fecfd7ba85b540934a79cd3bd556287db508 SHA512: 5b1c467b73e130af9b5f923a49893d87eb80b6db7fa0362128e167b9995723c85f8229b0510cbc4091f14e510084ad50d6ea149c4ba0c69cad5175950535dd0f 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: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-actuar Filename: pool/dists/noble/main/r-cran-tailplots_0.1.1-1.ca2404.1_amd64.deb Size: 109756 MD5sum: 49f82f28d41177345d23b21ce2111d79 SHA1: 89c2b9c63385e38e811d280ce8e27e1bc8ed6e94 SHA256: 2890a4d7415a57128577a79ecd479d74c8d0b4947aec93df48270e1c15ab4280 SHA512: 206181eb5dcbc38e7d4418f2a2e2e22abda66a5ee2ad89513198b4e650e044399ea4ebc89eac3c3d55fe5fd4796c4058b6af1c9fa8131e785f15fce628d19ccf Homepage: https://cran.r-project.org/package=tailplots Description: CRAN Package 'tailplots' (Estimators and Plots for Gamma and Pareto Tail Detection) Estimators for two functionals used to detect Gamma, Pareto or Lognormal distributions, as well as distributions exhibiting similar tail behavior, as introduced by Iwashita and Klar (2023) and Klar (2024) . One of these functionals, g, originally proposed by Asmussen and Lehtomaa (2017) , distinguishes between log-convex and log-concave tail behavior. Furthermore the characterization of the lognormal distribution is based on the work of Mosimann (1970) . The package also includes methods for visualizing these estimators and their associated confidence intervals across various threshold values. Package: r-cran-talib Architecture: amd64 Version: 0.9-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18483 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-talib_0.9-2-1.ca2404.1_amd64.deb Size: 3618616 MD5sum: cdcb3fd5a242511189b198a871a4796b SHA1: c0ccbf868ecb1dd47b8dba689b6b704292a17858 SHA256: 55e43953ae142b8fde27b05044828478be250be342e2afdd9dd74d0e2ecfa037 SHA512: cdd793c53e37fea47110ff2944bfc1388571ea88583852acb0e0484016a17618f3671698f6395e596c9a22b9617a5d18fe4fb16385a556ba5d78a7cbc43cc1f0 Homepage: https://cran.r-project.org/package=talib Description: CRAN Package 'talib' (Interface to 'TA-Lib' for Technical Analysis and CandlestickPatterns) Interface to the 'TA-Lib' (Technical Analysis Library) 'C' library, providing access to 150+ indicators (e.g. Average Directional Movement Index (ADX), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), Stochastic Oscillator, Bollinger Bands), candlestick pattern recognition, and rolling-window utilities. Core computations are implemented in 'C' for fast Open-High-Low-Close-Volume (OHLCV) time-series feature engineering and rule-based signal generation, with optional interactive visualization via 'plotly'. Package: r-cran-tall Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4448 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-base64enc, r-cran-ca, r-cran-chromote, r-cran-curl, r-cran-doparallel, r-cran-dplyr, r-cran-dt, r-cran-fontawesome, r-cran-future, r-cran-ggplot2, r-cran-ggraph, r-cran-ggwordcloud, r-cran-httr2, r-cran-igraph, r-cran-jsonlite, r-cran-later, r-cran-matrix, r-cran-openxlsx, r-cran-pagedown, r-cran-pdftools, r-cran-plotly, r-cran-promises, r-cran-purrr, r-cran-ranger, r-cran-readr, r-cran-readtext, r-cran-readxl, r-cran-rlang, r-cran-rspectra, r-cran-shinycssloaders, r-cran-shinydashboardplus, r-cran-shinyfiles, r-cran-shinyjs, r-cran-shinywidgets, r-cran-sparkline, r-cran-stringr, r-cran-strucchange, r-cran-textrank, r-cran-tidygraph, r-cran-tidyr, r-cran-stm, r-cran-topicmodels, r-cran-udpipe, r-cran-umap, r-cran-visnetwork, r-cran-word2vec Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tall_1.0.0-1.ca2404.1_amd64.deb Size: 2174618 MD5sum: 659fc0dcdbd53e157edb16b068f1e7c3 SHA1: 152241d1ed5d4c9a9c1feac91ce674020fcab718 SHA256: 94fcfe8333d42e80cc3d2b1c509538a8f94b10b30b5b619c3f1e127302fe6b0f SHA512: fd14dd5544d94997a827f025a10496b3615b7c0dab271875a32bb0a8a068016acb57e1035d669dd4ef056b7ebedebaacaab2d5d6fbc7d19050e04db8fd61ca94 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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Package: r-cran-targeted Architecture: amd64 Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3154 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rcpp, r-cran-abind, r-cran-cli, r-cran-future.apply, r-cran-lava, r-cran-mets, r-cran-quadprog, r-cran-progressr, r-cran-rlang, r-cran-survival, r-cran-rcpparmadillo Suggests: r-cran-superlearner, r-cran-mass, r-cran-cmprsk, r-cran-data.table, r-cran-e1071, r-cran-earth, r-cran-glmnet, r-cran-grf, r-cran-hal9001, r-cran-mgcv, r-cran-nnls, r-cran-optimx, r-cran-polle, r-cran-pracma, r-cran-quarto, r-cran-randomforestsrc, r-cran-ranger, r-cran-riskregression, r-cran-scatterplot3d, r-cran-tinytest, r-cran-viridislite, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-targeted_0.7.1-1.ca2404.1_amd64.deb Size: 2060790 MD5sum: a9a224977e344426251dd9d84056a699 SHA1: 7106915ea0f76ccbab145bb7075606d4f1a6c94a SHA256: 477bd409a0cffe383ed07384b882ea28b8a501f686689285f0cabce46b6db13b SHA512: d78752b30a40799f8c5db9541cd957b527f884c195e15ee61f4004cf7d7072d8e8dcb35f17047ef1a651e89b6068ae782257bc586d29b291a55a3b96188798a9 Homepage: https://cran.r-project.org/package=targeted Description: CRAN Package 'targeted' (Targeted Inference) Various methods for targeted and semiparametric inference including augmented inverse probability weighted (AIPW) estimators for missing data and causal inference (Bang and Robins (2005) ), variable importance and conditional average treatment effects (CATE) (van der Laan (2006) ), estimators for risk differences and relative risks (Richardson et al. (2017) ), assumption lean inference for generalized linear model parameters (Vansteelandt et al. (2022) ). Package: r-cran-tau Architecture: amd64 Version: 0.0-28-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tm Filename: pool/dists/noble/main/r-cran-tau_0.0-28-1.ca2404.1_amd64.deb Size: 146098 MD5sum: e757731dc05b5c2b911344b93877a5c3 SHA1: 0ff2d1192feb1bc7ec77d7f564333d391c14d93c SHA256: e45a0ba5f2b5f82feeb1c360493bc8a35dc9ca86cd21cd4444a89d39d6bc08a7 SHA512: 7fdd96ac105e46f3e9d544a978048664a21b96da9cb5b61bbf5756f51b8c182ba1adbe20ef11cd9d50248719e00ce851055f58f7dee03b8d825ff5d3a7fd8f5f Homepage: https://cran.r-project.org/package=tau Description: CRAN Package 'tau' (Text Analysis Utilities) Utilities for text analysis. 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This package allows the user to download NCBI data dumps and create a local database for fast and local taxonomic assignment. 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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. 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Jacobs (1990) ; Marsh et al. (1991) ; Shafer and Gregg (1993) ; Schnider et al. (1998) ; Abuhelwa, Foster, and Upton (2015) ; Eleveld et al. (2018) . 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(2021) "Data Science: Time Complexity, Inferential Uncertainty, and Spacekime Analytics", De Gruyter STEM Series, ISBN 978-3-11-069780-3. . The package includes 18 core functions which can be separated into three groups. 1) draw longitudinal data, such as Functional magnetic resonance imaging(fMRI) time-series, and forecast or transform the time-series data. 2) simulate real-valued time-series data, e.g., fMRI time-courses, detect the activated areas, report the corresponding p-values, and visualize the p-values in the 3D brain space. 3) Laplace transform and kimesurface reconstructions of the fMRI data. Package: r-cran-tclust Architecture: amd64 Version: 2.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1824 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-mass, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-mclust, r-cran-cluster, r-cran-sn Filename: pool/dists/noble/main/r-cran-tclust_2.2-0-1.ca2404.1_amd64.deb Size: 1425080 MD5sum: 8a5e539a5a99cd6c932e30d0b6920678 SHA1: ed9dd15c1b978126abec8562c4e80f4c8ffca5b6 SHA256: 9c2118bde2c4254cd14f3d9ffe26ad03b0613e56e5787bf34981a65da958adaf SHA512: b4b2f7e9811bff7827af7789003588ac05851e3a946d2a9770fbeaf3376e97737cbc715e4f11a77f77bbf7278d81990c651fe9289d1642031a013e50d83b987f Homepage: https://cran.r-project.org/package=tclust Description: CRAN Package 'tclust' (Robust Trimmed Clustering) Provides functions for robust trimmed clustering. The methods are described in Garcia-Escudero (2008) , Fritz et al. (2012) , Garcia-Escudero et al. (2011) and others. 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Package: r-cran-tda Architecture: amd64 Version: 1.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2924 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn, r-cran-rcpp, r-cran-igraph, r-cran-scales, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-lintr Filename: pool/dists/noble/main/r-cran-tda_1.9.4-1.ca2404.1_amd64.deb Size: 1986576 MD5sum: 4805373c095c551ed12c7c67d42aae15 SHA1: a81aca9051026ff8cf1f74e4f4a81b885d739d74 SHA256: 8fa05c7aed29987a5b11c8fb55828925baad7149cd072fa10c22c00d6e8c7212 SHA512: e53b1d21fc2cbde1b869a80f9f9f4d1c120e6948f2c3858b3ad26ead43b4dc4a4507963f7b9936186d97ce71d1ec6fbfa51c4128ae2deb9167d1cf9ad6063ee6 Homepage: https://cran.r-project.org/package=TDA Description: CRAN Package 'TDA' (Statistical Tools for Topological Data Analysis) Tools for Topological Data Analysis. The package focuses on statistical analysis of persistent homology and density clustering. For that, this package provides an R interface for the efficient algorithms of the C++ libraries 'GUDHI' , 'Dionysus' , and 'PHAT' . This package also implements methods from Fasy et al. (2014) and Chazal et al. (2015) for analyzing the statistical significance of persistent homology features. Package: r-cran-tdakit Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-tdastats, r-cran-t4cluster, r-cran-energy, r-cran-ggplot2, r-cran-maotai, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-tdakit_0.1.3-1.ca2404.1_amd64.deb Size: 191896 MD5sum: 3cddaf0836de071985b7917e9ab9f2f6 SHA1: c9443008d0767dd42d6877bc47f71371a9873454 SHA256: 269dba2c3266090efedb3ebc9b1aabbdb540e3d4a208d56cb5beb9e185d744af SHA512: 6bb24ac3701f0cbd0f68595d1c09d9795b0e548befcba711b513c13a803098b42313f9d4c513a048f186af1c550e74cb53aaa377f532f8b3c58028c74c745ee4 Homepage: https://cran.r-project.org/package=TDAkit Description: CRAN Package 'TDAkit' (Toolkit for Topological Data Analysis) Topological data analysis studies structure and shape of the data using topological features. We provide a variety of algorithms to learn with persistent homology of the data based on functional summaries for clustering, hypothesis testing, visualization, and others. We refer to Wasserman (2018) for a statistical perspective on the topic. Package: r-cran-tdapplied Architecture: amd64 Version: 3.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8964 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-clue, r-cran-rdist, r-cran-parallelly, r-cran-kernlab, r-cran-iterators, r-cran-rcpp Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-tdastats, r-cran-reticulate, r-cran-tda, r-cran-igraph Filename: pool/dists/noble/main/r-cran-tdapplied_3.0.4-1.ca2404.1_amd64.deb Size: 3933590 MD5sum: d438319dae82899fc9aa01b5af611f4e SHA1: 38c2d60a9c49ba2f219803369525dc9aea8e4431 SHA256: 2d7186dca7dbff033dadebaa83c7dc8dfbee0cd94aa7b9b05569b45e401f3ea1 SHA512: 0bc3b83f589108f4abc8de1204e84696d0ac354bd63007c2cc5824358bae94a395628c9c0ce232787b611ae31e08fbce688ef8ef32f66995d58f3dc61f36e307 Homepage: https://cran.r-project.org/package=TDApplied Description: CRAN Package 'TDApplied' (Machine Learning and Inference for Topological Data Analysis) Topological data analysis is a powerful tool for finding non-linear global structure in whole datasets. The main tool of topological data analysis is persistent homology, which computes a topological shape descriptor of a dataset called a persistence diagram. 'TDApplied' provides useful and efficient methods for analyzing groups of persistence diagrams with machine learning and statistical inference, and these functions can also interface with other data science packages to form flexible and integrated topological data analysis pipelines. Package: r-cran-tdastats Architecture: amd64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 730 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-tdastats_0.4.2-1.ca2404.1_amd64.deb Size: 368008 MD5sum: 0cf2748741b277e992564920014438c2 SHA1: 3e472a010058d46536850f3aa8103aad33fd76a8 SHA256: 8585ede60847da73d08dfd5cef417a0b54e19691edd08b87a682163eec42bad5 SHA512: 6c5ceed2c3ac279258bebe79fdf1215bbf422d205f23d8af7468a8f5baa07281e4524693defafb66e331064b8e3cb12867d9e508db758916b8b78d0c2ea89dd7 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: amd64 Version: 0.1.41-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 595 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-tdastats Filename: pool/dists/noble/main/r-cran-tdavec_0.1.41-1.ca2404.1_amd64.deb Size: 251072 MD5sum: 5a3f19ad4efd71f2be81311647ab947e SHA1: 408dd2b06c48a9301052600a44b140da949fda0b SHA256: ac5804f668264ce6966838b497ad825d406193ffdf12561396c3280af3a7237b SHA512: 0ecbf782cd6aa8c4d753f445b36331c43de8cac2c32b425187bf2710aea71faacc8605da190fadc9a83486108fc9d38808d37b6538831a611180b74c580771e9 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. 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Package: r-cran-tdboost Architecture: amd64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Filename: pool/dists/noble/main/r-cran-tdboost_1.6-1.ca2404.1_amd64.deb Size: 124396 MD5sum: 7f12da5ad843981dfd9dcbdf501d4681 SHA1: 53cc194a07879574c5f9ff236c1e2fb539574a9f SHA256: d9f2aa69e48b95f527c992c241dff08a3c19baad4dfe8dbc464ba1dcf5073ba4 SHA512: e0b92d5aaaa5522c5c35a8248dac556dfdd1434dfd546c6faed3f0f02d9a2adf3c0449f40bb0ccb6d4d044adb88193c40684f4a3b1a568b6e83a7c6c8978aa5c Homepage: https://cran.r-project.org/package=TDboost Description: CRAN Package 'TDboost' (A Boosted Tweedie Compound Poisson Model) An implementation of a boosted Tweedie compound Poisson model proposed by Yang, Y., Qian, W. and Zou, H. (2018) . 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Package: r-cran-tdigest Architecture: amd64 Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-testthat, r-cran-covr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-tdigest_0.4.3-1.ca2404.1_amd64.deb Size: 44842 MD5sum: d7d4fae47dad6ab021b3ceb0c0b5d732 SHA1: a7242cf63fc08d1aedadd55bd3ebccbabbdbfbc1 SHA256: b80a9d8ca577d87583b9d098287653df2542196c3d8737bb1ff09cf52e760854 SHA512: 46de0466092aa464c0e8c794ba8365f2a508707580afe9f5fc9387866efdbdd852179b6fdfba2e9d1bbabf66d11d45fc02d492d1f09938f5bfb61ce70c78a690 Homepage: https://cran.r-project.org/package=tdigest Description: CRAN Package 'tdigest' (Wicked Fast, Accurate Quantiles Using t-Digests) The t-Digest construction algorithm, by Dunning, (2019) , uses a variant of 1-dimensional k-means clustering to produce a very compact data structure that allows accurate estimation of quantiles. 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Package: r-cran-tdroc Architecture: amd64 Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-tdroc_2.0-1.ca2404.1_amd64.deb Size: 117344 MD5sum: 6db9b84f3df3f76c485ea0698fdb1beb SHA1: 97fac6d7144c8afaf9e0e4fae702d92d6a8d3041 SHA256: 94e830ed992e0b032e7339a56fa66016dca2cbac15b5866751cce1b2411bcfb2 SHA512: c7ad92dc3f5185895d87fff6aec046323e7bbd80867d347e1a3e631c6448dd60f575fda2fe85ddfe722bd63f2606758c04230fdf0da95287ccb49a3d80cb9902 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-tdthap Architecture: amd64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tdthap_1.3-1.ca2404.1_amd64.deb Size: 355342 MD5sum: 54e9b4cbb66d930aceabdb217859c4ac SHA1: 2a10c0dbc01605958ad280613632c24d5ec6a51e SHA256: 9b07f6cee5c5dc7ee0c2ceba4f502288c09729c3285cb58043bca34a89781edd SHA512: 9d78efd13af0c7d99b3cefa410aa32432baf6091877f598e8710d7ef3633cea97a1cee1baa20755c8c1012c7b9570a888ab43447acb7779aa149648de0f0708c Homepage: https://cran.r-project.org/package=tdthap Description: CRAN Package 'tdthap' (TDT Tests for Extended Haplotypes) Functions and examples are provided for Transmission/disequilibrium tests for extended marker haplotypes, as in Clayton, D. and Jones, H. (1999) "Transmission/disequilibrium tests for extended marker haplotypes". Amer. J. Hum. Genet., 65:1161-1169, . 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Package: r-cran-tetrascatt Architecture: amd64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-tetrascatt_0.1.1-1.ca2404.1_amd64.deb Size: 75898 MD5sum: dbda0a8fff1f8068d653067b9e32e5cc SHA1: 6bb2e8f9dd92c90ce2221dc35773e90e68d237c9 SHA256: 2b4e15043dcb7cc27acbaaa492edb0725dae4e624eae7c3ad6b64136e1408ab4 SHA512: ab8a13bed9a06586f7c37a7cfdfe4416afeb773bf0088d11cfd3ea933cb70a5d538c635f327f5ba34c5c906e45c966b0312a9bd0d912a6e7b498bb7c9185ae9f Homepage: https://cran.r-project.org/package=tetrascatt Description: CRAN Package 'tetrascatt' (Acoustic Scattering for Complex Shapes by Using the DWBA) Uses the Distorted Wave Born Approximation (DWBA) to compute the acoustic backward scattering, the geometry of the object is formed by a volumetric mesh, composed of tetrahedrons. 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Package: r-cran-texexamrandomizer Architecture: amd64 Version: 1.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 861 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-assertthat, r-cran-stringr, r-cran-jsonlite Suggests: r-cran-optparse, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-texexamrandomizer_1.2.7-1.ca2404.1_amd64.deb Size: 334796 MD5sum: 23565804d1626c375f3bbcd90f7e93c7 SHA1: 83b5e55a91dae5bd6ab563f2aebdda2910ea0521 SHA256: 68d78ffcaae276e98aeda1ec644e0f69e2ec32fc884c17c8ced4f97c4e945598 SHA512: 94c977fc3c11579a0f3f7e64bf88b2aa07aee658ccd8373c7d962498d81b778726be73614f0888a837c63164157d9600c18f340b06c7ee145da29ae1c131ee42 Homepage: https://cran.r-project.org/package=TexExamRandomizer Description: CRAN Package 'TexExamRandomizer' (Personalizes and Randomizes Exams Written in 'LaTeX') Randomizing exams with 'LaTeX'. 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Package: r-cran-text.alignment Architecture: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1125 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-text.alignment_0.1.5-1.ca2404.1_amd64.deb Size: 356188 MD5sum: 87b955b85996c59774d28fec584ed171 SHA1: 4e344aac9569eb2b2141c48e7d66d38f1d2e00e8 SHA256: 2506a0e2d2c10894801d2981e8ec05eb9eff04c577d5312728a8141182532882 SHA512: 9502a5aa070b75efaacf93ca07d65a6c2e4a054b82c958b0496629cdee682b2fd654e0facb262b41f62a5d39ad3a16cd3093995e1082855500d9b14e3621b206 Homepage: https://cran.r-project.org/package=text.alignment Description: CRAN Package 'text.alignment' (Text Alignment with Smith-Waterman) Find similarities between texts using the Smith-Waterman algorithm. 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Package: r-cran-tfm Architecture: amd64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2231 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-relliptical, r-cran-sopc, r-cran-mass, r-cran-mvtnorm, r-cran-matrixcalc Suggests: r-cran-rmarkdown, r-cran-psych Filename: pool/dists/noble/main/r-cran-tfm_0.3.0-1.ca2404.1_amd64.deb Size: 2243464 MD5sum: d8f61da348a103635f9cb3d2083c1968 SHA1: 13858f0f7f8e2a8b9ce47441334d14187dae447d SHA256: b6ddb2cd02b5f490f1b7323dbaacf7ccf02f812882db37d9914d23d92fae4246 SHA512: 29d67ca99207d8fb5f839957d551fa75a78ae5b462c1e97daadb2df3ea195ec997fe9a2a5236f852fe14a992bf3a1d61899c7bc33beb3c866f65cd39f9a8b3f9 Homepage: https://cran.r-project.org/package=TFM Description: CRAN Package 'TFM' (Sparse Online Principal Component for Truncated Factor Model) The Truncated Factor Model is a statistical model designed to handle specific data structures in data analysis. 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Package: r-cran-tfmpvalue Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tfmpvalue_1.0.0-1.ca2404.1_amd64.deb Size: 57400 MD5sum: 9ec849258055dfbb536584852cdc5846 SHA1: 93c5db0b5e5b8c8389fe5ca0673eed35dd538bd4 SHA256: f8be4e493240b041e762772153d0f0e787a0fd0417b6cd3077498ae15cbea2f5 SHA512: c200d98a0f77ed2d7a9addc552435bc89bb1f41e3de4e72c5026cbc80ec582f87b535ab16a9dc41fe97b681a3fe1fa6f84f9c7a3a6d7f002977de382e9b5a178 Homepage: https://cran.r-project.org/package=TFMPvalue Description: CRAN Package 'TFMPvalue' (Efficient and Accurate P-Value Computation for Position WeightMatrices) In putative Transcription Factor Binding Sites (TFBSs) identification from sequence/alignments, we are interested in the significance of certain match score. 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The method was published in Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), "A Tuning-free Robust and Efficient Approach to High-dimensional Regression", Journal of the American Statistical Association, 115:532, 1700-1714(JASA’s discussion paper), . See also Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), "Rejoinder to “A tuning-free robust and efficient approach to high-dimensional regression". Journal of the American Statistical Association, 115, 1726-1729, ; Peng, B. and Wang, L. (2015), "An Iterative Coordinate Descent Algorithm for High-Dimensional Nonconvex Penalized Quantile Regression", Journal of Computational and Graphical Statistics, 24:3, 676-694, ; Clémençon, S., Colin, I., and Bellet, A. (2016), "Scaling-up empirical risk minimization: optimization of incomplete u-statistics", The Journal of Machine Learning Research, 17(1):2682–2717; Fan, J. and Li, R. (2001), "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties", Journal of the American Statistical Association, 96:456, 1348-1360, . 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Package: r-cran-thunder Architecture: amd64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1801 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-airthermo, r-cran-curl, r-cran-dplyr, r-cran-httr, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-thunder_1.1.5-1.ca2404.1_amd64.deb Size: 1447126 MD5sum: 9e585717fc51873581a0547b152e3520 SHA1: 7d262847758bae26848444bf9eba02793923c32a SHA256: 3c70f47ed6ed08d938d118d7797965bb0bafc8f86e856a076ca6b3deec2fff6d SHA512: ce03844fc731139aadbfeb0eefb42e35b24474497b7f8812446a5877b621e3471147577e0e2763765c8d3a60bd267d8d3cfc962ac7de41613384284113a9e71a Homepage: https://cran.r-project.org/package=thunder Description: CRAN Package 'thunder' (Computation and Visualisation of Atmospheric ConvectiveParameters) Allow to compute and visualise convective parameters commonly used in the operational prediction of severe convective storms. Core algorithm is based on a highly optimized 'C++' code linked into 'R' via 'Rcpp'. Highly efficient engine allows to derive thermodynamic and kinematic parameters from large numerical datasets such as reanalyses or operational Numerical Weather Prediction models in a reasonable amount of time. Package has been developed since 2017 by research meteorologists specializing in severe thunderstorms. The most relevant methods used in the package based on the following publications Stipanuk (1973) , McCann et al. (1994) , Bunkers et al. (2000) , Corfidi et al. (2003) , Showalter (1953) , Coffer et al. (2019) , Gropp and Davenport (2019) , Czernecki et al. (2019) , Taszarek et al. (2020) , Sherburn and Parker (2014) , Romanic et al. (2022) . Package: r-cran-thurstonianirt Architecture: amd64 Version: 0.12.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2918 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-magrittr, r-cran-mvtnorm, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-tibble, r-cran-tidyr, r-cran-lavaan, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-mplusautomation, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-thurstonianirt_0.12.5-1.ca2404.1_amd64.deb Size: 926216 MD5sum: bd202dd7d846bd1bff5e48ba14bdeee2 SHA1: 7d62181b97ba89ca3987764435c653ff4f5ed24e SHA256: a4efc367d77ae7970958fd0aef050d43d86c7eda2d2c084997f4997318bebf84 SHA512: c2b04cedf6846f020f3792224616f70fda543db705884dd4eece11ac4ee870547814ecfb86a56d3e918d6c129903c6b3524930d0b0c405cc2cd387bbe6e043fb Homepage: https://cran.r-project.org/package=thurstonianIRT Description: CRAN Package 'thurstonianIRT' (Thurstonian IRT Models) Fit Thurstonian Item Response Theory (IRT) models in R. This package supports fitting Thurstonian IRT models and its extensions using 'Stan', 'lavaan', or 'Mplus' for the model estimation. Functionality for extracting results, making predictions, and simulating data is provided as well. References: Brown & Maydeu-Olivares (2011) ; Bürkner et al. (2019) . 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This is done automatically or according to a given specification. A common use case is for nested lists coming from parsing 'JSON' files, or the 'JSON' responses of 'REST' 'APIs'. 'Rectangling' uses the 'vctrs' package, and therefore offers a wide support of vector types. Package: r-cran-ticm Architecture: amd64 Version: 1.0-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fica, r-cran-jade, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-ticm_1.0-0-1.ca2404.1_amd64.deb Size: 170780 MD5sum: 65c27676e1fe02ab15e069fd59778914 SHA1: 87896a1ec5afab998e100184101ac3c0c96d0478 SHA256: 40a4dcdab833fdcdfc06dfe513af744a4acfa038849815f19c23b5f5ddb8cd90 SHA512: 31dcde1baaf7327a5492d61a47be6afbdb79c18902063dced8177e0cbc05bf9e6da189da81b1f0dbd4be1020c2bb512521c069dcd5773698becc7fc04fcd4c0e Homepage: https://cran.r-project.org/package=TICM Description: CRAN Package 'TICM' (Testing the Validity of the Independent Component ModelAssumption) Description: Provides affine-invariant, distribution-free tests of multivariate independence, applied either directly to observed data or to estimated independent components. 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Package: r-cran-tidyfast Architecture: amd64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3046 Depends: libc6 (>= 2.11), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-cpp11 Suggests: r-cran-covr, r-cran-dplyr, r-cran-magrittr, r-cran-remotes, r-cran-spelling, r-cran-testthat, r-cran-tidyr, r-cran-knitr Filename: pool/dists/noble/main/r-cran-tidyfast_0.4.0-1.ca2404.1_amd64.deb Size: 2183942 MD5sum: 4e09b7859bd6aa4eb5fc84d22b37dd5d SHA1: 25510e5fe308b935f53eb48cfc73b5fb820d5738 SHA256: 9ca1620fc0f7101c5f31124cf38cee3d889e22c2075dbe39715499bc3754686a SHA512: 5aadd0f7ce1f9ce6497b0493acdac7090182179c2610d07eb8956d99f8a71253193c27808af2cbb5010bc7e1ccad82de9d1fe120e1e5a24b75c0605446e75814 Homepage: https://cran.r-project.org/package=tidyfast Description: CRAN Package 'tidyfast' (Fast Tidying of Data) Tidying functions built on 'data.table' to provide quick and efficient data manipulation with minimal overhead. Package: r-cran-tidygenomics Architecture: amd64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-purrr, r-cran-tidyr, r-cran-fuzzyjoin, r-bioc-iranges, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tidygenomics_0.1.2-1.ca2404.1_amd64.deb Size: 220700 MD5sum: f5ec467b9fb513b3d7c9952ecb6e08ee SHA1: a20525e42b3b59afe98397a5247dba35d5eaf20b SHA256: b55cee24f580709129b3d48118c278d4ab59fa1840a9b3caee3c11d812c55170 SHA512: c275eb88723d295942f5321866f77823091e36bbc626848931dd4d91f345af5700507564f5af710c6bc93d4926cdc4b473cdb43ec31f8f594d0291f72922a4a3 Homepage: https://cran.r-project.org/package=tidygenomics Description: CRAN Package 'tidygenomics' (Tidy Verbs for Dealing with Genomic Data Frames) Handle genomic data within data frames just as you would with 'GRanges'. This packages provides method to deal with genomic intervals the "tidy-way" which makes it simpler to integrate in the the general data munging process. The API is inspired by the popular 'bedtools' and the genome_join() method from the 'fuzzyjoin' package. 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'tidygraph' provides an approach to manipulate these two virtual data frames using the API defined in the 'dplyr' package, as well as provides tidy interfaces to a lot of common graph algorithms. Package: r-cran-tidylda Architecture: amd64 Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1150 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-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/noble/main/r-cran-tidylda_0.0.7-1.ca2404.1_amd64.deb Size: 755548 MD5sum: aae8b94ac3122664335065dd24088ac4 SHA1: 4fa31f4824949afb650dd6cb177dfab01d43266a SHA256: e7391dd5d4703e5d65a7fea3e3b4481f8055378b744dccac5733e8726bf6f692 SHA512: 33a3e44e5762369f8bb3679c38f0e31f17558500df2944c412c1541ea76cfb35d630d942be8c4b3a7031fbe58e7ddc3709b546df7de4a05d64fc6c90e60b50d2 Homepage: https://cran.r-project.org/package=tidylda Description: CRAN Package 'tidylda' (Latent Dirichlet Allocation Using 'tidyverse' Conventions) Implements an algorithm for Latent Dirichlet Allocation (LDA), Blei et at. (2003) , using style conventions from the 'tidyverse', Wickham et al. (2019), and 'tidymodels', Kuhn et al.. Fitting is done via collapsed Gibbs sampling. Also implements several novel features for LDA such as guided models and transfer learning. Package: r-cran-tidynorm Architecture: amd64 Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2896 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-options, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-rcpparmadillo Suggests: r-cran-ggdensity, r-cran-ggplot2, r-cran-knitr, r-cran-magick, r-cran-quarto, r-cran-reticulate, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-tidynorm_0.4.1-1.ca2404.1_amd64.deb Size: 2085960 MD5sum: 77bb82f3de05ba54a956a7d03eb88fe3 SHA1: ff95d83ea0c929f66a54c2e1df9fe10706cbd024 SHA256: c2b4ae729da9fe626b181b7722ac9841d7719ed4f0a733fdb4f5297245aa6a23 SHA512: 2ed3d278d1966648f2a33599a286f6d9abcc3d024708596b038aec75fb1eecf9ac7a960405d0887dc01f93f7bbe9a4721a63460b868e136ca64213a4113f1093 Homepage: https://cran.r-project.org/package=tidynorm Description: CRAN Package 'tidynorm' (Tools for Tidy Vowel Normalization) An implementation of tidy speaker vowel normalization. This includes generic functions for defining new normalization methods for points, formant tracks, and Discrete Cosine Transform coefficients, as well as convenience functions implementing established normalization methods. References for the implemented methods are: Johnson, Keith (2020) Lobanov, Boris (1971) Nearey, Terrance M. (1978) Syrdal, Ann K., and Gopal, H. S. (1986) Watt, Dominic, and Fabricius, Anne (2002) . Package: r-cran-tidypopgen Architecture: amd64 Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4229 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-tidypopgen_0.4.4-1.ca2404.1_amd64.deb Size: 2868288 MD5sum: 796104d8b3e682abb37851707910ca8a SHA1: 014d059b00444ad0d8485bd53c64811ea00cb3f8 SHA256: 4bd38ff344217b1fa48ca46a905a3c87f7480f739f6489b8205f80ae18c548f1 SHA512: 9dd1f6b4dc9f5c8d701eb4971915e9aadc815c381d2433aef72521632f5203665ebbcd0d64a5fc0bfd9a18a6f6ea3387c6a2d9b915ccb5c5f0d4318943ac120f 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-timp Architecture: amd64 Version: 1.13.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1036 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields, r-cran-colorspace, r-cran-desolve, r-cran-gclus, r-cran-gplots, r-cran-minpack.lm, r-cran-nnls Filename: pool/dists/noble/main/r-cran-timp_1.13.6-1.ca2404.1_amd64.deb Size: 895388 MD5sum: ac6c59f81d334b74e66941f899dbacb4 SHA1: ce54bc4d253173910826053e547c9bad94723c6a SHA256: bdce994a79e0d4efb4a20643c41bf4cb5f96407e93693e068192594380558c94 SHA512: 37f07fffd3d39d0362c6024e99cfe7501c1f64c87c184d8d6de9760147bbde59eab5b2776499cb7ea5bd4d542ab7cc0be05a4f41a92939aa8eb01401edf88059 Homepage: https://cran.r-project.org/package=TIMP Description: CRAN Package 'TIMP' (Fitting Separable Nonlinear Models in Spectroscopy andMicroscopy) A problem solving environment (PSE) for fitting separable nonlinear models to measurements arising in physics and chemistry experiments, as described by Mullen & van Stokkum (2007) for its use in fitting time resolved spectroscopy data, and as described by Laptenok et al. 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Package: r-cran-tinyimg Architecture: amd64 Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1753 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testit Filename: pool/dists/noble/main/r-cran-tinyimg_0.4-1.ca2404.1_amd64.deb Size: 651750 MD5sum: 57ae3f8e46130d16149987a6800683f0 SHA1: cb6b3d3990d8fda7ecbd08a3f3cbc15b7eca4af6 SHA256: 71564cf493a2ba2ce0836a3b533adea57867410d986342c7b06e71452169bfa1 SHA512: 853232d97e22f2e04113a0f8698cb6dc5c27b1e8b25085727f3ba7dbe8c87201913f509729aca609d8d68edf8df0c88ce237293572ca329087a043c2a8b7267b Homepage: https://cran.r-project.org/package=tinyimg Description: CRAN Package 'tinyimg' (Optimize and Compress Images) Optimize and compress images using 'Rust' libraries to reduce file sizes while maintaining image quality. 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Package: r-cran-tinyvast Architecture: amd64 Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8428 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-corpcor, r-cran-fmesher, r-cran-igraph, r-cran-matrix, r-cran-mgcv, r-cran-sem, r-cran-sf, r-cran-sfnetworks, r-cran-tmb, r-cran-units, r-cran-checkmate, r-cran-abind, r-cran-sdmtmb, r-cran-dsem, r-cran-insight, r-cran-cv, r-cran-sparseinv, r-cran-gstat, r-cran-cli, r-cran-gpgp, r-cran-gpvecchia, r-cran-rcppeigen Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-lattice, r-cran-mvtnorm, r-cran-pdp, r-cran-rmarkdown, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-testthat, r-cran-tweedie, r-cran-viridislite, r-cran-visreg, r-cran-plyr, r-cran-dharma, r-cran-glmmtmb, r-cran-tibble, r-cran-rann Filename: pool/dists/noble/main/r-cran-tinyvast_1.6.0-1.ca2404.1_amd64.deb Size: 5029194 MD5sum: c76637544047a349b1e8d5dcc27391a7 SHA1: 3a3128fe4f0a437faf888ac3b15f6afb6772aca8 SHA256: d023e90142efb27b88e8444b663fea50299fca4be7677896ad8e2848180a6f1e SHA512: be0721acb1fd33d4cd402f92292880d28ea4cf418bb88cd0caa744136053329740600b061975b8984ca7dafbb945091837b9060c473b9507c64c0b3d2348c9bc Homepage: https://cran.r-project.org/package=tinyVAST Description: CRAN Package 'tinyVAST' (Multivariate Spatio-Temporal Models using Structural Equations) Fits a wide variety of multivariate spatio-temporal models with simultaneous and lagged interactions among variables (including vector autoregressive spatio-temporal ('VAST') dynamics) for areal, continuous, or network spatial domains. It includes time-variable, space-variable, and space-time-variable interactions using dynamic structural equation models ('DSEM') as expressive interface, and the 'mgcv' package to specify splines via the formula interface. See Thorson et al. (2025) for more details. Package: r-cran-tipitaka.critical Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5185 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-tipitaka.critical_1.0.0-1.ca2404.1_amd64.deb Size: 5124548 MD5sum: 012e67e913ddab166669ac7f9d67cd63 SHA1: 8e7d40435b589946b7dea380d18024e874b36f02 SHA256: 9ac51d7f809b228da6c0a05492ed08271e2d481a53de7217043ee9d0bdb6983f SHA512: 95c5edb31691b8525537604e8fb6115a52c7f1a12b67f1c23675e957a6146fbe1c4b0d898367b0ae735498898da40453234f6e08752f031f82ca38ce797556d3 Homepage: https://cran.r-project.org/package=tipitaka.critical Description: CRAN Package 'tipitaka.critical' (Lemmatized Critical Edition of the Pali Canon) A lemmatized critical edition of the complete Pali Canon (Tipitaka), the canonical scripture of Theravadin Buddhism. Based on a five-witness collation of the Pali Text Society (PTS) edition (via 'GRETIL'), 'SuttaCentral', the Vipassana Research Institute (VRI) Chattha Sangayana edition, the Buddha Jayanti Tipitaka (BJT), and the Thai Royal Edition. All text is lemmatized using the 'Digital Pali Dictionary', grouping inflected forms by dictionary headword. Covers all three pitakas (Sutta, Vinaya, Abhidhamma) with 5,777 individual text units. The companion package 'tipitaka' provides the original VRI edition data and Pali text tools. For background on the collation method, see Zigmond (2026) . Package: r-cran-tipitaka Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3107 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-cpp11 Suggests: r-cran-dplyr, r-cran-magrittr, r-cran-stringi Filename: pool/dists/noble/main/r-cran-tipitaka_1.0.0-1.ca2404.1_amd64.deb Size: 3059714 MD5sum: 6d065ec1b07d96e955431a8ebfcc4ebe SHA1: 4113b117637562f705c88bf8dea71f89e4681338 SHA256: 8df6777cbfb7947f04a86dd077c01ec9895f140cc809855a9f93256ae886ffdf SHA512: fded7bb3a4ebeea1256dee99bab1a590612c929d29984c7a6b6b79eb049277ee829000176250d50f8e83744de5934633fd28539bf4c0de36b9d9b116db8c35ee Homepage: https://cran.r-project.org/package=tipitaka Description: CRAN Package 'tipitaka' (Data and Tools for Analyzing the Pali Canon) Provides access to the complete Pali Canon, or Tipitaka, the canonical scripture for Theravadin Buddhists worldwide. Based on the Chattha Sangayana Tipitaka version 4 (Vipassana Research Institute, 1990). Includes word frequency data and tools for Pali string sorting. For a lemmatized critical edition with sutta-level granularity, see the companion package 'tipitaka.critical'. Package: r-cran-tips Architecture: amd64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2046 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-inline, r-cran-rcpp, r-cran-stringr, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ape, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tips_1.3.0-1.ca2404.1_amd64.deb Size: 1026514 MD5sum: d497d8e8ef008ac58ebc112e671d6e78 SHA1: 0d963b5eccb0eca2de8ff1caa6bffdf1582e33eb SHA256: 307deb78ed564addcbe6991d973059563fd7f360f1c1bf7c4c0a5087eddc583c SHA512: d50325d29607341f0141cce25e5e2c93ef0c82f89e6ba615ce362cbc1c70f61cad544fc1b9c2e1c4928706de2078e7a5c860306e74e1c1df845508df2c255a13 Homepage: https://cran.r-project.org/package=TiPS Description: CRAN Package 'TiPS' (Trajectories and Phylogenies Simulator) Generates stochastic time series and genealogies associated with a population dynamics model. Times series are simulated using the Gillespie exact and approximate algorithms and a new algorithm we introduce that uses both approaches to optimize the time execution of the simulations. Genealogies are simulated from a trajectory using a backwards-in-time based approach. Methods are described in Danesh G et al (2022) . Package: r-cran-tipsae Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6283 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-rcpp, r-cran-rstan, r-cran-ggplot2, r-cran-nlme, r-cran-sp, r-cran-ggpubr, r-cran-rdpack, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rstantools, r-cran-callr, r-cran-sf, r-cran-dplyr, r-cran-leaflet, r-cran-tmap, r-cran-spam, r-cran-spdep, r-cran-gridextra, r-cran-r.rsp, r-cran-shinythemes, r-cran-shinyfeedback, r-cran-shinybusy, r-cran-shinywidgets, r-cran-shinyjs, r-cran-bayesplot, r-cran-dt, r-cran-loo Filename: pool/dists/noble/main/r-cran-tipsae_1.0.3-1.ca2404.1_amd64.deb Size: 4174510 MD5sum: a78a771d8732e270f56f3f7a954e64bd SHA1: bd44bb9f5b5ad0bcfd2fc9f920f8951f58657799 SHA256: ba54ad5a927967d940c75a30532e4afd45ad7685fdb7e397458607b88a4a87c8 SHA512: efeb13e44f0c39254f334838c1bba2735a3f501d947469c769fecec5d9256a6c16d14f9a1594de511de980e717a381457332b4446fb528d8d6244799ea59449c 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. 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Package: r-cran-tlars Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2963 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-patchwork, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tlars_1.0.1-1.ca2404.1_amd64.deb Size: 1470732 MD5sum: fa57eb80eaaf5f4a60b9c38c64c718f4 SHA1: 28d30da040b1b9c603463c0db32c3f4ee63580d1 SHA256: 3e220ce7f6be19a05362b94e7e52aa5d8f9c896454ca35ac82f7ecbe11a22e3a SHA512: 3861d6601fab1f25d37d1c54c58446c3a091413cb706e69c823f49b44c24fe5e6d9c6f655ac0040d051c85bf82212d0c1d9c3c4bbb483f92222dbae4eb575d94 Homepage: https://cran.r-project.org/package=tlars Description: CRAN Package 'tlars' (The T-LARS Algorithm: Early-Terminated Forward VariableSelection) Computes the solution path of the Terminating-LARS (T-LARS) algorithm. The T-LARS algorithm is a major building block of the T-Rex selector (see R package 'TRexSelector'). The package is based on the papers Machkour, Muma, and Palomar (2022) , Efron, Hastie, Johnstone, and Tibshirani (2004) , and Tibshirani (1996) . Package: r-cran-tlmoments Architecture: amd64 Version: 0.7.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1458 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-hypergeo, r-cran-ggplot2, r-cran-lmomco Suggests: r-cran-evd, r-cran-knitr, r-cran-magrittr, r-cran-lmom, r-cran-lmoments, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tlmoments_0.7.5.3-1.ca2404.1_amd64.deb Size: 1230490 MD5sum: 5a12c81021ec49f73fb7046d20f68c8f SHA1: 30821030f7e6b55db0607fa5010d7277c9e15360 SHA256: 96d20940810162f7e7f954ba75f078e6dce9189f1e8ddb0dcd5fca0efb008e3a SHA512: 8e7715d3f7207cbda9459e87da17c8268e1e5a4404db681326e47375a48d051d9f9222e0ab6aa4925c31c1c4bb1e26d0dc3b8bbcf087f78c8409a66e35fbf1ef Homepage: https://cran.r-project.org/package=TLMoments Description: CRAN Package 'TLMoments' (Calculate TL-Moments and Convert Them to Distribution Parameters) Calculates empirical TL-moments (trimmed L-moments) of arbitrary order and trimming, and converts them to distribution parameters. Package: r-cran-tlrmvnmvt Architecture: amd64 Version: 1.1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 422 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-tlrmvnmvt_1.1.2.1-1.ca2404.1_amd64.deb Size: 175028 MD5sum: f0f73fafb0671b239ef0f00968742084 SHA1: d872da63a79857eae956a84696f30354741e5884 SHA256: 5ef45d239cd6468fdf9f910c6f931b79243762aed81babe27387f9a4b99b0e7a SHA512: 9f7c39e0a383395d0daf51bc34d55871785f8a034f37f87c124f9887335c9ed97c15d45ecf09e2237bc16df637185e43bdfe43bb30916a9fe9dc88ad2c303e5e Homepage: https://cran.r-project.org/package=tlrmvnmvt Description: CRAN Package 'tlrmvnmvt' (Low-Rank Methods for MVN and MVT Probabilities) Implementation of the classic Genz algorithm and a novel tile-low-rank algorithm for computing relatively high-dimensional multivariate normal (MVN) and Student-t (MVT) probabilities. References used for this package: Foley, James, Andries van Dam, Steven Feiner, and John Hughes. "Computer Graphics: Principle and Practice". Addison-Wesley Publishing Company. Reading, Massachusetts (1987, ISBN:0-201-84840-6 1); Genz, A., "Numerical computation of multivariate normal probabilities," Journal of Computational and Graphical Statistics, 1, 141-149 (1992) ; Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. "Exploiting Low Rank Covariance Structures for Computing High-Dimensional Normal and Student- t Probabilities," Statistics and Computing, 31.1, 1-16 (2021) ; Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. "tlrmvnmvt: Computing High-Dimensional Multivariate Normal and Student-t Probabilities with Low-Rank Methods in R," Journal of Statistical Software, 101.4, 1-25 (2022) . Package: r-cran-tm Architecture: amd64 Version: 0.7-18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 995 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlp, r-cran-rcpp, r-cran-slam, r-cran-xml2, r-cran-bh Suggests: r-cran-antiword, r-cran-filehash, r-cran-pdftools, r-bioc-rgraphviz, r-cran-rpoppler, r-cran-snowballc, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tm_0.7-18-1.ca2404.1_amd64.deb Size: 618276 MD5sum: 39204e4b86e3c294c1a7393b4c69642d SHA1: 583f48fb95997bde074a2c546f7372153cbb4431 SHA256: 1017eb4872e5faa44adc2523e139fecd6ede8a91c55a6d022dd4e1ac15f6eedf SHA512: 35bfad73402ac5484e49ec847900051382b7185fc496927779b3d6bda861bd5ca7a24f5ac44b7a080115291c2e53e03828daa509ae0b1268db3ab9cb8bde6fa8 Homepage: https://cran.r-project.org/package=tm Description: CRAN Package 'tm' (Text Mining Package) A framework for text mining applications within R. Package: r-cran-tma Architecture: amd64 Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1073 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rlang, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-rstudioapi, r-cran-knitr, r-cran-rmarkdown, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-tma_0.3.1-1.ca2404.1_amd64.deb Size: 856630 MD5sum: 6886186333e71be1308291e1411599c0 SHA1: 3b591979f3293b3869119c504d6ae62784fa1fd6 SHA256: ad37a8ae394b89e085a0ed5534602e765c2e934001e918a6cddd8c548914cb84 SHA512: c31122eeb1216bb8f8f4fbe3fa98434648ce63316e89d228be7fc195d090331591aa3d6a917e2a1fcfc51d6a914fc2fefe192e8951e11b3eb5119298e6c41277 Homepage: https://cran.r-project.org/package=tma Description: CRAN Package 'tma' (Transmodal Analysis (TMA)) A robust computational framework for analyzing complex multimodal data. Extends existing state-dependent models to account for diverse data streams, addressing challenges such as varying temporal scales and learner characteristics to improve the robustness and interpretability of findings. For methodological details, see Shaffer, Wang, and Ruis (2025) "Transmodal Analysis" . Package: r-cran-tmb Architecture: amd64 Version: 1.9.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3649 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcppeigen Suggests: r-cran-numderiv Filename: pool/dists/noble/main/r-cran-tmb_1.9.21-1.ca2404.1_amd64.deb Size: 828012 MD5sum: f07f04cd597fce7aac136b23a6e765f8 SHA1: 07f9d95f3dc46dd9554bda23d519a1b36f4a862e SHA256: d77fc78ce7134889049271969d081c2a2dc055cdf8e8ca8cb0f65ceecd38387b SHA512: 8eed85ee8822a806efc1b99180b61be342caed02b950d5aed5863d3f8f79d76201ead47d997d70e304edfb9be0058a6ddd7d18beaeba94d72040a793864bd890 Homepage: https://cran.r-project.org/package=TMB Description: CRAN Package 'TMB' (Template Model Builder: A General Random Effect Tool Inspired by'ADMB') With this tool, a user should be able to quickly implement complex random effect models through simple C++ templates. The package combines 'CppAD' (C++ automatic differentiation), 'Eigen' (templated matrix-vector library) and 'CHOLMOD' (sparse matrix routines available from R) to obtain an efficient implementation of the applied Laplace approximation with exact derivatives. Key features are: Automatic sparseness detection, parallelism through 'BLAS' and parallel user templates. Package: r-cran-tmbstan Architecture: amd64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1360 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-rcpp, r-cran-tmb, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-rtmb Filename: pool/dists/noble/main/r-cran-tmbstan_1.1.0-1.ca2404.1_amd64.deb Size: 509910 MD5sum: 0ff3691a0319de76cfc44fa49c23cd4f SHA1: 42f8133aea94183ccfde6444c188c09d6d0b4bec SHA256: 24ec8577f2c0cf6c62b5a5ab55ca29bcf1349be9cba78aa40340f51beca01e2d SHA512: 3f4472a33cc84b5b784227db5c72b839d8b03fa0cac11cb92d856a11af03dbde9c7071508849c3ee1c648fa28825c6863e52747f5e76e421989850a352547810 Homepage: https://cran.r-project.org/package=tmbstan Description: CRAN Package 'tmbstan' (MCMC Sampling from 'TMB' Model Object using 'Stan') Enables all 'rstan' functionality for a 'TMB' model object, in particular MCMC sampling and chain visualization. Sampling can be performed with or without Laplace approximation for the random effects. This is demonstrated in Monnahan & Kristensen (2018) . Package: r-cran-tmcn Architecture: amd64 Version: 0.2-13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1053 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tm Filename: pool/dists/noble/main/r-cran-tmcn_0.2-13-1.ca2404.1_amd64.deb Size: 1026552 MD5sum: cd7781a6811443f3356595182eddbcd0 SHA1: 56ca89a4d2654fa5dc5e2995cce717e934266c2a SHA256: 2b51b8ec7e8d13a760dddf52a2f7be0c9fccf65985fa9c65e081b683491525da SHA512: 5fc9a870d7b7b2ad5a1cee8bf9200f22b4c623e2b46f56ec43fdc335123341c9cd082819ab849f70416a90b36343908f0d95c36232530a11a6f0bc089f518226 Homepage: https://cran.r-project.org/package=tmcn Description: CRAN Package 'tmcn' (A Text Mining Toolkit for Chinese) A Text mining toolkit for Chinese, which includes facilities for Chinese string processing, Chinese NLP supporting, encoding detecting and converting. Moreover, it provides some functions to support 'tm' package in Chinese. Package: r-cran-tmt Architecture: amd64 Version: 0.3.6-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1250 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp, r-cran-rlang Suggests: r-cran-roxygen2, r-cran-erm, r-cran-knitr, r-cran-prettydoc, r-cran-psychotools, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tmt_0.3.6-4-1.ca2404.1_amd64.deb Size: 921264 MD5sum: dff8d025961a290c755d641f62118bed SHA1: 6719b2c02d62cf73af3eb283d345389115c6f8bb SHA256: 76c972aba5d76b9eccd64fd71248b0d1567539c81fb032c70627c32aefe6407f SHA512: 1319da9f03cfb663226002907d9b87a0e973cdfb6ade7ce915bcbc2ac62d2da8f814317f88c90069b544f383dd5c7fb18a80f03fe6e7f9ba9f1af7d0cfb3dafb Homepage: https://cran.r-project.org/package=tmt Description: CRAN Package 'tmt' (Estimation of the Rasch Model for Multistage Tests) Provides conditional maximum likelihood (CML) item parameter estimation of both sequential and cumulative deterministic multistage designs (Zwitser & Maris, 2015, ) and probabilistic sequential and cumulative multistage designs (Steinfeld & Robitzsch, 2024, ). Supports CML item parameter estimation of conventional linear designs and additional functions for the likelihood ratio test (Andersen, 1973, ) as well as functions for simulating various types of multistage designs. Package: r-cran-tmti Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-tmti_1.0.3-1.ca2404.1_amd64.deb Size: 159688 MD5sum: a410fb017b569b2d351b28c446e3a770 SHA1: 372257ffd59046f623d1920618035f449d9769e1 SHA256: 38cdb5a1bd8e79e69cba3d8715816331332a892893e8dda594ef838eeb9fd4a6 SHA512: 1432457b8819c564d63829691a2133e02e907412acb34a8b51fce4deb3b1e189b5d0b45260197998d29286b5fb2d537ce4e5e136bb1381979f9988d04a8471a5 Homepage: https://cran.r-project.org/package=TMTI Description: CRAN Package 'TMTI' (Too Many, Too Improbable (TMTI) Test Procedures) Methods for computing joint tests, controlling the Familywise Error Rate (FWER) and getting lower bounds on the number of false hypotheses in a set. The methods implemented here are described in Mogensen and Markussen (2021) . Package: r-cran-tmvnsim Architecture: amd64 Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tmvnsim_1.0-2-1.ca2404.1_amd64.deb Size: 20046 MD5sum: 90cc218a955f8265721ddb2b844b832e SHA1: f92b3ad72fe1a74b53fb2db9f950640852b49c86 SHA256: 5e37b547ddb4f7e8df5a7a3e7f75d9c819c1c0d06bfd955a94dd5015d93e1caf SHA512: 67f5656fcad5014d335de787b931c5b4dde1e811da7f0b107ba9670548630b7eb709f55d186da7017332f086287892f818082c1ab5af3727c9d307c0b738ea1f Homepage: https://cran.r-project.org/package=tmvnsim Description: CRAN Package 'tmvnsim' (Truncated Multivariate Normal Simulation) Importance sampling from the truncated multivariate normal using the GHK (Geweke-Hajivassiliou-Keane) simulator. Unlike Gibbs sampling which can get stuck in one truncation sub-region depending on initial values, this package allows truncation based on disjoint regions that are created by truncation of absolute values. The GHK algorithm uses simple Cholesky transformation followed by recursive simulation of univariate truncated normals hence there are also no convergence issues. Importance sample is returned along with sampling weights, based on which, one can calculate integrals over truncated regions for multivariate normals. Package: r-cran-tmvtnorm Architecture: amd64 Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 638 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-matrix, r-cran-gmm Suggests: r-cran-lattice, r-cran-rgl Filename: pool/dists/noble/main/r-cran-tmvtnorm_1.7-1.ca2404.1_amd64.deb Size: 572544 MD5sum: a13dd988f2b7e9a332cb3ade697e2c40 SHA1: d8e9ea5e1f98e776119a43f8add19dec2cffbe65 SHA256: 8d16e24ee5d9b107d5da528d9113e593beb6afca44bd98929a2a8003ca167558 SHA512: b064d115734459b9aaf35ac6dd6aaa209c497801e0c27d6f623835c5e7b0d382884bf5f8fac3ecffd79aec460e1653a190ce30ae29d78925bbbe08661d5dec64 Homepage: https://cran.r-project.org/package=tmvtnorm Description: CRAN Package 'tmvtnorm' (Truncated Multivariate Normal and Student t Distribution) Random number generation for the truncated multivariate normal and Student t distribution. Computes probabilities, quantiles and densities, including one-dimensional and bivariate marginal densities. Computes first and second moments (i.e. mean and covariance matrix) for the double-truncated multinormal case. 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Ghosh (2015) . Package: r-cran-tok Architecture: amd64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5396 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-cli Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-hfhub, r-cran-withr Filename: pool/dists/noble/main/r-cran-tok_0.2.2-1.ca2404.1_amd64.deb Size: 2069382 MD5sum: c0ec8c33229adb5110b7cfc49b66fa9a SHA1: ef395fc6ae992fecf51e65e80b9ed8f4b710bb7d SHA256: 336867da9dc75fbf33774a7328b907aee8c3b49182bf014de647a5baa9676d71 SHA512: 307d8fcabf0249e1a9254c6b83e3705061fcbd532f5fbf21f558eec28deeb9c1a7cfb2bcdce09a8378776cba6f84148e571bf96155e71fb6381172c7af46bc3c Homepage: https://cran.r-project.org/package=tok Description: CRAN Package 'tok' (Fast Text Tokenization) Interfaces with the 'Hugging Face' tokenizers library to provide implementations of today's most used tokenizers such as the 'Byte-Pair Encoding' algorithm . It's extremely fast for both training new vocabularies and tokenizing texts. 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Package: r-cran-trackdem Architecture: amd64 Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1013 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-trackdem_0.7.2-1.ca2404.1_amd64.deb Size: 593834 MD5sum: aff9085ff8371015ab5d391b6c8d3933 SHA1: 19591d491201de44bb0e1c4e44a75dbb40a64229 SHA256: 410527d97da825d113e382ad645e66053cc1dd5573d7af2152ebd9e34799f9ec SHA512: 3075621dee8c566357611cedfc8b549302e53fc06a12001258e716358f2df61f20e3a9b33d9c21bb0b09898655bd000dc921c91bb9f6d60bcf8fc59a7cdb75f1 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: amd64 Version: 0.11.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3022 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-minpack.lm, r-cran-numderiv, r-cran-ucminf, r-cran-mass, r-cran-capushe, r-cran-rcpparmadillo Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-trajer_0.11.1-1.ca2404.1_amd64.deb Size: 1310880 MD5sum: 8091fb0a2d553e6a368fb64bfea51796 SHA1: 8e7a8339c58eaeecd90c91058f2557acae6fba78 SHA256: 4a50f3afda1c576beabee8461e5bbd23b499ba77376965b48bc94fe6a1d96549 SHA512: 7ba847a302387c346db6734243cab8e7e461b08119af10d08d7c87f5fb26dd024172ef4c204c7d1462397dbe7b5e895e1320b249e25e69bb18073acc8fa937db 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: amd64 Version: 2.2-13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2663 Depends: libc6 (>= 2.14), 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/noble/main/r-cran-traminer_2.2-13-1.ca2404.1_amd64.deb Size: 2231014 MD5sum: 6a606f94e1d242e7cddde24dee8bd178 SHA1: 1c56301727acb5ecaff2630aac05b141711f06a6 SHA256: 6cfa4314617267c88ca9e5add4fa39f3674573e91077e54f8b5cfa78fd3e4a9a SHA512: db5b56e7febfd2f3708da958da025f72ca77f4bbc9aec184d69a6f50295f2795396fca6d2f68fd54ccad657fc13d681deb26f88fa844449650df537d81656faf 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: amd64 Version: 0.6.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: r-base-core (>= 4.4.0), 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/noble/main/r-cran-traminerextras_0.6.8-1.ca2404.1_amd64.deb Size: 413180 MD5sum: f30439691a0ebd7318590cdec5d1893c SHA1: df35272b9f98ebcb86d202ff42129bb82486801f SHA256: 8d9a0b1e679b07f967baffc157ca9efdb171eadceac01e394e7fed1365e98d57 SHA512: a032ef83b14e869e170dbf28f34ed04f4971bcead40998462a6835dffe9b9d35b74f7a05f2ccc7e1462e15fb76203705a4ba817f018b59ca6e50295986bc0599 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. 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Package: r-cran-tramme Architecture: amd64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4417 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-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/noble/main/r-cran-tramme_1.0.8-1.ca2404.1_amd64.deb Size: 3187632 MD5sum: 16f236e8b0f3dcba699f0010bf8c497d SHA1: ada9317ae41ab8fdef468b91dc7e0ca974d8f255 SHA256: fcfcd38ab9975f6e89eea13ab96680253d3024f556faf5c645690e4e7cf3567c SHA512: dc0fb035f0a97540cafd1d8122d90941457c68a8b46e1a92919664429a4c9ef565570d04d84d80cc07855565341ed4f746f8b9da48797fd3d96462d8a9eaeab3 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-transda Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-transda_1.0.0-1.ca2404.1_amd64.deb Size: 70346 MD5sum: 6f6f924c930883c87bbea0ca42b7572e SHA1: f098735d21e751e6ecadbb5652369286060eb269 SHA256: d9ccd2466ea772ea271c557d951d0ec1e0a474b7ccef9f85be72fc8169645dd6 SHA512: 5db82fa96b2fd80f375820e02ea41dbaf8b5b223b6ab046bc68689f7c6b67f0dfd318ec72cbb471c4121f4b2140be5bff0b2126c28078d738509f39b15b6ac49 Homepage: https://cran.r-project.org/package=transDA Description: CRAN Package 'transDA' (Transformation Discriminant Analysis) Performs transformation discrimination analysis and non-transformation discrimination analysis. It also includes functions for Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Mixture Discriminant Analysis. 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Inverse modelling enables to estimate net rainfall from streamflow measurements following Boudhraâ et al. (2018) . Resulting net rainfall is then estimated on the ungauged catchments by spatial interpolation in order to finally simulate streamflow following de Lavenne et al. (2016) . 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C++ functions are used to compute complex loops. The coefficient vector and cumulative baseline hazard function can be estimated, along with the corresponding standard errors and P values. 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Package: r-cran-transurv Architecture: amd64 Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rootsolve, r-cran-truncsp, r-cran-survival, r-cran-squarem Suggests: r-cran-mass, r-cran-boot Filename: pool/dists/noble/main/r-cran-transurv_1.2.4-1.ca2404.1_amd64.deb Size: 178582 MD5sum: 51282421f04b3fba99cdf64506d0a9d9 SHA1: d5ff9b64b2e0ee75680672beed849f40f1188b8a SHA256: 1e83f727858c6349e6f61a10be3619a7cf6366c98cd963353b02f54d829f28f9 SHA512: 73d0b2325a00bd03f18f2804618afa830e50b1ef2bea01e3d12a134473386b68083c98ce3a0f07b7e1950bf39888beb3a738a3f93e92b01eeb01e3f1afef7efc 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. 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Truncated rank correlation as a robust measure of test-retest reliability in mass spectrometry data. For more details see Lim et al. (2019) . Package: r-cran-treats Architecture: amd64 Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-disprity, r-cran-geiger, r-cran-mass, r-cran-rgl Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-treats_1.1.6-1.ca2404.1_amd64.deb Size: 305764 MD5sum: 3479bb57f42134e4a762c5d36aa3055e SHA1: 819d6f87dcbe36ee95d2ccc9aa6b987f0c3b1720 SHA256: 4e522614534dba68a94586c7314c7e27f27f04d5fb4796be608148e491e86a64 SHA512: 0420d90309f7233bce3befb917c5c79100cdad1e4dcb205f541865444b124290086866abed3fcaf9a592d106e4e18f95d386aca2aa8c1e40e4ceaa34c31d01ff Homepage: https://cran.r-project.org/package=treats Description: CRAN Package 'treats' (Trees and Traits Simulations) A modular package for simulating phylogenetic trees and species traits jointly. 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Package: r-cran-tree.interpreter Architecture: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-mass, r-cran-randomforest, r-cran-ranger, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-tree.interpreter_0.1.3-1.ca2404.1_amd64.deb Size: 116634 MD5sum: 9d79314e54d35f90b27ada780c173450 SHA1: 2fe9d6c4408818c4088c3161e4d19a0dc64a083f SHA256: 559bdf29238a89c52c61ceb82264ec6e62d491c2745b6b12d1e9b37707400140 SHA512: f28000ab4258b11dc9b7e2983a9cfa519b320fb86b3eac30bc497f4726a965871dc1ef75ade96c3938be71fdc9719347cc60edcfde4f55cad9848e265f2a2c4c Homepage: https://cran.r-project.org/package=tree.interpreter Description: CRAN Package 'tree.interpreter' (Random Forest Prediction Decomposition and Feature ImportanceMeasure) An R re-implementation of the 'treeinterpreter' package on PyPI . Each prediction can be decomposed as 'prediction = bias + feature_1_contribution + ... + feature_n_contribution'. This decomposition is then used to calculate the Mean Decrease Impurity (MDI) and Mean Decrease Impurity using out-of-bag samples (MDI-oob) feature importance measures based on the work of Li et al. (2019) . Package: r-cran-tree Architecture: amd64 Version: 1.0-45-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: libc6 (>= 2.3.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass, r-cran-islr2 Filename: pool/dists/noble/main/r-cran-tree_1.0-45-1.ca2404.1_amd64.deb Size: 153268 MD5sum: 6459a261b431e3b73586aa1b0cf0ff61 SHA1: ae9cb9d86dad4a094dedc788a72c1b3bdd5388e9 SHA256: afda054264232ca9db8b887058293f8be3330df2cd3c647aebe82e602b631b1c SHA512: 235d0f5ab9bcf91a5b2b8c22d39365098b0649388c10a83bca69c322877cf8a90185ed23753a88c3c359e986bb215b4abaebba05a768c5eec7d7ef7d0b2f5b28 Homepage: https://cran.r-project.org/package=tree Description: CRAN Package 'tree' (Classification and Regression Trees) Classification and regression trees. Package: r-cran-treebugs Architecture: amd64 Version: 1.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1612 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-runjags, r-cran-rjags, r-cran-coda, r-cran-hypergeo, r-cran-logspline, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-treebugs_1.5.3-1.ca2404.1_amd64.deb Size: 1280076 MD5sum: 818eed23ba0ac7df67de1ec69c2a3839 SHA1: d3b0843077aece392e529728b96117be5e02bda9 SHA256: 0d50f8adc65269a4c603bf9f3f895b8791c8ca7c5dbe0bdd475aa874741b6c84 SHA512: a97e8311d8009ec24aa43fd3792e5db31d9744068b71385a3574273641e6d2ecf69167ea54caf2960ffd350e36c320981e8bd9657cd583af74a29aaa3a922753 Homepage: https://cran.r-project.org/package=TreeBUGS Description: CRAN Package 'TreeBUGS' (Hierarchical Multinomial Processing Tree Modeling) User-friendly analysis of hierarchical multinomial processing tree (MPT) models that are often used in cognitive psychology. Implements the latent-trait MPT approach (Klauer, 2010) and the beta-MPT approach (Smith & Batchelder, 2010) to model heterogeneity of participants. MPT models are conveniently specified by an .eqn-file as used by other MPT software and data are provided by a .csv-file or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides tests of heterogeneity and MPT-tailored summaries and plotting functions. A detailed documentation is available in Heck, Arnold, & Arnold (2018) and a tutorial on MPT modeling can be found in Schmidt, Erdfelder, & Heck (2023) . Package: r-cran-treeclim Architecture: amd64 Version: 2.0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 548 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-abind, r-cran-plyr, r-cran-ggplot2, r-cran-lmtest, r-cran-lmodel2, r-cran-np, r-cran-boot, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-treeclim_2.0.7.1-1.ca2404.1_amd64.deb Size: 337376 MD5sum: 4eab183e46bb8e28c34c12cdff3d8120 SHA1: 5ac9c71338e2a637b6ac9b7a9e792100b9dacf7a SHA256: 63cf41c0a7c7138f5dd13bc1b1e6944fbbd5ea4b95aab11095ab3ef84719a92f SHA512: dc25f155b429c02a695cfbb804e41737b0fd73a422a343a408260fa1c1fe572a0f55ce44c50163546b4ecb8d4b9158b6318d405bb09086d22110bf5940c4a015 Homepage: https://cran.r-project.org/package=treeclim Description: CRAN Package 'treeclim' (Numerical Calibration of Proxy-Climate Relationships) Bootstrapped response and correlation functions, seasonal correlations and evaluation of reconstruction skills for use in dendroclimatology and dendroecology, see Zang and Biondi (2015) . Package: r-cran-treedimensiontest Architecture: amd64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1848 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlpack, r-cran-fitdistrplus, r-cran-igraph, r-cran-nfactors, r-cran-rcpp, r-cran-rcolorbrewer, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-treedimensiontest_0.0.2-1.ca2404.1_amd64.deb Size: 1575876 MD5sum: c8b613b3cd68ea4c20f3b3abaf875c34 SHA1: f7ac7a3c90a41c056aed08c5f3f30cc14dddabb8 SHA256: 1b426ccb2cf1842c9aacb2e6c2e3bad3d5fde0c71b371c6f3ce513123b213027 SHA512: 1945090b2e8ddaa2c99539c81a5a02641e52b73c3802f24fd6e13a62b9763d9ce3395f9d3dad4eae543b1b8d989b85ae258ebd5c1f934f86f1353c253ba5277e Homepage: https://cran.r-project.org/package=TreeDimensionTest Description: CRAN Package 'TreeDimensionTest' (Trajectory Presence and Heterogeneity in Multivariate Data) Testing for trajectory presence and heterogeneity on multivariate data. Two statistical methods (Tenha & Song 2022) are implemented. The tree dimension test quantifies the statistical evidence for trajectory presence. The subset specificity measure summarizes pattern heterogeneity using the minimum subtree cover. There is no user tunable parameters for either method. Examples are included to illustrate how to use the methods on single-cell data for studying gene and pathway expression dynamics and pathway expression specificity. Package: r-cran-treedist Architecture: amd64 Version: 2.14.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2677 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-cli, r-cran-colorspace, r-cran-rdpack, r-cran-shiny, r-cran-shinyjs, r-cran-treetools, r-cran-rcpp Suggests: r-cran-bookdown, r-cran-cluster, r-cran-ggplot2, r-cran-hypervolume, r-cran-kdensity, r-cran-knitr, r-cran-mass, r-cran-phangorn, r-cran-plotly, r-cran-plottools, r-cran-protoclust, r-cran-quartet, r-cran-readxl, r-cran-rmarkdown, r-cran-rgl, r-cran-rogue, r-cran-spelling, r-cran-tbrdist, r-cran-testthat, r-cran-ternary, r-cran-treesearch, r-cran-umatrix, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-treedist_2.14.0-1.ca2404.1_amd64.deb Size: 1437866 MD5sum: d974fda190cc958047adeedf0a59c260 SHA1: a3044df551cb06c6f359ecdec4f13e0052964ffb SHA256: 3eaaf0653f3c2e10ab175f9e01ab2a374e218f1fad155ecc6e00c888d927a959 SHA512: 09fbda3c97a56edd0b0d433993b04ad0b9c28861ce252d6b6801270dc71e375f310d632437c7c4d907e726f0db0797ab12c95c85eb0edfc356c981fa2f62c7f1 Homepage: https://cran.r-project.org/package=TreeDist Description: CRAN Package 'TreeDist' (Calculate and Map Distances Between Phylogenetic Trees) Implements measures of tree similarity, including information-based generalized Robinson-Foulds distances (Phylogenetic Information Distance, Clustering Information Distance, Matching Split Information Distance; Smith 2020) ; Jaccard-Robinson-Foulds distances (Bocker et al. 2013) , including the Nye et al. (2006) metric ; the Matching Split Distance (Bogdanowicz & Giaro 2012) ; the Hierarchical Mutual Information (Perotti et al. 2015) ; Maximum Agreement Subtree distances; the Kendall-Colijn (2016) distance , and the Nearest Neighbour Interchange (NNI) distance, approximated per Li et al. (1996) . Includes tools for visualizing mappings of tree space (Smith 2022) , for identifying islands of trees (Silva and Wilkinson 2021) , for calculating the median of sets of trees, and for computing the information content of trees and splits. Package: r-cran-treenomial Architecture: amd64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 602 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ape, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-treenomial_1.1.4-1.ca2404.1_amd64.deb Size: 227882 MD5sum: fb39becf42dcb120d29399ae578968ab SHA1: 09c54d785ce9e8fb87535250e7db2af36fdc65d4 SHA256: dce3a414ebcc865cef0e6619743fc641d7cac73f775f86e0c1044de032d44e10 SHA512: 99317234759fba58a0da696dd0b25a0d0d6c4b993dbb6fccaf44cbd4d482d7ac5d874104dde7b80ac40984a447f515f235510f6a5189f9553b3eb5692eb60dd2 Homepage: https://cran.r-project.org/package=treenomial Description: CRAN Package 'treenomial' (Comparison of Trees using a Tree Defining Polynomial) Provides functionality for creation and comparison of polynomials that uniquely describe trees as introduced in Liu (2019, ). The core method converts rooted unlabeled phylo objects from 'ape' to the tree defining polynomials described with coefficient matrices. Additionally, a conversion for rooted binary trees with binary trait labels is also provided. Once the polynomials of trees are calculated there are functions to calculate distances, distance matrices and plot different distance trees from a target tree. Manipulation and conversion to the tree defining polynomials is implemented in C++ with 'Rcpp' and 'RcppArmadillo'. Furthermore, parallel programming with 'RcppThread' is used to improve performance converting to polynomials and calculating distances. Package: r-cran-treesearch Architecture: amd64 Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4849 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-cli, r-cran-cluster, r-cran-fastmap, r-cran-fastmatch, r-cran-fs, r-cran-future, r-cran-plottools, r-cran-promises, r-cran-protoclust, r-cran-rcpp, r-cran-rdpack, r-cran-rogue, r-cran-shiny, r-cran-shinyjs, r-cran-stringi, r-cran-treedist, r-cran-treetools Suggests: r-cran-knitr, r-cran-phangorn, r-cran-quartet, r-cran-readxl, r-cran-rmarkdown, r-cran-shinytest, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-treesearch_1.7.0-1.ca2404.1_amd64.deb Size: 2496238 MD5sum: e401483d4f282d6d1cb20aa86d175ba3 SHA1: 065fc9d326968895af2504b49beee872e70bed27 SHA256: f2dbb96442667e6be07070c493f4d7b3a0c58bbbe5136777e445b71ba2e317cb SHA512: 218548c2bcea3771eb0c8874013d99d2ecfe8df0f1d673e2e06c12f3ad00195806f38f43603e2622eaf07076f312d1fac30a51da1dd7fb07499559822741338c Homepage: https://cran.r-project.org/package=TreeSearch Description: CRAN Package 'TreeSearch' (Phylogenetic Analysis with Discrete Character Data) Reconstruct phylogenetic trees from discrete data. Inapplicable character states are handled using the algorithm of Brazeau, Guillerme and Smith (2019) with the "Morphy" library, under equal or implied step weights. Contains a "shiny" user interface for interactive tree search and exploration of results, including character visualization, rogue taxon detection, tree space mapping, and cluster consensus trees (Smith 2022a, b) , . Profile Parsimony (Faith and Trueman, 2001) , Successive Approximations (Farris, 1969) and custom optimality criteria are implemented. Package: r-cran-treeshap Architecture: amd64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1364 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-gbm, r-cran-jsonlite, r-cran-lightgbm, r-cran-gpboost, r-cran-randomforest, r-cran-ranger, r-cran-scales, r-cran-survival, r-cran-testthat, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-treeshap_0.4.0-1.ca2404.1_amd64.deb Size: 1209688 MD5sum: 0cbf0df68773c270577d3b5fbfe3ca7e SHA1: 6d563d5978f22c3a73267f0ba4c6c10eb533743e SHA256: 885e3f7da1025478b36d3c181505384f16015208d4c8e9f6415e9d271997d8e1 SHA512: bd0011c4bcb0e531766a60d2722d62ea80013bd4f5f516cc0e9dd266c9cdd613df8859981a68bf5363b2bd3ef27638761b6d8a4a798b02a6453cf21424daa19d Homepage: https://cran.r-project.org/package=treeshap Description: CRAN Package 'treeshap' (Compute SHAP Values for Your Tree-Based Models Using the'TreeSHAP' Algorithm) An efficient implementation of the 'TreeSHAP' algorithm introduced by Lundberg et al., (2020) . 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Package: r-cran-treesitter.c Architecture: amd64 Version: 0.0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 903 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-treesitter Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-treesitter.c_0.0.4.2-1.ca2404.1_amd64.deb Size: 175508 MD5sum: 27431fdedb5c36a63a07a119e089db8b SHA1: 2ce673ed7ab2abc1b95197fd47989f8101c9b9b6 SHA256: f78d78f735602a4131b51ba3dbe4b3f888a417811ab47040f562eb8623571da7 SHA512: 58d8e3c1ebe54fa6e77c84c078fb8634612db399ec8bb955b3eed1db02a6f7f2403913d3757711a83f4be1ac5ef461ce54550ad4b790be122cc5ae9e48001129 Homepage: https://cran.r-project.org/package=treesitter.c Description: CRAN Package 'treesitter.c' ('R' Bindings to the 'C' Grammar for Tree-Sitter) Provides bindings to a 'C' grammar for Tree-sitter, to be used alongside the 'treesitter' package. Tree-sitter builds concrete syntax trees for source files and can efficiently update them or generate code like producing R C API wrappers from C functions, structs and global definitions from header files. 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'Tree-sitter' builds concrete syntax trees for source files of any language, and can efficiently update those syntax trees as the source file is edited. 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This package includes a 'shiny' interface which can be started from R using treespaceServer(). For further details see Jombart et al. (2017) . Package: r-cran-treess Architecture: amd64 Version: 0.1.44-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1260 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/noble/main/r-cran-treess_0.1.44-1.ca2404.1_amd64.deb Size: 1066086 MD5sum: f117ccbd073277ee37ee744275f0dcd7 SHA1: e1e4b02be9f6539b15e1d61a5af09ab83c210d75 SHA256: 0722a9a5bb448cf82f57e395a59d4f49b0661569b53ce5e56fb6df00e1fe8ef9 SHA512: ba61038b79fb6b7227effb3ceefc89364616fd66e4217c531a396d2295cd739fdb5394c3da10ef54a10bcc5e65e2897e5598aff5243e6ecaddc1d118593d4db3 Homepage: https://cran.r-project.org/package=treeSS Description: CRAN Package 'treeSS' (Tree-Spatial Scan Statistic for Cluster Detection) Implements the tree-spatial scan statistic for detecting clusters that combine both spatial and hierarchical structures, as proposed by Cancado et al. (2025) . The method extends Kulldorff (1997) circular spatial scan statistic and the tree-based scan statistic of Kulldorff et al. (2003) by searching for anomalies in both geographic regions and branches of hierarchical trees simultaneously. The package also provides standalone implementations of Kulldorff's circular spatial scan statistic and the tree-based scan statistic. Statistical significance is assessed via Monte Carlo simulation under a Poisson or binomial model, with optional 'OpenMP' parallelization. Package: r-cran-treestats Architecture: amd64 Version: 1.70.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8144 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-ape, r-cran-nloptr, r-cran-treebalance, r-cran-ddd, r-cran-rspectra, r-cran-rcpparmadillo Suggests: r-cran-phytools, r-cran-phylotop, r-cran-testthat, r-cran-geiger, r-cran-nltt, r-cran-castor, r-cran-adephylo, r-cran-ggplot2, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-picante, r-cran-lintr, r-cran-rmarkdown, r-cran-knitr, r-cran-igraph, r-cran-matrix, r-cran-pheatmap, r-cran-ggdendro, r-cran-dendextend, r-cran-treesim, r-cran-nlme Filename: pool/dists/noble/main/r-cran-treestats_1.70.11-1.ca2404.1_amd64.deb Size: 4378956 MD5sum: d0cf44acf94cb83e3e29d8b20af58c0c SHA1: ed4636c10e76619ed6f747fcc5174fc7f771f475 SHA256: b50e28f73089371c2782fc3fc472c75ca434f7e74162c35b52a6f9d002d80832 SHA512: f0cd35a8432e7315a7a4d17d656e68ddf7a89b92a0443b8ef625e8f9acd0b1ef846bd10fc5d8cb87ae3ed93887ce2894d5e50380c3651e45d7e09903ee6acc5f Homepage: https://cran.r-project.org/package=treestats Description: CRAN Package 'treestats' (Phylogenetic Tree Statistics) Collection of phylogenetic tree statistics, collected throughout the literature. 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Applications include: generation of trees with specified shapes; tree rearrangement; analysis of tree shape; rooting of trees and extraction of subtrees; calculation and depiction of split support; plotting the position of rogue taxa (Klopfstein & Spasojevic 2019) ; calculation of ancestor-descendant relationships, of 'stemwardness' (Asher & Smith, 2022) , and of tree balance (Mir et al. 2013, Lemant et al. 2022) , ; artificial extinction (Asher & Smith, 2022) ; import and export of trees from Newick, Nexus (Maddison et al. 1997) , and TNT formats; and analysis of splits and cladistic information. Package: r-cran-trend Architecture: amd64 Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: libc6 (>= 2.2.5), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-extradistr Suggests: r-cran-strucchange, r-cran-kendall, r-cran-psych Filename: pool/dists/noble/main/r-cran-trend_1.1.6-1.ca2404.1_amd64.deb Size: 374464 MD5sum: cd5753ee7b7bd23101d18d56f0ae81ac SHA1: 9b3e02dc1f0ac69b1a806bacd14e2a00415e0d23 SHA256: 1f1afcb88c80b47c4619f46aac43a1f3d5b848d4addd3a9c6e4de628c7ad2c9a SHA512: 39c26daf98fcbc769346947cab30f9e8ef4d9b69c3587bda1a9fedff092ae5b6bc583e0d77a6be5ec94f2faaea3e9d013fe361cbef82e03a66742afa14152839 Homepage: https://cran.r-project.org/package=trend Description: CRAN Package 'trend' (Non-Parametric Trend Tests and Change-Point Detection) The analysis of environmental data often requires the detection of trends and change-points. This package includes tests for trend detection (Cox-Stuart Trend Test, Mann-Kendall Trend Test, (correlated) Hirsch-Slack Test, partial Mann-Kendall Trend Test, multivariate (multisite) Mann-Kendall Trend Test, (Seasonal) Sen's slope, partial Pearson and Spearman correlation trend test), change-point detection (Lanzante's test procedures, Pettitt's test, Buishand Range Test, Buishand U Test, Standard Normal Homogeinity Test), detection of non-randomness (Wallis-Moore Phase Frequency Test, Bartels rank von Neumann's ratio test, Wald-Wolfowitz Test) and the two sample Robust Rank-Order Distributional Test. 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(1990) ; EffTox by Thall & Cook (2004) ; the two-parameter logistic method of Neuenschwander, Branson & Sponer (2008) ; and the Augmented Binary method by Wason & Seaman (2013) ; and more. We provide functions to aid model-fitting and analysis. The 'rstan' implementations may also serve as a cookbook to anyone looking to extend or embellish these models. We hope that this package encourages the use of Bayesian methods in clinical trials. There is a preponderance of early phase trial designs because this is where Bayesian methods are used most. If there is a method you would like implemented, please get in touch. 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References: Samuel Kotz, Johan Ren Van Dorp (2004) and Acerbi, Carlo and Tasche, Dirk. (2002) . 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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) ). 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Distributions include the Normal, Student and GED as well as their skewed variants ('Fernandez and Steel'), the 'Johnson SU', and the Generalized Hyperbolic. Also included is the semi-parametric piece wise distribution ('spd') with Pareto tails and kernel interior. Package: r-cran-tsdyn Architecture: amd64 Version: 11.0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4008 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mnormt, r-cran-mgcv, r-cran-nnet, r-cran-tserieschaos, r-cran-tseries, r-cran-vars, r-cran-urca, r-cran-forecast, r-cran-mass, r-cran-matrix, r-cran-foreach, r-cran-generics Suggests: r-cran-sm, r-cran-scatterplot3d, r-cran-rgl, r-cran-rugarch, r-cran-broom, r-cran-dplyr, r-cran-stringr, r-cran-purrr, r-cran-tibble, r-cran-tidyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tsdyn_11.0.5.2-1.ca2404.1_amd64.deb Size: 3763810 MD5sum: a8905e336e33f95b1e2ca9defbdcabb7 SHA1: e111122645fec6c9704a23ce41c18f3ed72c9041 SHA256: a908177836b16c85b9b1d99215d1e254a245152cadcda252576210cd8f88f56b SHA512: d1febe286c18c677273c8c06bb4790a1473bb5134b9935dfa6537fbb1dbb879098cace99b77f8c9072e7faabee725486aaf4b6d8298f90884affa0f217fb87b6 Homepage: https://cran.r-project.org/package=tsDyn Description: CRAN Package 'tsDyn' (Nonlinear Time Series Models with Regime Switching) Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006). Package: r-cran-tsentropies Architecture: amd64 Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tsentropies_0.9-1.ca2404.1_amd64.deb Size: 42358 MD5sum: c8bcc1c07cc3a12bc4edd97dbb5d6ff4 SHA1: 6921580cc3a0bbe7cf8f15bb974a0e2bed89b7a8 SHA256: dd5cc11282eb4aa4d7ab24b20f5a4efceb72d5a35b7a6f06a2af9e0dead46c6f SHA512: ba869baf6a00885bffb78eac969f412dbb78563385ca2e1c920f09463d83d00e97a75572d4fc8e8fca8981ce5aa0bb27a34846c0418461a6588b27e33dce409d Homepage: https://cran.r-project.org/package=TSEntropies Description: CRAN Package 'TSEntropies' (Time Series Entropies) Computes various entropies of given time series. This is the initial version that includes ApEn() and SampEn() functions for calculating approximate entropy and sample entropy. Approximate entropy was proposed by S.M. Pincus in "Approximate entropy as a measure of system complexity", Proceedings of the National Academy of Sciences of the United States of America, 88, 2297-2301 (March 1991). Sample entropy was proposed by J. S. Richman and J. R. Moorman in "Physiological time-series analysis using approximate entropy and sample entropy", American Journal of Physiology, Heart and Circulatory Physiology, 278, 2039-2049 (June 2000). This package also contains FastApEn() and FastSampEn() functions for calculating fast approximate entropy and fast sample entropy. These are newly designed very fast algorithms, resulting from the modification of the original algorithms. The calculated values of these entropies are not the same as the original ones, but the entropy trend of the analyzed time series determines equally reliably. Their main advantage is their speed, which is up to a thousand times higher. A scientific article describing their properties has been submitted to The Journal of Supercomputing and in present time it is waiting for the acceptance. Package: r-cran-tseries Architecture: amd64 Version: 0.10-61-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 478 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-zoo, r-cran-quantmod, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-tseries_0.10-61-1.ca2404.1_amd64.deb Size: 390420 MD5sum: f1701e1702bc314e56fec7460f3cf698 SHA1: 6f4f3763a48904a93a45611c94507fe57a88ee58 SHA256: fd7db8c3824190242f586a58866ac9e9a3eb6aa4d04526fd61afd9b8dc07ac99 SHA512: 58782b3f73ed63fa3664abc25c5da207abfe613838e0456942775ba97d34246d356a145f271bffca09e770faa16c427b46e3fa4c78eb7df82858170433b8141b Homepage: https://cran.r-project.org/package=tseries Description: CRAN Package 'tseries' (Time Series Analysis and Computational Finance) Time series analysis and computational finance. Package: r-cran-tserieschaos Architecture: amd64 Version: 0.1-13.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve Suggests: r-cran-scatterplot3d Filename: pool/dists/noble/main/r-cran-tserieschaos_0.1-13.1-1.ca2404.1_amd64.deb Size: 139892 MD5sum: 1ad32b438792a7731251cfbacc9399c7 SHA1: 98eacfae30438884f8320c6c78febe74fe06345c SHA256: f5f9c1c01033f758d7c1d8b97e65e6c326074fdabc8e12d2089d6d27d0f8b589 SHA512: 88fa359399db383f8142aa63f95e1be09f425fa4742646c338f02d5466f2dfa519c8ac99914b291856fbe68ecdd8f7c848f248a84d56f5b8f9ddd2890ed43dc0 Homepage: https://cran.r-project.org/package=tseriesChaos Description: CRAN Package 'tseriesChaos' (Analysis of Nonlinear Time Series) Routines for the analysis of nonlinear time series. This work is largely inspired by the TISEAN project, by Rainer Hegger, Holger Kantz and Thomas Schreiber: . Package: r-cran-tseriesentropy Architecture: amd64 Version: 0.7-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubature, r-cran-ks Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tseriesentropy_0.7-2-1.ca2404.1_amd64.deb Size: 334798 MD5sum: 001c5da8cac5f495228f2d9795764725 SHA1: 546e3ce5913507bd2a652c924bd63b9ba0a29830 SHA256: 7a5cf53b95c49f9792e22ccef1ec01b925a31fc7b240e9832947b2f9d92f9266 SHA512: e6cae88a3285fddc7f4659e9db30a11b70f0722b11408f98c408dad2d7c97afa59154b67803d801f6619f7987c168f1094c7eaf2d28649aad33a8b006045027d Homepage: https://cran.r-project.org/package=tseriesEntropy Description: CRAN Package 'tseriesEntropy' (Entropy Based Analysis and Tests for Time Series) Implements an Entropy measure of dependence based on the Bhattacharya-Hellinger-Matusita distance. Can be used as a (nonlinear) autocorrelation/crosscorrelation function for continuous and categorical time series. The package includes tests for serial and cross dependence and nonlinearity based on it. Some routines have a parallel version that can be used in a multicore/cluster environment. The package makes use of S4 classes. Package: r-cran-tseriestarma Architecture: amd64 Version: 0.5-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 502 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsolnp, r-cran-lbfgsb3c, r-cran-matrix, r-cran-rdpack, r-cran-mathjaxr, r-cran-rugarch, r-cran-zoo, r-cran-fitdistrplus Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tseriestarma_0.5-2-1.ca2404.1_amd64.deb Size: 313298 MD5sum: ed33f51b88a9f165e2fe047673206574 SHA1: f3f08b7ce25527b14e95955d154babaf1d04e2bd SHA256: a57aae4b676b41dca2b614ae2fe7454f831b55794f9dfafb83fc173a3947fa73 SHA512: 7f8e3c509ca87384ea6b08fe449a72b0b42a389d3d9b0d28ca4298cf9099278c0b06d428b6b5b2d93c11a87b4addb74bb2e403b1dddc270814e691897b2fda61 Homepage: https://cran.r-project.org/package=tseriesTARMA Description: CRAN Package 'tseriesTARMA' (Analysis of Nonlinear Time Series Through ThresholdAutoregressive Moving Average Models (TARMA) Models) Routines for nonlinear time series analysis based on Threshold Autoregressive Moving Average (TARMA) models. It provides functions and methods for: TARMA model fitting and forecasting, including robust estimators, see Goracci et al. JBES (2025) ; tests for threshold effects, see Giannerini et al. JoE (2024) , Goracci et al. Statistica Sinica (2023) , Angelini et al. (2024) OBES ; unit-root tests based on TARMA models, see Chan et al. Statistica Sinica (2024) . Package: r-cran-tsfgrnn Architecture: amd64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tsfgrnn_1.0.5-1.ca2404.1_amd64.deb Size: 155600 MD5sum: b3d2e693acb30ebcf317a29d80b52dad SHA1: 28e68eab13cfabe91bfc8af01c322987ef34358a SHA256: a78401fad23726952cd5cf45c97268761e37e6f248ce7bcba2b7157e4f36e1c9 SHA512: d70b253a761b8488d665c18e44f92fbd27f434c4644834a013bc9d2fb584b1c034b0dfa1face14892ce86860cb799e6d4386686d7fabdc121568aadf40e66e09 Homepage: https://cran.r-project.org/package=tsfgrnn Description: CRAN Package 'tsfgrnn' (Time Series Forecasting Using GRNN) A general regression neural network (GRNN) is a variant of a Radial Basis Function Network characterized by a fast single-pass learning. 'tsfgrnn' allows you to forecast time series using a GRNN model Francisco Martinez et al. (2019) and Francisco Martinez et al. (2022) . When the forecasting horizon is higher than 1, two multi-step ahead forecasting strategies can be used. The model built is autoregressive, that is, it is only based on the observations of the time series. You can consult and plot how the prediction was done. It is also possible to assess the forecasting accuracy of the model using rolling origin evaluation. Package: r-cran-tsfknn Architecture: amd64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1058 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tsfknn_0.6.0-1.ca2404.1_amd64.deb Size: 434624 MD5sum: 4a3630270b1a71fe9dd9b5cc3f07fd5c SHA1: 8f39bbdd7bb6008727dce7435c2970a1bbfd445a SHA256: 85f37dd306676266c9f159b22d1246be58a5feef0b17f8de3a14c8f2dae00acd SHA512: 7728c0be974050b51bd10a9c97d9af32d25975eed89bf06985dd0d0203c810959adf244bca63ee886ffc8b7cff035fd26944a080a051f2f26cca5ab8aa9a7009 Homepage: https://cran.r-project.org/package=tsfknn Description: CRAN Package 'tsfknn' (Time Series Forecasting Using Nearest Neighbors) Allows forecasting time series using nearest neighbors regression Francisco Martinez, Maria P. Frias, Maria D. Perez-Godoy and Antonio J. Rivera (2019) . When the forecasting horizon is higher than 1, two multi-step ahead forecasting strategies can be used. The model built is autoregressive, that is, it is only based on the observations of the time series. The nearest neighbors used in a prediction can be consulted and plotted. Package: r-cran-tsgarch Architecture: amd64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8843 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-tsmethods, r-cran-tmb, r-cran-rcpp, r-cran-nloptr, r-cran-rdpack, r-cran-numderiv, r-cran-xts, r-cran-zoo, r-cran-future.apply, r-cran-future, r-cran-progressr, r-cran-flextable, r-cran-data.table, r-cran-tsdistributions, r-cran-lubridate, r-cran-sandwich, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tsgarch_1.0.4-1.ca2404.1_amd64.deb Size: 2426320 MD5sum: 0c8b6ff9223e9f815f6d9cdedaa1b7a9 SHA1: 010e3d747064dd308e5d503a758ff7b6e2aa61a4 SHA256: 750844793fdb83f85624a22b23e25f625f8c5fc6d48ad77edde45b1b06f9dad3 SHA512: 7ae671878ccfc274f01495fdd397f33f021d46048a51402428550e9de3c7ef7b9570dfccc2d41c14aa2e437b16290cb391faf4120c011fe92264f2766cc6b1f0 Homepage: https://cran.r-project.org/package=tsgarch Description: CRAN Package 'tsgarch' (Univariate GARCH Models) Multiple flavors of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model with a large choice of conditional distributions. Methods for specification, estimation, prediction, filtering, simulation, statistical testing and more. Represents a partial re-write and re-think of 'rugarch', making use of automatic differentiation for estimation. 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Methods for estimation using automatic differentiation, automatic model selection and ensembling, prediction, filtering, simulation and backtesting. Based on the model described in Hyndman et al (2012) . Package: r-cran-tsitter Architecture: amd64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 761 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli Suggests: r-cran-magrittr, r-cran-pillar, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-tsitter_0.1.0-1.ca2404.1_amd64.deb Size: 351920 MD5sum: 4d3fa9c568dc1f6bc47e8c4280702003 SHA1: 513b2c35a6733225afb30a91464c17982611dcef SHA256: b080e6a5b6892846ba60bb1176c46e2c9fbce1f0648973e8df94684214db5236 SHA512: 13104bd6021ef6a5e503e89259901d745cfaa51a2f6209373ef60b21a723d6674bb8444e2bb3f303aad34555b100d8e2c423a90c9509f077b3d9d5cb98515988 Homepage: https://cran.r-project.org/package=tsitter Description: CRAN Package 'tsitter' (Tree-Sitter Parsing Tools) Common tree-sitter () parsing tools for R. It is meant to be used by other packages that specialize in particular languages and file formats. Package: r-cran-tsla Architecture: amd64 Version: 0.1.2-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-proc, r-cran-prroc, r-cran-ape, r-cran-phytools, r-cran-data.tree, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-tsla_0.1.2-1.ca2404.2_amd64.deb Size: 199246 MD5sum: a4cc2a80c18e0ea8b64038eaefc7013a SHA1: 8323b27f550d8ca547c7531f2196a665c5680ede SHA256: 0f25ac5cbfb6ea3ff1af710252f981616e6fc99beb5c9c3f1a3e13d22977fde0 SHA512: 148f6e762c23ab9e400e87404227775189136686eef22a7952db567a6aa1481d9683c6400a946e1244e24107779d44c70b338559d53654b1940e46fe85cf43fd Homepage: https://cran.r-project.org/package=TSLA Description: CRAN Package 'TSLA' (Tree-Guided Rare Feature Selection and Logic Aggregation) Implementation of the tree-guided feature selection and logic aggregation approach introduced in Chen et al. (2024) . The method enables the selection and aggregation of large-scale rare binary features with a known hierarchical structure using a convex, linearly-constrained regularized regression framework. The package facilitates the application of this method to both linear regression and binary classification problems by solving the optimization problem via the smoothing proximal gradient descent algorithm (Chen et al. (2012) ). Package: r-cran-tsmarch Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3969 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tsmethods, r-cran-rcpp, r-cran-rcppparallel, r-cran-tsgarch, r-cran-tsdistributions, r-cran-rcppbessel, r-cran-rsolnp, r-cran-nloptr, r-cran-numderiv, r-cran-abind, r-cran-shape, r-cran-rdpack, r-cran-xts, r-cran-zoo, r-cran-lubridate, r-cran-sandwich, r-cran-future.apply, r-cran-future, r-cran-data.table, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tstests Filename: pool/dists/noble/main/r-cran-tsmarch_1.0.0-1.ca2404.1_amd64.deb Size: 2780298 MD5sum: cfcbd61da3d1b1e52f4071dd3592a9f0 SHA1: fe2e9ba4d1c4f09e1dfbb7d620e1cd6d8f4d6fe5 SHA256: 562bc6349e83169e6d8ba904a978f6e24af91be1d83adba0ae14037da0273e3e SHA512: 81a5163af9e62127f333822b9fc49a833e8e0950b5cdab8cff8c8c83c006916f5c113f571b6de10153a6ccfc67c1dbbb7879dda1d64c1892bb1ca286b1b1532d Homepage: https://cran.r-project.org/package=tsmarch Description: CRAN Package 'tsmarch' (Multivariate ARCH Models) Feasible Multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models including Dynamic Conditional Correlation (DCC), Copula GARCH and Generalized Orthogonal GARCH with Generalized Hyperbolic distribution. 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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Provides automatic mesh generation from point coordinates with boundary constraints, Ruppert refinement for mesh quality, finite element method (FEM) matrix assembly (mass, stiffness, projection), barrier models, spherical meshes via icosahedral subdivision, and metric graph meshes for network geometries. Built on the 'CDT' header-only C++ library (Amirkhanov 2024 ). Designed as the mesh backend for the 'tulpa' Bayesian hierarchical modelling engine but usable standalone for any spatial triangulation task. Package: r-cran-tunepareto Architecture: amd64 Version: 2.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.4.0), 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/noble/main/r-cran-tunepareto_2.5.3-1.ca2404.1_amd64.deb Size: 213912 MD5sum: 65c91fc7dd0a488985f645f2042c7877 SHA1: 38ef9c91cfc3397d3eb84a0a7ff4b8f323c14e71 SHA256: a2aa2f3f2ab2f90003a3489671db6406118540a63eaae55baf8a53b674a4e8dd SHA512: 0b384773339a727034cede943de7fb41d949b35f6099bddd250dd2934372157d2044d52abd0abe81da6b0ab0347cdaf4ee3d37b7714ace45842676305e5eaafa Homepage: https://cran.r-project.org/package=TunePareto Description: CRAN Package 'TunePareto' (Multi-Objective Parameter Tuning for Classifiers) Generic methods for parameter tuning of classification algorithms using multiple scoring functions (Muessel et al. (2012), ). 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Package: r-cran-tuwmodel Architecture: amd64 Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 993 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tuwmodel_1.1-1-1.ca2404.1_amd64.deb Size: 902254 MD5sum: f45a9af7c2068e42e145c511d564fa2e SHA1: 78256bcb66b88c6cae0f3c4c4e882c7bfc421761 SHA256: ef2f650aceee84cae51944ac8ab1d39eae618667d0ee6cf0cd65e60192eefd29 SHA512: 0fad24a03aaf6d5afc1bea199b39faf459fc30a87ed4b61a7a74b1bd17778cf351934d547eb82657264e46def8de9c6cbb05b638b67d390fdaeb3fbc62cb71a5 Homepage: https://cran.r-project.org/package=TUWmodel Description: CRAN Package 'TUWmodel' (Lumped/Semi-Distributed Hydrological Model for EducationPurposes) The model, developed at the Vienna University of Technology, is a lumped conceptual rainfall-runoff model, following the structure of the HBV model. The model can also be run in a semi-distributed fashion and with dual representation of soil layer. The model runs on a daily or shorter time step and consists of a snow routine, a soil moisture routine and a flow routing routine. See Parajka, J., R. Merz, G. Bloeschl (2007) Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments, Hydrological Processes, 21, 435-446. Package: r-cran-tvdenoising Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tvdenoising_1.0.0-1.ca2404.1_amd64.deb Size: 212760 MD5sum: 56681aeb9ca39ebaac64f240ba421291 SHA1: e409545502b463e7e475d2df8609dfa7253e2644 SHA256: e2ba7537e498c6b8edba6e66006351080446d21c8b118ea23da0b081dd2fc6e9 SHA512: 01ec5eb8055a033ba69000a5ac37b6418f95244e534ade6fa03ea63c044b59fc638fa432396e78c6c61d19d34e0402caead8b7cca8b26a160e31d46e479d8437 Homepage: https://cran.r-project.org/package=tvdenoising Description: CRAN Package 'tvdenoising' (Univariate Total Variation Denoising) Total variation denoising can be used to approximate a given sequence of noisy observations by a piecewise constant sequence, with adaptively-chosen break points. 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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) . 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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. 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This package provides a range of functions for creating tweened data that can be used as basis for animation. Furthermore it adds a number of vectorized interpolaters for common R data types such as numeric, date and colour. 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Package: r-cran-twophaseind Architecture: amd64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 615 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-twophaseind_1.1.2-1.ca2404.1_amd64.deb Size: 541058 MD5sum: b7c163bb8044b15ee3c8c69b10de042f SHA1: 73cceae18e90c6c6ffa31595c8750fa2f12ee5e9 SHA256: 76d57b39e5d9230d9249eada1d9ef17075a92dd355d05d42df1d2192e3a1885a SHA512: 11fee1402d484d7dcf4c85ebfbe79ca624caa988fd8be07f0d35ff49ac5eafef34651e75e09fc0104e0ce008c88dd07384f5909aeb553e6563b527da3cb71ba7 Homepage: https://cran.r-project.org/package=TwoPhaseInd Description: CRAN Package 'TwoPhaseInd' (Estimate Gene-Treatment Interaction Exploiting Randomization) Estimation of gene-treatment interactions in randomized clinical trials exploiting gene-treatment independence. Methods used in the package refer to J. Y. Dai, M. LeBlanc, and C. Kooperberg (2009) Biometrics . 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Included tests are: Kolmogorov-Smirnov, Kuiper, Cramer-von Mises, Anderson-Darling, Wasserstein, and DTS. The default test (two_sample) is based on the DTS test statistic, as it is the most powerful, and thus most useful to most users. The DTS test statistic builds on the Wasserstein distance by using a weighting scheme like that of Anderson-Darling. See the companion paper at or for details of that test statistic, and non-standard uses of the package (parallel for big N, weighted observations, one sample tests, etc). We also include the permutation scheme to make test building simple for others. 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Harvey AC (1989) . Pedregal DJ and Young PC (2002) . Durbin J and Koopman SJ (2012) . Hyndman RJ, Koehler AB, Ord JK, and Snyder RD (2008) . Gómez V, Maravall A (2000) . Pedregal DJ, Trapero JR and Holgado E (2024) . 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The result of the projection by ESOM is a grid of neurons which can be visualised as a three dimensional landscape in form of the Umatrix. Further details can be found in the referenced publications (see url). This package offers tools for calculating and visualising the ESOM as well as Umatrix, Pmatrix and UStarMatrix. All the functionality is also available through graphical user interfaces implemented in 'shiny'. Based on the recognized data structures, the method can be used to generate new data. 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Package: r-cran-uuidx Architecture: amd64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1711 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/noble/main/r-cran-uuidx_0.0.1-1.ca2404.1_amd64.deb Size: 617754 MD5sum: f12e215469c218d6a7c8d8459582e15d SHA1: ebad73924dbd937fe1a7c5b3f68dc03c478479b2 SHA256: 3f6f0ed9cb7ae87aa21506f8ab5e8580b0996b98bf2a5bc7d5d520b3dfab4e02 SHA512: f8373ee6dbfb1e80ab2b9e0304223ebb7d76c6d3a644b1e254f0c9bc647c1fef11448a204d6b24b6705c50faf71bd5deaa0784c3bfbdb3a0b9e18df585588732 Homepage: https://cran.r-project.org/package=uuidx Description: CRAN Package 'uuidx' (Modern UUIDs for R with a Rust Backend) Generate, parse, and validate RFC 9562 UUIDs from R using the Rust 'uuid' crate via 'extendr'. Developed by Thomas Bryce Kelly at Icy Seas Co-Laboratory LLC. 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Package: r-cran-uwot Architecture: amd64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2107 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.4), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-fnn, r-cran-irlba, r-cran-rcpp, r-cran-rcppannoy, r-cran-rspectra, r-cran-dqrng, r-cran-rcppprogress Suggests: r-cran-bigstatsr, r-cran-covr, r-cran-knitr, r-cran-rcpphnsw, r-cran-rmarkdown, r-cran-rnndescent, r-cran-testthat Filename: pool/dists/noble/main/r-cran-uwot_0.2.4-1.ca2404.1_amd64.deb Size: 949628 MD5sum: 25226d6402b96f0aed9fd87fb4048087 SHA1: c5e26d39afdb771bd99ee659182f074b08c0ad52 SHA256: 24df60f71aa4228a4a8a06be91adb58dab9aa4bc9f44d154600ae9aac3210371 SHA512: 6dfcf2da38d9b9c424c431f00a2e0414f78b30d896625fbe9fd4b294143d1720dbc373a2e584cfa844071d7531beef4173628e6542aea4db8e80315db154e7bd Homepage: https://cran.r-project.org/package=uwot Description: CRAN Package 'uwot' (The Uniform Manifold Approximation and Projection (UMAP) Methodfor Dimensionality Reduction) An implementation of the Uniform Manifold Approximation and Projection dimensionality reduction by McInnes et al. (2018) . It also provides means to transform new data and to carry out supervised dimensionality reduction. An implementation of the related LargeVis method of Tang et al. (2016) is also provided. This is a complete re-implementation in R (and C++, via the 'Rcpp' package): no Python installation is required. See the uwot website () for more documentation and examples. Package: r-cran-v8 Architecture: amd64 Version: 8.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 35373 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.4), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-jsonlite, r-cran-curl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-v8_8.2.0-1.ca2404.1_amd64.deb Size: 8902192 MD5sum: caf7b0c78ea4e81313d70e32182db307 SHA1: 9676c8ba9f36efb00e0505e90356d2f93c33d6b4 SHA256: e23141b4e9dab0281612bac59fe3f1285ccba0b6f5fd4b9dd2f1ead454c0f247 SHA512: 5dd162c85806aac7c116853d1d9c5202b269ad8f4fb6aacc6b25f4a965aa30ad1903520acd3e71540f26c50ccdb977c37dcd6955aab7248b1c28d177f0350942 Homepage: https://cran.r-project.org/package=V8 Description: CRAN Package 'V8' (Embedded JavaScript and WebAssembly Engine for R) An R interface to V8 : Google's open source JavaScript and WebAssembly engine. 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Package: r-cran-validate Architecture: amd64 Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3413 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-settings, r-cran-yaml Suggests: r-cran-rsdmx, r-cran-tinytest, r-cran-knitr, r-cran-bookdown, r-cran-lumberjack, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-validate_1.1.7-1.ca2404.1_amd64.deb Size: 1926360 MD5sum: 9d7001a3ee57cb1fc846989a028ea355 SHA1: cd92ff05a28c550d24c58f4a08df6abf3f6da32f SHA256: a03b08b82d32547cd909f66d9cbb3ccb40c6d9875d902417cb75b90e22142eeb SHA512: 8e9eb57d3038b54986387188a1f3bea75dc00859d8ca60702af04fcab98516ad64ade183ad428dd0d3c88942d3685784b12b8cfe8aa86bc17c39e4e0333f8dff Homepage: https://cran.r-project.org/package=validate Description: CRAN Package 'validate' (Data Validation Infrastructure) Declare data validation rules and data quality indicators; confront data with them and analyze or visualize the results. The package supports rules that are per-field, in-record, cross-record or cross-dataset. Rules can be automatically analyzed for rule type and connectivity. Supports checks implied by an SDMX DSD file as well. See also Van der Loo and De Jonge (2018) , Chapter 6 and the JSS paper (2021) . 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VALORATE estimates the null distribution and the p-value of the log-rank test based on a recent formulation. For a given number of alterations that define the size of survival groups, the estimation involves a weighted sum of distributions that are conditional on a co-occurrence term where mutations and events are both present. The estimation of conditional distributions is quite fast allowing the analysis of large datasets in few minutes . Package: r-cran-valr Architecture: amd64 Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1493 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-cli, r-cran-cpp11bigwig, r-cran-dplyr, r-cran-ggplot2, r-cran-lifecycle, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-cpp11 Suggests: r-cran-bench, r-cran-covr, r-cran-cowplot, r-cran-curl, r-cran-dbi, r-cran-dbplyr, r-cran-devtools, r-cran-dt, r-bioc-genomicranges, r-bioc-iranges, r-cran-knitr, r-cran-purrr, r-cran-rmariadb, r-cran-rmarkdown, r-bioc-s4vectors, r-cran-testthat, r-cran-tidyr, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-valr_0.9.1-1.ca2404.1_amd64.deb Size: 957156 MD5sum: ce264b9426d64249602020da3a17b2c3 SHA1: 78c34a44799b886b83e957291a1b565667014227 SHA256: 68c8260c8d4c90c79bee5c5f93e61568606dcef1f1a4103e36906a853f0cc4be SHA512: 6026b4369dacd92d33c89f0adc0f6619107a387e3e8960311f1accb2d60608dfa397845edf0f260a7d6be6dc2f38219f76bf51db55605c7984971724cb7ea547 Homepage: https://cran.r-project.org/package=valr Description: CRAN Package 'valr' (Genome Interval Arithmetic) Read and manipulate genome intervals and signals. 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'GDAL' is the 'Geospatial Data Abstraction Library' a translator for raster and vector geospatial data formats that presents a single raster abstract data model and single vector abstract data model to the calling application for all supported formats . This package is focussed on providing exactly and only what GDAL does, to enable developing further tools. Package: r-cran-varband Architecture: amd64 Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 533 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-varband_0.9.0-1.ca2404.1_amd64.deb Size: 296934 MD5sum: 2fd71708db75b3f59f926609823849fc SHA1: fe6d9ca60897a3cf57740e278f2b4d7bf12a9322 SHA256: d6518eb9fe2ce197432d380ed9649e0d7d22f86a5ebb66e4d89c04baa319630e SHA512: 54a7a878a3e31c879569f12432390787c794d738caa27d094b68c27fe26bbcc9a7a703ca656038a368fa31bd9c6738c7bb451d10cd63e175a1a8517ca658e234 Homepage: https://cran.r-project.org/package=varband Description: CRAN Package 'varband' (Variable Banding of Large Precision Matrices) Implementation of the variable banding procedure for modeling local dependence and estimating precision matrices that is introduced in Yu & Bien (2016) and is available at . Package: r-cran-varbvs Architecture: amd64 Version: 2.6-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2753 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-lattice, r-cran-latticeextra, r-cran-rcpp, r-cran-nor1mix Suggests: r-cran-curl, r-cran-glmnet, r-cran-qtl, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-varbvs_2.6-10-1.ca2404.1_amd64.deb Size: 2472604 MD5sum: dada101bc67d21e58a1786992e252e1a SHA1: 5deca117690eb1afceb47cb32b5c3cd2177ce7e7 SHA256: 1491035aabef9a069000e984f32a7869c3123f41e0902bdb2c28b35ae0db3b43 SHA512: b9380797c7c045af8490fa3d33c15837774d4172692642cca99629acb37347aff6c1101855f840578ea2d966bf51a38bc2bf2316130acf8a9cfbd0b1450ed982 Homepage: https://cran.r-project.org/package=varbvs Description: CRAN Package 'varbvs' (Large-Scale Bayesian Variable Selection Using VariationalMethods) Fast algorithms for fitting Bayesian variable selection models and computing Bayes factors, in which the outcome (or response variable) is modeled using a linear regression or a logistic regression. The algorithms are based on the variational approximations described in "Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies" (P. Carbonetto & M. Stephens, 2012, ). This software has been applied to large data sets with over a million variables and thousands of samples. Package: r-cran-vardetect Architecture: amd64 Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1369 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mts, r-cran-igraph, r-cran-pracma, r-cran-mvtnorm, r-cran-sparsevar, r-cran-lattice, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-vardetect_0.1.8-1.ca2404.1_amd64.deb Size: 1160456 MD5sum: 52b8ae589ebd3b6b3e30f138f4d56b1d SHA1: e4f516a99207e237e6032ca5581a7f5fb72567bb SHA256: 74e12ccabad2df0874fd9a96269945504cbc99716ce061c93fa206140ca6c3f0 SHA512: 288cc986418d1778c9e3d0b26779eec9aeba115417cd2ec54a2e8396adf8987e25dd2b018d2baff6e36e0a45f2ee6fc0355105a5bd93306dcdd0347970825a58 Homepage: https://cran.r-project.org/package=VARDetect Description: CRAN Package 'VARDetect' (Multiple Change Point Detection in Structural VAR Models) Implementations of Thresholded Block Segmentation Scheme (TBSS) and Low-rank plus Sparse Two Step Procedure (LSTSP) algorithms for detecting multiple changes in structural VAR models. The package aims to address the problem of change point detection in piece-wise stationary VAR models, under different settings regarding the structure of their transition matrices (autoregressive dynamics); specifically, the following cases are included: (i) (weakly) sparse, (ii) structured sparse, and (iii) low rank plus sparse. It includes multiple algorithms and related extensions from Safikhani and Shojaie (2020) and Bai, Safikhani and Michailidis (2020) . Package: r-cran-varpro Architecture: amd64 Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9219 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-randomforestsrc, r-cran-glmnet, r-cran-foreach, r-cran-gbm, r-cran-bart, r-cran-survival Suggests: r-cran-mlbench, r-cran-domc, r-cran-caret, r-cran-mass, r-cran-igraph Filename: pool/dists/noble/main/r-cran-varpro_3.1.0-1.ca2404.1_amd64.deb Size: 9262098 MD5sum: f198308ff2d35d87becb7677d7817c01 SHA1: f475740ac0a9ef95e4479e4ea4cdbb400acd3361 SHA256: ed1e47be007233c048428ad3aed8dbb05e7cd8048e62ef9b812a0144f1685548 SHA512: 9ab4452fe7530bcf0ae5424077ad53a7cb5e5c98ac1735f954d485406edafb45884e73f23d65c4c9aaf0280cf3c424fcd3f5470247fcb0994dad7b11491dc40e Homepage: https://cran.r-project.org/package=varPro Description: CRAN Package 'varPro' (Model-Independent Variable Selection via the Rule-Based VariablePriority) A new framework of variable selection, which instead of generating artificial covariates such as permutation importance and knockoffs, creates release rules to examine the affect on the response for each covariate where the conditional distribution of the response variable can be arbitrary and unknown. Package: r-cran-varselectexposure Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-varselectexposure_1.0.3-1.ca2404.1_amd64.deb Size: 80424 MD5sum: 09e73e0fd929695568fd21354e8d05fd SHA1: c5b978f196fca7c499b23db21b2908c5423ce463 SHA256: ac01bffd1f742de2c1c1d4bf0ea18290d69c174d7acd7a88a61af03c9f2e5557 SHA512: 33575bb9e0fddbea07cd7dd994d5f753fa0fffff7f404031a4f181e0098aa416e47088f9021a1c0d7a73e6d0e99443ee0c53dcea12e4080f41c7e9012cb8bc25 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. Package: r-cran-varsellcm Architecture: amd64 Version: 2.1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1714 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mgcv, r-cran-ggplot2, r-cran-shiny, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-htmltools, r-cran-scales, r-cran-plyr Filename: pool/dists/noble/main/r-cran-varsellcm_2.1.3.2-1.ca2404.1_amd64.deb Size: 682058 MD5sum: dfbf3a37b9e730caee05d3ca79526d51 SHA1: 6f364195aeb061d8e6ff65a4e4d8c715be7a7e4b SHA256: 95a4fa0f0846f104786032199afcd39e807db5b0f4e823e4dde45463f6891cef SHA512: 46b7008975d73c51348ca32ae45bc44af0175d3101ea4e12573539579549b50876f7b32f8f188eeefecdef7cbe6fcaa1262f9889412efc4e092b1c9af563ddfb Homepage: https://cran.r-project.org/package=VarSelLCM Description: CRAN Package 'VarSelLCM' (Variable Selection for Model-Based Clustering of Mixed-Type DataSet with Missing Values) Full model selection (detection of the relevant features and estimation of the number of clusters) for model-based clustering (see reference here ). Data to analyze can be continuous, categorical, integer or mixed. Moreover, missing values can occur and do not necessitate any pre-processing. Shiny application permits an easy interpretation of the results. Package: r-cran-vartests Architecture: amd64 Version: 2.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 586 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-sn, r-cran-rcpparmadillo Suggests: r-cran-vars Filename: pool/dists/noble/main/r-cran-vartests_2.0.7-1.ca2404.1_amd64.deb Size: 310996 MD5sum: ebe3872db8ca5df8aaa9334b8c9ee8cc SHA1: 92f5f8f90726897c47706c04eb1fe1672d0a521b SHA256: 6ed12abcbc70cf5706e6ce6fa6eeffe8db33f97189956dcc2c64887d48f88097 SHA512: 6bb8a5662f17161fbb8caaa461b1960d58af26c9ddc906ca11d6500d9c456469085a4dec17ee6ebab4d9ca0fd3fd7d592213f793b5e25c6617988ec2defcc416 Homepage: https://cran.r-project.org/package=VARtests Description: CRAN Package 'VARtests' (Bootstrap Tests for Cointegration and Autocorrelation in VARs) Implements wild bootstrap tests for autocorrelation in Vector Autoregressive (VAR) models based on Ahlgren and Catani (2016) , a combined Lagrange Multiplier (LM) test for Autoregressive Conditional Heteroskedasticity (ARCH) in VAR models from Catani and Ahlgren (2016) , and bootstrap-based methods for determining the cointegration rank from Cavaliere, Rahbek, and Taylor (2012) and Cavaliere, Rahbek, and Taylor (2014) . Package: r-cran-vasicekreg Architecture: amd64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-gamlss, r-cran-gamlss.dist, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-vasicekreg_1.0.2-1.ca2404.1_amd64.deb Size: 93036 MD5sum: a114b9183038644d51d2e308d389e8db SHA1: d56074d291832237f092030bfb7f2536067b9449 SHA256: 3fdbd88cf0b03fcb092afa31b846aa606bb10351078bed06b86031e86af6348c SHA512: f6b4ca6d2bdada1541295eb4686031f9baea1806d5de3c13728281a4855a05c04ab28840850dd57aed63ffcf2c4aef765d1b2b1fd8f31d97ba20806b0e125468 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: amd64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 704 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/noble/main/r-cran-vaster_0.6.0-1.ca2404.1_amd64.deb Size: 434736 MD5sum: e76930cd710ad35198dea8b4191b9525 SHA1: 637897c07ce71c6728294dcd526b4da6168aad89 SHA256: f850d0fc500d973a6bf1933e9854b3cc1df58eb69d4dc4503d3202467edfbc6e SHA512: 33ad9409f3284d01a06d1f3c6a695ba4eb8d649af282db7fc482286c91f36c4fde61e52df22d63528bb78f216416b93737d7f3269299374fbe2453eedc590218 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: amd64 Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-mvtnorm, r-cran-testthat Filename: pool/dists/noble/main/r-cran-vbel_1.1.7-1.ca2404.1_amd64.deb Size: 111890 MD5sum: 78dea38248cced3bed23a843f434e92c SHA1: cda2c8291ff7d6153b1829a7e6aa6b8c82f6b5a7 SHA256: 8fb5843510db68af9b839ddc16b1ee5befc26eb8c06461cf3aa0362665e2ff81 SHA512: b0b3d42d420396b07c0882d96f22a8ea701ea1d193118ff156144ff477973342ac01b4cc7003c7c666e98f77e31faa7f7372853cc95da7e5f88d36138c9c9dcb Homepage: https://cran.r-project.org/package=VBel Description: CRAN Package 'VBel' (Variational Bayes for Fast and Accurate Empirical LikelihoodInference) Computes the Gaussian variational approximation of the Bayesian empirical likelihood posterior. This is an implementation of the function found in Yu, W., & Bondell, H. D. (2023) . 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One major limitation of joint models is that they could be computationally expensive for complex models where the number of the shared random effects is large. This package can be used to fit complex multivariate joint models using our newly developed algorithm Jieqi Tu and Jiehuan Sun (2023) , which is based on Gaussian variational approximate inference and is computationally efficient. Package: r-cran-vblpcm Architecture: amd64 Version: 2.4.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: libc6 (>= 2.29), libgsl27 (>= 2.7.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ergm, r-cran-network, r-cran-mclust, r-cran-sna Filename: pool/dists/noble/main/r-cran-vblpcm_2.4.9-1.ca2404.1_amd64.deb Size: 157824 MD5sum: 0b7c1b0cdd9aa62984af5d3f72abbf21 SHA1: 00ac356fa0174f035cd957c344cba52727ea6b81 SHA256: 4c81c45f61be49e18ab47e024094d6ad349f490ccd8cbfd809e2b9d7d3cfe259 SHA512: b4096cfc508798d7cada4675e60e78e5a9a4402ad3874f9ccdd81c01164f3a1eaafa50c221d42ac1e7a31524cce1ee7ce07602c42bb97b93c30e5e67d6cacf9f 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: amd64 Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 629 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-vinecopula Suggests: r-cran-lattice, r-cran-testthat Filename: pool/dists/noble/main/r-cran-vc2copula_0.1.6-1.ca2404.1_amd64.deb Size: 404944 MD5sum: 84c26adbe66ca9df965a14a1c9791ac3 SHA1: 1b388bc434e0c1d4454145eaa75eead3ccbea104 SHA256: 669abb6d12cb2915cebabc51795ab3c40e3c917af0bca3b26f67d5f0b3e565ea SHA512: 3db8072f179c3a93fdf858352d77cf79846d75f691e9aafbd3bcf0d738fd1d57f28899daafb2aacf9e812e6ce3e113d565e489c3f3e65ffc97f72942f4fe1230 Homepage: https://cran.r-project.org/package=VC2copula Description: CRAN Package 'VC2copula' (Extend the 'copula' Package with Families and Models from'VineCopula') Provides new classes for (rotated) BB1, BB6, BB7, BB8, and Tawn copulas, extends the existing Gumbel and Clayton families with rotations, and allows to set up a vine copula model using the 'copula' API. Corresponding objects from the 'VineCopula' API can easily be converted. Package: r-cran-vca Architecture: amd64 Version: 1.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1299 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-numderiv Suggests: r-cran-vfp, r-cran-stb, r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc, r-cran-runit Filename: pool/dists/noble/main/r-cran-vca_1.5.2-1.ca2404.1_amd64.deb Size: 977222 MD5sum: c3e99fe3e2c0338d3461128ba310199f SHA1: 6e3611dcaf9e3a41604046465c6179a7be4ddda7 SHA256: ab73edd09a8689a71d9340674f4b5a29da2cd9baf22b7c9b718f268b2ee3af19 SHA512: c3cc2eb98192b05a3fc749285f06df2157fcd25648a317bd96b07c7bc9ef4c2b7c8ee43e8d4245b262796e83300f3777c2b012557b1d39187ad448f44a682ec2 Homepage: https://cran.r-project.org/package=VCA Description: CRAN Package 'VCA' (Variance Component Analysis) ANOVA and REML estimation of linear mixed models is implemented, once following Searle et al. (1991, ANOVA for unbalanced data), once making use of the 'lme4' package. The primary objective of this package is to perform a variance component analysis (VCA) according to CLSI EP05-A3 guideline "Evaluation of Precision of Quantitative Measurement Procedures" (2014). There are plotting methods for visualization of an experimental design, plotting random effects and residuals. For ANOVA type estimation two methods for computing ANOVA mean squares are implemented (SWEEP and quadratic forms). The covariance matrix of variance components can be derived, which is used in estimating confidence intervals. Linear hypotheses of fixed effects and LS means can be computed. LS means can be computed at specific values of covariables and with custom weighting schemes for factor variables. See ?VCA for a more comprehensive description of the features. Package: r-cran-vcbart Architecture: amd64 Version: 1.2.5-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-vcbart_1.2.5-1.ca2404.1_amd64.deb Size: 215802 MD5sum: 75a1cd45d8a7d7638ca4413bbcc1d66e SHA1: e8f50f487fc3092222f78c29aa8817d23d2bcc82 SHA256: 014f7a25f1c7fa2564e68f59eb40408c3b9813e506f7ecd8873e1d4858d10ada SHA512: 32907dc399e4445d62cebc9ea892ecfccf18902fc19017c1644b2ca273d0833d80351b68f80815ac31527d5a2dd6954199426aeb999c2fea5c3c630d40716a81 Homepage: https://cran.r-project.org/package=VCBART Description: CRAN Package 'VCBART' (Fit Varying Coefficient Models with Bayesian Additive RegressionTrees) Fits linear varying coefficient (VC) models, which assert a linear relationship between an outcome and several covariates but allow that relationship (i.e., the coefficients or slopes in the linear regression) to change as functions of additional variables known as effect modifiers, by approximating the coefficient functions with Bayesian Additive Regression Trees. Implements a Metropolis-within-Gibbs sampler to simulate draws from the posterior over coefficient function evaluations. VC models with independent observations or repeated observations can be fit. For more details see Deshpande et al. (2026) . Package: r-cran-vcfppr Architecture: amd64 Version: 0.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3732 Depends: libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), libdeflate0 (>= 1.0), libgcc-s1 (>= 3.0), liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 13.1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-codetools, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-vcfppr_0.8.3-1.ca2404.1_amd64.deb Size: 2311644 MD5sum: 727b866fbd0f966da09ca8bc51559f77 SHA1: 7a43512de287e82348ca9164d03bb7ead8585ff1 SHA256: 2d540f43fd56b27167f87dbdac48f0b113c1070f85f06dd985330d98d4924881 SHA512: b16b99494ee7dafbda74617914e19270b14df541db536ca24053d8ccc834ee660db31c2a1526ce94c14eab85ec2754eea35c52d0e701e95754a37af3fe35b477 Homepage: https://cran.r-project.org/package=vcfppR Description: CRAN Package 'vcfppR' (Rapid Manipulation of the Variant Call Format (VCF)) The 'vcfpp.h' () provides an easy-to-use 'C++' 'API' of 'htslib', offering full functionality for manipulating Variant Call Format (VCF) files. The 'vcfppR' package serves as the R bindings of the 'vcfpp.h' library, enabling rapid processing of both compressed and uncompressed VCF files. Explore a range of powerful features for efficient VCF data manipulation. 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Functions are provided to rapidly read from and write to VCF files. Once VCF data is read into R a parser function extracts matrices of data. This information can then be used for quality control or other purposes. Additional functions provide visualization of genomic data. Once processing is complete data may be written to a VCF file (*.vcf.gz). It also may be converted into other popular R objects (e.g., genlight, DNAbin). VcfR provides a link between VCF data and familiar R software. 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Package: r-cran-vetr Architecture: amd64 Version: 0.2.22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 555 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-unitizer Filename: pool/dists/noble/main/r-cran-vetr_0.2.22-1.ca2404.1_amd64.deb Size: 283270 MD5sum: 39ed58259019da134da1cdd689f5862b SHA1: f47ffa0f8278524e0d44dd409d31d8272fbe4208 SHA256: 7e8bceb5546e4a5ea9022274e7e4fa989c42539382dda7ddaf2138ec3e9ce7a8 SHA512: e0fdf3edc448b7473f1a78d02abc7c63097fd4b118f09720633fdd6bc90d132109b0b896f37dcd300f3004a5063321087497e77efe8763225329a9675b633df4 Homepage: https://cran.r-project.org/package=vetr Description: CRAN Package 'vetr' (Trust, but Verify) Declarative template-based framework for verifying that objects meet structural requirements, and auto-composing error messages when they do not. Package: r-cran-vewaningvariant Architecture: amd64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1005 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-vewaningvariant_1.4-1.ca2404.1_amd64.deb Size: 789490 MD5sum: 9e582426fd56b54001049f6f2ed9fca5 SHA1: 41ca583d492ec536bf8dfe476b1de866cfbce6ec SHA256: 760da807936d5bfe46a0611d1e19c427370531c8f869be5b87bbe48a3f0fe6a1 SHA512: 376711d457d76b28848ba0519e86c5f4793b7c217d3eb37967046c15c2a56ff88d45349cd5a3a81d7f390ec89ee19a4f2b3e916739f61ab52a2caf50d46437e2 Homepage: https://cran.r-project.org/package=VEwaningVariant Description: CRAN Package 'VEwaningVariant' (Vaccine Efficacy Over Time - Variant Aware) Implements methods for inference on potential waning of vaccine efficacy and for estimation of vaccine efficacy at a user-specified time after vaccination based on data from a randomized, double-blind, placebo-controlled vaccine trial in which participants may be unblinded and placebo subjects may be crossed over to the study vaccine. The methods also for variant stratification and allow adjustment for possible confounding via inverse probability weighting through specification of models for the trial entry process, unblinding mechanisms, and the probability an unblinded placebo participant accepts study vaccine. Package: r-cran-vgam Architecture: amd64 Version: 1.1-14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8472 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-vgamextra, r-cran-mass, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-vgam_1.1-14-1.ca2404.1_amd64.deb Size: 7805352 MD5sum: 1b460311cc353570c0cc8f64807ad0f9 SHA1: f4a625fa2a2f94064899af1a17173f83770dea78 SHA256: a1f92423a620866b4f4afc6873a7de697bf7f25359a2ddba7b4a024a3c10e0e7 SHA512: f62afa068e171ab5d90bb1f6ce4eda8c0c117a01efbbd58d5ee71949ce7aaf2ac8f57f9c4d96dba77e3568edaa5d592ce1af510f100213379661996e38fe1e7c Homepage: https://cran.r-project.org/package=VGAM Description: CRAN Package 'VGAM' (Vector Generalized Linear and Additive Models) An implementation of about 6 major classes of statistical regression models. The central algorithm is Fisher scoring and iterative reweighted least squares. At the heart of this package are the vector generalized linear and additive model (VGLM/VGAM) classes. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are data-driven VGLMs that use smoothing. The book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) gives details of the statistical framework and the package. Currently only fixed-effects models are implemented. Many (100+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE. The other classes are RR-VGLMs (reduced-rank VGLMs), quadratic RR-VGLMs, doubly constrained RR-VGLMs, quadratic RR-VGLMs, reduced-rank VGAMs, RCIMs (row-column interaction models)---these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO). Hauck-Donner effect detection is implemented. Note that these functions are subject to change; see the NEWS and ChangeLog files for latest changes. Package: r-cran-vgamextra Architecture: amd64 Version: 0.0-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1132 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vgam Suggests: r-cran-vgamdata Filename: pool/dists/noble/main/r-cran-vgamextra_0.0-9-1.ca2404.1_amd64.deb Size: 1039286 MD5sum: 404b8b8a05c9ee92f1f241ba4098a754 SHA1: a0d1ef98e0393e3aad2bac5012bc55e35985d62b SHA256: 22c188ed18dcdb49e54455fc12cf5cf05ad3f0498ccd5dc35bf11375eb5709af SHA512: 6b7dbfe869aa16f294bfaafaa8302557ace0413c3994f781ce1718e96c0906f7ed5c4bbf10bb495514afc8d235bfafee971a6e1856a508a51e2971f42f0f5390 Homepage: https://cran.r-project.org/package=VGAMextra Description: CRAN Package 'VGAMextra' (Additions and Extensions of the 'VGAM' Package) Extending the functionalities of the 'VGAM' package with additional functions and datasets. At present, 'VGAMextra' comprises new family functions (ffs) to estimate several time series models by maximum likelihood using Fisher scoring, unlike popular packages in CRAN relying on optim(), including ARMA-GARCH-like models, the Order-(p, d, q) ARIMAX model (non- seasonal), the Order-(p) VAR model, error correction models for cointegrated time series, and ARMA-structures with Student-t errors. For independent data, new ffs to estimate the inverse- Weibull, the inverse-gamma, the generalized beta of the second kind and the general multivariate normal distributions are available. In addition, 'VGAMextra' incorporates new VGLM-links for the mean-function, and the quantile-function (as an alternative to ordinary quantile modelling) of several 1-parameter distributions, that are compatible with the class of VGLM/VGAM family functions. Currently, only fixed-effects models are implemented. All functions are subject to change; see the NEWS for further details on the latest changes. Package: r-cran-vglmer Architecture: amd64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 664 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-lme4, r-cran-cholwishart, r-cran-mvtnorm, r-cran-matrix, r-cran-lmtest, r-cran-mgcv, r-cran-rcppeigen Suggests: r-cran-superlearner, r-cran-mass, r-cran-tictoc, r-cran-testthat, r-cran-gkrls Filename: pool/dists/noble/main/r-cran-vglmer_1.0.6-1.ca2404.1_amd64.deb Size: 441260 MD5sum: 252b20af35526704dc7ff628dccd8a39 SHA1: da5f3d40bb662ef6a8eb63f2ec96001cfdf8d764 SHA256: 61e4bc93ae69de209b1da68530fc8798a1ba9c69820854e241ee97a853ab823b SHA512: cd0f0e86eb22bf6f911532a2fc0ef2d6f872603734cb349bac5eb9f9c06b42a5ad06c095aaad337440543efc49a7e83312301e52e6abc04358134e0a19d4112c Homepage: https://cran.r-project.org/package=vglmer Description: CRAN Package 'vglmer' (Variational Inference for Hierarchical Generalized Linear Models) Estimates hierarchical models using variational inference. 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Package: r-cran-vic5 Architecture: amd64 Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1210 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-rcpp, r-cran-foreach, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-vic5_0.2.6-1.ca2404.1_amd64.deb Size: 794182 MD5sum: 3899ca4fd5adb561d5b105d8fb390eca SHA1: b312df0526f7642716ad02dd3a3a3fb1a8b15471 SHA256: 5e651b1fd1f1d5f67542d956492776bde452e17510c0762b6fe8840f382f473a SHA512: b38063e2649398744cb1774cce5cc2a77489e168540d52e34093bdb64a6f3b561b210e0f2579d95a82742ccb7d476070f61842e8f941422cac39b2380846e887 Homepage: https://cran.r-project.org/package=VIC5 Description: CRAN Package 'VIC5' (The Variable Infiltration Capacity (VIC) Hydrological Model) The Variable Infiltration Capacity (VIC) model is a macroscale hydrologic model that solves full water and energy balances, originally developed by Xu Liang at the University of Washington (UW). The version of VIC source code used is of 5.0.1 on , see Hamman et al. (2018). Development and maintenance of the current official version of the VIC model at present is led by the UW Hydro (Computational Hydrology group) in the Department of Civil and Environmental Engineering at UW. VIC is a research model and in its various forms it has been applied to most of the major river basins around the world, as well as globally . References: "Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges (1994), A simple hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res., 99(D7), 14415-14428, "; "Hamman, J. J., Nijssen, B., Bohn, T. J., Gergel, D. R., and Mao, Y. (2018), The Variable Infiltration Capacity model version 5 (VIC-5): infrastructure improvements for new applications and reproducibility, Geosci. Model Dev., 11, 3481-3496, ". Package: r-cran-vicatmix Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-klar, r-cran-matrixstats, r-cran-mcclust, r-cran-rcpp, r-cran-gtools, r-cran-rcpparmadillo Suggests: r-cran-doparallel, r-cran-dorng, r-cran-foreach Filename: pool/dists/noble/main/r-cran-vicatmix_1.0-1.ca2404.1_amd64.deb Size: 158388 MD5sum: b6f795708da1c936dad320b733492d9e SHA1: 00867b59627aa36846fa74b8668c7790d5c5954d SHA256: a87f12cbbbc25a57c5863e9c0937c3e17d3186907e865c662ffabda40ffbe840 SHA512: 3499196d20ca4b69318016c39cbdd1a716f754be958a1933530b0f3458c4f860fd2b06ccdedf9c780a10d489f1e7cb237732db40b7d11e87c6937d77ff61849d Homepage: https://cran.r-project.org/package=VICatMix Description: CRAN Package 'VICatMix' (Variational Mixture Models for Clustering Categorical Data) A variational Bayesian finite mixture model for the clustering of categorical data, and can implement variable selection and semi-supervised outcome guiding if desired. 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Package: r-cran-vigor Architecture: amd64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-vigor_1.1.5-1.ca2404.1_amd64.deb Size: 268388 MD5sum: 4f6ad049a997553fefe24ed2e7176dda SHA1: ba1c9f588b56d8b081164d173d4a8311bebb3e02 SHA256: 9ea76b85affb860a1365bce61ba9ddf3ad3074eefdd17d05af5b038c5551d05f SHA512: 7c9cefeef65e003b3e8a8e80dc4f675c6deeed0bd3b2d70bea7fa6c8d690f2b527d98a2dc128d34e98aa35033a10d57472a6c6dd5a2f0b80c976cf0ad48d9349 Homepage: https://cran.r-project.org/package=VIGoR Description: CRAN Package 'VIGoR' (Variational Bayesian Inference for Genome-Wide Regression) Conducts linear regression using variational Bayesian inference, particularly optimized for genome-wide association mapping and whole-genome prediction which use a number of DNA markers as the explanatory variables. 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Package: r-cran-vim Architecture: amd64 Version: 7.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7170 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-vim_7.0.0-1.ca2404.1_amd64.deb Size: 3604052 MD5sum: 7c34c905fa851352c8f25ea5dde23556 SHA1: dac81635b9b6d5d97193f244a52b9fba235ecc12 SHA256: ea71c6ab077e0d864dbb6456bcc4fc4880990bccb5d52f050dea000b4a9b711c SHA512: 15fc760963188bd23ab737b722d9358c0256eeded55946d37e35cfda42409a8e241249b9f11704a68b91c10dfd50ed12fdf4c9de0728d785a10b07c37fe0aa4e Homepage: https://cran.r-project.org/package=VIM Description: CRAN Package 'VIM' (Visualization and Imputation of Missing Values) Provides methods for imputation and visualization of missing values. It includes graphical tools to explore the amount, structure and patterns of missing and/or imputed values, supporting exploratory data analysis and helping to investigate potential missingness mechanisms (details in Alfons, Templ and Filzmoser, . The quality of imputations can be assessed visually using a wide range of univariate, bivariate and multivariate plots. The package further provides several imputation methods, including efficient implementations of k-nearest neighbour and hot-deck imputation (Kowarik and Templ 2013, , iterative robust model-based multiple imputation (Templ 2011, ; Templ 2023, ), and machine learning–based approaches such as robust GAM-based multiple imputation (Templ 2024, ) as well as gradient boosting (XGBoost) and transformer-based methods (Niederhametner et al., ). General background and practical guidance on imputation are provided in the Springer book by Templ (2023) . 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Package: r-cran-vinecopula Architecture: amd64 Version: 2.6.1-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1514 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm, r-cran-adgoftest, r-cran-lattice Suggests: r-cran-tsp, r-cran-shiny, r-cran-testthat, r-cran-numderiv, r-cran-kdecopula, r-cran-network Filename: pool/dists/noble/main/r-cran-vinecopula_2.6.1-1.ca2404.2_amd64.deb Size: 1197602 MD5sum: 6af603c7b85bde0b47e13853758b1c3c SHA1: 29250cb549ca01d8e132289b67b28c5a0bede52c SHA256: 30a762c4734314c97c4b6cc4e0ecf2444d50c97cbfe9ff1a7b9b9e34b6c3c9c6 SHA512: 35bb61ef49a2303cec8d701986716191d6a4bd2bea729799a12dada0df61a7c5e01a16c26bd7851974993d25befbba59f0f223891248115bf827a3e45861f402 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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Typical designs of Phase I trials use toxicity as the primary endpoint and aim to find the maximum tolerable dosage. However, these designs are poorly applicable for the development of cancer therapeutic vaccines because the expected safety concerns for these vaccines are not as much as cytotoxic agents. The primary objectives of a cancer therapeutic vaccine phase I trial thus often include determining whether the vaccine shows biologic activity and the minimum dose necessary to achieve a full immune or even clinical response. This package implements a Bayesian Phase I cancer vaccine trial design that allows simultaneous evaluation of safety and immunogenicity outcomes. See Wang et al. (2019) for further details. Package: r-cran-vispedigree Architecture: amd64 Version: 1.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10055 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-igraph, r-cran-matrix, r-cran-rcpp, r-cran-lattice, r-cran-rcpparmadillo Suggests: r-cran-nadiv, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-vispedigree_1.8.1-1.ca2404.1_amd64.deb Size: 6008592 MD5sum: ffeb5fa3205e120c42950c3e31a22419 SHA1: 76e705990e3213dac850aebdda2a60eb704e9089 SHA256: 117d030038cb70c8ee608513c1ca0573661b34fd8c777ced936615a36a3bf173 SHA512: 82008ae991355f02effcb52cbfe62b778714bb39905e787174115d7ffffd139598846c36718555bda816761ac6dfe2d4591420249e31306894f33616e2f1fb72 Homepage: https://cran.r-project.org/package=visPedigree Description: CRAN Package 'visPedigree' (Tidying, Analysis, and Fast Visualization of Animal and PlantPedigrees) Provides tools for the analysis and visualization of animal and plant pedigrees. 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Package: r-cran-vistla Architecture: amd64 Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-vistla_2.1.2-1.ca2404.1_amd64.deb Size: 217042 MD5sum: 65bb193d63dbf4966178b046ce9a2a33 SHA1: 7fc3388e3ebb51873201001e48a34d1859514948 SHA256: 6aa31543781d7560898349a2fcca7d544622a4528fabdab76b6336ea2ebeac10 SHA512: a07585022c8b4031c4776b3e92c705785c35d5a9bac92d88ce96fd20482844e9199a46f65455f226ae1dd177a0a1f455dc36ab5bd7773abc42fe8aec94fe9fb4 Homepage: https://cran.r-project.org/package=vistla Description: CRAN Package 'vistla' (Detecting Influence Paths with Information Theory) Traces information spread through interactions between features, utilising information theory measures and a higher-order generalisation of the concept of widest paths in graphs. 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Package: r-cran-vita Architecture: amd64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-randomforest Suggests: r-cran-mnormt Filename: pool/dists/noble/main/r-cran-vita_1.0.0-1.ca2404.1_amd64.deb Size: 138466 MD5sum: 1795fa1d1684f779dcf5c26bb8278deb SHA1: 42a8c23440d0e4cc1123b9e64a3e27132e59c8b8 SHA256: daa1b30448436508b74704e1c081ce44354fac3a8ae1f6f0213ba379a412e293 SHA512: ef845962e82882bd206b678c20d78cc4a4fa93c62d5099254594f5f69af6338b0431b81acd31cc9bc06c0dcdc1c967ad6e273956fc72d054eed0c11c7464578f Homepage: https://cran.r-project.org/package=vita Description: CRAN Package 'vita' (Variable Importance Testing Approaches) Implements the novel testing approach by Janitza et al.(2015) for the permutation variable importance measure in a random forest and the PIMP-algorithm by Altmann et al.(2010) . Janitza et al.(2015) do not use the "standard" permutation variable importance but the cross-validated permutation variable importance for the novel test approach. The cross-validated permutation variable importance is not based on the out-of-bag observations but uses a similar strategy which is inspired by the cross-validation procedure. The novel test approach can be applied for classification trees as well as for regression trees. However, the use of the novel testing approach has not been tested for regression trees so far, so this routine is meant for the expert user only and its current state is rather experimental. 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For more information, see (i) 'Variational Mode Decomposition' by K. Dragomiretskiy and D. Zosso in IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531-544, Feb.1, 2014, ; (ii) 'Two-Dimensional Variational Mode Decomposition' by Dragomiretskiy, K., Zosso, D. (2015), In: Tai, XC., Bae, E., Chan, T.F., Lysaker, M. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2015. Lecture Notes in Computer Science, vol 8932. Springer, . 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Package: r-cran-volesti Architecture: amd64 Version: 1.1.2-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2761 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-volesti_1.1.2-10-1.ca2404.1_amd64.deb Size: 953770 MD5sum: fc4c38ba50105463874edb3de1c1a197 SHA1: 73d150fa2def53dd2b1e2ddc866b2efd372bfc08 SHA256: d69d7d6c54aade9318a5e109afe7df73aa25fb9861703a804fa0f2f81a213605 SHA512: afe4c12f793f45076b5d5cd045b49e7f8c798d721278662fe57015cb8cf2b0043536336344a110659b39105f47dbfacdf459f7f40ea83ccd6ee88af57d37f055 Homepage: https://cran.r-project.org/package=volesti Description: CRAN Package 'volesti' (Volume Approximation and Sampling of Convex Polytopes) Provides an R interface for 'volesti' C++ package. 'volesti' computes estimations of volume of polytopes given by (i) a set of points, (ii) linear inequalities or (iii) Minkowski sum of segments (a.k.a. zonotopes). There are three algorithms for volume estimation as well as algorithms for sampling, rounding and rotating polytopes. Moreover, 'volesti' provides algorithms for estimating copulas useful in computational finance. Methods implemented in 'volesti' are described in A. Chalkis and V. Fisikopoulos (2022) and references therein. Package: r-cran-voronoifortune Architecture: amd64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-voronoifortune_1.0-1.ca2404.1_amd64.deb Size: 33358 MD5sum: 722b194d74592bdafca7d2a4d947f825 SHA1: f8f0181fbe6c4b8933725dca2df5bff83171256c SHA256: ddc655b491971d3b94de3c898fb272ccf100e1cf43aa5d3b64b81b9dfa59137e SHA512: 5af02c7c1620dafe2941a31f39200ec6a98affcebd1534bd1d988a0c5f1ecb32eceaa2d19d35f69fbf06a9e8b2c1f1767e1b23fa2a9bac76875cdb0500924deb Homepage: https://cran.r-project.org/package=voronoifortune Description: CRAN Package 'voronoifortune' (Voronoi Tessellation by Fortune Algorithm) Fortune's (1987, ) algorithm is a very efficient method to perform Voronoi tessellation and Delaunay triangulation. This package is a port of the original code published in the early 1990's by Steven Fortune. Package: r-cran-vostokr Architecture: amd64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4428 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-lidr, r-cran-data.table, r-cran-terra, r-cran-sf, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-vostokr_0.2.1-1.ca2404.1_amd64.deb Size: 4310490 MD5sum: a8466e7054fb499abfa8338f6a53a722 SHA1: e18379ef9917344afd3cbf9eafb3803c289b123d SHA256: 4c1f1093e7efcf1c4b69c9811abd988d64c0bdff0e9e4b037a4fa668d013acb2 SHA512: b6a3262ca0f0198e16fe1119c8afce48943f979eab2c45e00bc976c9786781772c70c73d0e21bbef6f374b4e44f19095b1aaf5a25ec81281cbb28802653ecb67 Homepage: https://cran.r-project.org/package=vostokR Description: CRAN Package 'vostokR' (Solar Potential Calculation for Point Clouds using 'VOSTOK') Calculate solar potential for LiDAR point clouds using the 'VOSTOK' (Voxel Octree Solar Toolkit) algorithm. 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Package: r-cran-vpdtw Architecture: amd64 Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-vpdtw_2.2.1-1.ca2404.1_amd64.deb Size: 212624 MD5sum: eb1c67c97ffe5468f8d53c07e71c65b7 SHA1: 04e301b2bcf7030de543c638c836fedaec7f9c6f SHA256: 1ea996349872581a0328f827ed7c086f73b123e9ba597c77e0cb1fcc4563ed24 SHA512: 4c9dafd1e3845cac96b80aeb3dca425b997180b9ffb6d58232b97404386265cf62edce28f27ccc21d1c1ec78c63fc4f30337cdb41a6eab9cd9e3578b80b201f6 Homepage: https://cran.r-project.org/package=VPdtw Description: CRAN Package 'VPdtw' (Variable Penalty Dynamic Time Warping) Variable Penalty Dynamic Time Warping (VPdtw) for aligning chromatographic signals. With an appropriate penalty this method performs good alignment of chromatographic data without deforming the peaks (Clifford, D., Stone, G., Montoliu, I., Rezzi S., Martin F., Guy P., Bruce S., and Kochhar S.(2009) ; Clifford, D. and Stone, G. (2012) ). 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Package: r-cran-watson Architecture: amd64 Version: 1.0.0-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-tinflex, r-cran-rcpparmadillo Suggests: r-cran-hsaur3, r-cran-testthat Filename: pool/dists/noble/main/r-cran-watson_1.0.0-1.ca2404.1_amd64.deb Size: 277434 MD5sum: 0ea2754fd80103f67fee63c3fa1a1c22 SHA1: 2c635dd217f2dd4408a6640fa53ee4c3b383b512 SHA256: f934b4e368103fc7621c5f6bc17f0c820591b0f4086fab9c9fe26d5fb1000151 SHA512: de2d8c6580a987dce399c6e35a5a0cf0daa58ce2e4b50095b3444cb4f7e15cab15e9275e66a43b8f2e50ae8b34ad65081400622d95c4244253edc2395faf7a25 Homepage: https://cran.r-project.org/package=watson Description: CRAN Package 'watson' (Fitting and Simulating Mixtures of Watson Distributions) Tools for fitting and simulating mixtures of Watson distributions. The package is described in Sablica, Hornik and Leydold (2026) . The random sampling scheme of the package offers two sampling algorithms that are based of the results of Sablica, Hornik and Leydold (2022) . What is more, the package offers a smart tool to combine these two methods, and based on the selected parameters, it approximates the relative sampling speed for both methods and picks the faster one. In addition, the package offers a fitting function for the mixtures of Watson distribution, that uses the expectation-maximization (EM) algorithm. Special features are the possibility to use multiple variants of the E-step and M-step, sparse matrices for the data representation and state of the art methods for numerical evaluation of needed special functions using the results of Sablica and Hornik (2022) and Sablica and Hornik (2024) . Package: r-cran-wav Architecture: amd64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-testthat, r-cran-patrick Filename: pool/dists/noble/main/r-cran-wav_0.2.0-1.ca2404.1_amd64.deb Size: 71292 MD5sum: 692369f430d79d86c7bce0c7d0f23376 SHA1: 8979c7d18d79983ecab2628eff13c06ee9174f93 SHA256: 12144cb5bbd8ddaa893415b638c93436bafc1afaf191470ee55f6438dec3ad23 SHA512: 18d164c376e5174c4501fe94f6d468129ea5effc886c3dcdc57bf621d3431833ab37b10b878745a861ce93d22a3835c9b152f970e15439fd53c0c382332cba6b Homepage: https://cran.r-project.org/package=wav Description: CRAN Package 'wav' (Read and Write WAV Files) Efficiently read and write Waveform (WAV) audio files . Support for unsigned 8 bit Pulse-code modulation (PCM), signed 12, 16, 24 and 32 bit PCM and other encodings. Package: r-cran-waveband Architecture: amd64 Version: 4.7.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-wavethresh Filename: pool/dists/noble/main/r-cran-waveband_4.7.4-1.ca2404.1_amd64.deb Size: 78508 MD5sum: a48e3331342d2cd0ebd389b3d3d5ea43 SHA1: 088de757596abd194f26beb232854088c46fcd11 SHA256: 0199a04186c6d4b2ff9c6ef5df0465d3190965159c4407d9fde4c5ed1f390f2d SHA512: 85b93ab194e5d40d5f49403d846377e24e0df6d7708ae553fd381479378d04e9d21f8a6018c4358f702c44e4eda7fcf48c70f3ac33d6d1e84eb577450ae7442a Homepage: https://cran.r-project.org/package=waveband Description: CRAN Package 'waveband' (Computes Credible Intervals for Bayesian Wavelet Shrinkage) Computes Bayesian wavelet shrinkage credible intervals for nonparametric regression. The method uses cumulants to derive Bayesian credible intervals for wavelet regression estimates. The first four cumulants of the posterior distribution of the estimates are expressed in terms of the observed data and integer powers of the mother wavelet functions. These powers are closely approximated by linear combinations of wavelet scaling functions at an appropriate finer scale. Hence, a suitable modification of the discrete wavelet transform allows the posterior cumulants to be found efficiently for any data set. Johnson transformations then yield the credible intervals themselves. Barber, S., Nason, G.P. and Silverman, B.W. (2002) . Package: r-cran-wavelets Architecture: amd64 Version: 0.3-0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 386 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-wavelets_0.3-0.2-1.ca2404.1_amd64.deb Size: 342698 MD5sum: 576f4e51fb6fecdabb5d1694343eb01a SHA1: 4ad6e2725dea8c9b1962784bb28b4ff383cda159 SHA256: e9f8c3f61f751b10c4aae30be6b591336f27bca52d482de2bbd84edf1a255628 SHA512: db6d2c456287d4ce840c5783644b710f20a7cf819099d7242ab25c9b378ba37bdbcd6a15927995c97a5d9d76e270babbfc78a9b751127db74d4bc7c992e51000 Homepage: https://cran.r-project.org/package=wavelets Description: CRAN Package 'wavelets' (Functions for Computing Wavelet Filters, Wavelet Transforms andMultiresolution Analyses) Contains functions for computing and plotting discrete wavelet transforms (DWT) and maximal overlap discrete wavelet transforms (MODWT), as well as their inverses. Additionally, it contains functionality for computing and plotting wavelet transform filters that are used in the above decompositions as well as multiresolution analyses. Package: r-cran-wavesampling Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-sampling, r-cran-balancedsampling Filename: pool/dists/noble/main/r-cran-wavesampling_0.1.4-1.ca2404.1_amd64.deb Size: 183206 MD5sum: 899ade6691d56385d431ffb8e4cd37ea SHA1: 0bf55049af27ea179622668b9f5b91e42eb7a1ed SHA256: 592194fab82c1c85d6910acfdbcfb89e3521b80efd996b3dad924f56953c140c SHA512: a03a6bcb268841d0aa76179c3083f02bfa157fd4afa83d6cec0bedfbd8c410ead6e116f612c9792a13d38bf8ee6420527449b13b2796bfad5fcc0e8e9c7ce209 Homepage: https://cran.r-project.org/package=WaveSampling Description: CRAN Package 'WaveSampling' (Weakly Associated Vectors (WAVE) Sampling) Spatial data are generally auto-correlated, meaning that if two units selected are close to each other, then it is likely that they share the same properties. For this reason, when sampling in the population it is often needed that the sample is well spread over space. A new method to draw a sample from a population with spatial coordinates is proposed. This method is called wave (Weakly Associated Vectors) sampling. It uses the less correlated vector to a spatial weights matrix to update the inclusion probabilities vector into a sample. For more details see Raphaël Jauslin and Yves Tillé (2019) . Package: r-cran-waveslim Architecture: amd64 Version: 1.8.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 844 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-multitaper Suggests: r-cran-fftw, r-cran-covr Filename: pool/dists/noble/main/r-cran-waveslim_1.8.5-1.ca2404.1_amd64.deb Size: 767508 MD5sum: 83a31109deed95e311b4a41e9b735567 SHA1: 82dd31f8f7fbd4a743f098998bbc7f150d743433 SHA256: 4c990197300fdfd36e8d650adff699cd40a713b73a989f0fc6c1c988cbdcad0a SHA512: 736076721ad943040086f3ff5c6b2b491c5cf5996b368d90cc382b29b78f0be5649a6a26e2cc574eca2fbf4372e8242b5ec9fb2f0e46cab6a60ef730f24da55a Homepage: https://cran.r-project.org/package=waveslim Description: CRAN Package 'waveslim' (Basic Wavelet Routines for One-, Two-, and Three-DimensionalSignal Processing) Basic wavelet routines for time series (1D), image (2D) and array (3D) analysis. The code provided here is based on wavelet methodology developed in Percival and Walden (2000); Gencay, Selcuk and Whitcher (2001); the dual-tree complex wavelet transform (DTCWT) from Kingsbury (1999, 2001) as implemented by Selesnick; and Hilbert wavelet pairs (Selesnick 2001, 2002). All figures in chapters 4-7 of GSW (2001) are reproducible using this package and R code available at the book website(s) below. Package: r-cran-wavethresh Architecture: amd64 Version: 4.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1887 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-wavethresh_4.7.3-1.ca2404.1_amd64.deb Size: 1694972 MD5sum: 8a7583aedfbcaa3c39ef41f3b3dd4afb SHA1: 25f7bdd2a9ffdd8852c77472f4ad7c9eb6e8a349 SHA256: 6f0b3fe5b6052aa03677568f97f3e6afbadf73baa20588063724d4763925f500 SHA512: 6dea7856f66bcc3c65647ea890299cc97a0ce146a4781bd74048c39e36a57d7459399837a582174af78ac79dc8965f99a7e133f9720479592c3122cfbb536f01 Homepage: https://cran.r-project.org/package=wavethresh Description: CRAN Package 'wavethresh' (Wavelets Statistics and Transforms) Performs 1, 2 and 3D real and complex-valued wavelet transforms, nondecimated transforms, wavelet packet transforms, nondecimated wavelet packet transforms, multiple wavelet transforms, complex-valued wavelet transforms, wavelet shrinkage for various kinds of data, locally stationary wavelet time series, nonstationary multiscale transfer function modeling, density estimation. 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The diagnostic "theta" can be used to detect and weed out bootstrap-based procedures that provably have size equal to one for a given testing problem. The implementation covers a large variety of bootstrap-based procedures, cf. the above mentioned article for details. A function for computing bootstrap p-values is provided. 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The method is described in "Boker, S. M., Rotondo, J. L., Xu, M., & King, K. (2002). Windowed cross-correlation and peak picking for the analysis of variability in the association between behavioral time series. Psychological Methods, 7(3), 338." 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Rewiring unweighted networks with given assortativity coefficients. Generating general preferential attachment networks. 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'WebSocket' is a protocol for low-overhead real-time communication: . Package: r-cran-webutils Architecture: amd64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-jsonlite Suggests: r-cran-httpuv, r-cran-testthat Filename: pool/dists/noble/main/r-cran-webutils_1.2.2-1.ca2404.1_amd64.deb Size: 35644 MD5sum: a6c7de9ff236782df35d2fbf4d92a487 SHA1: 0ba3564d158ae1538d253c204f0228a45915a36c SHA256: 63053675b00602e6d3fbba2afc32684c32e6f90328f21ddb61666d1c0f11872b SHA512: 1dad6e3b041d3ef4e8785fbc59cada7ccd9cb168869ce8429684c70a5e57d3c924b002165964e5678c0aa2ce069acea23dc02752c687398aed31f8c8f0e97cda Homepage: https://cran.r-project.org/package=webutils Description: CRAN Package 'webutils' (Utility Functions for Developing Web Applications) Parses http request data in application/json, multipart/form-data, or application/x-www-form-urlencoded format. 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Package: r-cran-weibullr Architecture: amd64 Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 813 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-weibullr_1.2.4-1.ca2404.1_amd64.deb Size: 564706 MD5sum: ea23845a832ed613a164fb2a1a2b94a3 SHA1: 69cff6043364f8240726b1ff55e0cab8a8fbeab4 SHA256: 4ec428032acc1460083f07a38a82608682793d325807bfc4845f4c4341cae02c SHA512: d7aded91201b2e95ea4279d39d0c2075b4fb57342832f8e0893cca123d1f53f8a0dd8a3d8277d794d70d2de607a3e238b8fc77a052fede294ef80ce9d77fb414 Homepage: https://cran.r-project.org/package=WeibullR Description: CRAN Package 'WeibullR' (Weibull Analysis for Reliability Engineering) Life data analysis in the graphical tradition of Waloddi Weibull. Methods derived from Robert B. Abernethy (2008, ISBN 0-965306-3-2), Wayne Nelson (1982, ISBN: 9780471094586), William Q. Meeker and Lois A. Escobar (1998, ISBN: 1-471-14328-6), John I. McCool, (2012, ISBN: 9781118217986). 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Comprises a compact and easily accessible set of methods and visualization tools that make the examination and adjustment as well as the analysis and interpretation of field data (and bench tests) as simple as possible. Non-parametric estimators like Median Ranks, Kaplan-Meier (Abernethy, 2006, ), Johnson (Johnson, 1964, ), and Nelson-Aalen for failure probability estimation within samples that contain failures as well as censored data are included. The package supports methods like Maximum Likelihood and Rank Regression, (Genschel and Meeker, 2010, ) for the estimation of multiple parametric lifetime distributions, as well as the computation of confidence intervals of quantiles and probabilities using the delta method related to Fisher's confidence intervals (Meeker and Escobar, 1998, ) and the beta-binomial confidence bounds. If desired, mixture model analysis can be done with segmented regression and the EM algorithm. Besides the well-known Weibull analysis, the package also contains Monte Carlo methods for the correction and completion of imprecisely recorded or unknown lifetime characteristics. (Verband der Automobilindustrie e.V. (VDA), 2016, ). Plots are created statically ('ggplot2') or interactively ('plotly') and can be customized with functions of the respective visualization package. The graphical technique of probability plotting as well as the addition of regression lines and confidence bounds to existing plots are supported. Package: r-cran-weightedcl Architecture: amd64 Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matlab, r-cran-rootsolve, r-cran-sure, r-cran-mass Filename: pool/dists/noble/main/r-cran-weightedcl_0.7-1.ca2404.1_amd64.deb Size: 127074 MD5sum: cbf6013ea7791649d59ab526e9188d33 SHA1: 632cba2a5d59bcab3df076254c6fe8bbac20d347 SHA256: cef6b1504d22ac60fda8d9244e9f090a756bcaf5f17c7daed8787def55a6e6bb SHA512: 44697d2a9d198b083845b250cb9bb4f84cd6b7480ff96cb2bb7cbd6c3a5d458809815f535b2ac81b3cd9d36a2e400cbe6dcc641633fd207c123bea0037cbabd7 Homepage: https://cran.r-project.org/package=weightedCL Description: CRAN Package 'weightedCL' (Efficient and Feasible Inference for High-Dimensional NormalCopula Regression Models) Estimates high-dimensional multivariate normal copula regression models with the weighted composite likelihood estimating equations in Nikoloulopoulos (2023) . 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It provides an optimized weighted PAM algorithm as well as functions for aggregating replicated cases, computing cluster quality measures for a range of clustering solutions, sequence analysis typology validation using parametric bootstraps and plotting (fuzzy) clusters of state sequences. It further provides a fuzzy and crisp CLARA algorithm to cluster large database with sequence analysis, and a methodological framework for Robustness Assessment of Regressions using Cluster Analysis Typologies (RARCAT). Package: r-cran-weightedscores Architecture: amd64 Version: 0.9.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-weightedscores_0.9.5.3-1.ca2404.1_amd64.deb Size: 278982 MD5sum: 0165bc0c0065b80a1e4b4662acdd0455 SHA1: 1e3f1990f2c6fb17af5aff9037ae698e7423356d SHA256: da7e0c0018209aec406052bf504bfa06bdaf6482d6f1afd0838b852136ea4975 SHA512: 075ad86fef8aed65159de5feac0ea87a69655464e1d5fec59523319244d668c77b8d867ba140b77277b23007aa31defb10b424580eb9c9657593d48f03f2a0d0 Homepage: https://cran.r-project.org/package=weightedScores Description: CRAN Package 'weightedScores' (Weighted Scores Method for Regression Models with Dependent Data) The weighted scores method and composite likelihood information criteria as an intermediate step for variable/correlation selection for longitudinal ordinal and count data in Nikoloulopoulos, Joe and Chaganty (2011) , Nikoloulopoulos (2016) and Nikoloulopoulos (2017) . Package: r-cran-weightedtreemaps Architecture: amd64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2798 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspace, r-cran-dplyr, r-cran-lattice, r-cran-rcpp, r-cran-scales, r-cran-sf, r-cran-sp, r-cran-tibble, r-cran-bh, r-cran-rcppcgal Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-weightedtreemaps_0.1.4-1.ca2404.1_amd64.deb Size: 1958664 MD5sum: 379746c781a3e1eea350ddd9b0b07bb6 SHA1: 62478a7037f5323949be7edbb01cba4df14fd89c SHA256: 7230c96caaac9914855073b407f69127c1f41746588d83ef76fc04fb397ce7a3 SHA512: 1bd7047f39afe0aeef1fad44b710a798ab00e9d8f78d87aaf8dcb45f91fce7ded19d4d40537b33d57cad81b43596ca34150acf84eef9e80780b69a71d1a4c1bd 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. 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It uses a modified version of 'libsvm' and is compatible with package 'e1071'. It also allows user defined kernel matrix. Package: r-cran-wfe Architecture: amd64 Version: 1.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-arm, r-cran-matrix, r-cran-mass Filename: pool/dists/noble/main/r-cran-wfe_1.9.1-1.ca2404.1_amd64.deb Size: 158532 MD5sum: 1f5a8a882b4d30859ff384d9f48e979f SHA1: c96ccc98ef3422d2005f80dcc0aff440debc01db SHA256: bb5a58a4101679aabb0847aa0338179b407da0183cca8cbe6e678998d7962ec5 SHA512: 6b869a07b63272d4f1308216361940e544ea57e00c235fe0ed3444faf81a1dde1c1c253d2863436ffe954d64ec6066ad192d4b2083eea72f3611e9eabf39a774 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. 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Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization. Package: r-cran-wh Architecture: amd64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 492 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wh_2.0.0-1.ca2404.1_amd64.deb Size: 290856 MD5sum: a85e0a7c511ba6394f9f9b3e47182115 SHA1: df3a7dfaa898dd4f1da547abf26be86ee988a36f SHA256: 3df38639b5efabeac55df2ef2c17443fa22fd0e2e396655ecc0474bb1b5a0e8e SHA512: 5394c21e48826d8c5b63e10bbbcae7b20e96b2e197b1fc538dd6841c9af9090bcd078f1a1b7e606198682a43031a941f34c56f1d02bc1a54097b672093fcfc61 Homepage: https://cran.r-project.org/package=WH Description: CRAN Package 'WH' (Enhanced Implementation of Whittaker-Henderson Smoothing) An enhanced implementation of Whittaker-Henderson smoothing for the graduation of one-dimensional and two-dimensional actuarial tables used to quantify Life Insurance risks. 'WH' is based on the methods described in Biessy (2025) . Among other features, it generalizes the original smoothing algorithm to maximum likelihood estimation, automatically selects the smoothing parameter(s) and extrapolates beyond the range of data. Package: r-cran-whitelabrt Architecture: amd64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7897 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-whitelabrt_1.0.1-1.ca2404.1_amd64.deb Size: 5149674 MD5sum: 6918e7f6e06fb26fc584f7892db867a8 SHA1: 11b2069eb52ad5a32eecc01ca67b605a1b963f90 SHA256: ff6ec95d4228361d8e17af13cf95892161a385d3a6857e2ec55d7dca5242d055 SHA512: 8b90fc80fef41d12e38714125088baec7a6de360faa9106d1058c66baae2d8745ac2674ddb906b3c81a40efe35f11cdca64f6cd92d8ed3f34330629f2c9b3c9a Homepage: https://cran.r-project.org/package=WhiteLabRt Description: CRAN Package 'WhiteLabRt' (Novel Methods for Reproduction Number Estimation,Back-Calculation, and Forecasting) A collection of functions related to novel methods for estimating R(t), created by the lab of Professor Laura White. Currently implemented methods include two-step Bayesian back-calculation and now-casting for line-list data with missing reporting delays, adapted in 'STAN' from Li (2021) , and calculation of time-varying reproduction number assuming a flux between various adjacent states, adapted into 'STAN' from Zhou (2021) . Package: r-cran-whoa Architecture: amd64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 637 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-tibble, r-cran-tidyr, r-cran-rcpp, r-cran-vcfr, r-cran-viridis, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-whoa_0.0.2-1.ca2404.1_amd64.deb Size: 470918 MD5sum: 2a536fb1d5e56333c9b99e117f585cb7 SHA1: cf29d6d7c1d7bdea6e20176bd6e84c07d88398f1 SHA256: 28ad6eff794599ac37b0609c124a51101364a3902d16338d2e2c090d1f74cb11 SHA512: 15895e59f07389b281edf0bf33bd97ee2d8869c1b58b117db82c1c3c707a5e4902849470f6f3f9d8bf04e142204e8bda2161802f843768dc561f6704242a436c Homepage: https://cran.r-project.org/package=whoa Description: CRAN Package 'whoa' (Evaluation of Genotyping Error in Genotype-by-Sequencing Data) This is a small, lightweight package that lets users investigate the distribution of genotypes in genotype-by-sequencing (GBS) data where they expect (by and large) Hardy-Weinberg equilibrium, in order to assess rates of genotyping errors and the dependence of those rates on read depth. It implements a Markov chain Monte Carlo (MCMC) sampler using 'Rcpp' to compute a Bayesian estimate of what we call the heterozygote miscall rate for restriction-associated digest (RAD) sequencing data and other types of reduced representation GBS data. It also provides functions to generate plots of expected and observed genotype frequencies. Some background on these topics can be found in a recent paper "Recent advances in conservation and population genomics data analysis" by Hendricks et al. (2018) , and another paper describing the MCMC approach is in preparation with Gordon Luikart and Thierry Gosselin. Package: r-cran-widals Architecture: amd64 Version: 0.6.2-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 563 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-snowfall Suggests: r-cran-sssimple Filename: pool/dists/noble/main/r-cran-widals_0.6.2-1.ca2404.2_amd64.deb Size: 461048 MD5sum: 61ddf3f2cd4c8258ec9e9e790c339f93 SHA1: 118b2a20b248e6286a12006cb8566f22d4793991 SHA256: d5e2ca2b6c0b316c921fbd7624dc387978edb58d9a318788d98bc372c18f2acf SHA512: fb57912d574cbe2f073cf35815b314bb74cfdbb63891555d0a680cecf1ac59d497c6217c47fec609d0feef50c2b4c4efafda1d43c6218950df9a386af9dd2fc5 Homepage: https://cran.r-project.org/package=widals Description: CRAN Package 'widals' (Weighting by Inverse Distance with Adaptive Least Squares) Computationally easy modeling, interpolation, forecasting of massive temporal-spacial data. Package: r-cran-wienr Architecture: amd64 Version: 0.3-15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-wienr_0.3-15-1.ca2404.1_amd64.deb Size: 366188 MD5sum: 7c745289fc51a1a680a9ba77e1ab0710 SHA1: cf45665ba5d7aaa54e6b6219c96d3281be6159f4 SHA256: 677ad8fb3a305fd22881b3c69d132962744a008e6150d2a8c8c0e24b7e8447cc SHA512: 073a1fae6391b858dff5566cce51a9320452d4913f9e9dc45262cc1c4188d47d3e4259bd147789f7c2f31d1e4ec58b4080ba384a8c67383c988ad13acfa48484 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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Utilities to parse coordinates from data frames, plot well-known geometry vectors, extract meta information from well-known geometry vectors, and calculate bounding boxes are provided. 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Bayesian Prediction of Racial Category UsingSurname, First Name, Middle Name, and Geolocation) Predicts individual race/ethnicity using surname, first name, middle name, geolocation, and other attributes, such as gender and age. The method utilizes Bayes' Rule (with optional measurement error correction) to compute the posterior probability of each racial category for any given individual. The package implements methods described in Imai and Khanna (2016) "Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records" Political Analysis and Imai, Olivella, and Rosenman (2022) "Addressing census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements" . The package also incorporates the data described in Rosenman, Olivella, and Imai (2023) "Race and ethnicity data for first, middle, and surnames" . 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See Nishimura et al. (2020) for the original Discontinuous Hamiltonian Monte Carlo; Hoffman et al. (2014) and Betancourt (2016) for the definition of possible Hamiltonian Monte Carlo termination criteria. 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Package: r-cran-ymd Architecture: amd64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1818 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ymd_0.1.5-1.ca2404.1_amd64.deb Size: 611908 MD5sum: b40d84009653f95fddb23074158070f2 SHA1: 040d993a77b3a4e8ac84473a498c0bbc31657754 SHA256: 82092ce69c6976439c59dd29a089725f98f3a4451bc7c37c245d24b0334a39f9 SHA512: 0fe1c2d39bd3fa1a28a4cac769f91d4895fe5d87b9fc493cc403a4d95ce95ad7cf576c0d3033e6790314d22bcbf22bdac692a6cb527aaf4b04b8e9bb9654d364 Homepage: https://cran.r-project.org/package=ymd Description: CRAN Package 'ymd' (Parse 'YMD' Format Number or String to Date) Convert 'YMD' format number or string to Date efficiently, using Rust's standard library. 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Details about the model can be found in Demarqui et al. (2019) . Model fitting can be carried out via both maximum likelihood and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times. Package: r-cran-ypinterimtesting Architecture: amd64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass Filename: pool/dists/noble/main/r-cran-ypinterimtesting_1.0.3-1.ca2404.1_amd64.deb Size: 88702 MD5sum: d0b455778edb8dc26f475fea4beda7d3 SHA1: 870beeb3ccab1e01add451048aac752de5a01ec7 SHA256: 0b0573313a2b8aa889e4ecdd5942d27f61d95d1a051f5d56e84bf2bcbcd089e1 SHA512: b84720d8a5b5fd6a93ef7e1357f05f86100c23eff7a5882bba19dfbba758ec1168ee320482cc7e7ec8c29336f53f8bda17328ed66e7349796bf2c33019eec942 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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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: amd64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 474 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-ztpln_0.1.3-1.ca2404.1_amd64.deb Size: 213918 MD5sum: b1eab9e5e8110dbcfc2e860c92209b34 SHA1: 2af607cf9d55bad7f6d9af22b59d52bc1995662b SHA256: 31ba20636fa9e47bc0c3c52c455a2aecc6eec04f685d82b4674dd095a4d375ed SHA512: 5298dcb8f9e4bbb18366d8d13e054048ad7083d11d1290aa58a504f956e820a20704ac3e4622df7f5ea5b158debe687492403c2cc402acc4e2e39d01dc3d4ad9 Homepage: https://cran.r-project.org/package=ztpln Description: CRAN Package 'ztpln' (Zero-Truncated Poisson Lognormal Distribution) Functions for obtaining the density, random variates and maximum likelihood estimates of the Zero-truncated Poisson lognormal distribution and their mixture distribution. Package: r-cran-zvcv Architecture: amd64 Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1096 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-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/noble/main/r-cran-zvcv_2.1.3-1.ca2404.1_amd64.deb Size: 517846 MD5sum: df51653ee31f0d3ce386078648ef2de4 SHA1: 58b55f8e5b6d4e95cc9a825d50bdfcbcad70efea SHA256: 7f15f4431ceb1eb09f62766e94a04ebfaaa139381ab4c449372d80828c64efcb SHA512: 0fc07f6801fccff07c22717e7dc39ebce52ce5a52d6a1485ea4ac801fb1f018f65499367c44f6e1f804d961b9ad07ede6801ee5015caa0f5ebd7841fe9ac5b54 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.