{ "_id": "6a26a4ae24555f66ed522e3c", "Package": "rsparse", "Type": "Package", "Title": "Statistical Learning on Sparse Matrices", "Version": "0.5.3", "Authors@R": "c(\nperson(\"Dmitriy\", \"Selivanov\", role=c(\"aut\", \"cre\", \"cph\"), email=\"selivanov.dmitriy@gmail.com\",\ncomment = c(ORCID = \"0000-0001-5413-1506\")),\nperson(\"David\", \"Cortes\", role=\"ctb\"),\nperson(\"Drew\", \"Schmidt\", role=\"ctb\", comment=\"configure script for BLAS, LAPACK detection\"),\nperson(\"Wei-Chen\", \"Chen\", role=\"ctb\", comment=\"configure script and work on linking to float package\")\n)", "Maintainer": "Dmitriy Selivanov ", "Description": "Implements many algorithms for statistical learning on\nsparse matrices - matrix factorizations, matrix completion,\nelastic net regressions, factorization machines. Also 'rsparse'\nenhances 'Matrix' package by providing methods for\nmultithreaded matrix products and native\nslicing of the sparse matrices in Compressed Sparse Row (CSR)\nformat. List of the algorithms for regression problems: 1)\nElastic Net regression via Follow The Proximally-Regularized\nLeader (FTRL) Stochastic Gradient Descent (SGD), as per McMahan\net al(, ) 2) Factorization\nMachines via SGD, as per Rendle (2010,\n) List of algorithms for matrix\nfactorization and matrix completion: 1) Weighted Regularized\nMatrix Factorization (WRMF) via Alternating Least Squares (ALS)\n- paper by Hu, Koren, Volinsky (2008,\n) 2) Maximum-Margin Matrix\nFactorization via ALS, paper by Rennie, Srebro (2005,\n) 3) Fast Truncated Singular Value\nDecomposition (SVD), Soft-Thresholded SVD, Soft-Impute matrix\ncompletion via ALS - paper by Hastie, Mazumder et al. (2014,\n) 4) Linear-Flow matrix\nfactorization, from 'Practical linear models for large-scale\none-class collaborative filtering' by Sedhain, Bui, Kawale et\nal (2016, ISBN:978-1-57735-770-4) 5) GlobalVectors (GloVe)\nmatrix factorization via SGD, paper by Pennington, Socher,\nManning (2014, ) Package is\nreasonably fast and memory efficient - it allows to work with\nlarge datasets - millions of rows and millions of columns. This\nis particularly useful for practitioners working on recommender\nsystems.", "License": "GPL (>= 2)", "Encoding": "UTF-8", "LazyData": "true", "ByteCompile": "true", "StagedInstall": "TRUE", "URL": "https://github.com/dselivanov/rsparse", "BugReports": "https://github.com/dselivanov/rsparse/issues", "RoxygenNote": "7.3.1", "NeedsCompilation": "yes", "Repository": "https://blaserlab.r-universe.dev", "Date/Publication": "2025-02-17 00:59:04 UTC", "RemoteUrl": "https://github.com/dselivanov/rsparse", "RemoteRef": "HEAD", "RemoteSha": "54f7e6a8e81d568c0f22199d84129a0b1cbbf27c", "Packaged": { "Date": "2026-06-08 11:03:57 UTC", "User": "root" }, "Author": "Dmitriy Selivanov [aut, cre, cph] (ORCID:\n),\nDavid Cortes [ctb],\nDrew Schmidt [ctb] (configure script for BLAS, LAPACK detection),\nWei-Chen Chen [ctb] (configure script and work on linking to float\npackage)", "MD5sum": "e2a7f50c68759aadd2bdf8d6172fc57e", "_user": "blaserlab", "_type": "src", "_file": "rsparse_0.5.3.tar.gz", 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