Statistical Learning on Sparse Matrices R package rsparse version 0.5.3 CRAN Task View: MissingData (https://CRAN.R-project.org/view=MissingData) 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 <sparse, dense> 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(, <doi:10.1145/2487575.2488200>) 2) Factorization Machines via SGD, as per Rendle (2010, <doi:10.1109/ICDM.2010.127>) 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, <doi:10.1109/ICDM.2008.22>) 2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro (2005, <doi:10.1145/1102351.1102441>) 3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD, Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder et al. (2014, <doi:10.48550/arXiv.1410.2596>) 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, <https://aclanthology.org/D14-1162/>) 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. Software Depends: R (>= 3.6.0), methods, Matrix (>= 1.3) Imports: MatrixExtra (>= 0.1.7), Rcpp (>= 0.11), data.table (>= 1.10.0), float (>= 0.2-2), RhpcBLASctl, lgr (>= 0.2) LinkingTo: Rcpp, RcppArmadillo (>= 0.9.100.5.0) Suggests: testthat, covr Dmitriy Selivanov <selivanov.dmitriy@gmail.com> Comprehensive R Archive Network (CRAN) Dmitriy Selivanov [aut, cre, cph] (ORCID: <https://orcid.org/0000-0001-5413-1506>), David Cortes [ctb], Drew Schmidt [ctb] (configure script for BLAS, LAPACK detection), Wei-Chen Chen [ctb] (configure script and work on linking to float package) GPL (>= 2) 2025-02-17 application/tgz https://CRAN.R-project.org/package=rsparse doi:10.32614/CRAN.package.rsparse