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 <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, <>) 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.

Version: 0.5.2
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 (≥
Suggests: testthat, covr
Published: 2024-06-28
DOI: 10.32614/CRAN.package.rsparse
Author: Dmitriy Selivanov ORCID iD [aut, cre, cph], 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)
Maintainer: Dmitriy Selivanov <selivanov.dmitriy at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: README NEWS
In views: MissingData
CRAN checks: rsparse results


Reference manual: rsparse.pdf


Package source: rsparse_0.5.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): rsparse_0.5.2.tgz, r-oldrel (arm64): rsparse_0.5.2.tgz, r-release (x86_64): rsparse_0.5.2.tgz, r-oldrel (x86_64): rsparse_0.5.2.tgz
Old sources: rsparse archive

Reverse dependencies:

Reverse imports: LSX, PsychWordVec, text2vec


Please use the canonical form to link to this page.