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, <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.
| Version: |
0.5.3 |
| 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 |
| Published: |
2025-02-17 |
| DOI: |
10.32614/CRAN.package.rsparse |
| Author: |
Dmitriy Selivanov 
[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 gmail.com> |
| BugReports: |
https://github.com/dselivanov/rsparse/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/dselivanov/rsparse |
| NeedsCompilation: |
yes |
| Materials: |
README, NEWS |
| In views: |
MissingData |
| CRAN checks: |
rsparse results |
Documentation:
Downloads:
Reverse dependencies:
Linking:
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