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:	0.5.2
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 (≥ 0.9.100.5.0)
Suggests:	testthat, covr
Published:	2024-06-28
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:

Reference manual:

rsparse.pdf

Downloads:

Package source:	rsparse_0.5.2.tar.gz
Windows binaries:	r-devel: rsparse_0.5.2.zip, r-release: rsparse_0.5.2.zip, r-oldrel: rsparse_0.5.2.zip
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

Linking:

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