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, <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://www.aclweb.org/anthology/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: 0.4.0
Depends: R (≥ 3.6.0), methods
Imports: Matrix (≥ 1.2), 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: 2020-04-01
Author: Dmitriy Selivanov ORCID iD [aut, cre, cph], 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 <ds at rexy.ai>
BugReports: https://github.com/rexyai/rsparse/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/rexyai/rsparse
NeedsCompilation: yes
Materials: README NEWS
In views: MissingData
CRAN checks: rsparse results

Downloads:

Reference manual: rsparse.pdf
Package source: rsparse_0.4.0.tar.gz
Windows binaries: r-devel: rsparse_0.4.0.zip, r-release: rsparse_0.4.0.zip, r-oldrel: rsparse_0.4.0.zip
macOS binaries: r-release: rsparse_0.4.0.tgz, r-oldrel: rsparse_0.4.0.tgz
Old sources: rsparse archive

Reverse dependencies:

Reverse imports: LSX, text2vec
Reverse suggests: cmfrec

Linking:

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