Package: rsparse
Type: Package
Title: Statistical Learning on Sparse Matrices
Version: 0.5.3
Authors@R: c(
    person("Dmitriy", "Selivanov", role=c("aut", "cre", "cph"), email="selivanov.dmitriy@gmail.com", 
    comment = c(ORCID = "0000-0001-5413-1506")),
    person("David", "Cortes", role="ctb"),
    person("Drew", "Schmidt", role="ctb", comment="configure script for BLAS, LAPACK detection"), 
    person("Wei-Chen", "Chen", role="ctb", comment="configure script and work on linking to float package")
    )
Maintainer: Dmitriy Selivanov <selivanov.dmitriy@gmail.com>
Description: 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.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
ByteCompile: true
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
StagedInstall: TRUE
URL: https://github.com/dselivanov/rsparse
BugReports: https://github.com/dselivanov/rsparse/issues
RoxygenNote: 7.3.1
NeedsCompilation: yes
Repository: https://dselivanov.r-universe.dev
Date/Publication: 2025-02-17 00:59:04 UTC
RemoteUrl: https://github.com/dselivanov/rsparse
RemoteRef: HEAD
RemoteSha: 54f7e6a8e81d568c0f22199d84129a0b1cbbf27c
Packaged: 2026-06-24 10:13:09 UTC; root
Author: 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)