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message: 'To cite package "rsparse" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'rsparse: Statistical Learning on Sparse Matrices'
version: 0.5.3
doi: 10.32614/CRAN.package.rsparse
abstract: '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(, <https://doi.org/10.1145/2487575.2488200>)
  2) Factorization Machines via SGD, as per Rendle (2010, <https://doi.org/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, <https://doi.org/10.1109/ICDM.2008.22>) 2) Maximum-Margin Matrix
  Factorization via ALS, paper by Rennie, Srebro (2005, <https://doi.org/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, <https://doi.org/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.'
authors:
- family-names: Selivanov
  given-names: Dmitriy
  email: selivanov.dmitriy@gmail.com
  orcid: https://orcid.org/0000-0001-5413-1506
repository: https://dselivanov.r-universe.dev
repository-code: https://github.com/dselivanov/rsparse
commit: 54f7e6a8e81d568c0f22199d84129a0b1cbbf27c
url: https://github.com/dselivanov/rsparse
date-released: '2025-02-17'
contact:
- family-names: Selivanov
  given-names: Dmitriy
  email: selivanov.dmitriy@gmail.com
  orcid: https://orcid.org/0000-0001-5413-1506