DESCRIPTION

Package: rsparse
Type: Package
Title: Statistical Learning on Sparse Matrices
Version: 0.5.2
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