Package: rsparse
Type: Package
Title: Statistical Learning on Sparse Matrices
Version: 0.4.0
Authors@R: c(
    person("Dmitriy", "Selivanov", role=c("aut", "cre", "cph"), email="ds@rexy.ai", 
    comment = c(ORCID = "0000-0001-5413-1506")),
    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 <ds@rexy.ai>
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, <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.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
ByteCompile: true
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
StagedInstall: TRUE
URL: https://github.com/rexyai/rsparse
BugReports: https://github.com/rexyai/rsparse/issues
RoxygenNote: 7.1.0
NeedsCompilation: yes
Packaged: 2020-04-01 16:55:58 UTC; dselivanov
Author: Dmitriy Selivanov [aut, cre, cph]
    (<https://orcid.org/0000-0001-5413-1506>),
  Drew Schmidt [ctb] (configure script for BLAS, LAPACK detection),
  Wei-Chen Chen [ctb] (configure script and work on linking to float
    package)
Repository: CRAN
Date/Publication: 2020-04-01 17:50:02 UTC
Built: R 4.0.2; x86_64-apple-darwin17.0; 2020-07-16 00:41:22 UTC; unix
Archs: rsparse.so.dSYM
