Package: L0Learn
Type: Package
Title: Fast Algorithms for Best Subset Selection
Version: 2.1.0
Date: 2023-03-04
Authors@R: c(
    person("Hussein", "Hazimeh", email = "husseinhaz@gmail.com", role = c("aut", "cre")),
    person("Rahul", "Mazumder", email = "rahulmaz@mit.edu", role = "aut"),
    person("Tim", "Nonet", email = "tim.nonet@gmail.com", role = "aut"))
Description: Highly optimized toolkit for approximately solving L0-regularized learning problems (a.k.a. best subset selection).
    The algorithms are based on coordinate descent and local combinatorial search.
    For more details, check the paper by Hazimeh and Mazumder (2020) <doi:10.1287/opre.2019.1919>.
URL: https://github.com/hazimehh/L0Learn
        https://pubsonline.informs.org/doi/10.1287/opre.2019.1919
BugReports: https://github.com/hazimehh/L0Learn/issues
License: MIT + file LICENSE
Depends: R (>= 3.3.0)
Imports: Rcpp (>= 0.12.13), Matrix, methods, ggplot2, reshape2, MASS
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.2.3
Encoding: UTF-8
Suggests: knitr, rmarkdown, testthat, pracma, raster, covr
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2023-03-04 19:02:37 UTC; hh
Author: Hussein Hazimeh [aut, cre],
  Rahul Mazumder [aut],
  Tim Nonet [aut]
Maintainer: Hussein Hazimeh <husseinhaz@gmail.com>
Repository: CRAN
Date/Publication: 2023-03-07 08:00:18 UTC
Built: R 4.2.3; x86_64-w64-mingw32; 2024-04-24 01:57:02 UTC; windows
ExperimentalWindowsRuntime: ucrt
Archs: x64
