DESCRIPTION

Package: L0Learn
Type: Package
Title: Fast Algorithms for Best Subset Selection
Version: 2.0.5
Date: 2022-02-13
Authors@R: c(
    person("Hussein", "Hazimeh", email = "hazimeh@mit.edu", 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) <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)
SystemRequirements: C++11
Imports: Rcpp (>= 0.12.13), Matrix, methods, ggplot2, reshape2, MASS
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.1.1
Encoding: UTF-8
Suggests: 
    knitr,
    rmarkdown,
    testthat,
    pracma,
    raster,
    covr
VignetteBuilder: knitr