L0Learn: Fast Algorithms for Best Subset Selection

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); the link is provided in the URL field below.

Version: 2.0.3
Depends: R (≥ 3.3.0)
Imports: Rcpp (≥ 0.12.13), Matrix, methods, ggplot2, reshape2, MASS
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, testthat, pracma, raster
Published: 2021-04-03
Author: Hussein Hazimeh [aut, cre], Rahul Mazumder [aut], Tim Nonet [aut]
Maintainer: Hussein Hazimeh <hazimeh at mit.edu>
BugReports: https://github.com/hazimehh/L0Learn/issues
License: MIT + file LICENSE
URL: https://pubsonline.informs.org/doi/10.1287/opre.2019.1919 https://github.com/hazimehh
NeedsCompilation: yes
SystemRequirements: C++11
Materials: ChangeLog
CRAN checks: L0Learn results

Downloads:

Reference manual: L0Learn.pdf
Vignettes: L0Learn Vignette
Package source: L0Learn_2.0.3.tar.gz
Windows binaries: r-devel: L0Learn_2.0.3.zip, r-devel-UCRT: L0Learn_2.0.3.zip, r-release: L0Learn_2.0.3.zip, r-oldrel: L0Learn_2.0.3.zip
macOS binaries: r-release (arm64): L0Learn_2.0.3.tgz, r-release (x86_64): L0Learn_2.0.3.tgz, r-oldrel: L0Learn_2.0.3.tgz
Old sources: L0Learn archive

Reverse dependencies:

Reverse suggests: susieR

Linking:

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