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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) <doi:10.1287/opre.2019.1919>.

Version: 2.1.0
Depends: R (≥ 3.3.0)
Imports: Rcpp (≥ 0.12.13), Matrix, methods, ggplot2, reshape2, MASS
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, testthat, pracma, raster, covr
Published: 2023-03-07
DOI: 10.32614/CRAN.package.L0Learn
Author: Hussein Hazimeh [aut, cre], Rahul Mazumder [aut], Tim Nonet [aut]
Maintainer: Hussein Hazimeh <husseinhaz at gmail.com>
BugReports: https://github.com/hazimehh/L0Learn/issues
License: MIT + file LICENSE
URL: https://github.com/hazimehh/L0Learn https://pubsonline.informs.org/doi/10.1287/opre.2019.1919
NeedsCompilation: yes
Materials: ChangeLog
CRAN checks: L0Learn results

Documentation:

Reference manual: L0Learn.pdf
Vignettes: L0Learn Vignette

Downloads:

Package source: L0Learn_2.1.0.tar.gz
Windows binaries: r-devel: L0Learn_2.1.0.zip, r-release: L0Learn_2.1.0.zip, r-oldrel: L0Learn_2.1.0.zip
macOS binaries: r-release (arm64): L0Learn_2.1.0.tgz, r-oldrel (arm64): L0Learn_2.1.0.tgz, r-release (x86_64): L0Learn_2.1.0.tgz, r-oldrel (x86_64): L0Learn_2.1.0.tgz
Old sources: L0Learn archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=L0Learn to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.