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.html , L0Learn.pdf
Vignettes:	L0Learn Vignette (source, R code)

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

Reverse dependencies:

Reverse imports:

inferCSN, modelSelection

Linking:

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