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