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 + fileLICENSE |
| URL: | https://github.com/hazimehh/L0Learnhttps://pubsonline.informs.org/doi/10.1287/opre.2019.1919 |
| NeedsCompilation: | yes |
| Materials: | ChangeLog |
| CRAN checks: | L0Learn results |
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