L0Learn: Fast Algorithms for Best Subset Selection (original) (raw)
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 |
| BugReports: | https://github.com/hazimehh/L0Learn/issues |
| License: | MIT + file |
| URL: | https://github.com/hazimehh/L0Learn https://pubsonline.informs.org/doi/10.1287/opre.2019.1919 |
| NeedsCompilation: | yes |
| Materials: | |
| CRAN checks: | L0Learn results |
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