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:
Downloads:
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