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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 |
| 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 |
| Reference manual: | L0Learn.pdf |
| Vignettes: |
L0Learn Vignette |
| 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 |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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