Highly optimized toolkit for approximately solving L0-regularized learning problems (aka best subset selection). The algorithms are based on coordinate descent and local combinatorial search. For more details, check the paper by Hazimeh and Mazumder (2018) <arXiv:1803.01454>; the link is provided in the URL field below.
| Version: | 1.2.0 |
| Depends: | R (≥ 3.3.0) |
| Imports: | Rcpp (≥ 0.12.13), Matrix, methods, ggplot2, reshape2 |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | knitr, rmarkdown |
| Published: | 2019-08-30 |
| Author: | Hussein Hazimeh, Rahul Mazumder |
| Maintainer: | Hussein Hazimeh <hazimeh at mit.edu> |
| License: | MIT + file LICENSE |
| URL: | https://arxiv.org/abs/1803.01454 |
| NeedsCompilation: | yes |
| Materials: | ChangeLog |
| CRAN checks: | L0Learn results |
| Reference manual: | L0Learn.pdf |
| Vignettes: |
L0Learn Vignette |
| Package source: | L0Learn_1.2.0.tar.gz |
| Windows binaries: | r-devel: L0Learn_1.2.0.zip, r-release: L0Learn_1.2.0.zip, r-oldrel: L0Learn_1.2.0.zip |
| macOS binaries: | r-release: L0Learn_1.2.0.tgz, r-oldrel: L0Learn_1.2.0.tgz |
| Old sources: | L0Learn archive |
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