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);
the link is provided in the URL field below.
| Version: |
2.0.3 |
| Depends: |
R (≥ 3.3.0) |
| Imports: |
Rcpp (≥ 0.12.13), Matrix, methods, ggplot2, reshape2, MASS |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
knitr, rmarkdown, testthat, pracma, raster |
| Published: |
2021-04-03 |
| Author: |
Hussein Hazimeh [aut, cre],
Rahul Mazumder [aut],
Tim Nonet [aut] |
| Maintainer: |
Hussein Hazimeh <hazimeh at mit.edu> |
| BugReports: |
https://github.com/hazimehh/L0Learn/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://pubsonline.informs.org/doi/10.1287/opre.2019.1919
https://github.com/hazimehh |
| NeedsCompilation: |
yes |
| SystemRequirements: |
C++11 |
| Materials: |
ChangeLog |
| CRAN checks: |
L0Learn results |
Documentation:
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=L0Learn
to link to this page.