Package: L0Learn 2.1.0

Hussein Hazimeh

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>.

Authors:Hussein Hazimeh [aut, cre], Rahul Mazumder [aut], Tim Nonet [aut]

L0Learn_2.1.0.tar.gz
L0Learn_2.1.0.tar.gz(r-4.5-noble)L0Learn_2.1.0.tar.gz(r-4.4-noble)
L0Learn_2.1.0.tgz(r-4.4-emscripten)L0Learn_2.1.0.tgz(r-4.3-emscripten)
L0Learn.pdf |L0Learn.html✨
L0Learn/json (API)

# Install 'L0Learn' in R:

install.packages('L0Learn', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/hazimehh/l0learn/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

2.96 score 91 scripts 811 downloads 1 mentions 3 exports 34 dependencies

Last updated 2 years agofrom:3912974251. Checks:OK: 1 NOTE: 1. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Sep 29 2024
R-4.5-linux-x86_64	NOTE	Sep 29 2024

Exports:GenSynthetic L0Learn.cvfit L0Learn.fit

Dependencies:cli colorspace fansi farver ggplot2 glue gtable isoband labeling lattice lifecycle magrittr MASS Matrix mgcv munsell nlme pillar pkgconfig plyr R6 RColorBrewer Rcpp RcppArmadillo reshape2 rlang scales stringi stringr tibble utf8 vctrs viridisLite withr

L0Learn Vignette

Hussein Hazimeh, Rahul Mazumder, and Tim Nonet, Massachusetts Institute of Technology (MIT)

Rendered fromL0Learn-vignette.Rmdusingknitr::rmarkdownon Sep 29 2024.

Last update: 2023-03-07
Started: 2018-07-01

Citation

Readme and manuals

Help Manual

Help page	Topics
A package for L0-regularized learning	L0Learn-package
Extract Solutions	coef.L0Learn coef.L0LearnCV
Generate Synthetic Data	GenSynthetic
Generate Exponential Correlated Synthetic Data	GenSyntheticHighCorr
Generate Logistic Synthetic Data	GenSyntheticLogistic
Cross Validation	L0Learn.cvfit
Fit an L0-regularized model	L0Learn.fit
Plot Regularization Path	plot.L0Learn
Plot Cross-validation Errors	plot.L0LearnCV
Predict Response	predict.L0Learn predict.L0LearnCV
Print L0Learn.fit object	print.L0Learn print.L0LearnCV