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'))

Peer review:

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

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

openblascpp

2.96 score 92 scripts 877 downloads 1 mentions 3 exports 34 dependencies

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

TargetResultDate
Doc / VignettesOKNov 28 2024
R-4.5-linux-x86_64NOTENov 28 2024

Exports:GenSyntheticL0Learn.cvfitL0Learn.fit

Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrR6RColorBrewerRcppRcppArmadilloreshape2rlangscalesstringistringrtibbleutf8vctrsviridisLitewithr

L0Learn Vignette

Rendered fromL0Learn-vignette.Rmdusingknitr::rmarkdownon Nov 28 2024.

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

Readme and manuals

Help Manual

Help pageTopics
A package for L0-regularized learningL0Learn-package
Extract Solutionscoef.L0Learn coef.L0LearnCV
Generate Synthetic DataGenSynthetic
Generate Exponential Correlated Synthetic DataGenSyntheticHighCorr
Generate Logistic Synthetic DataGenSyntheticLogistic
Cross ValidationL0Learn.cvfit
Fit an L0-regularized modelL0Learn.fit
Plot Regularization Pathplot.L0Learn
Plot Cross-validation Errorsplot.L0LearnCV
Predict Responsepredict.L0Learn predict.L0LearnCV
Print L0Learn.fit objectprint.L0Learn print.L0LearnCV