L0Learn

Fast Algorithms for Best Subset Selection

v2.1.0 · Mar 7, 2023 · MIT + file LICENSE

Description

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>.

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r-devel-linux-x86_64-debian-clang	NOTE	333.4s
r-devel-linux-x86_64-debian-gcc	NOTE	231.1s
r-devel-linux-x86_64-fedora-clang	NOTE	458.6s
r-devel-linux-x86_64-fedora-gcc	NOTE	592.2s
r-devel-macos-arm64	NOTE	53s
r-devel-windows-x86_64	NOTE	395s
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r-oldrel-windows-x86_64	NOTE	454s
r-patched-linux-x86_64	NOTE	315.5s
r-release-linux-x86_64	NOTE	293.3s
r-release-macos-arm64	NOTE
r-release-macos-x86_64	NOTE	189s
r-release-windows-x86_64	NOTE	360s

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NOTE 0 OK · 14 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026

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Reverse Dependencies (2)

imports

inferCSN modelSelection

Dependency Network

Version History

new 2.1.0 Mar 10, 2026

updated 2.1.0 ← 2.0.3 diff Mar 6, 2023

updated 2.0.3 ← 2.0.0 diff Apr 2, 2021

updated 2.0.0 ← 1.2.0 diff Mar 3, 2021

updated 1.2.0 ← 1.1.0 diff Aug 29, 2019

updated 1.1.0 ← 1.0.8 diff Apr 7, 2019

updated 1.0.8 ← 1.0.7 diff Oct 28, 2018

updated 1.0.7 ← 1.0.2 diff Sep 10, 2018

updated 1.0.2 ← 1.0.0 diff Jul 2, 2018

new 1.0.0 Jun 30, 2018

Maintainer

Hussein Hazimeh

Dependencies

Depends

R (>= 3.3.0)

Imports

Rcpp (>= 0.12.13) Matrix methods ggplot2 reshape2 MASS

Suggests

knitr rmarkdown testthat pracma raster covr

Compilation

Needs compilation

First Published

Jun 30, 2018

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