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L0Learn  

Fast Algorithms for Best Subset Selection
View on CRAN: Click here


Download and install L0Learn package within the R console
Install from CRAN:
install.packages("L0Learn")

Install from Github:
library("remotes")
install_github("cran/L0Learn")

Install by package version:
library("remotes")
install_version("L0Learn", "2.1.0")



Attach the package and use:
library("L0Learn")
Maintained by
Hussein Hazimeh
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-07-01
Latest Update: 2023-03-07
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) .
How to cite:
Hussein Hazimeh (2018). L0Learn: Fast Algorithms for Best Subset Selection. R package version 2.1.0, https://cran.r-project.org/web/packages/L0Learn. Accessed 22 Dec. 2024.
Previous versions and publish date:
1.0.0 (2018-07-01 18:20), 1.0.2 (2018-07-03 00:50), 1.0.7 (2018-09-11 06:30), 1.0.8 (2018-10-29 01:00), 1.1.0 (2019-04-08 22:33), 1.2.0 (2019-08-30 17:00), 2.0.0 (2021-03-04 07:20), 2.0.3 (2021-04-03 15:50)
Other packages that cited L0Learn R package
View L0Learn citation profile
Other R packages that L0Learn depends, imports, suggests or enhances
Complete documentation for L0Learn
Functions, R codes and Examples using the L0Learn R package
Some associated functions: GenSynthetic . GenSyntheticHighCorr . L0Learn-package . L0Learn.cvfit . L0Learn.fit . coef.L0Learn . plot.L0Learn . plot.L0LearnCV . predict.L0Learn . print.L0Learn . 
Some associated R codes: L0Learn.R . RcppExports.R . coef.R . cvfit.R . fit.R . genhighcorr.R . gensynthetic.R . plot.R . predict.R . print.R .  Full L0Learn package functions and examples
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