Package: L0Learn 2.1.0
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:
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
Last updated 2 years agofrom:3912974251. Checks:OK: 1 NOTE: 1. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 28 2024 |
R-4.5-linux-x86_64 | NOTE | Dec 28 2024 |
Exports:GenSyntheticL0Learn.cvfitL0Learn.fit
Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrR6RColorBrewerRcppRcppArmadilloreshape2rlangscalesstringistringrtibbleutf8vctrsviridisLitewithr
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 |