Introduction | Installation | Tutorial | Fitting L0 Regression Models | Fitting L0L2 and L0L1 Regression Models | Higher-quality Solutions using Local Search | Cross-validation | Fitting Classification Models | Advanced Options | Sparse Matrix Support | Selection on Subset of Variables | Coefficient Bounds | User-specified Lambda Grids | References
Package: L0Learn 2.0.5
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) <10.1287/opre.2019.1919>.
Authors:
L0Learn_2.0.5.tar.gz
L0Learn_2.0.5.zip(r-4.7)L0Learn_2.0.5.zip(r-4.6)L0Learn_2.0.5.zip(r-4.5)
L0Learn_2.0.5.tgz(r-4.6-x86_64)L0Learn_2.0.5.tgz(r-4.6-arm64)L0Learn_2.0.5.tgz(r-4.5-x86_64)L0Learn_2.0.5.tgz(r-4.5-arm64)
L0Learn_2.0.5.tar.gz(r-4.7-arm64)L0Learn_2.0.5.tar.gz(r-4.7-x86_64)L0Learn_2.0.5.tar.gz(r-4.6-arm64)L0Learn_2.0.5.tar.gz(r-4.6-x86_64)
L0Learn_2.0.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
L0Learn/json (API)
| # Install 'L0Learn' in R: |
| install.packages('L0Learn', repos = c('https://hazimehh.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/hazimehh/l0learn/issues
compressed-sensingfeature-selectionl0-regularizationl0learnmachine-learningregularizationsparse-modelingsparse-regressionopenblascpp
Last updated from:3f695c567e. Checks:8 WARNING, 2 OK, 3 FAIL. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | WARNING | 253 | ||
| linux-devel-x86_64 | WARNING | 247 | ||
| source / vignettes | OK | 363 | ||
| linux-release-arm64 | WARNING | 268 | ||
| linux-release-x86_64 | WARNING | 253 | ||
| macos-release-arm64 | WARNING | 206 | ||
| macos-release-x86_64 | WARNING | 403 | ||
| macos-oldrel-arm64 | FAIL | 110 | ||
| macos-oldrel-x86_64 | FAIL | 327 | ||
| windows-devel | WARNING | 289 | ||
| windows-release | WARNING | 286 | ||
| windows-oldrel | FAIL | 85 | ||
| wasm-release | OK | 215 |
Exports:GenSyntheticL0Learn.cvfitL0Learn.fit
Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixplyrR6RColorBrewerRcppRcppArmadilloreshape2rlangS7scalesstringistringrvctrsviridisLitewithr
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 Expoentential 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 |
