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:Hussein Hazimeh [aut, cre], Rahul Mazumder [aut], Tim Nonet [aut]

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Peer review:

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

On CRAN:

compressed-sensingfeature-selectionl0-regularizationl0learnmachine-learningregularizationsparse-modelingsparse-regression

3 exports 93 stars 4.12 score 34 dependencies 1 mentions 91 scripts 1.3k downloads

Last updated 1 years agofrom:3f695c567e. Checks:OK: 1 WARNING: 8. Indexed: yes.

Exports:GenSyntheticL0Learn.cvfitL0Learn.fit

Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrR6RColorBrewerRcppRcppArmadilloreshape2rlangscalesstringistringrtibbleutf8vctrsviridisLitewithr