APML0: Augmented and Penalized Minimization Method L0

Fit linear, logistic and Cox models regularized with L0, lasso (L1), elastic-net (L1 and L2), or net (L1 and Laplacian) penalty, and their adaptive forms, such as adaptive lasso / elastic-net and net adjusting for signs of linked coefficients. It solves L0 penalty problem by simultaneously selecting regularization parameters and performing hard-thresholding or selecting number of non-zeros. This augmented and penalized minimization method provides an approximation solution to the L0 penalty problem, but runs as fast as L1 regularization problem. The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients. It could deal with very high dimensional data and has superior selection performance.

Version: 0.10
Depends: Matrix (≥ 1.2-10)
Imports: Rcpp (≥ 0.12.12)
LinkingTo: Rcpp, RcppEigen
Published: 2020-01-19
Author: Xiang Li, Shanghong Xie, Donglin Zeng and Yuanjia Wang
Maintainer: Xiang Li <spiritcoke at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: APML0 results


Reference manual: APML0.pdf


Package source: APML0_0.10.tar.gz
Windows binaries: r-devel: APML0_0.10.zip, r-release: APML0_0.10.zip, r-oldrel: APML0_0.10.zip
macOS binaries: r-release (arm64): APML0_0.10.tgz, r-oldrel (arm64): APML0_0.10.tgz, r-release (x86_64): APML0_0.10.tgz, r-oldrel (x86_64): APML0_0.10.tgz
Old sources: APML0 archive


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