Package: APML0
Type: Package
Title: Augmented and Penalized Minimization Method L0
Version: 0.11
Date: 2026-06-13
Authors@R: c(
    person("Xiang", "Li", email = "spiritcoke@gmail.com", role = c("aut", "cre")),
    person("Shanghong", "Xie", role = "aut"),
    person("Donglin", "Zeng", role = "aut"),
    person("Yuanjia", "Wang", role = "aut"))
Description: 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 the L0 penalty problem by simultaneously selecting
        regularization parameters and performing hard-thresholding or
        selecting the 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. The
        package uses a one-step coordinate descent algorithm and runs
        extremely fast by taking into account the sparsity structure of
        coefficients. It can handle very high dimensional data and has
        superior selection performance.
License: GPL (>= 2)
Encoding: UTF-8
Language: en-US
URL: https://github.com/LeeSprite/APML0
BugReports: https://github.com/LeeSprite/APML0/issues
Imports: Rcpp (>= 0.12.12)
LinkingTo: Rcpp, RcppEigen
Depends: Matrix (>= 1.2-10)
NeedsCompilation: yes
Packaged: 2026-06-19 18:38:03 UTC; root
Author: Xiang Li [aut, cre], Shanghong Xie [aut], Donglin Zeng [aut],
        Yuanjia Wang [aut]
Maintainer: Xiang Li <spiritcoke@gmail.com>
Repository: https://cran.r-universe.dev
Date/Publication: 2026-06-19 18:11:21 UTC
RemoteUrl: https://github.com/cran/APML0
RemoteRef: HEAD
RemoteSha: 6f232b0dc82bde5cf48e9e99623797603958a57b