Package: l0ara 0.1.6

Wenchuan Guo

l0ara: Sparse Generalized Linear Model with L0 Approximation for Feature Selection

An efficient procedure for feature selection for generalized linear models with L0 penalty, including linear, logistic, Poisson, gamma, inverse Gaussian regression. Adaptive ridge algorithms are used to fit the models.

Authors:Wenchuan Guo, Shujie Ma, Zhenqiu Liu

l0ara_0.1.6.tar.gz
l0ara_0.1.6.tar.gz(r-4.5-noble)l0ara_0.1.6.tar.gz(r-4.4-noble)
l0ara_0.1.6.tgz(r-4.4-emscripten)l0ara_0.1.6.tgz(r-4.3-emscripten)
l0ara.pdf |l0ara.html
l0ara/json (API)
NEWS

# Install 'l0ara' in R:
install.packages('l0ara', repos = 'https://cloud.r-project.org')
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

1.70 score 153 downloads 2 exports 2 dependencies

Last updated 5 years agofrom:30303f9789. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 21 2025
R-4.5-linux-x86_64OKMar 21 2025
R-4.4-linux-x86_64OKMar 21 2025

Exports:cv.l0aral0ara

Dependencies:RcppRcppArmadillo

Citation

To cite package ‘l0ara’ in publications use:

Guo W, Ma S, Liu Z (2020). l0ara: Sparse Generalized Linear Model with L0 Approximation for Feature Selection. R package version 0.1.6, https://CRAN.R-project.org/package=l0ara.

ATTENTION: This citation information has been auto-generated from the package DESCRIPTION file and may need manual editing, see ‘help("citation")’.

Corresponding BibTeX entry:

  @Manual{,
    title = {l0ara: Sparse Generalized Linear Model with L0
      Approximation for Feature Selection},
    author = {Wenchuan Guo and Shujie Ma and Zhenqiu Liu},
    year = {2020},
    note = {R package version 0.1.6},
    url = {https://CRAN.R-project.org/package=l0ara},
  }

Readme and manuals

l0ara

What is it?

l0ara fits regularization for linear or generalized linear models with L0 penalty. Adaptive ridge algorithms are used to fit the models.

Installation

To install the latest version from github :

install.packages("devtools")
devtools::install_github("wguo1990/l0ara")

To install from CRAN:

install.packages("l0ara")