Package: l0ara 0.1.7

Wenchuan Guo

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

Fits sparse generalized linear models using an adaptive ridge approximation to an L0 penalty. Supported model families include Gaussian, logistic, Poisson, gamma, and inverse Gaussian regression. The package also provides cross-validation for selecting the penalty parameter.

Authors:Wenchuan Guo [aut, cre], Shujie Ma [aut], Zhenqiu Liu [aut]

l0ara_0.1.7.tar.gz
l0ara_0.1.7.zip(r-4.7-x86_64)l0ara_0.1.7.zip(r-4.7-arm64)l0ara_0.1.7.zip(r-4.6-x86_64)l0ara_0.1.7.zip(r-4.6-arm64)
l0ara_0.1.7.tar.gz(r-4.7-arm64)l0ara_0.1.7.tar.gz(r-4.7-x86_64)l0ara_0.1.7.tar.gz(r-4.6-arm64)l0ara_0.1.7.tar.gz(r-4.6-x86_64)
l0ara_0.1.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
l0ara/json (API)

# Install 'l0ara' in R:
install.packages('l0ara', repos = c('https://cran.r-universe.dev', '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 3 scripts 330 downloads 2 exports 2 dependencies

Last updated from:1cb2c0bf3d. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK112
linux-devel-x86_64OK100
source / vignettesOK163
linux-release-arm64OK123
linux-release-x86_64OK104
windows-devel-arm64OK179
windows-devel-x86_64OK126
windows-release-arm64OK205
windows-release-x86_64OK110
wasm-releaseOK94

Exports:cv.l0aral0ara

Dependencies:RcppRcppArmadillo