Package: l0ara 0.1.7

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.zip(r-4.5-x86_64)
l0ara_0.1.7.tgz(r-4.6-x86_64)l0ara_0.1.7.tgz(r-4.6-arm64)l0ara_0.1.7.tgz(r-4.5-x86_64)l0ara_0.1.7.tgz(r-4.5-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://wgost.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 435 downloads 2 exports 2 dependencies

Last updated from:1cb2c0bf3d. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK125
linux-devel-x86_64OK108
source / vignettesOK185
linux-release-arm64OK120
linux-release-x86_64OK168
macos-release-arm64OK199
macos-release-x86_64OK354
macos-oldrel-arm64OK204
macos-oldrel-x86_64OK316
windows-devel-arm64OK305
windows-devel-x86_64OK116
windows-release-arm64OK361
windows-release-x86_64OK101
windows-oldrel-x86_64OK104
wasm-releaseOK117

Exports:cv.l0aral0ara

Dependencies:RcppRcppArmadillo