Package: glinternet 1.0.13

Michael Lim

glinternet: Learning Interactions via Hierarchical Group-Lasso Regularization

Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015) <doi:10.1080/10618600.2014.938812>.

Authors:Michael Lim [aut, cre], Trevor Hastie [aut]

glinternet_1.0.13.tar.gz
glinternet_1.0.13.zip(r-4.7-x86_64)glinternet_1.0.13.zip(r-4.7-arm64)glinternet_1.0.13.zip(r-4.6-x86_64)glinternet_1.0.13.zip(r-4.6-arm64)
glinternet_1.0.13.tar.gz(r-4.7-arm64)glinternet_1.0.13.tar.gz(r-4.7-x86_64)glinternet_1.0.13.tar.gz(r-4.6-arm64)glinternet_1.0.13.tar.gz(r-4.6-x86_64)
glinternet_1.0.13.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
glinternet/json (API)

# Install 'glinternet' in R:
install.packages('glinternet', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

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

openmp

3.67 score 14 stars 2 packages 1 scripts 1.1k downloads 5 mentions 2 exports 0 dependencies

Last updated from:df62cf0e5e. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK77
linux-devel-x86_64OK136
source / vignettesOK139
linux-release-arm64OK70
linux-release-x86_64OK89
windows-devel-arm64OK204
windows-devel-x86_64OK100
windows-release-arm64OK205
windows-release-x86_64OK56
wasm-releaseOK71

Exports:glinternetglinternet.cv

Dependencies: