Package: glmgraph 1.0.3

Li Chen

glmgraph: Graph-Constrained Regularization for Sparse Generalized Linear Models

We propose to use sparse regression model to achieve variable selection while accounting for graph-constraints among coefficients. Different linear combination of a sparsity penalty(L1) and a smoothness(MCP) penalty has been used, which induces both sparsity of the solution and certain smoothness on the linear coefficients.

Authors:Li Chen, Jun Chen

glmgraph_1.0.3.tar.gz
glmgraph_1.0.3.zip(r-4.7-x86_64)glmgraph_1.0.3.zip(r-4.7-arm64)glmgraph_1.0.3.zip(r-4.6-x86_64)glmgraph_1.0.3.zip(r-4.6-arm64)glmgraph_1.0.3.zip(r-4.5-x86_64)
glmgraph_1.0.3.tgz(r-4.6-x86_64)glmgraph_1.0.3.tgz(r-4.6-arm64)glmgraph_1.0.3.tgz(r-4.5-x86_64)glmgraph_1.0.3.tgz(r-4.5-arm64)
glmgraph_1.0.3.tar.gz(r-4.7-arm64)glmgraph_1.0.3.tar.gz(r-4.7-x86_64)glmgraph_1.0.3.tar.gz(r-4.6-arm64)glmgraph_1.0.3.tar.gz(r-4.6-x86_64)
glmgraph_1.0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
glmgraph/json (API)

# Install 'glmgraph' in R:
install.packages('glmgraph', repos = c('https://djghosh1123.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • 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.

cpp

1.94 score 4 stars 22 scripts 4 downloads 2 exports 2 dependencies

Last updated from:c018301951 (on master). Checks:15 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK125
linux-devel-x86_64OK129
source / vignettesOK178
linux-release-arm64OK116
linux-release-x86_64OK127
macos-release-arm64OK129
macos-release-x86_64OK333
macos-oldrel-arm64OK98
macos-oldrel-x86_64OK451
windows-devel-arm64OK361
windows-devel-x86_64OK172
windows-release-arm64OK359
windows-release-x86_64OK140
windows-oldrel-x86_64OK130
wasm-releaseOK113

Exports:cv.glmgraphglmgraph

Dependencies:RcppRcppArmadillo