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.

Version: 1.0.3
Depends: Rcpp (≥ 0.11.0)
LinkingTo: Rcpp, RcppArmadillo
Published: 2015-07-19
Author: Li Chen, Jun Chen
Maintainer: Li Chen <li.chen at>
License: GPL-2
NeedsCompilation: yes
CRAN checks: glmgraph results


Reference manual: glmgraph.pdf
Package source: glmgraph_1.0.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: glmgraph_1.0.3.tgz
OS X Mavericks binaries: r-oldrel: glmgraph_1.0.3.tgz
Old sources: glmgraph archive


Please use the canonical form to link to this page.