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.

AuthorLi Chen, Jun Chen
Date of publication2015-07-19 09:52:47
MaintainerLi Chen <>

View on CRAN

Questions? Problems? Suggestions? or email at

All documentation is copyright its authors; we didn't write any of that.