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.

Package details

AuthorLi Chen, Jun Chen
MaintainerLi Chen <[email protected]>
Package repositoryView on CRAN
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glmgraph documentation built on May 1, 2019, 7:04 p.m.