glmgraph (1.0.1)

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.

Maintainer: Li Chen
Author(s): Li Chen, Jun Chen

License: GPL-2

Uses: Does not use any package

Released over 4 years ago.