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.


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install.packages("glmgraph")

1.0.3 by Li Chen, 4 years ago


Browse source code at https://github.com/cran/glmgraph


Authors: Li Chen , Jun Chen


Documentation:   PDF Manual  


GPL-2 license


Depends on Rcpp

Linking to Rcpp, RcppArmadillo


See at CRAN