glmgraph (1.0.3)

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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: Rcpp

Released over 4 years ago.

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