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.
| Version: | 1.0.3 |
| Depends: | Rcpp (≥ 0.11.0) |
| LinkingTo: | Rcpp, RcppArmadillo |
| Published: | 2015-07-19 |
| Author: | Li Chen, Jun Chen |
| Maintainer: | Li Chen <li.chen at emory.edu> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| CRAN checks: | glmgraph results |
| Reference manual: | glmgraph.pdf |
| Package source: | glmgraph_1.0.3.tar.gz |
| Windows binaries: | r-devel: glmgraph_1.0.3.zip, r-release: glmgraph_1.0.3.zip, r-oldrel: glmgraph_1.0.3.zip |
| macOS binaries: | r-release: glmgraph_1.0.3.tgz, r-oldrel: glmgraph_1.0.3.tgz |
| Old sources: | glmgraph archive |
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