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.
|Author||Li Chen, Jun Chen|
|Maintainer||Li Chen <[email protected]>|
|Package repository||View on CRAN|
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