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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.
Package details |
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Author | Li Chen, Jun Chen |
Maintainer | Li Chen <li.chen@emory.edu> |
License | GPL-2 |
Version | 1.0.3 |
Package repository | View on CRAN |
Installation |
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