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We propose to use sparse regression model to achieve variable selection while accounting for graphconstraints 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 


Author  Li Chen, Jun Chen 
Date of publication  20150719 09:52:47 
Maintainer  Li Chen <li.chen@emory.edu> 
License  GPL2 
Version  1.0.3 
Package repository  View on CRAN 
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