The package implements the concave 1norm and 2norm group penalty in linear and logistic regression. The concave 1norm group penalty includes 1norm group SCAD and 1norm group MCP. The concave 1norm group penalty has bilevel selection features. That is it selects variables at group and individual levels with proper tuning parameters. The concave 1norm group penalty is robust to misspecified group information. The concave 2norm group penalty includes 2norm group SCAD and 2norm group MCP. The concave 2norm group penalty select variable at group level only. The package can also fit group Lasso, which is a special case of concave 2norm group penalty when the regularization parameter kappa equals zero. The highly efficient (block) coordinate descent algorithm (CDA) is used to compute the solutions for both penalties in linear models. The highly stable and efficient (block) CDA and minimizationmajorization approach are used to compute the solution for both penalties in logistic models. In the computation of solution surface, the solution path along kappa is implemented. This provides a better solution path compared to the solution path along lambda. The package also provides a tuning parameter selection method based on crossvalidation for both linear and logistic models.
Package details 


Author  Dingfeng Jiang <dingfengjiang at gmail.com> 
Date of publication  20140217 22:40:28 
Maintainer  Dingfeng Jiang <dingfengjiang@gmail.com> 
License  GPL (>= 2) 
Version  2.10 
URL  http://www.rproject.org 
Package repository  View on CRAN 
Installation 
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