Extends the glmnet package with "relaxation", done by running glmnet once on the entire predictor matrix, then again on each different subset of variables from along the regularization path. Relaxation may lead to improved prediction accuracy for truly sparse data generating models, as well as fewer false positives (i.e. fewer noncontributing predictors in the final model). Penalty may be lasso (alpha = 1) or elastic net (0 < alpha < 1). For this version, family may be "gaussian" or "binomial" only. Takes advantage of fast FORTRAN code from the glmnet package.
Package details 


Author  Stephan Ritter, Alan Hubbard 
Date of publication  20130816 18:29:00 
Maintainer  Stephan Ritter <stephanritterRpacks@gmail.com> 
License  GPL (>= 2) 
Version  0.32 
URL  http://cran.rproject.org/package=relaxnet 
Package repository  View on CRAN 
Installation 
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