Packaged: Sat Feb 16 23:20:27 2008; jjgoeman

Description: A package for fitting possibly high dimensional penalized regression models. The penalty structure can be any combination of an L1 penalty (lasso), an L2 penalty (ridge) and a positivity constraint on the regression coefficients. The supported regression models are linear, logistic and poisson regression and the Cox Proportional Hazards model. Cross-validation routines allow optimization of the tuning parameters.
Collate: penfit.R breslow.R penalized.R core.R checkinput.R cvl.R contrasts.R Brent.R plotpath.R cox.R logit.R linear.R poisson.R
Packaged: Fri Apr 25 16:31:56 2008; jjgoeman

Packaged: Mon Sep 29 16:02:00 2008; jjgoeman

Title: L1 (lasso) and L2 (ridge) penalized estimation in GLMs and in
Description: A package for fitting possibly high dimensional penalized
regression models. The penalty structure can be any combination
of an L1 penalty (lasso), an L2 penalty (ridge) and a
positivity constraint on the regression coefficients. The
supported regression models are linear, logistic and poisson
regression and the Cox Proportional Hazards model.
Cross-validation routines allow optimization of the tuning
Collate: penfit.R breslow.R penalized.R core.R checkinput.R cvl.R
contrasts.R Brent.R plotpath.R cox.R logit.R linear.R poisson.R
Packaged: Thu Mar 12 17:57:33 2009; jjgoeman

Packaged: Fri Aug  3 15:40:02 2007; jjgoeman

Depends: R (>= 2.10.0), methods, survival

Author: Jelle Goeman <j.j.goeman@lumc.nl>, Rosa Meijer
Collate: onattach.R penfit.R breslow.R penalized.R core.R checkinput.R
cvl.R contrasts.R Brent.R plotpath.R cox.R logit.R linear.R

Collate: objects.R breslow.R user.R core.R contrasts.R Brent.R plotpath.R cox.R logit.R linear.R
Packaged: Mon Aug 13 17:42:14 2007; jjgoeman

Title: L1 (lasso and fused lasso) and L2 (ridge) penalized estimation
Author: Jelle Goeman <j.j.goeman@lumc.nl>, Rosa Meijer, Nimisha
of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge)
and a positivity constraint on the regression coefficients. The