Flexible R-Vines Estimation Using Bivariate Penalized Splines
R (>= 2.15.1), lattice,igraph, fda, Matrix
quadprog, latticeExtra, doParallel, copula, foreach
Offers routines for estimating densities and copula distribution of R-vines using penalized splines.

Penalized estimation in Generalized Linear Models
L1 (lasso) and L2 (ridge) estimation in Generalized Linear Models and in the Cox Proportional Hazards model
objects.R user.R core.R contrasts.R Brent.R plotpath.R cox.R logit.R linear.R

L1 (lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model
L1 (lasso) and L2 (ridge) penalized estimation in Generalized Linear Models and in the Cox Proportional Hazards model

objects.R breslow.R user.R core.R cvl.R contrasts.R Brent.R plotpath.R cox.R logit.R linear.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

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 and logistic regression and the Cox Proportional Hazards model. Cross-validation routines allow optimization of the tuning parameters.

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.
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

L1 (lasso) and L2 (ridge) penalized estimation in GLMs and in
the Cox model
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.
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