person("A. Bradley", "Duthie", email = "brad.duthie@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-8343-4995"))
A. Bradley Duthie [aut, cre] (<https://orcid.org/0000-0001-8343-4995>)

This is a package with different card games (e.g. uno,
poker, ...) and using a NWS Server as card table. You can play
the games with your friends in the whole world. Just install a
NWS Server at one machine, send the login data to your friends
and start the game.

This package provides log penalized linear model estimation.
It calculates regularization paths via coordinate descent.  The log penalty, s*log(r+|b|), spans the range of non-convex cost functions between L0 and L1 norms.  As much as possible, usage is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms).

This package implements the gamma lasso algorithm for regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms.  As much as possible, usage is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms).
Taddy (2013), The Gamma Lasso.
http://arxiv.org/abs/1308.5623

The gamma lasso algorithm provides regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms.  As much as possible, usage for this package is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms).  For details see: Taddy (2015), One-Step Estimator Paths for Concave Regularization, http://arxiv.org/abs/1308.5623.

R (>= 2.15), Matrix, methods, graphics, stats

The gamma lasso algorithm provides regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms.  As much as possible, usage for this package is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms).  For details see: Taddy (2017 JCGS), One-Step Estimator Paths for Concave Regularization, <arXiv:1308.5623>.