Description: penalized linear model estimation
References: Taddy (2013), The Gamma Lasso

Description: 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).
References: Taddy (2013), "The Gamma Lasso".

Description: 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).
References: Taddy (2013), The Gamma Lasso.

Description: 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.

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

Description: 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>.

Description: 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>.