Title: Regularization paths for regression models with grouped covariates
Description: Efficient algorithms for fitting the regularization path for linear or logistic regression models penalized by the group lasso, group bridge, or group MCP methods.  The algorithm is based on the idea of a locally approximated coordinate descent.  A technical report describing the methods and algorithms is available at <http://www.stat.uiowa.edu/techrep/tr393.pdf>.
Packaged: Sun Nov 23 07:10:47 2008; pbreheny

Title: Regularization paths for regression models with grouped
Description: Efficient algorithms for fitting the regularization path
for linear or logistic regression models penalized by the group
lasso, group bridge, or group MCP methods.  The algorithm is
based on the idea of a locally approximated coordinate descent.

Suggests: testthat, glmnet, ncvreg, Matrix
of linear or logistic regression models with grouped penalties,
such as group lasso, group MCP, and group SCAD.  The algorithms
are based on the idea of either locally approximated coordinate
descent or group descent, depending on the penalty.

Description: Efficient algorithms for fitting the regularization path of
linear or logistic regression models with grouped penalties.  This
includes group selection methods such as group lasso, group MCP, and
group SCAD as well as bi-level selection methods such as the group
exponential lasso, the composite MCP, and the group bridge.

Title: Regularization Paths for Regression Models with Grouped

Author: Patrick Breheny [aut, cre], Yaohui Zeng [ctb]

person("Patrick", "Breheny", role=c("aut","cre"), email="patrick-breheny@uiowa.edu", comment=c(ORCID="000-0002-0650-1119")),
Description: Efficient algorithms for fitting the regularization path of linear
regression, GLM, and Cox regression models with grouped penalties.  This
Author: Patrick Breheny [aut, cre] (<https://orcid.org/000-0002-0650-1119>),

person("Patrick", "Breheny", role=c("aut","cre"), email="patrick-breheny@uiowa.edu", comment=c(ORCID="0000-0002-0650-1119")),
Author: Patrick Breheny [aut, cre] (<https://orcid.org/0000-0002-0650-1119>),