comment = "Code adapted from 'SLOPE' version 0.1.3, as well as from http://statweb.stanford.edu/~candes/SortedL1/software.html under GNU GPL-3"),
person("Chiara", "Sabatti", role = "ctb",
person("Evan", "Patterson", role = "ctb",
comment = "Code adapted from 'SLOPE' version 0.1.3, as well as from http://statweb.stanford.edu/~candes/SortedL1/software.html under GNU GPL-3"))
Suggests: testthat, knitr, rmarkdown, pander, isotone
Ewout van den Berg [ctb] (Code adapted from 'SLOPE' version 0.1.3, as
Emmanuel Candes [ctb] (Code adapted from 'SLOPE' version 0.1.3, as well
Chiara Sabatti [ctb] (Code adapted from 'SLOPE' version 0.1.3, as well
Evan Patterson [ctb] (Code adapted from 'SLOPE' version 0.1.3, as well

Suggests: testthat, knitr, rmarkdown, pander, isotone, SLOPE

Description: Fits user specified models with group lasso penalty
Packaged: Mon Aug 21 17:31:54 2006; meier

Title: Fitting user specified models with group lasso penalty
Packaged: Tue Feb  6 13:49:12 2007; meier

Title: Fitting user specified models with Group Lasso penalty
Description: Fits user specified models with Group Lasso penalty
Packaged: Wed Jun 27 17:47:52 2007; meier

Packaged: Wed Nov 28 15:28:35 2007; meier

Description: Fits user specified (GAM-) models with Group Lasso penalty
Packaged: Thu Aug  7 16:45:18 2008; meier

Description: Fits user specified (GLM-) models with Group Lasso penalty
Packaged: Wed Feb 11 15:31:32 2009; meier

Packaged: Mon Mar 30 15:52:36 2009; meier

Packaged: Wed Apr 1 11:36:10 2009; meier

Title: Fitting User-Specified Models with Group Lasso Penalty
Description: Fits user-specified (GLM-) models with group lasso penalty.

Title: Standardization for Group Lasso Models with Categorical
Author: Felicitas Detmer and Martin Slawski
Description: Implements the simple and computationally efficient standardization scheme for group lasso models with categorical predictors described in Detmer, Cebral, Slawski (2019) <arXiv:1805.06915>.

Package: grppenalty
Version: 2.1-0
Date: 2013-03-17
Title: Concave 1-norm and 2-norm group penalty in linear and logistic
regression
Author: Dingfeng Jiang <dingfengjiang at gmail.com>
Maintainer: Dingfeng Jiang <dingfengjiang@gmail.com>
Depends: R (>= 2.9.0)
Description: The package implements the concave 1-norm and 2-norm group penalty in linear and logistic regression.
The concave 1-norm group penalty includes 1-norm group SCAD and 1-norm group MCP.
The concave 1-norm group penalty has bi-level selection features. That is it selects variables at group and individual levels with proper tuning parameters.
The concave 1-norm group penalty is robust to mis-specified group information.
The concave 2-norm group penalty includes 2-norm group SCAD and 2-norm group MCP. The concave 2-norm group penalty select variable at group level only.
The package can also fit group Lasso, which is a special case of concave 2-norm group penalty when the regularization parameter kappa equals zero.
The highly efficient (block) coordinate descent algorithm (CDA) is used to compute the solutions for both penalties in linear models.
The highly stable and efficient (block) CDA and minimization-majorization approach are used to compute the solution for both penalties in logistic models.
In the computation of solution surface, the solution path along kappa is implemented.
This provides a better solution path compared to the solution path along lambda.
The package also provides a tuning parameter selection method based on cross-validation for both linear and logistic models.
License: GPL (>= 2)
URL: http://www.r-project.org
Packaged: 2014-02-17 18:12:02 UTC; luwenjing
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-02-17 22:40:28