grpnet: Group Elastic Net Regularized GLMs and GAMs

Efficient algorithms for fitting generalized linear and additive models with group elastic net penalties as described in Helwig (2024) <doi:10.1080/10618600.2024.2362232>. Implements group LASSO, group MCP, and group SCAD with an optional group ridge penalty. Computes the regularization path for linear regression (gaussian), logistic regression (binomial), multinomial logistic regression (multinomial), log-linear count regression (poisson and negative.binomial), and log-linear continuous regression (gamma and inverse gaussian). Supports default and formula methods for model specification, k-fold cross-validation for tuning the regularization parameters, and nonparametric regression via tensor product reproducing kernel (smoothing spline) basis function expansion.

Version:	0.6
Depends:	R (≥ 3.5.0)
Published:	2024-10-11
DOI:	10.32614/CRAN.package.grpnet
Author:	Nathaniel E. Helwig [aut, cre]
Maintainer:	Nathaniel E. Helwig <helwig at umn.edu>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
Citation:	grpnet citation info
Materials:	ChangeLog
CRAN checks:	grpnet results

Documentation:

Reference manual:

grpnet.pdf

Downloads:

Package source:	grpnet_0.6.tar.gz
Windows binaries:	r-devel: grpnet_0.6.zip, r-release: grpnet_0.6.zip, r-oldrel: grpnet_0.6.zip
macOS binaries:	r-release (arm64): grpnet_0.6.tgz, r-oldrel (arm64): grpnet_0.6.tgz, r-release (x86_64): grpnet_0.6.tgz, r-oldrel (x86_64): grpnet_0.6.tgz
Old sources:	grpnet archive

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