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: 0.5
Depends: R (≥ 3.5.0)
Published: 2024-07-13
DOI: 10.32614/CRAN.package.grpnet
Author: Nathaniel E. Helwig [aut, cre]
Maintainer: Nathaniel E. Helwig <helwig at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: grpnet citation info
Materials: ChangeLog
CRAN checks: grpnet results


Reference manual: grpnet.pdf


Package source: grpnet_0.5.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): grpnet_0.5.tgz, r-oldrel (arm64): grpnet_0.5.tgz, r-release (x86_64): grpnet_0.5.tgz, r-oldrel (x86_64): grpnet_0.5.tgz
Old sources: grpnet archive


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