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: |
0.4 |
| Depends: |
R (≥ 3.5.0) |
| Published: |
2024-06-05 |
| 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:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=grpnet
to link to this page.