grpnet: Group Elastic Net Regularized GLMs and GAMs
Efficient algorithms for fitting generalized linear and additive models with group elastic net penalties. 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: |
0.3 |
| Depends: |
R (≥ 3.5.0) |
| Published: |
2024-02-20 |
| 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 |
| Materials: |
ChangeLog |
| CRAN checks: |
grpnet results |
Documentation:
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