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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 |
| Reference manual: | grpnet.pdf |
| 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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