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), loglinear count regression (poisson and negative.binomial), and loglinear continuous regression (gamma and inverse gaussian). Supports default and formula methods for model specification, kfold crossvalidation for tuning the regularization parameters, and nonparametric regression via tensor product reproducing kernel (smoothing spline) basis function expansion.
Package details 


Author  Nathaniel E. Helwig [aut, cre] 
Maintainer  Nathaniel E. Helwig <helwig@umn.edu> 
License  GPL (>= 2) 
Version  0.4 
Package repository  View on CRAN 
Installation 
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