Group Elastic Net Regularized GLMs and GAMs
Extract Coefficients for cv.grpnet and grpnet Fits
Compare Multiple cv.grpnet Solutions
Cross-Validation for grpnet
Prepare 'family' Argument for grpnet
Fit a Group Elastic Net Regularized GLM/GAM
Internal 'grpnet' Functions
Plot Cross-Validation Curve for cv.grpnet Fits
Plot Regularization Path for grpnet Fits
Predict Method for cv.grpnet Fits
Predict Method for grpnet Fits
S3 'print' Methods for grpnet
Construct Design Matrices via Reproducing Kernels
Reproducing Kernel Basis
Row-Wise Kronecker Product
Startup Message for grpnet
Plots grpnet Penalty Function or its Derivative
Plots grpnet Shrinkage Operator or its Estimator
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.