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  "Title": "Group Elastic Net Regularized GLMs and GAMs",
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  "Description": "Efficient algorithms for fitting generalized linear and\nadditive models with group elastic net penalties as described\nin Helwig (2025) <doi:10.1080/10618600.2024.2362232>.\nImplements group LASSO, group MCP, and group SCAD with an\noptional group ridge penalty. Computes the regularization path\nfor linear regression (gaussian), multivariate regression\n(multigaussian), smoothed support vector machines (svm1),\nsquared support vector machines (svm2), logistic regression\n(binomial), proportional odds logistic regression (ordinal),\nmultinomial logistic regression (multinomial), log-linear count\nregression (poisson and negative.binomial), and log-linear\ncontinuous regression (gamma and inverse gaussian). Supports\ndefault and formula methods for model specification, k-fold\ncross-validation for tuning the regularization parameters, and\nnonparametric regression via tensor product reproducing kernel\n(smoothing spline) basis function expansion.",
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        "grpnet.formula"
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