Statistics, Department of
The R Journal
Date of this Version
6-2021
Document Type
Article
Citation
The R Journal (June 2021) 13(1); Editor: Dianne Cook
Abstract
The tramnet package implements regularized linear transformation models by combining the flexible class of transformation models from tram with constrained convex optimization implemented in CVXR. Regularized transformation models unify many existing and novel regularized regression models under one theoretical and computational framework. Regularization strategies implemented for transformation models in tramnet include the Lasso, ridge regression, and the elastic net and follow the parameterization in glmnet. Several functionalities for optimizing the hyperparameters, including model-based optimization based on the mlrMBO package, are implemented. A multitude of S3 methods is deployed for visualization, handling, and simulation purposes. This work aims at illustrating all facets of tramnet in realistic settings and comparing regularized transformation models with existing implementations of similar models.
Included in
Numerical Analysis and Scientific Computing Commons, Programming Languages and Compilers Commons
Comments
Copyright 2021, The R Foundation. Open access material. License: CC BY 4.0 International