The R Journal: accepted article

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Regularized Transformation Models: The tramnet Package PDF download
Lucas Kook and Torsten Hothorn

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 parametrization in glmnet. Several functionalities for optimizing the hyperparameters, including model-based optimization based on the mlrMBO package, are implemented. A multitude of S3 methods are 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.

Received: 2021-01-15; online 2021-06-08


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@article{RJ-2021-054,
  author = {Lucas Kook and Torsten Hothorn},
  title = {{Regularized Transformation Models: The tramnet Package}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-054},
  url = {https://journal.r-project.org/archive/2021/RJ-2021-054/index.html}
}