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.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2021-054.zip
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For attribution, please cite this work as
Hothorn, "The R Journal: Regularized Transformation Models: The tramnet Package", The R Journal, 2021
BibTeX citation
@article{RJ-2021-054,
author = {Hothorn, Lucas Kook, Torsten},
title = {The R Journal: Regularized Transformation Models: The tramnet Package},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-054},
doi = {10.32614/RJ-2021-054},
volume = {13},
issue = {1},
issn = {2073-4859},
pages = {581-594}
}