Header

UZH-Logo

Maintenance Infos

Regularized Transformation Models: The tramnet Package


Kook, Lucas; Hothorn, Torsten (2021). Regularized Transformation Models: The tramnet Package. R Journal, 13(1):581.

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.

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.

Statistics

Citations

Dimensions.ai Metrics
2 citations in Web of Science®
2 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

30 downloads since deposited on 18 Nov 2021
11 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Physical Sciences > Statistics and Probability
Physical Sciences > Numerical Analysis
Social Sciences & Humanities > Statistics, Probability and Uncertainty
Uncontrolled Keywords:Statistics, Probability and Uncertainty, Numerical Analysis, Statistics and Probability
Language:English
Date:1 January 2021
Deposited On:18 Nov 2021 15:33
Last Modified:26 Jun 2024 01:43
Publisher:R Foundation for Statistical Computing
ISSN:2073-4859
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.32614/rj-2021-054
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)