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tramME: Mixed-Effects Transformation Models Using Template Model Builder


Tamási, Bálint; Hothorn, Torsten (2021). tramME: Mixed-Effects Transformation Models Using Template Model Builder. R Journal, 13(2):398-418.

Abstract

Linear transformation models constitute a general family of parametric regression models for discrete and continuous responses. To accommodate correlated responses, the model is extended by incorporating mixed effects. This article presents the R package tramME, which builds on existing implementations of transformation models (mlt and tram packages) as well as Laplace approximation and automatic differentiation (using the TMB package), to calculate estimates and perform likelihood inference in mixed-effects transformation models. The resulting framework can be readily applied to a wide range of regression problems with grouped data structures.

Abstract

Linear transformation models constitute a general family of parametric regression models for discrete and continuous responses. To accommodate correlated responses, the model is extended by incorporating mixed effects. This article presents the R package tramME, which builds on existing implementations of transformation models (mlt and tram packages) as well as Laplace approximation and automatic differentiation (using the TMB package), to calculate estimates and perform likelihood inference in mixed-effects transformation models. The resulting framework can be readily applied to a wide range of regression problems with grouped data structures.

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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
Language:English
Date:2021
Deposited On:04 Feb 2022 05:34
Last Modified:26 Jun 2024 01:52
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-2021-075
Related Items:
  • Content: Published Version
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)