The R Journal: accepted article

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tramME: Mixed-Effects Transformation Models Using Template Model Builder PDF download
Bálint Tamási and Torsten Hothorn

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.

Received: 2020-11-02; online 2021-08-17, supplementary material, (6 Kb)
CRAN packages: nlme, lme4, tramME, mlt, tram, TMB, glmmTMB, survival, boxcoxmix, ordinalCont, coxme, parfm, frailtypack, ordinal
CRAN Task Views implied by cited CRAN packages: Survival, Econometrics, Psychometrics, SocialSciences, Environmetrics, OfficialStatistics, SpatioTemporal, ChemPhys, ClinicalTrials, Finance, Spatial


CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2021-075,
  author = {Bálint Tamási and Torsten Hothorn},
  title = {{tramME: Mixed-Effects Transformation Models Using Template
          Model Builder}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-075},
  url = {https://journal.r-project.org/archive/2021/RJ-2021-075/index.html}
}