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.
nlme, lme4, tramME, mlt, tram, TMB, glmmTMB, survival, boxcoxmix, ordinalCont, coxme, parfm, frailtypack, ordinal
Survival, Econometrics, Psychometrics, SocialSciences, Environmetrics, OfficialStatistics, SpatioTemporal, ChemPhys, ClinicalTrials, Finance, Spatial
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For attribution, please cite this work as
Tamási & Hothorn, "The R Journal: tramME: Mixed-Effects Transformation Models Using Template Model Builder", The R Journal, 2021
BibTeX citation
@article{RJ-2021-075,
author = {Tamási, Bálint and Hothorn, Torsten},
title = {The R Journal: tramME: Mixed-Effects Transformation Models Using Template Model Builder},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-075},
doi = {10.32614/RJ-2021-075},
volume = {13},
issue = {2},
issn = {2073-4859},
pages = {398-418}
}