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Mixed-Effects Additive Transformation Models with the R Package tramME

Bálint Tamási

Journal of Statistical Software2025https://doi.org/10.18637/jss.v114.i11article
ABDC A
Weight
0.37

Abstract

Regression models that accommodate correlated observations and potential nonlinear predictor-outcome relationships are fundamental in analyzing experimental and observational data. Unlike traditional parametric approaches, transformation models make weaker assumptions on the conditional response distribution, thus allowing for a more universal applicability to at least ordered univariate outcomes. This flexibility makes transformation models an attractive choice for modeling complex relationships in a wide range of domains. The R package tramME extends the transformation model framework with general random effect structures and penalized smooth terms to adapt to dependent data and nonlinear predictor-outcome relationships. This paper presents the statistical framework and implementation details of tramME, including its integration with other popular R packages for transformation modeling (mlt), mixed-effects (lme4) and additive models (mgcv). The package employs the efficient Template Model Builder framework (TMB) for fully parametric likelihood-based estimation and inference. Two illustrations demonstrate that tramME can readily model complex, dependent data structures under settings where the choice of the outcome distribution type is challenging.

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@article{bálint2025,
  title        = {{Mixed-Effects Additive Transformation Models with the R Package tramME}},
  author       = {Bálint Tamási},
  journal      = {Journal of Statistical Software},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.18637/jss.v114.i11},
}

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Mixed-Effects Additive Transformation Models with the R Package tramME

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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