Mixed-Effects Additive Transformation Models with the R Package tramME

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.

• Publication year

  2025

• Venue

  Journal of Statistical Software

• Publication date

  Unknown publication date

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.18637/jss.v114.i11
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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