Transformation Models with Mixed Effects

Likelihood-based estimation of mixed-effects transformation models using the Template Model Builder (TMB, Kristensen et al., 2016, ). The technical details of transformation models are given in Hothorn et al. (2018, ). Likelihood contributions of exact, randomly censored (left, right, interval) and truncated observations are supported. The random effects are assumed to be normally distributed on the scale of the transformation function, the marginal likelihood is evaluated using the Laplace approximation, and the gradients are calculated with automatic differentiation (AD).


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0.1.2 by Balint Tamasi, 3 months ago

Browse source code at

Authors: Balint Tamasi [aut, cre] , Torsten Hothorn [ctb]

Documentation:   PDF Manual  

GPL-2 license

Imports alabama, lme4, Matrix, methods, nlme, TMB, stats, variables, basefun, mvtnorm, numDeriv, MASS, coneproj

Depends on tram, mlt

Suggests multcomp, parallel, survival, knitr, coxme, ordinal, ordinalCont, ggplot2

Linking to TMB, RcppEigen

See at CRAN