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 (Tamasi & Hothorn,
2021) . Penalized smooth shift terms can be
defined using 'mgcv'.