is.pd

Likelihood-based estimation of mixed-effects transformation models using the Template
Model Builder ('TMB', Kristensen et al., 2016, <doi:10.18637/jss.v070.i05>). The technical details
of transformation models are given in Hothorn et al. (2018, <doi:10.1111/sjos.12291>). 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 and Hothorn, 2021,
<doi:10.32614/RJ-2021-075>). Penalized smooth shift terms can be defined using 'mgcv'.

Balint Tamasi

tramME

is.pd: Check positive definiteness

Description

Usage

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