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 & Hothorn, 2021) <doi:10.32614/RJ-2021-075>. Penalized smooth shift terms can be defined using 'mgcv'.
Version: 1.0.7 Depends: R (≥ 3.6.0), tram (≥ 0.3.2), mlt (≥ 1.1.0) Imports: alabama, Matrix, methods, mgcv (≥ 1.8.34), nlme, TMB (≥ 1.7.15), stats, variables (≥ 1.0.2), basefun (≥ 1.0.6), numDeriv, MASS, coneproj, mvtnorm, reformulas LinkingTo: TMB, RcppEigen Suggests: lme4 (≥ 1.1.19), multcomp, parallel, survival, knitr, coxme, ordinal, ordinalCont, gamm4, gamlss.dist, glmmTMB, xtable Published: 2024-11-29 DOI: 10.32614/CRAN.package.tramME Author: Balint Tamasi [aut, cre], Torsten Hothorn [ctb] Maintainer: Balint Tamasi <balint.tamasi+tramME at gmail.com> License: GPL-2 URL: http://ctm.R-forge.R-project.org NeedsCompilation: yes Citation: tramME citation info Materials: NEWS CRAN checks: tramME results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=tramME to link to this page.
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