*** Apologies for cross posting *** Dear Colleagues, Dear R-users, I would like to announce the release of the new version of package JMbayes available from CRAN (http://CRAN.R-project.org/package=JMbayes). This package fits joint models for longitudinal and time-to-event data under a Bayesian approach using MCMC. These models are applicable in mainly two settings. First, when focus is on the survival outcome and we wish to account for the effect of an endogenous (aka internal) time-dependent covariates measured with error. Second, when focus is on the longitudinal outcome and we wish to correct for nonrandom dropout. Some basic features of the new version: * The MCMC in now implemented with efficient custom-made code and no longer relies on JAGS, OpenBUGS or WinBUGS. * The user can now specify her own density function for the longitudinal responses using argument 'densLong' (default is the normal pdf). Among others, this allows to fit joint models with categorical and left-censored longitudinal responses and robust joint models with Student's-t error terms. In addition, using the 'df.RE' argument, the user can also change the distribution of the random effects from multivariate normal to a multivariate Student's-t with prespecified degrees of freedom. * The user has now the option to define custom transformation functions for the terms of the longitudinal submodel that enter into the linear predictor of the survival submodel (argument 'transFun'). For example, interactions terms, nonlinear terms (polynomials, splines), etc. * The baseline hazard is now only estimated using B-splines (penalized (default) or regression). * Dynamic predictions: - function survfitJM.JMbayes(), which computes dynamic survival probabilities, is now faster; - the new generic function aucJM() calculates time-dependent AUCs for joint models; - the new generic function dynCJM() calculates a dynamic discrimination index (weighted average of time-dependent AUCs) for joint models; - the new generic function prederrJM() calculates prediction errors for joint models; - the new function bma.combine() combines predictions using Bayesian model averaging; posterior model weights can be calculated using logLik.JMbayes() and marglogLik(). * a method has been added for the xtable() generic from package xtable for producing a LaTeX table with the results of the joint model. * Backward-incompatible version; the aforementioned changes require refitting joint models that have been fitted with previous versions. As always, any kind of feedback (e.g., questions, suggestions, bug-reports, etc.) is more than welcome. Kind regards, Dimitris -- Dimitris Rizopoulos Assistant Professor Department of Biostatistics Erasmus University Medical Center Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands Tel: +31/(0)10/7043478 Fax: +31/(0)10/7043014 Web: http://www.erasmusmc.nl/biostatistiek/ _______________________________________________ R-packages mailing list R-packages at r-project.org https://stat.ethz.ch/mailman/listinfo/r-packages