[R-pkgs] package JMbayes -- version 0.5-0

[D. Rizopoulos]

*** 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/