[R-pkgs] package JMbayes -- version 0.5-0
d.rizopoulos at erasmusmc.nl
Mon Jan 13 08:48:25 CET 2014
*** 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
- the new generic function dynCJM() calculates a dynamic
discrimination index (weighted average of time-dependent AUCs) for joint
- 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.
Department of Biostatistics
Erasmus University Medical Center
Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
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