Changes in version: JMbayes_0.4-0

  * the new function bma.combine() combines predictions using Bayesian model averaging.

  * logLik.JMbayes() can now calculate marginal log-likelihoods averaging over the random effects and the parameters.

  * the new function marglogLik() calculates marginal likelihood contributions for individual subjects.

  * 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.

  * jointModelBayes() can now fit robust joint models in which both the error terms for the longitudinal outcome
    and the random effects are assumed to follow a Student's t distribution. This is controled by the arguments
    'robust' and 'df' for the error terms, and 'robust.b' and 'df.b' for the random effects.

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Changes in version: JMbayes_0.2-0

  * the new control argument 'ordSpline' sets the order of the spline for the B-spline basis (i.e.,
    it is passed to the 'ord' argument of splineDesign()). By setting to 1 a piecewise-constant baseline
    hazard is fitted.

  * corrected some typos in .Rd files.
   



