Changes in version: JMbayes_0.5-3
* Small bug fixes.
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Changes in version: JMbayes_0.5-2
* Updates for estimating the weight function.
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Changes in version: JMbayes_0.5-1
* A shiny web application has been added in the demo folder.
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Changes in version: JMbayes_0.5-0
* The MCMC is now implemented with efficient custom-made code and no longer relies on JAGS, WinBUGS or OpenBUGS.
* The user can specify her own density function for the longitudinal outcome (default is the normal). Among others,
this allows fitting joint models with categorical or left-censored longitudinal responses.
* The baseline hazard is now only estimated with B-splines (regression or penalized).
* The user has now the option to define custom transformation functions for the longitudinal model terms that
enter into the linear predictor of the survival submodel.
* survfitJM.JMbayes() is faster.
* Backward-incompatible version; the aforementioned changes require refitting joint models that have been fitted
with previous versions.
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Changes in version: JMbayes_0.4-1
* new versions of functions ins() and ibs() with updated 'weight.fun' argument, and makepredictcall() methods.
* methods have been added for the fitted() and residuals() generics to calculate fitted values and residuals,
respectively.
* a method has been added for the xtable() generic from package xtable for producing a LaTeX table with the
results of the joint model.
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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 controlled 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.