Changes in version: JMbayes_0.8-7

  * corrected several small bugs in the code.
  * several updates in the shiny app.
  * added functions to select the optimal intervention time.

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Changes in version: JMbayes_0.8-69

  * corrected floor ambiguity in C++.
  * added number of false positives and false negatives, as well as F1 score and Youden in
  rocJM().
  * several updates in the shiny app.

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Changes in version: JMbayes_0.8-68

  * vignette in doc/ directory for dynamic predictions.

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Changes in version: JMbayes_0.8-67

  * the shiny app for dynamic prediction now works for multivariate models.

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Changes in version: JMbayes_0.8-66

  * added function for dynamic predictions from multivariate models.
  * new methods for aucJM(), rocJM() and prederrJM() for multivariate models.
  * handling of interval censored data.
  * allow for time-varying effects in the survival submodel.
  * added a vignette in the doc/ directory for multivariate models.
  * faster C++ implementation.
  * corrected issues in predVars in model.frames.

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Changes in version: JMbayes_0.8-6

  * extractFrames() now correctly constructs the design matrix for hierarchical centering.

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Changes in version: JMbayes_0.8-3

  * added function mvglmer() for fitting multivariate mixed models using JAGS.
  * added function mvJointModelBayes() for fitting multivariate joint models.

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Changes in version: JMbayes_0.8-1

  * shiny app for dynamic predictions from mixed models.

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Changes in version: JMbayes_0.8-0

  * package version for JSS paper v72i07.

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Changes in version: JMbayes_0.7-9

  * aucJM() and prederrJM() now work with left truncated data.

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Changes in version: JMbayes_0.7-8

  * support the use of offset().

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Changes in version: JMbayes_0.7-6

  * resolve small bugs in summary.JMbayes().

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Changes in version: JMbayes_0.7-5

  * resolve imports from other packages in NAMESPACE.

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Changes in version: JMbayes_0.7-2

  * jointModelBayes() can now accept Cox models with left-truncation.

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Changes in version: JMbayes_0.7-0

  * New function rocJM() calculates dynamic sensitivity and specificity and the plot produce the ROC curve plot.
  * The new function cvDCL() calculates an estimate of the dynamic cross-entropy.
  * The new function dynInfo() calculates the dynamic Kullaback-Leibler information provided by an extra longitudinal measurement.
  * Function jointModelBayes() can now handle survival submodels with exogenous time-varying covariates.
  * The MCMC algorithm implements now hierarchical centering for the parameters of mixed effects model.
  * Several improvements in the shiny web app for calculating dynamic predictions.

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Changes in version: JMbayes_0.6-1

  * Impovements for estimating the weight function.
  * Faster implementation of the MCMC.
  * First internal implementation of optimal screening frequency.

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Changes in version: JMbayes_0.6-0

  * Impovements in the shiny web interface.
  * Added functionality for estimating the weight function of the cumulative effect parameterization.
  * Small bug fixes.

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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.
   



