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.