Changes in version: JMbayes_0.8-72 * Support for fitting multi-state joint models. --------------------------------------- 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. --------------------------------------- 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. --------------------------------------- Changes in version: JMbayes_0.8-68 * vignette in doc/ directory for dynamic predictions. --------------------------------------- Changes in version: JMbayes_0.8-67 * the shiny app for dynamic prediction now works for multivariate models. --------------------------------------- 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. --------------------------------------- Changes in version: JMbayes_0.8-6 * extractFrames() now correctly constructs the design matrix for hierarchical centering. --------------------------------------- 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. --------------------------------------- Changes in version: JMbayes_0.8-1 * shiny app for dynamic predictions from mixed models. --------------------------------------- Changes in version: JMbayes_0.8-0 * package version for JSS paper v72i07. --------------------------------------- Changes in version: JMbayes_0.7-9 * aucJM() and prederrJM() now work with left truncated data. --------------------------------------- Changes in version: JMbayes_0.7-8 * support the use of offset(). --------------------------------------- Changes in version: JMbayes_0.7-6 * resolve small bugs in summary.JMbayes(). --------------------------------------- Changes in version: JMbayes_0.7-5 * resolve imports from other packages in NAMESPACE. --------------------------------------- Changes in version: JMbayes_0.7-2 * jointModelBayes() can now accept Cox models with left-truncation. --------------------------------------- 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. --------------------------------------- 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. --------------------------------------- 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. --------------------------------------- Changes in version: JMbayes_0.5-3 * Small bug fixes. --------------------------------------- Changes in version: JMbayes_0.5-2 * Updates for estimating the weight function. --------------------------------------- Changes in version: JMbayes_0.5-1 * A shiny web application has been added in the demo folder. --------------------------------------- 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. --------------------------------------- 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. --------------------------------------- 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. --------------------------------------- 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.