coef() and logLik() to extract the estimated coefficients and log likelihood values.summary().JMMLSM().survfitjmcs(), survfitJMMLSM(), and survfitmvjmcs(), and add survfitJM() as a unified interface function to make dynamic predictions for new subjects.survfitJM(), DynPredAcc(), and Concordance().DynPredAccjmcs() to calculate all available evaluation metrics for fitted joint models.AUCjmcs(), MAEQjmcs(), PEjmcs(), summary.AUCjmcs(), summary.MAEQjmcs(), and summary.PEjmcs().survfitmvjmcs().mvjmcs() to support ties in obtaining the estimator of the baseline hazards.mvjmcs() to support multivariate joint modeling.jmcs().AUCjmcs() area under the ROC curve (AUC) to assess the prediction performance of joint models.surviftjmcs().plot.surviftjmcs() due to theoretical problem.PEjmcs() and MAEQjmcs().surviftjmcs() for the competing risk and add summary() for providing parameter estimates and SE for both sub-models.jmcs().Provide support for handling categorical variables in both sub-models.
Provide the anova() function to compare two fitted joint models.
Add the simulate argument in the survfitjmcs() function to obtain the conditional probabilities using the Gauss-Hermite quadrature rule for numerical integration.
Adjust the label position of y axis for clarity purposes when include.y = TRUE in the plot.survfitjmcs() function.