Provides model fitting, prediction, and plotting for joint models of longitudinal and multiple time-to-event data, including methods from Rizopoulos (2012) <doi:10.1201/b12208>. Useful for handling complex survival and longitudinal data in clinical research.

Provides analysis tools for big data where the sample size is very large. It offers
             a suite of functions for fitting and predicting joint models, which allow for the simultaneous
             analysis of longitudinal and time-to-event data. This statistical methodology is particularly 
             useful in medical research where there is often interest in understanding the relationship 
             between a longitudinal biomarker and a clinical outcome, such as survival or disease progression.
             This can be particularly useful in a clinical setting where it is important to be able to predict 
             how a patient's health status may change over time. Overall, this package provides a 
             comprehensive set of tools for joint modeling of BIG data obtained as survival and 
             longitudinal outcomes with both Bayesian and non-Bayesian approaches. Its versatility
             and flexibility make it a valuable resource for researchers in many different fields,
             particularly in the medical and health sciences.

Fit joint mean-covariance models for longitudinal data. The models
    and their components are represented using S4 classes and methods. The core
    computational algorithms are implemented using the 'Armadillo' C++ library
    for numerical linear algebra and 'RcppArmadillo' glue.

Joint mean and dispersion effects models fit the mean and dispersion parameters of a response variable by two separate linear models, the mean and dispersion submodels, simultaneously. It also allows the users to choose either the deviance or the Pearson residuals as the response variable of the dispersion submodel. Furthermore, the package provides the possibility to nest the submodels in one another, if one of the parameters has significant explanatory power on the other. Wu & Li (2016) <doi:10.1016/j.csda.2016.04.015>.