  Joint models for longitudinal and time-to-event data constitute an attractive
modeling framework that has received a lot of interest in the recent years.
This paper presents the capabilities of the R package JMbayes for fitting these
models under a Bayesian approach using Markon chain Monte Carlo algorithms.
JMbayes can fit a wide range of joint models, including among others joint
models for continuous and categorical longitudinal responses, and provides
several options for modeling the association structure between the two
outcomes. In addition, this package can be used to derive dynamic predictions
for both outcomes, and offers several tools to validate these predictions in
terms of discrimination and calibration. All these features are illustrated
using a real data example on patients with primary biliary cirrhosis.
