Description As of version 1.2.0, joineR fits the joint model proposed by Williamson et al. (2008) for joint models of longitudinal data and competing risks. Here, the longitudinal data submodel remains as per that analyzed in Henderson et al. (2000), namely
[Y_i(t) = X_i(t)^\top \beta + Z_i(t)^\top U_i + \epsilon_i(t),]
where (Y_i(t)) is a repeated measurement on subject (i) at time (t), (U_i) is a latent vector that follows a zero-mean multivariate normal distribution, (X_i(t)) and (Z_i(t)) are vectors of explanatory variables that may be time-constant or time-varying, and the (\epsilon_i(t)) are mutually independent errors, (\epsilon_i(t) \sim N(0, \tau^2)).