The new RANDOM statement simplifies the construction of hierarchical random-effects models and significantly reduces simulation time while improving convergence, especially in models with a large number of subjects or clusters. This statement defines random effects that can enter the model in a linear or nonlinear fashion and supports univariate and multivariate prior distributions.

In addition to the default Metropolis-based algorithms, PROC MCMC now takes advantages of certain forms of conjugacy in the model in order to sample directly from the target conditional distributions. In many situations, the conjugate sampler increases sampling efficiency and provides a substantial reduction in computing time.

The MCMC procedure now supports multivariate distributions including the Dirichlet, inverse Wishart, multivariate normal, and multinomial distributions.