Stata 16 offers extensive additions to Stata’s Bayesian suite of commands, which include

 

Multiple chains

Gelman–Rubin convergence diagnostics

Bayesian predictions

Posterior summaries of simulated values

MCMC replicates

Posterior predictive p-values

 

In addition, bayes: and bayesmh support new priors pareto(), dirichlet(), and geometric() for specifying, respectively, Pareto, multivariate beta (Dirichlet), and geometric prior distributions. Pareto is a power-law-based distribution. Dirichlet can be used for specifying priors for probability vector parameters. Geometric priors are suitable for modeling count parameters.

 

Last but not least is that bayes: with multilevel models such as bayes: mixed now runs faster!