Yes, Bayesian analysis is enabled by specifying the 'bayes' parameter. This allows you to define prior distributions for the model parameters using the 'prior' subparameter. The action then uses MCMC methods, such as the No-U-Turn Sampler ('NUTS') or Random Walk Metropolis ('RWM'), to generate posterior samples for inference. You can control aspects like the number of burn-in iterations, samples, and chains.
