Bayesian Synthetic Control Models // Implements the synthetic control method of Abadie, Diamond, and Hainmueller (2010) doi:10.1198/jasa.2009.ap08746 within a Bayesian framework, enabling straightforward uncertainty quantification of treatment effects and other quantities of interest. Supports time-varying covariates with potentially time-varying effects, single or multiple treated units, and staggered treatment adoption. Provides methods for model assessment, comparison, and selection based on placebo studies, cross-validation, and posterior predictive checks. Posterior sampling is performed using Markov chain Monte Carlo via Stan.