Implements Bayesian polynomial chaos expansion methods for surrogate modeling, uncertainty quantification, and sensitivity analysis. Includes sparse regression-based approaches and adaptive Bayesian models based on reversible-jump Markov chain Monte Carlo. Optional screening and basis-enrichment strategies are provided to improve scalability in moderate to high dimensions.