BSCB - Bayesian Simultaneous Credible Bands for Polynomial Regression

Provides functions to construct two-sided Bayesian simultaneous credible bands (BSCBs) for the regression curve in univariate polynomial regression over a finite covariate interval. Six methods are implemented, including Normal-Gamma conjugate priors (with empirical Bayes, unit-information, and g-prior hyperparameter specifications), non-conjugate priors fitted via Hamiltonian Monte Carlo (HMC) using 'cmdstanr', and a non-informative independent Jeffreys prior approach. Also includes functions for computing the empirical simultaneous coverage rate (ESCR) and posterior simultaneous coverage probability (PSCP), enabling performance comparison across methods. The methodology is described in: Yang, F., Han, Y., Liu, W., & Hall, I. (2026). "Bayesian simultaneous credible bands for polynomial regression" <doi:10.48550/arXiv.2606.28015>.