Bayesian Unit Root Test for AR(1) Model with Trend Approximated by Linear Spline Function // Performs Bayesian unit root testing for autoregressive time series models with non-linear trend components approximated by linear spline functions, as proposed by Kumar et al. (2020) doi:10.19139/soic-2310-5070-786. The package 'BayesSplineUR' computes posterior odds ratios, Bayes factors, and posterior probabilities for the unit root hypothesis against trend- stationary alternatives in models with linear spline trends or maintained polynomial trends as developed by Chaturvedi and Kumar (2005) doi:10.1016/j.spl.2005.04.044. Includes automatic knot selection using information criteria (AIC/BIC) and theoretical foundations for Bayesian unit root testing under structural breaks and maintained trends drawing from Schotman and van Dijk (1991) doi:10.1016/0304-4076(91)90038-F, Phillips and Perron (1988) doi:10.1093/biomet/75.2.335, Ouliaris et al. (1988) doi:10.1007/978-94-009-2953-1_10, and Perron (1989) doi:10.2307/1913683.