Comprehensive Regime Change Detection in Time Series // A unified framework for detecting regime changes (changepoints) in time series data. Implements both frequentist methods including Cumulative Sum (CUSUM, Page (1954) doi:10.1093/biomet/41.1-2.100), Pruned Exact Linear Time (PELT, Killick, Fearnhead, and Eckley (2012) doi:10.1080/01621459.2012.737745), Binary Segmentation, and Wild Binary Segmentation, as well as Bayesian methods such as Bayesian Online Changepoint Detection (BOCPD, Adams and MacKay (2007) doi:10.48550/arXiv.0710.3742 and Shiryaev-Roberts. Supports offline analysis for retrospective detection and online monitoring for real-time surveillance. Provides rigorous uncertainty quantification through confidence intervals and posterior distributions. Handles univariate and multivariate series with detection of changes in mean, variance, trend, and distributional properties.