Jarrett Byrnes (@jebyrnes.bsky.social)

RegimeChange: 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) <<a href="https://doi.org/10.1093%2Fbiomet%2F41.1-2.100" target="_top">doi:10.1093/biomet/41.1-2.100</a>>), Pruned Exact Linear Time (PELT, Killick, Fearnhead, and Eckley (2012) <<a href="https://doi.org/10.1080%2F01621459.2012.737745" target="_top">doi:10.1080/01621459.2012.737745</a>>), Binary Segmentation, and Wild Binary Segmentation, as well as Bayesian methods such as Bayesian Online Changepoint Detection (BOCPD, Adams and MacKay (2007) <<a href="https://doi.org/10.48550%2FarXiv.0710.3742" target="_top">doi:10.48550/arXiv.0710.3742</a>> 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. https://CRAN.R-project.org/package=RegimeChange