Statistical volume anomaly detection for trade streams - Hawkes process, CUSUM, and Bayesian Online Changepoint Detection (BOCPD). Zero dependencies. TypeScript.
Changepoint detection toolkit for offline and online in Rust with Python bindings
Workspace-scale adverse-event horizon scanner — BOCPD + Poisson z-score + CUSUM ensemble with Benjamini–Hochberg FDR control. Workspace-scope A2A agent, signed FHIR DetectedIssue output.
Bayesian changepoint detection for insurance pricing — BOCPD and PELT, exposure-weighted Poisson-Gamma, UK regulatory event priors, Consumer Duty reports
Regime detection without religion — six algorithms (HMM, BOCPD, CUSUM, GMM, BinSeg, Ensemble), one harness, reproducible leaderboard.
Ultra-fast Bayesian Online Change Point Detection in C. Hand-tuned AVX2 assembly delivers 3 million observations/sec: that's 33x faster than naive and 4,100x faster than Python. Sub-microsecond latency, full numerical precision, production-ready.
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