Conformal Anomaly Detection in Python: Moving Beyond Heuristic Thresholds with 'nonconform'

ArXi:2605.13642v1 Announce Type: cross Most anomaly detection systems output scores rather than calibrated decisions, leaving practitioners to choose thresholds heuristically and without clear statistical interpretation. Conformal anomaly detection addresses this limitation by converting anomaly scores into calibrated p-values that are valid under the statistical assumption of data exchangeability, with a growing literature extending this idea beyond that setting.