NEWS.md

In retestR: Model-Based Anomaly Detection for Repeat Test-Takers

retestR 0.1.0

• Initial release.
• Expected-gain model for repeaters fitted by marginal ML, robust to preknowledge (rt_fit()).
• Posterior-predictive evidence: score gain, exposed-item performance, response speed (rt_evidence()).
• Risk index calibrated by parametric bootstrap with explicit false-positive rates (rt_risk()).
• Raw-gain baseline (rt_raw_gain()) and known-truth simulation (rt_simulate()).

retestR documentation built on Oct. 8, 2026, 5:08 p.m.