Detecting, Decomposing, and Stress-Testing Temporal Change in Repeated Decision Systems // Tools for detecting, decomposing, and stress-testing temporal drift in repeated binary decision systems. Complements the 'decisionpaths' package by shifting focus from path construction to system-level change over time. Implements five core analytic modules: (1) prevalence drift did the overall decision rate change over time?; (2) transition drift did the probability of switching or persisting change?; (3) entropy and stability trends did path complexity evolve?; (4) group-differential drift did the system drift differently across subgroups?; (5) change-point and regime-shift detection did the system change abruptly after a policy or model update? Additionally provides a robustness module for testing stability of drift conclusions across analytic choices, and a sensitivity module for probing vulnerability to data problems including missingness, miscoding, and threshold shifts. Defines four original drift indices: the Decision Drift Index (DDI), Transition Drift Index (TDI), Group Differential Drift (GDD), and Cumulative Drift Burden (CDB). Applications include algorithmic audit, AI governance, education, health, and organisational research.