Detect abrupt changes in time series with local fluctuations as a random walk process and autocorrelated noise as an AR(1) process. See Romano, G., Rigaill, G., Runge, V., Fearnhead, P. (2021) <doi:10.1080/01621459.2021.1909598>.

Calculates various estimates for measures of educational
        differentials, the relative importance of primary and secondary
        effects in the creation of such differentials and compares the
        estimates obtained from two datasets.

Provides constrained triangulation of polygons. Ear cutting (or 
 ear clipping) applies constrained triangulation by successively 'cutting'
 triangles from a polygon defined by path/s. Holes are supported by introducing
 a bridge segment between polygon paths. This package wraps the 'header-only' 
 library 'earcut.hpp' <https://github.com/mapbox/earcut.hpp> which includes
 a reference to the method used by Held, M. (2001) <doi:10.1007/s00453-001-0028-4>.

Contains a function called dmur() which accepts four parameters like possible values, probabilities of the values, selling cost and preparation cost. The dmur() function generates various numeric decision parameters like MEMV (Maximum (optimum) expected monitory value), best choice, EPPI (Expected profit with perfect information), EVPI (Expected value of the perfect information), EOL (Expected opportunity loss), which facilitate effective decision-making.

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.