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.git> 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.

Supporting the quantitative analysis of binary welfare based
    decision making processes using Monte Carlo simulations. Decision support
    is given on two levels: (i) The actual decision level is to choose between
    two alternatives under probabilistic uncertainty. This package calculates
    the optimal decision based on maximizing expected welfare. (ii) The meta
    decision level is to allocate resources to reduce the uncertainty in the
    underlying decision problem, i.e to increase the current information to
    improve the actual decision making process. This problem is dealt with
    using the Value of Information Analysis. The Expected Value of
    Information for arbitrary prospective estimates can be calculated as
    well as Individual Expected Value of Perfect Information.
    The probabilistic calculations are done via Monte Carlo
    simulations. This Monte Carlo functionality can be used on its own.