Orders a data-set consisting of an ensemble of probability density functions on the same x-grid.  Visualizes a box-plot of these functions based on the notion of distance determined by the user.  Reports outliers based on the distance chosen and the scaling factor for an interquartile range rule.  For further details, see: Alexander C. Murph et al. (2023). "Visualization and Outlier Detection for Probability Density Function Ensembles." <https://sirmurphalot.github.io/publications>.

Specify debug messages as special string constants, and
    control debugging of packages via environment variables.

Tool to print out the value of R objects/expressions while running
  an R script. Outputs can be made dependent on user-defined conditions/criteria. 
  Debug messages only appear when a global option for debugging is set. 
  This way, 'debugr' code can even remain in the debugged code for later use 
  without any negative effects during normal runtime.

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.