Different evidential classifiers, which provide
    outputs in the form of Dempster-Shafer mass functions. The methods are: 
    the evidential K-nearest neighbor rule, the evidential neural 
    network, radial basis function neural networks, logistic regression,
    feed-forward neural networks.

Combine pieces of evidence in the form of uncertainty representations.

Extends simulation, distribution, quantile and density
        functions to univariate and multivariate parametric extreme
        value distributions, and provides fitting functions which
        calculate maximum likelihood estimates for univariate and
        bivariate maxima models, and for univariate and bivariate
        threshold models.

Provides functions for the Bayesian analysis of extreme
        value models, using Markov chain Monte Carlo methods. Allows
		the construction of both uninformative and informed prior 
		distributions for common statistical models applied to extreme
        event data, including the generalized extreme value distribution.

The confusion matrix (CM) is used to get a classifier's
             evaluation measure in order to select a method among many.
	     A stochastic matrix and its transformation are computed from
	     the CM. The eigenvalues of the transformed symmetric matrix
	     are used to get an entropy which appears to be a good
	     evaluation measure. Many other measures, commonly used, are
	     provided for comparison purpose.

We quantitatively evaluated the assertion that says if one suit is found to be evenly distributed among the 4 players, the rest of the suits are more likely to be evenly distributed. Our mathematical analyses show that, if one suit is found to be evenly distributed, then a second suit has a slightly elevated probability (ranging between 10% to 15%) of being evenly distributed. If two suits are found to be evenly distributed, then a third suit has a substantially elevated probability (ranging between 30% to 50%) of being evenly distributed.This package refers to methods and authentic data from Ely Culbertson <https://www.bridgebum.com/law_of_symmetry.php>, Gregory Stoll <https://gregstoll.com/~gregstoll/bridge/math.html>, and details of performing the probability calculations from Jeremy L. Martin <https://jlmartin.ku.edu/~jlmartin/bridge/basics.pdf>, Emile Borel and Andre Cheron (1954) "The Mathematical Theory of Bridge",Antonio Vivaldi and Gianni Barracho (2001, ISBN:0 7134 8663 5)  "Probabilities and Alternatives in Bridge", Ken Monzingo (2005) "Hand and Suit Patterns" <http://web2.acbl.org/documentlibrary/teachers/celebritylessons/handpatternsrevised.pdf>Ken Monzingo (2005) "Hand and Suit Patterns" <http://web2.acbl.org/documentlibrary/teachers/celebritylessons/handpatternsrevised.pdf>.