A way to apply Distance-Based Common Spatial Patterns
    (DB-CSP) techniques in different fields, both classical Common Spatial
    Patterns (CSP) as well as DB-CSP. The method is composed of two
    phases: applying the DB-CSP algorithm and performing a classification.
    The main idea behind the CSP is to use a linear transform to project
    data into low-dimensional subspace with a projection matrix, in such a
    way that each row consists of weights for signals. This transformation
    maximizes the variance of two-class signal matrices.The dbcsp object
    is created to compute the projection vectors. For exploratory and
    descriptive purpose, plot and boxplot functions can be used. Functions
    train, predict and selectQ are implemented for the classification
    step.

A metric called 'Density-Based Clustering Validation index' (DBCV) index to evaluate clustering results, following the <https://github.com/pajaskowiak/clusterConfusion/blob/main/R/dbcv.R> 'R' implementation by Pablo Andretta Jaskowiak. Original 'DBCV' index article: Moulavi, D., Jaskowiak, P. A., Campello, R. J., Zimek, A., and Sander, J. (April 2014), "Density-based clustering validation", Proceedings of SDM 2014 -- the 2014 SIAM International Conference on Data Mining (pp. 839-847), <doi:10.1137/1.9781611973440.96>. A more recent article on the 'DBCV' index: Chicco, D., Sabino, G.; Oneto, L.; Jurman, G. (August 2025), "The DBCV index is more informative than DCSI, CDbw, and VIASCKDE indices for unsupervised clustering internal assessment of concave-shaped and density-based clusters", PeerJ Computer Science 11:e3095 (pp. 1-), <doi:10.7717/peerj-cs.3095>.

Tools for working with a new versatile
	discrete distribution, the db ("discretised Beta")
	distribution.  This package provides density (probability),
	distribution, inverse distribution (quantile) and random
	data generation functions for the db family.  It provides
	functions to effect conveniently maximum likelihood
	estimation of parameters, and a variety of useful plotting
	functions.  It provides goodness of fit tests and functions
	to calculate the Fisher information, different estimates of
	the hessian of the log likelihood and Monte Carlo estimation
	of the covariance matrix of the maximum likelihood parameter
	estimates.  In addition it provides analogous tools for
	working with the beta-binomial distribution which has been
	proposed as a competitor to the db distribution.