Calculate a set of corrected test statistics for cases when samples
    are not independent, such as when classification accuracy values are obtained
    over resamples or through k-fold cross-validation, as proposed by Nadeau and Bengio (2003) <doi:10.1023/A:1024068626366> 
    and presented in Bouckaert and Frank (2004) <doi:10.1007/978-3-540-24775-3_3>.

Lightweight package for computing different kinds
    of correlations, such as partial correlations, Bayesian correlations,
    multilevel correlations, polychoric correlations, biweight
    correlations, distance correlations and more. Part of the 'easystats'
    ecosystem. References: Makowski et al. (2020) <doi:10.21105/joss.02306>.

Speeds up exploratory data analysis (EDA)
    by providing a succinct workflow and interactive visualization tools for understanding
    which features have relationships to target (response). Uses binary correlation analysis
    to determine relationship. Default correlation method is the Pearson method. 
    Lian Duan, W Nick Street, Yanchi Liu, Songhua Xu, and Brook Wu (2014) <doi:10.1145/2637484>.

Calculates the probabilities of k successes given n trials of a binomial random variable with non-negative correlation across trials. The function takes as inputs the scalar values the level of correlation or association between trials, the success probability, the number of trials, an optional input specifying the number of bits of precision used in the calculation, and an optional input specifying whether the calculation approach to be used is from Witt (2014) <doi:10.1080/03610926.2012.725148> or from Kuk (2004) <doi:10.1046/j.1467-9876.2003.05369.x>. The output is a (trials+1)-dimensional vector containing the likelihoods of 0, 1, ..., trials successes.

Routines for the graphical representation of correlation matrices by means of correlograms, MDS maps and biplots obtained by PCA, PFA or WALS (weighted alternating least squares); See Graffelman & De Leeuw (2023) <doi: 10.1080/00031305.2023.2186952>.