Fits predictive and symmetric co-correspondence analysis (CoCA) models to relate one data matrix to another data matrix. More specifically, CoCA maximises the weighted covariance between the weighted averaged species scores of one community and the weighted averaged species scores of another community. CoCA attempts to find patterns that are common to both communities.

Provides a set of functions to implement the Combined Compromise Solution (CoCoSo) Method created by Yazdani, Zarate, Zavadskas and Turskis (2019) <doi:10.1108/MD-05-2017-0458>. This method is based on an integrated simple additive weighting and compromise exponentially weighted product model.

A common misconception is that the Hochberg procedure comes up with adequate overall type I error control when test statistics are positively correlated. However, unless the test statistics follow some standard distributions, the Hochberg procedure requires a more stringent positive dependence assumption, beyond mere positive correlation, to ensure valid overall type I error control. To fill this gap, we formulate statistical tests grounded in rank correlation coefficients to validate fulfillment of the positive dependence through stochastic ordering (PDS) condition.
    See Gou, J., Wu, K. and Chen, O. Y. (2024). Rank correlation coefficient based tests on positive dependence through stochastic ordering with application in cancer studies, Technical Report.

Statistical tests for the comparison between two or more alpha
    coefficients based on either dependent or independent groups of individuals.
    A web interface is available at http://comparingcronbachalphas.org. A plugin
    for the R GUI and IDE RKWard is included. Please install RKWard from https://
    rkward.kde.org to use this feature. The respective R package 'rkward' cannot be
    installed directly from a repository, as it is a part of RKWard.

Provides functions for summarizing and plotting the
	output from Markov Chain Monte Carlo (MCMC) simulations, as
	well as diagnostic tests of convergence to the equilibrium
	distribution of the Markov chain.