Implementation of two-dimensional (2D) correlation analysis based
    on the Fourier-transformation approach described by Isao Noda (I. Noda
    (1993) <DOI:10.1366/0003702934067694>). Additionally there are two plot
    functions for the resulting correlation matrix: The first one creates
    colored 2D plots, while the second one generates 3D plots.

The goal of 'corrarray' is to create a multi-sample
  correlation array by combining the correlation matrices of a data set
  stratified by a grouping variable. 
  For two specified levels of the variable, 'corrarray' displays one level's 
  correlation matrix in the lower triangular matrix and the other level's
  correlation matrix in the upper triangular matrix.
  Such an output can enable visualization of correlations from two samples
  in a single correlation matrix or corrgram.

Implements non-parametric analyses for clustered
    binary and multinomial data. The elements of the cluster are assumed
    exchangeable, and identical joint distribution (also known as marginal
    compatibility, or reproducibility) is assumed for clusters of different
    sizes. A trend test based on stochastic ordering is implemented. 
    Szabo A, George EO. (2010) <doi:10.1093/biomet/asp077>;
    George EO, Cheon K, Yuan Y, Szabo A (2016) <doi:10.1093/biomet/asw009>.

Using a computationally efficient method, the package can
    be used to find the corrected coverage estimate of a credible set 
    of putative causal variants from Bayesian genetic fine-mapping. 
    The package can also be used to obtain a corrected credible set
    if required; that is, the smallest set of variants required such 
    that the corrected coverage estimate of the resultant credible set is  
    within some user defined accuracy of the desired coverage.
    Maller et al. (2012) <doi:10.1038/ng.2435>,
    Wakefield (2009) <doi:10.1002/gepi.20359>,
    Fortune and Wallace (2018) <doi:10.1093/bioinformatics/bty898>.

Can be useful for finding associations among different positions in a position-wise aligned sequence dataset. The approach adopted for finding associations among positions is based on the latent multivariate normal distribution.