Provide the 'CrossClustering' algorithm (Tellaroli et al.
    (2016) <doi:10.1371/journal.pone.0152333>), which is a partial
    clustering algorithm that combines the Ward's minimum variance and
    Complete Linkage algorithms, providing automatic estimation of a
    suitable number of clusters and identification of outlier elements.

Contains functions for the construction of carryover
        balanced crossover designs. In addition contains functions to
        check given designs for balance.

A local haplotyping visualization toolbox to capture major patterns 
    of co-inheritance between clusters of linked variants, whilst connecting findings 
    to phenotypic and demographic traits across individuals. 'crosshap' enables users 
    to explore and understand genomic variation across a trait-associated region. 
    For an example of successful local haplotype analysis, see Marsh et al. (2022) 
    <doi:10.1007/s00122-022-04045-8>.

Linear or nonlinear cross-lagged panel model can be built from input data. Users can choose the appropriate method from three methods for constructing nonlinear cross lagged models. These three methods include polynomial regression, generalized additive model and generalized linear mixed model.In addition, a function for determining linear relationships is provided. 
    Relevant knowledge of cross lagged models can be learned through the paper by Fredrik Falkenström (2024) <doi:10.1016/j.cpr.2024.102435> and the paper by A Gasparrini (2010) <doi:10.1002/sim.3940>.

Provides an extension to the 'purrr' family of mapping
    functions to apply a function to each combination of elements in a
    list of inputs.  Also includes functions for automatically detecting
    output type in mapping functions, finding every combination of
    elements of lists or rows of data frames, and applying multiple models
    to multiple subsets of a dataset.

Performs the cross-match test that is an exact, 
		distribution free test of equality of 2 high dimensional multivariate distributions.   
		The input is a distance matrix and the labels of
        the two groups to be compared, the output is the number of
        cross-matches and a p-value. See Rosenbaum (2005) <doi:10.1111/j.1467-9868.2005.00513.x>.