Contains the functions for construction and visualization of underlying and reflexivity graphs of 
            the three families of the proximity catch digraphs (PCDs), see (Ceyhan (2005) ISBN:978-3-639-19063-2),
            and for computing the edge density of these PCD-based graphs which are then
            used for testing the patterns of segregation and association against complete spatial randomness (CSR))
            or uniformity in one and two dimensional cases. 
            The PCD families considered are Arc-Slice PCDs, Proportional-Edge (PE) PCDs (Ceyhan et al. (2006) <doi:10.1016/j.csda.2005.03.002>) 
            and Central Similarity PCDs (Ceyhan et al. (2007) <doi:10.1002/cjs.5550350106>). 
            See also (Ceyhan (2016) <doi:10.1016/j.stamet.2016.07.003>) for edge density of the underlying and 
            reflexivity graphs of PE-PCDs.
            The package also has tools for visualization of PCD-based graphs for one, two, and three dimensional data.

Functions for generating progressively Type-II censored data in
    a mixture structure and fitting models using a constrained EM algorithm. It can also
    create a progressive Type-II censored version of a given real dataset to be considered for
    model fitting.

Principal component of explained variance (PCEV) is a statistical
    tool for the analysis of a multivariate response vector. It is a dimension-
    reduction technique, similar to Principal component analysis (PCA), that seeks
    to maximize the proportion of variance (in the response vector) being explained
    by a set of covariates.

Provides convenience functions and pre-programmed
    Stan models related to the paired comparison factor model. Its purpose
    is to make fitting paired comparison data using Stan easy. This
    package is described in Pritikin (2020) <doi:10.1016/j.heliyon.2020.e04821>.

We provide several algorithms to compute the genotype ancestry scores (such as eigenvector projections) in the case where highly correlated individuals are involved.