Compute Gower's distance (or similarity) coefficient between records. Compute 
    the top-n matches between records. Core algorithms are executed in parallel on systems
    supporting OpenMP.

Provides tools for applying the Bayesian Gower agreement methodology (presented in the package vignette) to nominal or ordinal data. The framework can accommodate any number of units, any number of coders, and missingness; and can handle both one-way and two-way random study designs. Influential units and/or coders can be identified easily using leave-one-out statistics.

It provides an effective, efficient, and fast way to explore the Gene Ontology (GO). 
             Given a set of genes, the package contains functions to assess the GO and obtain the 
             terms associated with the genes and the levels of the GO terms. The package provides 
             functions for the three different GO ontology. We discussed the methods explicitly in 
             the following article <doi:10.1038/s41598-020-73326-3>.

A collection of tools which extract a model documentation from 'GAMS' code and comments. 
             In order to use the package you need to install 'pandoc' and 'pandoc-citeproc' 
             first (<https://pandoc.org/>).

Functions to estimate the parameters of the generalized Poisson distribution with or without covariates using maximum likelihood. The references include Nikoloulopoulos A.K. & Karlis D. (2008). "On modeling count data: a comparison of some well-known discrete distributions". Journal of Statistical Computation and Simulation, 78(3): 437--457, <doi:10.1080/10629360601010760> and Consul P.C. & Famoye F. (1992). "Generalized Poisson regression model". Communications in Statistics - Theory and Methods, 21(1): 89--109, <doi:10.1080/03610929208830766>.

Aligning multiple visualisations by utilising generalised orthogonal Procrustes analysis (GPA) before combining coordinates into a single biplot display as described in Nienkemper-Swanepoel, le Roux and Lubbe (2023)<doi:10.1080/03610918.2021.1914089>. This is mainly suitable to combine visualisations constructed from multiple imputations, however, it can be generalised to combine variations of visualisations from the same datasets (i.e. resamples).