Implements the MST-kNN clustering algorithm proposed by
    Inostroza-Ponta (2008) <https://trove.nla.gov.au/work/28729389>.
    The algorithm determines the number of clusters automatically by
    recursively intersecting the Minimum Spanning Tree (MST) and the
    k-Nearest Neighbor (kNN) proximity graphs constructed from a pairwise
    distance matrix.  The value of k is selected via a connectivity
    criterion (the smallest k such that the kNN graph is connected,
    bounded by floor(log(n))).  The package requires only a distance
    matrix as input and returns cluster assignments, an 'igraph' network,
    and partition metadata.

Distance between multivariate t distributions, as presented by N. Bouhlel and D. Rousseau (2023) <doi:10.1109/LSP.2023.3324594>.

Estimators for multivariate symmetrical uncertainty based
    on the work of Gustavo Sosa et al. (2016) <arXiv:1709.08730>,
    total correlation, information gain and symmetrical uncertainty of
    categorical variables.

Defines colour palettes and themes for Michigan State
  University (MSU) publications and presentations. Palettes and
  themes are supported in both base R and 'ggplot2' graphics, and are intended
  to provide consistency between those creating documents and presentations.

Estimation, inference and diagnostics for Univariate Autoregressive Markov Switching Models for Linear and Generalized Models. Distributions for the series include gaussian, Poisson, binomial and gamma cases. The EM algorithm is used for estimation (see Perlin (2012) <doi:10.2139/ssrn.1714016>).

Functions for metabolomics data analysis: data preprocessing, 
  orthogonal signal correction, PCA analysis, PCA-DA analysis, 
	PLS-DA analysis, classification, feature selection, correlation 
	analysis, data visualisation and re-sampling strategies.

Implements survival analyses across multiple abundance thresholds,
    repeatedly partitioning samples into groups and evaluating survival
    differences to assess taxonomic associations with outcomes.