Clustering methods, which (if asked) can provide step-by-step
    explanations of the algorithms used, as described in Ezugwu et. al., (2022)
    <doi:10.1016/j.engappai.2022.104743>; and datasets to test them on, which
    highlight the strengths and weaknesses of each technique, as presented in
    the clustering section of 'scikit-learn' (Pedregosa et al., 2011)
    <https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html>.

Multiple comparison techniques are typically applied following an F
    test from an ANOVA to decide which means are significantly different from
    one another. As an alternative to traditional methods, cluster analysis
    can be performed to group the means of different treatments into
    non-overlapping clusters. Treatments in different groups are considered
    statistically different. Several approaches have been proposed, with varying
    clustering methods and cut-off criteria. This package implements
    cluster-based multiple comparisons tests and also provides a visual
    representation in the form of a dendrogram. Di Rienzo, J. A., Guzman, A. W., 
    & Casanoves, F. (2002) <jstor.org/stable/1400690>. Bautista, M. G., Smith, D. 
    W., & Steiner, R. L. (1997) <doi:10.2307/1400402>.

Model-based clustering of mixed data (i.e. data which consist of
    continuous, binary, ordinal or nominal variables) using a parsimonious
    mixture of latent Gaussian variable models.

Functions to perform k-prototypes partitioning clustering for
    mixed variable-type data according to Z.Huang (1998): Extensions to the k-Means
    Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining
    and Knowledge Discovery 2, 283-304.

Network-based clustering using a Bayesian network mixture model with optional covariate adjustment.

Cluster analysis of a set of variables. Variables can be quantitative, qualitative or a mixture of both.

Biclustering, row clustering and column clustering using the proportional odds model (POM), ordered stereotype model (OSM) or binary model for ordinal categorical data. Fernández, D., Arnold, R., Pledger, S., Liu, I., & Costilla, R. (2019) <doi:10.1007/s11634-018-0324-3>.