Deciding what resolution to use can be a difficult question when
    approaching a clustering analysis. One way to approach this problem is to
    look at how samples move as the number of clusters increases. This package
    allows you to produce clustering trees, a visualisation for interrogating
    clusterings as resolution increases.

Returns an edit-distance based clusterization of an input vector of strings.
    Each cluster will contain a set of strings w/ small mutual edit-distance
    (e.g., Levenshtein, optimum-sequence-alignment, Damerau-Levenshtein), as computed by
    stringdist::stringdist(). The set of all mutual edit-distances is then used by
    graph algorithms (from package 'igraph') to single out subsets of high connectivity.

An interactive document on  the topic of cluster analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/ClusterAnalysis/>.

Functions for the clustering of variables around Latent Variables, for 2-way or 3-way data. Each cluster of variables, which may be defined as a local or directional cluster, is associated with a latent variable. External variables measured on the same observations or/and additional information on the variables can be taken into account. A "noise" cluster or sparse latent variables can also be defined.

Variable selection for Gaussian model-based clustering as implemented in the 'mclust' package. The methodology allows to find the (locally) optimal subset of variables in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting 'mclust' models. By default the algorithm uses a sequential search, but parallelisation is also available.

Gaussian-Based Visualization of Gaussian and Non-Gaussian Model-Based Clustering done on any type of data. Visualization is based on the probabilities of classification.