Fits a spatio-temporal finite mixture model using 'TMB'.
    Covariate, spatial and temporal random effects can be incorporated
    into the gating formula using multinomial logistic regression, the
    expert formula using a generalized linear mixed model framework, or
    both.

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.

Package contains most of the popular internal and external
        cluster validation methods ready to use for the most of the
        outputs produced by functions coming from package "cluster".
        Package contains also functions and examples of usage for
        cluster stability approach that might be applied to algorithms
        implemented in "cluster" package as well as user defined
        clustering algorithms.

Statistical and biological validation of clustering results. This package implements Dunn Index, Silhouette, Connectivity, Stability, BHI and BSI. Further information can be found in Brock, G et al. (2008) <doi: 10.18637/jss.v025.i04>.