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/>.

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.

A tool that implements the clustering algorithms from 'mothur' (Schloss PD et al. (2009) <doi:10.1128/AEM.01541-09>). 'clustur' make use of the cluster() and make.shared() command from 'mothur'. Our cluster() function has five different algorithms implemented: 'OptiClust', 'furthest', 'nearest', 'average', and 'weighted'. 'OptiClust' is an optimized clustering method for Operational Taxonomic Units, and you can learn more here, (Westcott SL, Schloss PD (2017) <doi:10.1128/mspheredirect.00073-17>). The make.shared() command is always applied at the end of the clustering command. This functionality allows us to generate and create clustering and abundance data efficiently.

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.