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.

Provides various tools of for clustering multivariate angular 
  data on the torus. The package provides angular 
  adaptations of usual clustering methods such as the k-means 
  clustering, pairwise angular distances, which can be used as an 
  input for distance-based clustering algorithms, and implements
  clustering based on the conformal prediction framework. Options 
  for the conformal scores include scores based on a kernel density 
  estimate, multivariate von Mises mixtures, and naive k-means clusters. 
  Moreover, the package provides some basic data handling tools for 
  angular data.

Clusters longitudinal trajectories over time (can be unequally 
    spaced, unequal length time series and/or partially overlapping series) on
    a common time axis. Performs k-means clustering on a single continuous 
    variable measured over time, where each mean is defined by a thin plate 
    spline fit to all points in a cluster. Distance is MSE across trajectory 
    points to cluster spline. Provides graphs of derived cluster splines, 
    silhouette plots, and Adjusted Rand Index evaluations of the number
    of clusters. Scales well to large data with multicore parallelism available
    to speed computation.

Monitor and trace changes in clustering solutions of accumulating datasets 
    at successive time points. The clusters can adopt External and Internal transition at 
    succeeding time points. The External transitions comprise of Survived, Merged, Split, 
    Disappeared, and newly Emerged candidates. In contrast, Internal transition includes changes 
    in location and cohesion of the survived clusters. The package uses MONIC framework developed by 
    Spiliopoulou, Ntoutsi, Theodoridis, and Schult (2006)<doi:10.1145/1150402.1150491> .