A method for determining groups in multiple 
    curves with an automatic selection of their number based on k-means or 
    k-medians algorithms. The selection of the optimal number is provided by 
    bootstrap methods or other approaches with lower computational cost. The methodology can be applied both in regression and survival framework.
     Implemented methods are:
    Grouping multiple survival curves described by Villanueva et al. (2018) <doi:10.1002/sim.8016>.

Clustering method to cluster both effects curves, through quantile regression coefficient modeling, and curves in functional data analysis. Sottile G. and Adelfio G. (2019) <doi:10.1007/s00180-018-0817-8>.

Methods for Cluster analysis.  Much extended the original from
	Peter Rousseeuw, Anja Struyf and Mia Hubert,
	based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".

Test for cluster tendency (clusterability) of a data set.
    The methods implemented - reducing the data set to a single dimension using principal component analysis or computing
    pairwise distances, and performing a multimodality test like the Dip Test or Silverman's Critical Bandwidth Test - 
    are described in Adolfsson, Ackerman, and Brownstein (2019) <doi:10.1016/j.patcog.2018.10.026>. Such methods can inform whether clustering algorithms
    are appropriate for a data set.

Provides functionality for the analysis of clustered data using the cluster bootstrap.

Package providing functions for computing a collection of clustering validation or quality criteria and partition comparison indices.

A collection of data sets for teaching cluster analysis.

Identification and visualization of groups of closely spaced mutations in the DNA sequence of cancer genome. The extremely mutated zones are searched in the symmetric dissimilarity matrix using the anti-Robinson matrix properties. Different data sets are obtained to describe and plot the clustered mutations information.

We developed the clusterGeneration package to provide functions 
        for generating random clusters, generating random 
        covariance/correlation matrices,
        calculating a separation index (data and population version)
        for pairs of clusters or cluster distributions, and 1-D and 2-D
        projection plots to visualize clusters.  The package also
        contains a function to generate random clusters based on
        factorial designs with factors such as degree of separation,
        number of clusters, number of variables, number of noisy
        variables.