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".

A collection of data sets for teaching cluster analysis.

Estimate and return the needed parameters for visualisations designed for 'OpenBudgets' <http://openbudgets.eu/> data. Calculate cluster analysis measures in Budget data of municipalities across Europe, according to the 'OpenBudgets' data model. It involves a set of techniques and algorithms used to find and divide the data into groups of similar observations. Also, can be used generally to extract visualisation parameters convert them to 'JSON' format and use them as input in a different graphical interface.

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.