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.

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> and Laborde et al. (2023) <doi: 10.1186/s12859-023-05210-6>. 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.

Nonparametric estimation of population-averaged transition
    probabilities, with cluster-bootstrap pointwise confidence intervals,
    simultaneous confidence bands, and two-sample Kolmogorov-Smirnov-type
    tests for clustered or independent multistate process data.
    Estimation follows Bakoyannis (2021) <doi:10.1111/biom.13327>;
    two-sample inference for the cluster-randomized and
    independent-samples designs follows Bakoyannis and Bandyopadhyay
    (2022) <doi:10.1007/s10463-021-00819-x>. Both methods use the
    working-independence Aalen-Johansen estimator. The package supports
    both progressive (acyclic) and non-monotone (e.g., illness-death
    with recovery) multistate processes, right censoring, left
    truncation, and informative cluster size. The user supplies data
    in interval format (one row per mutually-exclusive time interval
    per subject) and interacts with the package through a single
    formula-based function, patp().