Evaluates the stability and significance of clusters on 'igraph' graphs.
    Supports weighted and unweighted graphs. Implements the cluster evaluation methods
    defined by Arratia A, Renedo M (2021) <doi:10.7717/peerj-cs.600>. Also includes an
    implementation of the Reduced Mutual Information introduced by Newman et al. (2020) 
    <doi:10.1103/PhysRevE.101.042304>.

A set of tools for evaluating clustering robustness using 
    proportion of ambiguously clustered pairs (Senbabaoglu et al. (2014) 
    <doi:10.1038/srep06207>), as well as similarity across methods 
    and method stability using element-centric clustering comparison (Gates et 
    al. (2019) <doi:10.1038/s41598-019-44892-y>). Additionally, this package 
    enables stability-based parameter assessment for graph-based clustering 
    pipelines typical in single-cell data analysis.

Hierarchical and partitioning algorithms of blocks of variables. The partitioning algorithm includes an option called noise cluster to set aside atypical blocks of variables. The CLUSTATIS method (for quantitative blocks) (Llobell, Cariou, Vigneau, Labenne & Qannari (2020) <doi:10.1016/j.foodqual.2018.05.013>, Llobell, Vigneau & Qannari (2019) <doi:10.1016/j.foodqual.2019.02.017>)  and the CLUSCATA method (for Check-All-That-Apply data) (Llobell, Cariou, Vigneau, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2018.09.006>, Llobell, Giacalone, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2019.05.017>) are the core of this package. The CATATIS methods allows to compute some indices and tests to control the quality of CATA data. Multivariate analysis and clustering of subjects for quantitative multiblock data, CATA, RATA, Free Sorting and JAR experiments are available.

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".