Description: The package provides a function for measuring the difference between two independent or non-independent empirical distributions and returning a significance level of the difference.

Title: Measuring the Difference Between Two Empirical Distributions
Description: Provides a function for measuring the difference between two independent or non-independent empirical distributions and returning a significance level of the difference.

Title: Variable-Group Methods for Agglomerative Hierarchical Clustering
Authors@R: c(person("Alberto", "Fernandez", role = c("aut", "cre"),
person("Sergio", "Gomez", role = "aut", email = "sergio.gomez@urv.cat",
Description: A collection of methods for agglomerative hierarchical clustering
strategies on a matrix of distances, implemented using the variable-group
approach introduced in Fernandez and Gomez (2008)
<doi:10.1007/s00357-008-9004-x>. Descriptive measures to analyze the resulting
hierarchical trees are also provided. In addition to the usual clustering
methods, two parameterized methods are provided to explore an infinite family
of hierarchical clustering strategies. When there are ties in proximity
values, the hierarchical trees obtained are unique and independent of the
order of the elements in the input matrix.
Author: Alberto Fernandez [aut, cre] (<https://orcid.org/0000-0002-1241-1646>),

Title: Extended Agglomerative Hierarchical Clustering
Description: A comprehensive collection of linkage methods for agglomerative
hierarchical clustering on a matrix of proximity data (distances or
similarities), returning a multifurcated dendrogram or multidendrogram.
Multidendrograms can group more than two clusters when ties in proximity data
occur, and therefore they do not depend on the order of the input data.
Descriptive measures to analyze the resulting dendrogram are additionally
Authors@R: c(person(given = "Alberto", family = "Fernandez",
person(given = "Sergio", family = "G�mez",
Imports: graphics, grDevices, Rcpp, stats, utils
Suggests: ape, cluster, dendextend, knitr, rmarkdown