Bernstein.compute.pvalues
                        Function to compute the stability indices and
                        the p-values associated to a set of clusterings
                        according to Bernstein inequality.
Bernstein.p.value       Function to compute the p-value according to
                        Bernstein inequality.
Chi.square.compute.pvalues
                        Function to compute the stability indices and
                        the p-values associated to a set of clusterings
                        according to the chi-square test between
                        multiple proportions.
Compute.Chi.sq          Function to evaluate if a set of similarity
                        distributions significantly differ using the
                        chi square test.
Do.boolean.membership.matrix
                        Function to compute and build up a pairwise
                        boolean membership matrix.
Fuzzy.kmeans.sim.noise
                        Function to compute similarity indices using
                        noise injection techniques and fuzzy c-mean
                        clustering.
Fuzzy.kmeans.sim.projection
                        Function to compute similarity indices using
                        random projections and fuzzy c-mean clustering.
Fuzzy.kmeans.sim.resampling
                        Function to compute similarity indices using
                        resampling techniques and fuzzy c-mean
                        clustering.
Hierarchical.sim.noise
                        Function to compute similarity indices using
                        noise injection techniques and hierarchical
                        clustering.
Hierarchical.sim.projection
                        Function to compute similarity indices using
                        random projections and hierarchical clustering.
Hierarchical.sim.resampling
                        Function to compute similarity indices using
                        resampling techniques and hierarchical
                        clustering.
Hybrid.testing          Statistical test based on stability methods for
                        model order selection.
Hypothesis.testing      Function to select significant clusterings from
                        a given set of p-values
Intersect               Function to compute the intersection between
                        elements of two vectors
Kmeans.sim.noise        Function to compute similarity indices using
                        noise injection techniques and kmeans
                        clustering.
Kmeans.sim.projection   Function to compute similarity indices using
                        random projections and kmeans clustering.
Kmeans.sim.resampling   Function to compute similarity indices using
                        resampling techniques and kmeans clustering.
PAM.sim.noise           Function to compute similarity indices using
                        noise injection techniques and PAM clustering.
PAM.sim.projection      Function to compute similarity indices using
                        random projections and PAM clustering.
PAM.sim.resampling      Function to compute similarity indices using
                        resampling techniques and PAM clustering.
compute.cumulative.multiple
                        Function to compute the empirical cumulative
                        distribution function (ECDF) of the similarity
                        measures.
compute.integral        Functions to compute the integral of the ecdf
                        of the similarity values
do.similarity.noise     Function that computes sets of similarity
                        indices using injection of gaussian noise.
do.similarity.projection
                        Function that computes sets of similarity
                        indices using randomized maps.
do.similarity.resampling
                        Function that computes sets of similarity
                        indices using resampling techniques.
mosclust-package        Model order selection for clustering
perturb.by.noise        Function to generate a data set perturbed by
                        noise.
plot_cumulative         Function to plot the empirical cumulative
                        distribution function of the similarity values
plot_multiple.hist.similarity
                        Plotting histograms of similarity measures
                        between clusterings
plot_pvalues            Function to plot p-values for different tests
                        of hypothesis
sFM                     Similarity measures between pairs of
                        clusterings
