R: Model Order Selection for Clustering

# Model Order Selection for Clustering

## Documentation for package ‘mosclust’ version 1.0.2

- DESCRIPTION file
- User guides, package vignettes and other documentation

## Help Pages

mosclust-package | Model order selection for clustering
Bernstein.compute.pvalues | Function to compute the stability indices and the p-values associated to a set of clusterings according to Bernstein inequality.
Bernstein.ind.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.
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
compute.integral.from.similarity | Functions to compute the integral of the ecdf of the similarity values
cumulative.values | Function to compute the empirical cumulative distribution function (ECDF) of the similarity measures.
Do.boolean.membership.matrix | Function to compute and build up a pairwise boolean membership matrix.
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.
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.
mosclust | Model order selection for 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.
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_cumulative.multiple | Function to plot the empirical cumulative distribution function of the similarity values
plot_hist.similarity | Plotting histograms of similarity measures between clusterings
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
sJaccard | Similarity measures between pairs of clusterings
sM | Similarity measures between pairs of clusterings