Toplevel fitting functionsMain clustering function 


Model based hierarchical clustering 

Generic methods to explore a fitDescription of methods to extract coefficients and cut/plot a hierachical model fit. 

Method to extract the clustering results from an 

Generic method to extract the ICL value from an 

Generic method to get the number of clusters from an 

Generic method to extract the prior used to fit 

Extract parameters from an 

Show an IclPath object 

Generic method to cut a path solution to a desired number of cluster 

Plot an 

Extract a part of a 

Fitting algorithms classesDescription of classes that describe the possible fitting algorithms. 

Abstract optimization algorithm class 

Hybrid optimization algorithm 

Greedy algorithm with multiple start class 

Genetic optimization algorithm 

Greedy algorithm with seeded initialization 

Supported generative modelsDescription of classes that corresponds to the different type of generative models that greed may use. 

Stochastic Block Model Prior class 

Degree Corrected Stochastic Block Model Prior class 

Multinomial Stochastic Block Model Prior class 

Latent Class Analysis Model Prior class 

Mixture of Multinomial Model Prior description class 

Gaussian Mixture Model Prior description class 

Diagonal Gaussian Mixture Model Prior description class 

Multivariate mixture of regression Prior model description class 

Degree Corrected Latent Block Model for bipartite graph class 

Mixed Models classes 

Abstract class to represent a generative model for clustering 

Abstract class to represent a generative model for coclustering 

Models fit classesDescription of classes that describe a model fit. 

Abstract class to represent a clustering result 

Abstract class to represent a hierarchical clustering result 

Stochastic Block Model fit results class 

Degree Corrected Stochastic Block Model fit results class 

Multinomial Stochastic Block Model fit results class 

Latent Class Analysis fit results class 

Mixture of Multinomial fit results class 

Gaussian mixture model fit results class 

Diagonal Gaussian mixture model fit results class 

Clustering with a multivariate mixture of regression model fit results class 

Degree corrected Latent Block Model fit results class 

Mixed Models fit results class 

Hierarchical models fit classesDescription of classes that describe a hierachical model fit. 

Stochastic Block Model hierarchical fit results class 

Degree Corrected Stochastic Block Model hierarchical fit results class 

Multinomial Stochastic Block Model hierachical fit results class 

Latent Class Analysis hierarchical fit results class 

Mixture of Multinomial hierarchical fit results class 

Gaussian mixture model hierarchical fit results class 

Diagonal Gaussian mixture model hierarchical fit results class 

Multivariate mixture of regression model hierarchical fit results class 

Degree corrected Latent Block Model hierarchical fit results class 

Mixed Models hierarchical fit results class 

Methods to explore a hierarchical fitDescription of methods to extract coefficients and cut/plot a hierachical model fit. 

Make a matrix of plots with a given data and gmm fitted parameters 

Extract parameters from an 

Extract parameters from an 

Extract mixture parameters from 

Extract mixture parameters from 

Extract parameters from an 

Extract parameters from an 

Extract parameters from an 

Extract mixture parameters from 

Extract parameters from an 

Extract parameters from an 

Method to cut a DcLbmPath solution to a desired number of cluster 

Generic method to cut a path solution to a desired number of cluster 

Plot a 

Plot a 

Plot a 

Plot a 

Plot a 

Plot an 

Plot a 

Plot a 

Plot a 

Plot a 

Misc toolsMiscaelenous utility functions. 

Compute the normalized mutual information of two discrete samples 

Compute the entropy of a discrete sample 

Compute the mutual information of two discrete samples 

Regularized spectral clustering 

Convert a binary adjacency matrix with missing value to a cube 

Data generation functionFunctions to generate data from a specified generative model. 

Generate a graph adjacency matrix using a Stochastic Block Model 

Generates graph adjacency matrix using a degree corrected SBM 

Generate a graph adjacency matrix using a Stochastic Block Model 

Generate data using a Multinomial Mixture 

Generate data from a mixture of regression model 

Generate a data matrix using a Latent Block Model 

Generate data from lca model 

Data sets 

Political blogs network dataset 

Books about US politics network dataset 

American College football network dataset 

Jazz musicians network dataset 

Jazz musicians / Bands relations 

French Parliament votes dataset 

Fashion mnist dataset 

Mushroom data 

Fifa data 

Fifa_positions data 

French Parliament votes dataset 

Ndrangheta mafia covert network dataset 

Young People survey data 

NewGuinea data 

SevenGraders data 