Top-level fitting functions

Main clustering function

greed()

Model based hierarchical clustering

Generic methods to explore a fit

Description of methods to extract coefficients and cut/plot a hierachical model fit.

clustering()

Method to extract the clustering results from an IclFit-class object

ICL()

Generic method to extract the ICL value from an IclFit-class object

K()

Generic method to get the number of clusters from an IclFit-class object

prior()

Generic method to extract the prior used to fit IclFit-class object

coef(<IclFit>)

Extract parameters from an IclFit-class object

show(<IclFit>)

Show an IclPath object

cut(<IclPath>)

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

plot(<IclPath>,<missing>)

Plot an IclPath-class object

extractSubModel()

Extract a part of a MixedModelsPath-class object

Fitting algorithms classes

Description of classes that describe the possible fitting algorithms.

Alg-class

Abstract optimization algorithm class

Hybrid()

Hybrid optimization algorithm

Multistarts()

Greedy algorithm with multiple start class

Genetic()

Genetic optimization algorithm

Seed()

Greedy algorithm with seeded initialization

Supported generative models

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

SbmPrior() Sbm()

Stochastic Block Model Prior class

DcSbmPrior() DcSbm()

Degree Corrected Stochastic Block Model Prior class

MultSbmPrior() MultSbm()

Multinomial Stochastic Block Model Prior class

LcaPrior() Lca()

Latent Class Analysis Model Prior class

MoMPrior() MoM()

Mixture of Multinomial Model Prior description class

GmmPrior() Gmm()

Gaussian Mixture Model Prior description class

DiagGmmPrior() DiagGmm()

Diagonal Gaussian Mixture Model Prior description class

MoRPrior() MoR()

Multivariate mixture of regression Prior model description class

DcLbmPrior() DcLbm()

Degree Corrected Latent Block Model for bipartite graph class

MixedModels()

Mixed Models classes

DlvmPrior-class

Abstract class to represent a generative model for clustering

DlvmCoPrior-class

Abstract class to represent a generative model for co-clustering

Models fit classes

Description of classes that describe a model fit.

IclFit-class

Abstract class to represent a clustering result

IclPath-class

Abstract class to represent a hierarchical clustering result

SbmFit-class

Stochastic Block Model fit results class

DcSbmFit-class

Degree Corrected Stochastic Block Model fit results class

MultSbmFit-class

Multinomial Stochastic Block Model fit results class

LcaFit-class

Latent Class Analysis fit results class

MoMFit-class

Mixture of Multinomial fit results class

GmmFit-class

Gaussian mixture model fit results class

DiagGmmFit-class

Diagonal Gaussian mixture model fit results class

MoRFit-class

Clustering with a multivariate mixture of regression model fit results class

DcLbmFit-class

Degree corrected Latent Block Model fit results class

MixedModelsFit-class

Mixed Models fit results class

Hierarchical models fit classes

Description of classes that describe a hierachical model fit.

SbmPath-class

Stochastic Block Model hierarchical fit results class

DcSbmPath-class

Degree Corrected Stochastic Block Model hierarchical fit results class

MultSbmPath-class

Multinomial Stochastic Block Model hierachical fit results class

LcaPath-class

Latent Class Analysis hierarchical fit results class

MoMPath-class

Mixture of Multinomial hierarchical fit results class

GmmPath-class

Gaussian mixture model hierarchical fit results class

DiagGmmPath-class

Diagonal Gaussian mixture model hierarchical fit results class

MoRPath-class

Multivariate mixture of regression model hierarchical fit results class

DcLbmPath-class

Degree corrected Latent Block Model hierarchical fit results class

MixedModelsPath-class

Mixed Models hierarchical fit results class

Methods to explore a hierarchical fit

Description of methods to extract coefficients and cut/plot a hierachical model fit.

gmmpairs()

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

coef(<DcLbmFit>)

Extract parameters from an DcLbmFit-class object

coef(<DcSbmFit>)

Extract parameters from an DcSbmFit-class object

coef(<DiagGmmFit>)

Extract mixture parameters from DiagGmmFit-class object

coef(<GmmFit>)

Extract mixture parameters from GmmFit-class object

coef(<IclFit>)

Extract parameters from an IclFit-class object

coef(<LcaFit>)

Extract parameters from an LcaFit-class object

coef(<MoMFit>)

Extract parameters from an MoMFit-class object

coef(<MoRFit>)

Extract mixture parameters from MoRFit-class object using MAP estimation

coef(<MultSbmFit>)

Extract parameters from an MultSbmFit-class object

coef(<SbmFit>)

Extract parameters from an SbmFit-class object

cut(<DcLbmPath>)

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

cut(<IclPath>)

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

plot(<DcLbmFit>,<missing>)

Plot a DcLbmFit-class

plot(<DcLbmPath>,<missing>)

Plot a DcLbmPath-class

plot(<DcSbmFit>,<missing>)

Plot a DcSbmFit-class object

plot(<DiagGmmFit>,<missing>)

Plot a DiagGmmFit-class object

plot(<GmmFit>,<missing>)

Plot a GmmFit-class object

plot(<IclPath>,<missing>)

Plot an IclPath-class object

plot(<LcaFit>,<missing>)

Plot a LcaFit-class object

plot(<MoMFit>,<missing>)

Plot a MoMFit-class object

plot(<MultSbmFit>,<missing>)

Plot a MultSbmFit-class object

plot(<SbmFit>,<missing>)

Plot a SbmFit-class object

Misc tools

Miscaelenous utility functions.

NMI()

Compute the normalized mutual information of two discrete samples

H()

Compute the entropy of a discrete sample

MI()

Compute the mutual information of two discrete samples

spectral()

Regularized spectral clustering

to_multinomial()

Convert a binary adjacency matrix with missing value to a cube

Data generation function

Functions to generate data from a specified generative model.

rsbm()

Generate a graph adjacency matrix using a Stochastic Block Model

rdcsbm()

Generates graph adjacency matrix using a degree corrected SBM

rmultsbm()

Generate a graph adjacency matrix using a Stochastic Block Model

rmm()

Generate data using a Multinomial Mixture

rmreg()

Generate data from a mixture of regression model

rlbm()

Generate a data matrix using a Latent Block Model

rlca()

Generate data from lca model

Data sets

Blogs

Political blogs network dataset

Books

Books about US politics network dataset

Football

American College football network dataset

Jazz

Jazz musicians network dataset

Jazz_full

Jazz musicians / Bands relations

Xvlegislature

French Parliament votes dataset

fashion

Fashion mnist dataset

mushroom

Mushroom data

Fifa

Fifa data

Fifa

Fifa_positions data

FrenchParliament

French Parliament votes dataset

Ndrangheta

Ndrangheta mafia covert network dataset

Youngpeoplesurvey

Young People survey data

NewGuinea

NewGuinea data

SevenGraders

SevenGraders data