Reference

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 CombinedModelsPath-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
CombinedModels()	Combined 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
CombinedModelsFit-class	Combined 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 hierarchical 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
CombinedModelsPath-class	Combined 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
available_models()	Display the list of every currently available DLVM
available_algorithms()	Display the list of every currently available optimization algorithm
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
Books	Books about US politics network dataset
Football	American College football network dataset
Jazz	Jazz musicians network dataset
fashion	Fashion mnist dataset
mushroom	Mushroom data
Fifa	Fifa data
Ndrangheta	Ndrangheta mafia covert network dataset
Youngpeoplesurvey	Young People survey data
NewGuinea	NewGuinea data
SevenGraders	SevenGraders data