Top-level fitting functions

Main clustering functions

greed()

Model based hierarchical clustering

greed_cond()

Conditional model based hierarchical clustering

Fitting algorithms classes

Description of classes that describe the possible fitting algorithms.

hybrid-class

Hybrid optimization algorithm

multistarts-class

Greedy algorithm with multiple start class

genetic-class

Genetic optimization algorithm

seed-class

Greedy algorithm with seeded initialization

Supported generative models

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

sbm-class

Stochastic Block Model class

misssbm-class

Stochastic Block Model with sampling scheme class

dcsbm-class

Degree Corrected Stochastic Block Model class

multsbm-class

Multinomial Stochastic Block Model class

co_dcsbm-class

Degree Corrected Stochastic Block Model for bipartite graph class

mm-class

Mixture of Multinomial model description class

gmm-class

Gaussian mixture model description class

diaggmm-class

Diagonal Gaussian mixture model description class

mvmreg-class

Multivariate mixture of regression model description class

Models fit classes

Description of classes that describe a model fit.

sbm_fit-class

Stochastic Block Model fit results class

misssbm_fit-class

Stochastic Block Model with sampling scheme fit results class

dcsbm_fit-class

Degree Corrected Stochastic Block Model fit results class

multsbm_fit-class

Multinomial Stochastic Block Model fit results class

co_dcsbm_fit-class

Degree corrected stochastic block model for bipartite graph fit results class

mm_fit-class

Mixture of Multinomial fit results class

gmm_fit-class

Gaussian mixture model fit results class

diaggmm_fit-class

Diagonal Gaussian mixture model fit results class

mvmreg_fit-class

Clustering with a multivariate mixture of regression model fit results class

Hierarchical models fit classes

Description of classes that describe a hierachical model fit.

sbm_path-class

Stochastic Block Model hierarchical fit results class

misssbm_path-class

Stochastic Block Model with sampling scheme hierarchical fit results class

dcsbm_path-class

Degree Corrected Stochastic Block Model hierarchical fit results class

multsbm_path-class

Multinomial Stochastic Block Model hierachical fit results class

co_dcsbm_path-class

Degree corrected stochastic block model for bipartite graph hierarchical fit results class

mm_path-class

Mixture of Multinomial hierarchical fit results class

gmm_path-class

Gaussian mixture model hierarchical fit results class

diaggmm_path-class

Diagonal Gaussian mixture model hierarchical fit results class

mvmreg_path-class

Multivariate mixture of regression model 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(<co_dcsbm_fit>)

Extract parameters from an co_dcsbm_fit-class object

coef(<dcsbm_fit>)

Extract parameters from an dcsbm_fit-class object

coef(<diaggmm_fit>)

Extract mixture parameters from diaggmm_fit-class object

coef(<gmm_fit>)

Extract mixture parameters from gmm_fit-class object

coef(<misssbm_fit>)

Extract parameters from an misssbm_fit-class object

coef(<mm_fit>)

Extract parameters from an mm_fit-class object

coef(<multsbm_fit>)

Extract parameters from an multsbm_fit-class object

coef(<mvmreg_fit>)

Extract mixture parameters from mvmreg_fit-class object

coef(<sbm_fit>)

Extract parameters from an sbm_fit-class object

cut(<co_dcsbm_path>)

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

cut(<icl_path>)

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

plot(<co_dcsbm_fit>,<missing>)

plot a co_dcsbm_fit-class

plot(<co_dcsbm_path>,<missing>)

plot a co_dcsbm_path-class

plot(<dcsbm_fit>,<missing>)

plot a sbm_fit-class object

plot(<dcsbm_path>,<missing>)

plot a sbm_path-class object

plot(<diaggmm_path>,<missing>)

plot a diaggmm_path-class object

plot(<gmm_path>,<missing>)

plot a gmm_path-class object

plot(<misssbm_fit>,<missing>)

plot a misssbm_fit-class object

plot(<misssbm_path>,<missing>)

plot a misssbm_path-class object

plot(<mm_fit>,<missing>)

plot a mm_fit-class object

plot(<mm_path>,<missing>)

plot a mm_path-class object

plot(<multsbm_fit>,<missing>)

plot a multsbm_fit-class object

plot(<multsbm_path>,<missing>)

plot a sbm_path-class object

plot(<mvmreg_path>,<missing>)

plot a mvmreg_path-class object

plot(<sbm_fit>,<missing>)

plot a sbm_fit-class object

plot(<sbm_path>,<missing>)

plot a sbm_path-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

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

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