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mimiSBM

A mixture of multilayer Stochastic Block Models (SBM) to group co-membership matrices with similar information into components and to partition observations into different clusters.

Installation

This package can be installed from GitHub with :

# install.packages("remotes")
remotes::install_github("Kdesantiago/mimiSBM")

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Version

Install

install.packages('mimiSBM')

Monthly Downloads

149

Version

0.0.1.3

License

GPL-3

Maintainer

Kylliann De Santiago

Last Published

January 9th, 2024

Functions in mimiSBM (0.0.1.3)

fit_SBM_per_layer

SBM on each layer

Create probality-component list for clustering per view component.

fit_SBM_per_layer_parallel

SBM on each layer - parallelized

Loss_BayesianMSBM

mimiSBM Evidence Lower BOund

Clustering Matrix : One hot encoding

initialisation_params_bayesian

Initialization of mimiSBM parameters

Variational Bayes Expectation Maximization

Diagonal coefficient to 0 on each slice given the 3rd dimension.

log softmax of matrices (by row)

Label Switching

BayesianMixture_SBM_model

mimiSBM model for fixed K and Q

Sort the clustering matrix

Transposition of an array

Mixture of Multilayer Integrator SBM (mimiSBM)

Plot adjacency matrices

update_u_bayesian

Update of bayesian parameter u

partition_K_barre

Create a link between final clustering and clustering per view component.

update_eta_bayesian

Update of bayesian parameter eta

update_tau_bayesian

Update of bayesian parameter tau

Upper triangular Matrix/Array

update_xi_bayesian

Update of bayesian parameter xi

update_theta_bayesian

Update of bayesian parameter theta

one_hot_errormachine

One Hot Encoding with Error machine

update_beta_bayesian

Update of bayesian parameter beta

multinomial_lbeta_function

Calculation of Log multinomial Beta value.

Simulation of mixture multilayer Stochastick block model

Simulate data from the mimiSBM generative model.