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missSBM (version 1.0.5)

Handling Missing Data in Stochastic Block Models

Description

When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0 due to missing information between node pairs), it is possible to account for the underlying process that generates those NAs. 'missSBM', presented in 'Barbillon, Chiquet and Tabouy' (2022) , adjusts the popular stochastic block model from network data sampled under various missing data conditions, as described in 'Tabouy, Barbillon and Chiquet' (2019) .

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Version

Install

install.packages('missSBM')

Monthly Downloads

268

Version

1.0.5

License

GPL-3

Maintainer

Julien Chiquet

Last Published

March 13th, 2025

Functions in missSBM (1.0.5)

dyadSampling_fit

Class for fitting a dyad sampling
dyadSampler

Virtual class for all dyad-centered samplers
covarNodeSampling_fit

Class for fitting a node-centered sampling with covariate
degreeSampler

Class for defining a degree sampler
fitted.missSBM_fit

Extract model fitted values from object missSBM_fit, return by estimateMissSBM()
doubleStandardSampler

Class for defining a double-standard sampler
estimateMissSBM

Estimation of simple SBMs with missing data
degreeSampling_fit

Class for fitting a degree sampling
doubleStandardSampling_fit

Class for fitting a double-standard sampling
er_network

ER ego centered network
nodeSampler

Virtual class for all node-centered samplers
networkSamplingNodes_fit

Virtual class used to define a family of networkSamplingNodes_fit
networkSamplingDyads_fit

Virtual class used to define a family of networkSamplingDyads_fit
networkSampler

Definition of R6 Class 'networkSampling_sampler'
missSBM_collection

An R6 class to represent a collection of SBM fits with missing data
networkSampling

Definition of R6 Class 'networkSampling'
l1_similarity

L1-similarity
missSBM_fit

An R6 class to represent an SBM fit with missing data
missSBM-package

missSBM: Handling Missing Data in Stochastic Block Models
frenchblog2007

Political Blogosphere network prior to 2007 French presidential election
partlyObservedNetwork

An R6 Class used for internal representation of a partially observed network
plot.missSBM_fit

Visualization for an object missSBM_fit
snowballSampler

Class for defining a snowball sampler
simpleDyadSampler

Class for defining a simple dyad sampler
%>%

Pipe operator
predicted.missSBM_fit

Prediction of a missSBM_fit (i.e. network with imputed missing dyads)
nodeSampling_fit

Class for fitting a node sampling
war

War data set
simpleNodeSampler

Class for defining a simple node sampler
observeNetwork

Observe a network partially according to a given sampling design
summary.missSBM_fit

Summary method for a missSBM_fit
blockDyadSampler

Class for defining a block dyad sampler
covarDyadSampling_fit

Class for fitting a dyad sampling with covariates
SimpleSBM_fit

This internal class is designed to adjust a binary Stochastic Block Model in the context of missSBM.
blockNodeSampler

Class for defining a block node sampler
coef.missSBM_fit

Extract model coefficients
SimpleSBM_fit_MNAR

This internal class is designed to adjust a binary Stochastic Block Model in the context of missSBM.
SimpleSBM_fit_noCov

This internal class is designed to adjust a binary Stochastic Block Model in the context of missSBM.
blockNodeSampling_fit

Class for fitting a block-node sampling
SimpleSBM_fit_withCov

This internal class is designed to adjust a binary Stochastic Block Model in the context of missSBM.
blockDyadSampling_fit

Class for fitting a block-dyad sampling