Package: missSBM 1.0.5

Julien Chiquet

missSBM: Handling Missing Data in Stochastic Block Models

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) <doi:10.18637/jss.v101.i12>, adjusts the popular stochastic block model from network data sampled under various missing data conditions, as described in 'Tabouy, Barbillon and Chiquet' (2019) <doi:10.1080/01621459.2018.1562934>.

Authors:Julien Chiquet [aut, cre], Pierre Barbillon [aut], Timothée Tabouy [aut], Jean-Benoist Léger [ctb], François Gindraud [ctb], großBM team [ctb]

missSBM_1.0.5.tar.gz
missSBM_1.0.5.zip(r-4.7)missSBM_1.0.5.zip(r-4.6)missSBM_1.0.5.zip(r-4.5)
missSBM_1.0.5.tgz(r-4.6-x86_64)missSBM_1.0.5.tgz(r-4.6-arm64)missSBM_1.0.5.tgz(r-4.5-x86_64)missSBM_1.0.5.tgz(r-4.5-arm64)
missSBM_1.0.5.tar.gz(r-4.7-arm64)missSBM_1.0.5.tar.gz(r-4.7-x86_64)missSBM_1.0.5.tar.gz(r-4.6-arm64)missSBM_1.0.5.tar.gz(r-4.6-x86_64)
missSBM_1.0.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
missSBM/json (API)
NEWS

# Install 'missSBM' in R:
install.packages('missSBM', repos = c('https://grosssbm.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/grosssbm/misssbm/issues

Pkgdown/docs site:https://grosssbm.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • er_network - ER ego centered network
  • frenchblog2007 - Political Blogosphere network prior to 2007 French presidential election
  • war - War data set

On CRAN:

Conda:

missing-datanasnetwork-analysisnetwork-datasetstochastic-block-modelcpp

5.55 score 13 stars 18 scripts 270 downloads 6 exports 61 dependencies

Last updated from:edc94376f1. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK262
linux-devel-x86_64OK306
source / vignettesOK349
linux-release-arm64OK275
linux-release-x86_64OK254
macos-release-arm64OK276
macos-release-x86_64OK360
macos-oldrel-arm64OK239
macos-oldrel-x86_64OK525
windows-develOK290
windows-releaseOK267
windows-oldrelOK307
wasm-releaseOK152

Exports:%>%estimateMissSBMl1_similaritymissSBM_collectionmissSBM_fitobserveNetwork

Dependencies:alluvialaricodeblockmodelsclicodetoolscpp11data.tablediagramdigestdplyrfarverfuturefuture.applygenericsggplot2globalsglueGREMLINSgtableigraphisobandKernSmoothlabelinglatticelavalifecyclelistenvmagrittrMatrixnloptrnumDerivparallellypbmcapplypillarpkgconfigplyrprodlimprogressrpurrrR6RColorBrewerRcppRcppArmadilloRcppEigenreshape2rlangRSpectraS7sbmscalesshapeSQUAREMstringistringrsurvivaltibbletidyselectutf8vctrsviridisLitewithr

missSBM: a case study with war networks

missSBM team

Rendered fromcase_study_war_networks.Rmdusingknitr::rmarkdownon May 12 2026.

Last update: 2023-10-24
Started: 2019-04-12

Readme and manuals

Help Manual

Help pageTopics
Class for defining a block dyad samplerblockDyadSampler
Class for fitting a block-dyad samplingblockDyadSampling_fit
Class for defining a block node samplerblockNodeSampler
Class for fitting a block-node samplingblockNodeSampling_fit
Extract model coefficientscoef.missSBM_fit
Class for fitting a dyad sampling with covariatescovarDyadSampling_fit
Class for fitting a node-centered sampling with covariatecovarNodeSampling_fit
Class for defining a degree samplerdegreeSampler
Class for fitting a degree samplingdegreeSampling_fit
Class for defining a double-standard samplerdoubleStandardSampler
Class for fitting a double-standard samplingdoubleStandardSampling_fit
Virtual class for all dyad-centered samplersdyadSampler
Class for fitting a dyad samplingdyadSampling_fit
ER ego centered networker_network
Estimation of simple SBMs with missing dataestimateMissSBM
Extract model fitted values from object 'missSBM_fit', return by 'estimateMissSBM()'fitted.missSBM_fit
Political Blogosphere network prior to 2007 French presidential electionfrenchblog2007
L1-similarityl1_similarity
An R6 class to represent a collection of SBM fits with missing datamissSBM_collection
An R6 class to represent an SBM fit with missing datamissSBM_fit
Definition of R6 Class 'networkSampling_sampler'networkSampler
Definition of R6 Class 'networkSampling'networkSampling
Virtual class used to define a family of networkSamplingDyads_fitnetworkSamplingDyads_fit
Virtual class used to define a family of networkSamplingNodes_fitnetworkSamplingNodes_fit
Virtual class for all node-centered samplersnodeSampler
Class for fitting a node samplingnodeSampling_fit
Observe a network partially according to a given sampling designobserveNetwork
An R6 Class used for internal representation of a partially observed networkpartlyObservedNetwork
Visualization for an object 'missSBM_fit'plot.missSBM_fit
Prediction of a 'missSBM_fit' (i.e. network with imputed missing dyads)predict.missSBM_fit predicted.missSBM_fit
Class for defining a simple dyad samplersimpleDyadSampler
Class for defining a simple node samplersimpleNodeSampler
This internal class is designed to adjust a binary Stochastic Block Model in the context of missSBM.SimpleSBM_fit
This internal class is designed to adjust a binary Stochastic Block Model in the context of missSBM.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.SimpleSBM_fit_withCov
Class for defining a snowball samplersnowballSampler
Summary method for a 'missSBM_fit'summary.missSBM_fit
War data setwar