Package: missSBM 1.0.4

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_1.0.4.tar.gz
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missSBM.pdf |missSBM.html✨
missSBM/json (API)
NEWS

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

Peer review:

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

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:

missing-datanasnetwork-analysisnetwork-datasetstochastic-block-model

6 exports 12 stars 1.82 score 66 dependencies 19 scripts 322 downloads

Last updated 11 months agofrom:d379fadf69. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 03 2024
R-4.5-win-x86_64OKSep 03 2024
R-4.5-linux-x86_64OKSep 03 2024
R-4.4-win-x86_64OKSep 03 2024
R-4.4-mac-x86_64OKSep 03 2024
R-4.4-mac-aarch64OKSep 03 2024
R-4.3-win-x86_64OKSep 03 2024
R-4.3-mac-x86_64OKSep 03 2024
R-4.3-mac-aarch64OKSep 03 2024

Exports:%>%estimateMissSBMl1_similaritymissSBM_collectionmissSBM_fitobserveNetwork

Dependencies:alluvialaricodeblockmodelsclicodetoolscolorspacecpp11data.tablediagramdigestdplyrfansifarverfuturefuture.applygenericsggplot2globalsglueGREMLINSgtableigraphisobandKernSmoothlabelinglatticelavalifecyclelistenvmagrittrMASSMatrixmgcvmunsellnlmenloptrnumDerivparallellypbmcapplypillarpkgconfigplyrprodlimprogressrpurrrR6RColorBrewerRcppRcppArmadilloRcppEigenreshape2rlangRSpectrasbmscalesshapeSQUAREMstringistringrsurvivaltibbletidyselectutf8vctrsviridisLitewithr

missSBM: a case study with war networks

missSBM team

Rendered fromcase_study_war_networks.Rmdusingknitr::rmarkdownon Sep 03 2024.

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