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✨
DESCRIPTION |NEWS
card.svg |card.png
missSBM/json (API)

# 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:

missing-data nas network-analysis network-dataset stochastic-block-model cpp

5.59 score 13 stars 20 scripts 298 downloads 6 exports 61 dependencies

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

Target	Result	Time
linux-devel-arm64	OK	243
linux-devel-x86_64	OK	232
source / vignettes	OK	454
linux-release-arm64	OK	287
linux-release-x86_64	OK	223
macos-release-arm64	OK	226
macos-release-x86_64	OK	336
macos-oldrel-arm64	OK	213
macos-oldrel-x86_64	OK	415
windows-devel	OK	266
windows-release	OK	269
windows-oldrel	OK	315
wasm-release	OK	186

Exports:%>%estimateMissSBM l1_similarity missSBM_collection missSBM_fit observeNetwork

Dependencies:alluvial aricode blockmodels cli codetools cpp11 data.table diagram digest dplyr farver future future.apply generics ggplot2 globals glue GREMLINS gtable igraph isoband KernSmooth labeling lattice lava lifecycle listenv magrittr Matrix nloptr numDeriv parallelly pbmcapply pillar pkgconfig plyr prodlim progressr purrr R6 RColorBrewer Rcpp RcppArmadillo RcppEigen reshape2 rlang RSpectra S7 sbm scales shape SQUAREM stringi stringr survival tibble tidyselect utf8 vctrs viridisLite withr

missSBM: a case study with war networks

Rendered fromcase_study_war_networks.Rmdusingknitr::rmarkdown

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

Help page	Topics
Class for defining a block dyad sampler	blockDyadSampler
Class for fitting a block-dyad sampling	blockDyadSampling_fit
Class for defining a block node sampler	blockNodeSampler
Class for fitting a block-node sampling	blockNodeSampling_fit
Extract model coefficients	coef.missSBM_fit
Class for fitting a dyad sampling with covariates	covarDyadSampling_fit
Class for fitting a node-centered sampling with covariate	covarNodeSampling_fit
Class for defining a degree sampler	degreeSampler
Class for fitting a degree sampling	degreeSampling_fit
Class for defining a double-standard sampler	doubleStandardSampler
Class for fitting a double-standard sampling	doubleStandardSampling_fit
Virtual class for all dyad-centered samplers	dyadSampler
Class for fitting a dyad sampling	dyadSampling_fit
ER ego centered network	er_network
Estimation of simple SBMs with missing data	estimateMissSBM
Extract model fitted values from object 'missSBM_fit', return by 'estimateMissSBM()'	fitted.missSBM_fit
Political Blogosphere network prior to 2007 French presidential election	frenchblog2007
L1-similarity	l1_similarity
An R6 class to represent a collection of SBM fits with missing data	missSBM_collection
An R6 class to represent an SBM fit with missing data	missSBM_fit
Definition of R6 Class 'networkSampling_sampler'	networkSampler
Definition of R6 Class 'networkSampling'	networkSampling
Virtual class used to define a family of networkSamplingDyads_fit	networkSamplingDyads_fit
Virtual class used to define a family of networkSamplingNodes_fit	networkSamplingNodes_fit
Virtual class for all node-centered samplers	nodeSampler
Class for fitting a node sampling	nodeSampling_fit
Observe a network partially according to a given sampling design	observeNetwork
An R6 Class used for internal representation of a partially observed network	partlyObservedNetwork
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 sampler	simpleDyadSampler
Class for defining a simple node sampler	simpleNodeSampler
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 sampler	snowballSampler
Summary method for a 'missSBM_fit'	summary.missSBM_fit
War data set	war

Package: missSBM 1.0.5

missSBM: Handling Missing Data in Stochastic Block Models

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