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>.

Version: 1.0.4
Depends: R (≥ 3.4.0)
Imports: Rcpp, methods, igraph, nloptr, ggplot2, future.apply, R6, rlang, sbm, magrittr, Matrix, RSpectra
LinkingTo: Rcpp, RcppArmadillo, nloptr
Suggests: aricode, blockmodels, corrplot, future, testthat (≥ 2.1.0), covr, knitr, rmarkdown, spelling
Published: 2023-10-24
DOI: 10.32614/CRAN.package.missSBM
Author: Julien Chiquet ORCID iD [aut, cre], Pierre Barbillon ORCID iD [aut], Timothée Tabouy [aut], Jean-Benoist Léger [ctb] (provided C++ implementaion of K-means), François Gindraud [ctb] (provided C++ interface to NLopt), großBM team [ctb]
Maintainer: Julien Chiquet <julien.chiquet at inrae.fr>
BugReports: https://github.com/grossSBM/missSBM/issues
License: GPL-3
URL: https://grosssbm.github.io/missSBM/
NeedsCompilation: yes
Language: en-US
Citation: missSBM citation info
Materials: NEWS
In views: MissingData
CRAN checks: missSBM results


Reference manual: missSBM.pdf
Vignettes: missSBM: a case study with war networks


Package source: missSBM_1.0.4.tar.gz
Windows binaries: r-devel: missSBM_1.0.4.zip, r-release: missSBM_1.0.4.zip, r-oldrel: missSBM_1.0.4.zip
macOS binaries: r-release (arm64): missSBM_1.0.4.tgz, r-oldrel (arm64): missSBM_1.0.4.tgz, r-release (x86_64): missSBM_1.0.4.tgz, r-oldrel (x86_64): missSBM_1.0.4.tgz
Old sources: missSBM archive

Reverse dependencies:

Reverse suggests: gsbm


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