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' 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: 0.3.0
Depends: R (≥ 3.4.0)
Imports: Rcpp, methods, ape, igraph, nloptr, ggplot2, corrplot, R6, rlang, sbm, magrittr
LinkingTo: Rcpp, RcppArmadillo
Suggests: aricode, blockmodels, testthat (≥ 2.1.0), covr, knitr, rmarkdown, spelling
Published: 2020-11-18
Author: Julien Chiquet ORCID iD [aut, cre], Pierre Barbillon ORCID iD [aut], Timothée Tabouy [aut], 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: README NEWS
In views: MissingData
CRAN checks: missSBM results


Reference manual: missSBM.pdf
Vignettes: missSBM: a case study with war networks
Package source: missSBM_0.3.0.tar.gz
Windows binaries: r-devel: missSBM_0.3.0.zip, r-release: missSBM_0.3.0.zip, r-oldrel: missSBM_0.3.0.zip
macOS binaries: r-release: missSBM_0.3.0.tgz, r-oldrel: missSBM_0.3.0.tgz
Old sources: missSBM archive

Reverse dependencies:

Reverse suggests: gsbm


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