Handling Missing Data in Stochastic Block Models R package missSBM version 1.1.0 CRAN Task View: MissingData (https://CRAN.R-project.org/view=MissingData) 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>. Software Depends: R (>= 3.4.0) Imports: Rcpp, methods, igraph, ggplot2, future.apply, R6, rlang, sbm, magrittr, Matrix, RSpectra LinkingTo: Rcpp, RcppArmadillo Suggests: aricode, blockmodels, corrplot, future, testthat (>= 2.1.0), covr, knitr, rmarkdown, spelling Julien Chiquet <julien.chiquet@inrae.fr> Comprehensive R Archive Network (CRAN) Julien Chiquet [aut, cre] (ORCID: <https://orcid.org/0000-0002-3629-3429>), Pierre Barbillon [aut] (ORCID: <https://orcid.org/0000-0002-7766-7693>), Timothée Tabouy [aut], Jean-Benoist Léger [ctb] (provided C++ implementaion of K-means), François Gindraud [ctb] (provided C++ interface to NLopt), großBM team [ctb] GPL-3 2026-07-21 application/tgz https://CRAN.R-project.org/package=missSBM doi:10.32614/CRAN.package.missSBM en-US