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: 1.0.5 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: 2025-03-13 DOI: 10.32614/CRAN.package.missSBM Author: Julien Chiquet [aut, cre], Pierre Barbillon [aut], Timothée Tabouy [aut], Jean-Benoist Léger [ctb] (provided C++ implementaion of K-means), François Gindraud [ctb] (provided C++ interface to NLopt), groÃBM 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 Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=missSBM to link to this page.
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