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), 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 |