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 
[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 |