[aut, cre],
Pierre Barbillon
[aut],
Timothée Tabouy [aut],
großBM team [ctb]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
[aut, cre],
Pierre Barbillon
[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 |
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