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: |
0.2.1 |
| Depends: |
R (≥ 3.4.0) |
| Imports: |
Rcpp, methods, ape, igraph, nloptr, ggplot2, corrplot, R6, magrittr |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
aricode, blockmodels, testthat, covr, knitr, rmarkdown |
| Published: |
2019-09-16 |
| Author: |
Julien Chiquet 
[aut, cre],
Pierre Barbillon
[aut],
Timothée Tabouy [aut] |
| Maintainer: |
Julien Chiquet <julien.chiquet at inra.fr> |
| BugReports: |
https://github.com/jchiquet/missSBM/issues |
| License: |
GPL-3 |
| URL: |
https://jchiquet.github.io/missSBM |
| NeedsCompilation: |
yes |
| Citation: |
missSBM citation info |
| Materials: |
README NEWS |
| In views: |
MissingData |
| CRAN checks: |
missSBM results |