# missSBM:
Handling missing data in Stochastic Block Models

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) 10.18637/jss.v101.i12,
adjusts the popular stochastic block model from network data observed
under various missing data conditions, as described in ‘Tabouy,
Barbillon and Chiquet’ (2019) 10.1080/01621459.2018.1562934.

## Installation

The Last CRAN version is available via

`install.packages("missSBM")`

The development version is available via

`devtools::install_github("grossSBM/missSBM")`

## References

Please cite our work using the following references:

Barbillon, P., Chiquet, J., & Tabouy, T. (2022). missSBM: An R
Package for Handling Missing Values in the Stochastic Block Model.
*Journal of Statistical Software*, 101(12), 1–32. DOI: 10.18637/jss.v101.i12

Timothée Tabouy, Pierre Barbillon & Julien Chiquet (2019)
“Variational Inference for Stochastic Block Models from Sampled Data”,
*Journal of the American Statistical Association*, DOI: 10.1080/01621459.2018.1562934