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’ (2021) https://arxiv.org/abs/1906.12201, 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")

## Reference

Please cite our work using the following reference:

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

Pierre Barbillon, Julien Chiquet & Timothée Tabouy (2021) “missSBM: An R Package for Handling Missing Values in the Stochastic Block Model”, arXiv preprint, DOI: https://arxiv.org/abs/1906.12201