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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’ 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