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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', presented in 'Barbillon, Chiquet and Tabouy' (2022) <doi:10.18637/jss.v101.i12>, 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>.
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Package details |
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| Author | Julien Chiquet [aut, cre] (<https://orcid.org/0000-0002-3629-3429>), Pierre Barbillon [aut] (<https://orcid.org/0000-0002-7766-7693>), Timothée Tabouy [aut], Jean-Benoist Léger [ctb] (provided C++ implementaion of K-means), François Gindraud [ctb] (provided C++ interface to NLopt), großBM team [ctb] |
| Maintainer | Julien Chiquet <julien.chiquet@inrae.fr> |
| License | GPL-3 |
| Version | 1.0.4 |
| URL | https://grosssbm.github.io/missSBM/ |
| Package repository | View on CRAN |
| Installation |
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