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' (2021) , adjusts the popular stochastic block model from network data sampled under various missing data conditions, as described in 'Tabouy, Barbillon and Chiquet' (2019) .


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Reference manual

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install.packages("missSBM")

1.0.1 by Julien Chiquet, 5 months ago


https://grosssbm.github.io/missSBM/


Report a bug at https://github.com/grossSBM/missSBM/issues


Browse source code at https://github.com/cran/missSBM


Authors: Julien Chiquet [aut, cre] , Pierre Barbillon [aut] , Timothée Tabouy [aut] , Jean-Benoist Léger [ctb] (provided C++ implementaion of K-means) , François Gindraud [ctb] (provided C++ interface to NLopt) , großBM team [ctb]


Documentation:   PDF Manual  


Task views: Missing Data


GPL-3 license


Imports Rcpp, methods, igraph, nloptr, ggplot2, future.apply, R6, rlang, sbm, magrittr, Matrix

Suggests aricode, blockmodels, corrplot, future, testthat, covr, knitr, rmarkdown, spelling

Linking to Rcpp, RcppArmadillo, nloptr


Suggested by gsbm.


See at CRAN