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) .


Reference manual

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1.0.1 by Julien Chiquet, 5 months ago


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