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 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")

0.2.0 by Julien Chiquet, 3 months ago


https://jchiquet.github.io/missSBM


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


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


Authors: Julien Chiquet [aut, cre] , Pierre Barbillon [aut] , Timothée Tabouy [aut]


Documentation:   PDF Manual  


GPL-3 license


Imports Rcpp, methods, ape, igraph, nloptr, corrplot, R6, magrittr

Suggests aricode, blockmodels, testthat, covr, knitr, rmarkdown, ggplot2

Linking to Rcpp, RcppArmadillo


See at CRAN