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) .


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.2.1 by Julien Chiquet, a year ago


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  

Task views: Missing Data

GPL-3 license

Imports Rcpp, methods, ape, igraph, nloptr, ggplot2, corrplot, R6, magrittr

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

Linking to Rcpp, RcppArmadillo

Suggested by gsbm.

See at CRAN