Title: Handling Missing Data in Stochastic Block Models
person("Pierre", "Barbillon", role = c("aut"), email = "pierre.barbillon@agroparistech.fr",
person("Timothée", "Tabouy", role = c("aut"), email = "timothee.tabouy@agroparistech.fr")
Description: 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) <doi:10.1080/01621459.2018.1562934>.
Imports: Rcpp, methods, ape, igraph, nloptr, corrplot, R6, magrittr
Suggests: aricode, blockmodels, testthat, covr, knitr, rmarkdown,

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

person("Timothée", "Tabouy", role = c("aut"), email = "timothee.tabouy@agroparistech.fr"),
Suggests: aricode, blockmodels, testthat (>= 2.1.0), covr, knitr,

person("Pierre", "Barbillon", role = "aut", email = "pierre.barbillon@agroparistech.fr",
person("Timothée", "Tabouy", role = "aut", email = "timothee.tabouy@agroparistech.fr"),
person("Jean-Benoist", "Léger", role = "ctb", email = "jbleger@hds.utc.fr", comment = "provided C++ implementaion of K-means"),
person("François", "Gindraud", role = "ctb", email = "francois.gindraud@gmail.com", comment = "provided C++ interface to NLopt"),
that generates those NAs. 'missSBM', presented in 'Barbillon, Chiquet and Tabouy' (2021) <arXiv:1906.12201>,
adjusts the popular stochastic block model from network data sampled under various missing data conditions,
Imports: Rcpp, methods, igraph, nloptr (>= 1.2.0), ggplot2,
future.apply, R6, rlang, sbm, magrittr, Matrix
Suggests: aricode, blockmodels, corrplot, future, testthat (>= 2.1.0),
Jean-Benoist Léger [ctb] (provided C++ implementaion of K-means),
François Gindraud [ctb] (provided C++ interface to NLopt),

that generates those NAs. 'missSBM', presented in 'Barbillon, Chiquet and Tabouy' (2021) <doi:10.18637/jss.v101.i12>,
Imports: Rcpp, methods, igraph, nloptr, ggplot2, future.apply, R6,