Rcpp, methods, ape, igraph, nloptr, ggplot2, corrplot, R6,
magrittr
aricode, blockmodels, testthat, covr, knitr, rmarkdown,

c(
person("Julien", "Chiquet", role = c("aut", "cre"), email = "julien.chiquet@inrae.fr",
comment = c(ORCID = "0000-0002-3629-3429")),
person("Pierre", "Barbillon", role = c("aut"), email = "pierre.barbillon@agroparistech.fr",
comment = c(ORCID = "0000-0002-7766-7693")),
person("Timothée", "Tabouy", role = c("aut"), email = "timothee.tabouy@agroparistech.fr"),
person("großBM team", role = c("ctb"))
)
Rcpp, methods, ape, igraph, nloptr, ggplot2, corrplot, R6,
rlang, sbm, magrittr
'utils_missSBM.R' 'R6Class-networkSampling.R'
'R6Class-networkSampling_fit.R'
'R6Class-simpleSBM_fit_missSBM.R' 'R6Class-missSBM_fit.R'
'R6Class-missSBM_collection.R' 'R6Class-networkSampler.R'
'R6Class-partlyObservedNetwork.R' 'RcppExports.R' 'defunct.R'
'er_network.R' 'estimateMissSBM.R' 'frenchblog2007.R'
'missSBM-package.R' 'observeNetwork.R' 'utils_initialization.R'
'war.R'
aricode, blockmodels, testthat (>= 2.1.0), covr, knitr,
rmarkdown, spelling
Julien Chiquet [aut, cre] (<https://orcid.org/0000-0002-3629-3429>),
Pierre Barbillon [aut] (<https://orcid.org/0000-0002-7766-7693>),
Timothée Tabouy [aut],
großBM team [ctb]

c(
person("Julien", "Chiquet", role = c("aut", "cre"), email = "julien.chiquet@inrae.fr",
comment = c(ORCID = "0000-0002-3629-3429")),
person("Pierre", "Barbillon", role = "aut", email = "pierre.barbillon@agroparistech.fr",
comment = c(ORCID = "0000-0002-7766-7693")),
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"),
person("großBM team", role = c("ctb"))
)
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) <arXiv:1906.12201>,
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>.
Rcpp, methods, igraph, nloptr (>= 1.2.0), ggplot2,
future.apply, R6, rlang, sbm, magrittr, Matrix
'utils_missSBM.R' 'R6Class-networkSampling.R'
'R6Class-networkSampling_fit.R' 'R6Class-simpleSBM_fit.R'
'R6Class-missSBM_fit.R' 'R6Class-missSBM_collection.R'
'R6Class-networkSampler.R' 'R6Class-partlyObservedNetwork.R'
'RcppExports.R' 'er_network.R' 'estimateMissSBM.R'
'frenchblog2007.R' 'kmeans.R' 'missSBM-package.R'
'observeNetwork.R' 'war.R'
aricode, blockmodels, corrplot, future, testthat (>= 2.1.0),
covr, knitr, rmarkdown, spelling
Julien Chiquet [aut, cre] (<https://orcid.org/0000-0002-3629-3429>),
Pierre Barbillon [aut] (<https://orcid.org/0000-0002-7766-7693>),
Timothée Tabouy [aut],
Jean-Benoist Léger [ctb] (provided C++ implementaion of K-means),
François Gindraud [ctb] (provided C++ interface to NLopt),
großBM team [ctb]