The missSBM package provides five functions:
simulate
a function to define and draw network data according to a stochastic block model
sample
a function to sample an existing network according to a variety of sampling designs
estimate
a function to perform inference of SBM from network data with missing entries under various sampling designs.
prepare_data
a function to format real-world network data (adjacency matrix and covariates) to perform the estimation under missing data condition
smooth
a function to smooth an existing collection of SBM estimation, to avoid being trapped in local maxima.
These function leads to the manipulation of a variety of R object, with their respective fields and methods. They are all automatically generated by the top-level functions itemized above, so that the user should generally to use their constructor or internal methods directly. The user should only have a basic understanding of the fields of each object to manipulate the output in R. The main objects are the following:
sampledNetwork
an object to store sampled network data (i.e. with missing dyads)
SBM_sampler
an object to define a SBM to sample from
SBM_fit
an object to define and store an SBM fit
networkSampler
an object to define a network sampling to sample from
networkSampling
an object to define and store a network sampling fit
missSBM_fit
an object that put together an SBM fit and and network sampling fit - the main point of the missSBM package !
missSBM_collection
an object to store a collection of missSBM_fit, ordered by number of block
Timoth<U+00E9>e Tabouy, Pierre Barbillon & Julien Chiquet (2019) <U+201C>Variational Inference for Stochastic Block Models from Sampled Data<U+201D>, Journal of the American Statistical Association, <doi:10.1080/01621459.2018.1562934>