# R: Adjusting Stochastic Block Models under various missing data...

missSBM {missSBM} | R Documentation

## Adjusting Stochastic Block Models under various missing data conditions

### Description

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.

### Details

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

### Author(s)

Timothée Tabouy, Pierre Barbillon, Julien Chiquet

### References

Timothée Tabouy, Pierre Barbillon & Julien Chiquet (2019) “Variational Inference for Stochastic Block Models from Sampled Data”, Journal of the American Statistical Association, <doi:10.1080/01621459.2018.1562934>

[Package missSBM version 0.2.0 Index]