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README.Rmd
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README.Rmd
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---
output: github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# missSBM: Handling missing data in Stochastic Block Models
<!-- badges: start -->
[](https://grosssbm.github.io/missSBM/)
[](https://github.com/grosssbm/missSBM/actions)
[](https://cran.r-project.org/package=missSBM)
[](https://github.com/GrossSBM/missSBM/commits/master)
[](https://app.codecov.io/gh/GrossSBM/missSBM?branch=master)
[](https://github.com/GrossSBM/missSBM/actions)
[](https://github.com/GrossSBM/missSBM/actions/workflows/R-CMD-check.yaml)
<!-- badges: end -->
> 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' (2022)
[10.18637/jss.v101.i12](https://doi.org/10.18637/jss.v101.i12), adjusts the popular stochastic block model from network data observed under various missing data conditions, as described in 'Tabouy, Barbillon and Chiquet' (2019) [10.1080/01621459.2018.1562934](https://doi.org/10.1080/01621459.2018.1562934).
## Installation
The Last CRAN version is available via
```{r package CRAN, eval = FALSE}
install.packages("missSBM")
```
The development version is available via
```{r package github, eval = FALSE}
devtools::install_github("grossSBM/missSBM")
```
## References
Please cite our work using the following references:
Barbillon, P., Chiquet, J., & Tabouy, T. (2022). missSBM: An R Package for Handling Missing Values in the Stochastic Block Model. _Journal of Statistical Software_, 101(12), 1–32. DOI: [10.18637/jss.v101.i12](https://doi.org/10.18637/jss.v101.i12)
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](https://doi.org/10.1080/01621459.2018.1562934)