https://github.com/grosssbm/misssbm

An R package for adjusting Stochastic Block Models from networks data sampled under various missing data conditions
https://github.com/grosssbm/misssbm

missing-data nas network-analysis network-dataset stochastic-block-model

Last synced: 3 months ago
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An R package for adjusting Stochastic Block Models from networks data sampled under various missing data conditions

• Host: GitHub
• URL: https://github.com/grosssbm/misssbm
• Owner: GrossSBM
• License: gpl-3.0
• Created: 2017-09-02T02:27:02.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2025-03-13T09:04:48.000Z (11 months ago)
• Last Synced: 2025-10-22T05:58:52.634Z (3 months ago)
• Topics: missing-data, nas, network-analysis, network-dataset, stochastic-block-model
• Language: R
• Homepage: http://grosssbm.github.io/missSBM
• Size: 85.1 MB
• Stars: 12
• Watchers: 0
• Forks: 2
• Open Issues: 2
• Metadata Files:
  • Readme: README.Rmd
  • License: LICENSE.md

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README

          
---

output: github_document

---

```{r setup, include=FALSE}

knitr::opts_chunk$set(echo = TRUE)

```

# missSBM: Handling missing data in Stochastic Block Models

[![website](https://github.com/GrossSBM/missSBM/workflows/pkgdown/badge.svg)](https://grosssbm.github.io/missSBM/)

[![R-CMD-check](https://github.com/grosssbm/missSBM/workflows/R-CMD-check/badge.svg)](https://github.com/grosssbm/missSBM/actions)

[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/missSBM)](https://cran.r-project.org/package=missSBM)

[![](https://img.shields.io/github/last-commit/grossSBM/missSBM.svg)](https://github.com/GrossSBM/missSBM/commits/master)

[![Codecov test coverage](https://codecov.io/gh/GrossSBM/missSBM/branch/master/graph/badge.svg)](https://app.codecov.io/gh/GrossSBM/missSBM?branch=master)

[![R-CMD-check](https://github.com/GrossSBM/missSBM/workflows/R-CMD-check/badge.svg)](https://github.com/GrossSBM/missSBM/actions)

[![R-CMD-check](https://github.com/GrossSBM/missSBM/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/GrossSBM/missSBM/actions/workflows/R-CMD-check.yaml)

> 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)