README.Rmd

---
title: Shiny SBM
output: github_document
---

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`shinySbm` is a R package containing a shiny application. This application provides a user-friendly interface for network analysis based on the `sbm` package made by Chiquet J, Donnet S and Barbillon P (2023) [CRAN](https://CRAN.R-project.org/package=sbm). The `sbm` package regroups into a unique framework tools for estimating and manipulating variants of the stochastic block model.
`shinySbm` allows you to easily apply and explore the outputs of a Stochastic Block Model without programming. It is useful if you want to analyze your network data (adjacency matrix or list of edges) without knowing the `R` language or to learn the basics of the `sbm` package. 

Stochastic block models (SBMs) are probabilistic models in statistical analysis of graphs or networks, that can be used to discover or understand the (hidden/latent) structure of a network, as well as for clustering purposes. 

Stochastic Block Models are applied on network to simplify the information they gather, and help visualize the main behaviours/categories/relationships present in your network. It's a latent model which identify significant blocks (groups) of nodes with similar connectivity patterns. This could help you to know if your network: hides closed sub-communities, is hierarchical, or has another specific structure. 

With `shinySbm` you should also be able to: 

 - Easily run a Stochastic Block Model (set your model, infer associated parameters and choose the number of blocks)
 - Get some nice outputs as matrix and network plots organized by blocks
 - Get a summary of the modelling
 - Extract lists of nodes associated with their blocks
 
To learn more about `shinySbm` you can go to the [ShinySbm Website](https://shinysbm-theodore-vanrenterghem-b12616c23cfbfb3f0fe520178bcb95a.pages.mia.inra.fr/) 


## How to use the application

### On Shiny Migale

I you want to use shinySBM without having to code a single line, the app is available on [Migale](https://shiny.migale.inrae.fr/app/ShinySBM).

### With `R`

#### Installation

You can install the development version of shinySbm like so:

``` r
install.packages("shinySbm")
```

The shinySbm package should be installed. 

#### Running the application

From a new `R` session run

``` r
shinySbm::shinySbmApp()
```

### With `docker`

#### Installation

If you are familiar to `docker`, you can also download the docker image by running the command:

``` bash
docker pull registry.forgemia.inra.fr/theodore.vanrenterghem/shinysbm:latest
```
#### Running the application

Once installed you can run the command to launch the app:

``` bash
docker run -p 3838:3838 registry.forgemia.inra.fr/theodore.vanrenterghem/shinysbm:latest
```

And then from your browser find the address `http://localhost:3838/`

## Contact

Any questions, problems or comments regarding this application ?        
Contact us: [shiny.sbm.dev@gmail.com](mailto:shiny.sbm.dev@gmail.com)

## References

Chiquet J, Donnet S, Barbillon P (2023).  sbm: Stochastic Blockmodels. R package version 0.4.5,  
[https://CRAN.R-project.org/package=sbm](https://CRAN.R-project.org/package=sbm).