# overview

    library(bnRep)
    #> This is bnRep version 0.0.6
    #>
    #> - See https://github.com/manueleleonelli/bnRep if you want to contribute to the project.
    #>
    #> - If you are using any Bayesian network from bnRep, remember to cite:
    #>
    #> Leonelli, M. (2025). bnRep: A repository of Bayesian networks from the academic literature. Neurocomputing, 624, 129502.
    #>

The R package `bnRep` includes the largest repository of Bayesian networks, which were all collected from recent academic literature in a variety of fields! If you are using any Bayesian network from `bnRep` you should cite:

Leonelli, M (2024). “bnRep: A repository of Bayesian networks from the academic literature.” arXiv preprint arXiv:2409.19158.

    @Article{,
        title = {bn{R}ep: A repository of {B}ayesian networks from the academic literature},
        author = {Manuele Leonelli},
        journal = {arXiv preprint arXiv:2409.19158},
        year = {2024}
      }

Go to (link here) to explore the repository online!

## Contribution

If you are interested in having your Bayesian network included in `bnRep` you must prepare three objects:

- the Bayesian network as a `bn.fit` object (if not created with `bnlearn` you can always use import functions, such as `read.bif()`);

- an R file with the same name of the `bn.fit` object reporting the documentation of the Bayesian network;

- a vector/excel file with the required details to include in the `bnRep_summary` object.

You can submit the required objects directly via github (e.g fork/pull), or via email.

If you struggle with any of these steps, please get in touch and I will try to help!

## Overview

`bnRep` includes over 200 Bayesian networks from more than 150 academic publications. It includes discrete, Gaussian and conditional linear Gaussian Bayesian networks, all stored as appropriate `bn.fit` objects from `bnlearn`. They can be exported for use to other software (e.g. Phython libraries) using functions from `bnlearn` such as `write.bif()`. Recall that in order to plot the associated DAG, one must first convert it to a graph object with `bn.net()` from the `bnlearn` package.

### Installation

    # Install stable version from CRAN:
    install.packages("bnRep")

    # Or the development version from GitHub:
    remotes::install_github("manueleleonelli/bnRep")

### Usage

We will use the `lawschool` Bayesian network as an example. To load it in the environment simply call `data(lawschool)` and to then plot it (for instance using `graphviz.plot` from the `bnlearn` package)

    library(bnRep)
    library(bnlearn)
    data("lawschool")
    qgraph::qgraph(bn.net(lawschool))

Notice that the function `bn.net` function must be used in order to plot the network.

### Exploring bnRep

`bnRep` includes two features to explore the Bayesian networks in the repository:

- `bnRep_summary`: a dataframe with important details about each network in the repository.

- `bnRep_app`: a Shiny app to interactively explore `bnRep_summary` and filter the networks according to various criteria. The app is also available online at (link here).

Here’s the columns of `bnRep_summary`:

    #>  [1] "Name"                   "Type"                   "Structure"
    #>  [4] "Probabilities"          "Graph"                  "Area"
    #>  [7] "Nodes"                  "Arcs"                   "Parameters"
    #> [10] "Avg. Parents"           "Max Parents"            "Avg. Levels"
    #> [13] "Max Levels"             "Average Markov Blanket" "Year"
    #> [16] "Journal"                "Reference"

### An overview of the Bayesian networks in bnRep

The following plots show some summary statistics of the repository.