# BinomialBias
[](https://pypi.org/project/binomialbias)
[](https://github.com/thekerrlab/binomialbias/actions/workflows/tests.yaml?query=workflow)
This library computes and plots quantitative assessments of discrimination within organizations, based on the binomial distribution.
This code supports the following paper:
**Quantitative measures of discrimination with application to appointment processes.** Robinson PA, Kerr CC (2024). *PLoS ONE* 19(3): e0299870. https://doi.org/10.1371/journal.pone.0299870
There are several ways to use this library, described below.
## Webapp
A live webapp is running at https://binomialbias.sciris.org.
## Local installation and usage
### Python
To use locally with Python, run
pip install binomialbias
This can then be run via e.g.:
import binomialbias as bb
bb.plot_bias(n=20, n_e=10, n_a=7)
This example shows the statistics for the case where there were `n = 20` appointments (e.g., the size of a committee), out of which `n_e = 10` people were expected to belong to a given group (e.g., female), and for which `n_a = 7` actually were.
### Shiny
To run the [Shiny](https://shiny.posit.co/py/) app locally, clone the repository from GitHub, then install with
pip install -e .[app]
The Shiny app can then be run locally via the `run` script.
## Structure
- All code for the Python package is in the `binomialbias` folder.
- The script for generating the figure in the paper is in the `scripts` folder.
- Continuous integration tests are in the `tests` folder.
- Older Jupyter and Matplotlib versions are available in the `archive` folder.
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"description": "# BinomialBias\n\n[](https://pypi.org/project/binomialbias)\n[](https://github.com/thekerrlab/binomialbias/actions/workflows/tests.yaml?query=workflow)\n\nThis library computes and plots quantitative assessments of discrimination within organizations, based on the binomial distribution.\n\nThis code supports the following paper:\n\n**Quantitative measures of discrimination with application to appointment processes.** Robinson PA, Kerr CC (2024). *PLoS ONE* 19(3): e0299870. https://doi.org/10.1371/journal.pone.0299870\n\nThere are several ways to use this library, described below.\n\n\n## Webapp\n\nA live webapp is running at https://binomialbias.sciris.org.\n\n\n## Local installation and usage\n\n### Python\n\nTo use locally with Python, run\n\n pip install binomialbias\n\nThis can then be run via e.g.:\n\n import binomialbias as bb\n bb.plot_bias(n=20, n_e=10, n_a=7)\n\nThis example shows the statistics for the case where there were `n = 20` appointments (e.g., the size of a committee), out of which `n_e = 10` people were expected to belong to a given group (e.g., female), and for which `n_a = 7` actually were.\n\n### Shiny\n\nTo run the [Shiny](https://shiny.posit.co/py/) app locally, clone the repository from GitHub, then install with\n\n pip install -e .[app]\n\nThe Shiny app can then be run locally via the `run` script.\n\n\n## Structure\n\n- All code for the Python package is in the `binomialbias` folder.\n- The script for generating the figure in the paper is in the `scripts` folder.\n- Continuous integration tests are in the `tests` folder.\n- Older Jupyter and Matplotlib versions are available in the `archive` folder.\n",
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