alea-inference 0.3.4
A tool to perform toyMC-based inference constructions
Requires Python: <3.13,>=3.8

# alea
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alea is a flexible statistical inference framework. The Python package is designed for constructing, handling, and fitting statistical models, computing confidence intervals and conducting sensitivity studies. It is primarily developed for the [XENONnT dark matter experiment](https://xenonexperiment.org/), but can be used for any statistical inference problem.

Alea aims to model the statistical behaviour of an experiment, which again depends on your knowledge of the underlying physics-- this can range from the very simple, such as measuring a gaussian-distributed random variable, to complex likelihoods where each model component is created by physics simulations (GEANT4), fast detector simulations (for example [appletree](https://github.com/XENONnT/appletree/) for XENONnT) or a data-driven method.

If you use alea in your research, please consider citing the software published on [zenodo](https://zenodo.org/badge/latestdoi/654100988).

## Installation
You can install alea from PyPI using pip but **beware that it is listed there as alea-inference!** Thus, you need to run
```
pip install alea-inference
```

For the latest version, you can install directly from the GitHub repository by cloning the repository and running
```
cd alea
pip install .
```
You are now ready to use alea!

## Getting started
The best way to get started is to check out the [documentation](https://alea.readthedocs.io/en/latest/) and have a look at our [tutorial notebooks](https://github.com/XENONnT/alea/tree/main/notebooks). To explore the notebooks interactively, you can use [Binder](https://mybinder.org/v2/gh/XENONnT/alea/HEAD?labpath=notebooks).

## Acknowledgements
`alea` is a public package inherited the spirits of previously private XENON likelihood definition and inference construction code `binference` that based on the blueice repo https://github.com/JelleAalbers/blueice.

Binference was developed for XENON1T WIMP searches by Knut Dundas Morå, and for the first XENONnT results by Robert Hammann, Knut Dundas Morå and Tim Wolf.