Metadata-Version: 2.1
Name: jaxili
Version: 0.0.1
Summary: This package provides tools to execute and implement Implicit Likelihood Inference tools in JAX.
Author: Sacha Guerrini
Project-URL: Repository, https://github.com/sachaguer/jaxili
Requires-Python: <3.11,>=3.10
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: jax
Requires-Dist: jupyter
Requires-Dist: matplotlib
Requires-Dist: cmake
Requires-Dist: tensorflow
Requires-Dist: tensorflow-probability
Requires-Dist: optax
Requires-Dist: flax
Requires-Dist: sbibm
Requires-Dist: torch
Requires-Dist: pytorch-lightning
Requires-Dist: optuna
Provides-Extra: docs
Requires-Dist: flowMC; extra == "docs"
Requires-Dist: numpydoc; extra == "docs"
Requires-Dist: emcee==3.1.4; extra == "docs"
Requires-Dist: chainconsumer==0.34.0; extra == "docs"
Requires-Dist: jax-cosmo; extra == "docs"

# JaxILI

This is a package to run Neural Density Estimation using Jax.

**Work is still in progress**

## Installation

To install the package, clone the repository and create a conda environment using `conda_env.yml`.

```bash
git clone https://github.com/sachaguer/jaxili.git
cd jaxili
conda env create -f conda_env.yml
```

You can then use pip to install the library in your conda environment.

```bash
conda activate jaxili
pip install .
```
