change readme from rst to md

Author: ANazaret

README.md

@@ -0,0 +1,117 @@
+
+# Treeffuser
+
+[![PyPI version](https://badge.fury.io/py/treeffuser.svg)](https://badge.fury.io/py/treeffuser)
+[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
+[![GitHub Stars](https://img.shields.io/github/stars/blei-lab/treeffuser?style=flat&logo=GitHub)](https://github.com/blei-lab/treeffuser/stargazers)
+[![PyPI - Downloads](https://img.shields.io/pypi/dm/treeffuser)](https://pypi.org/project/treeffuser/)
+[![Website](https://img.shields.io/badge/website-visit-blue?label=website)](https://blei-lab.github.io/treeffuser/)
+[![Documentation](https://img.shields.io/badge/docs-passing-green)](https://blei-lab.github.io/treeffuser/docs/getting-started.html)
+[![arXiv](https://img.shields.io/badge/arXiv-2406.07658-red)](https://arxiv.org/abs/2406.07658)
+
+Treeffuser is an easy-to-use package for **probabilistic prediction on tabular data with tree-based diffusion models**.
+It estimates distributions of the form `p(y|x)` where `x` is a feature vector and `y` is a target vector.
+Treeffuser can model conditional distributions `p(y|x)` that are arbitrarily complex (e.g., multimodal, heteroscedastic, non-Gaussian, heavy-tailed, etc.).
+
+It is designed to adhere closely to the scikit-learn API and require minimal user tuning.
+
+<h3 align="center">
+        <b><a href="https://blei-lab.github.io/treeffuser/">Website</a></b> |
+        <b><a href="https://github.com/blei-lab/treeffuser/">GitHub</a></b> |
+        <b><a href="https://blei-lab.github.io/treeffuser/docs/getting-started.html">Documentation</a></b> |
+        <b><a href="https://arxiv.org/abs/2406.07658">Paper (NeurIPS 2024)</a></b>
+</h3>
+
+
+## Installation
+
+Install Treeffuser from PyPI:
+
+```bash
+pip install treeffuser
+```
+
+Install the development version:
+
+```bash
+pip install git+https://github.com/blei-lab/treeffuser.git@main
+```
+
+The GitHub repository is located at: https://github.com/blei-lab/treeffuser
+
+
+## Usage Example
+
+Here's a simple example demonstrating how to use Treeffuser.
+
+We generate a heteroscedastic response with two sinusoidal components and heavy tails.
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+from treeffuser import Treeffuser, Samples
+
+# Generate data
+seed = 0
+rng = np.random.default_rng(seed=seed)
+n = 5000
+x = rng.uniform(0, 2 * np.pi, size=n)
+z = rng.integers(0, 2, size=n)
+y = z * np.sin(x - np.pi / 2) + (1 - z) * np.cos(x) + rng.laplace(scale=x / 30, size=n)
+```
+
+We fit Treeffuser and generate samples. We then plot the samples against the raw data.
+
+```python
+# Fit the model
+model = Treeffuser(seed=seed)
+model.fit(x, y)
+
+# Generate and plot samples
+y_samples = model.sample(x, n_samples=1, seed=seed, verbose=True)
+plt.scatter(x, y, s=1, label="observed data")
+plt.scatter(x, y_samples[0, :], s=1, alpha=0.7, label="Treeffuser samples")
+```
+
+![Treeffuser on heteroscedastic data](README_example.png)
+
+Treeffuser accurately learns the target conditional densities and can generate samples from them.
+
+These samples can be used to compute any downstream estimates of interest:
+
+```python
+y_samples = model.sample(x, n_samples=100, verbose=True)  # y_samples.shape[0] is 100
+
+# Estimate downstream quantities of interest
+y_mean = y_samples.mean(axis=0)  # conditional mean
+y_std = y_samples.std(axis=0)    # conditional std
+```
+
+You can also use the `Samples` helper class:
+
+```python
+y_samples = Samples(y_samples)
+y_mean = y_samples.sample_mean()
+y_std = y_samples.sample_std()
+y_quantiles = y_samples.sample_quantile(q=[0.05, 0.95])
+```
+
+See the documentation for more information on available methods and parameters.
+
+---
+
+## Citing Treeffuser
+
+If you use Treeffuser in your work, please cite:
+
+```bibtex
+@article{beltranvelez2024treeffuser,
+  title={Treeffuser: Probabilistic Predictions via Conditional Diffusions with Gradient-Boosted Trees},
+  author={Nicolas Beltran-Velez and Alessandro Antonio Grande and Achille Nazaret and Alp Kucukelbir and David Blei},
+  year={2024},
+  eprint={2406.07658},
+  archivePrefix={arXiv},
+  primaryClass={cs.LG},
+  url={https://arxiv.org/abs/2406.07658},
+}
+```