Metadata-Version: 2.4
Name: bartrs
Version: 0.2.0
Classifier: Programming Language :: Rust
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Dist: numba>=0.60.0
Requires-Dist: pymc>=6.0.1
Requires-Dist: arviz>=1.1.0
Requires-Dist: pymc-bart>=0.12.0
Requires-Dist: pre-commit>=4.0.1 ; extra == 'dev'
Requires-Dist: pytest-cov>=6.0.0 ; extra == 'dev'
Requires-Dist: pytest>=8.3.4 ; extra == 'dev'
Requires-Dist: ruff>=0.8.3 ; extra == 'dev'
Provides-Extra: dev
License-File: LICENSE
Summary: Rust implementation of Bayesian Additive Regression Trees for Probabilistic programming with PyMC
Author-email: Otto Vintola <hello@ottovintola.com>
License-Expression: Apache-2.0
Requires-Python: >=3.10, <3.13
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
Project-URL: Homepage, https://github.com/pymc-devs/bartrs
Project-URL: Issues, https://github.com/pymc-devs/bartrs/issues

# PyMC-BART-rs

High-performance Rust implementation of [PyMC-BART](https://github.com/pymc-devs/pymc-bart). This implementation provides an optimized Particle Gibbs BART (PGBART) sampler designed for performance and extensibility.

## Table of Contents

- [Installation](#installation)
- [Usage](#usage)
- [Modifications](#modifications)

## Installation

PyMC-BART is available on PyPI with pre-built wheels for Linux (x86_64, aarch64), Windows (x64), and macOS (x86_64, aarch64). To install using `pip`

```bash
pip install pymc-bart-rs
```

## Usage

Get started by using PyMC-BART to set up a BART model

```python
import pymc as pm
import pymc_bartrs as pmb

X, y = ... # Your data replaces "..."
with pm.Model() as model:
    bart = pmb.BART('bart', X, y)
    ...
    idata = pm.sample()
```

