Hello 👋

We are always looking for contributions!

How to contribute?

Please have a look at https://docs.pymc.io/en/latest/contributing/

If you are unsure how to start reach out to us on a GitHub issue that piques your interest or open a topic in our Discourse

Where to contribute?

There are several active repositories under the PyMC organization where you can help:

• pymc: Probabilistic Programming Language (PPL). It specifies a user friendly model based API, probability transformations and sampling routines. Inference Button™
• pymc-examples Educative notebooks showcasing the applications of PyMC. Some date back to 1763.
• pymc-experimental: Where experimental and cutting edge features are implemented. Approach at your own risk!
• pytensor: Computational backend that powers PyMC. It defines objects that represent computational tensor graphs, rewrite routines for optimization and numerical stability, automatic differentiation, and compilation and transpilation to lower level platforms (C, Numba, JAX) for efficient evaluation. Where mathematics meets machine.
• pymc-bart: Bayesian Additive Regression Trees in PyMC models. Wild and environmentally friendly.
• nutpie: Sample PyMC models (and STAN models, but don't tell anyone!) with the next generation NUTS sampler implemented in Rust. Some say it is the fastest thing out there!