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Update README #659

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15 changes: 3 additions & 12 deletions README.md
Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@
[![Coverage Status](https://coveralls.io/repos/github/TuringLang/DynamicPPL.jl/badge.svg?branch=master)](https://coveralls.io/github/TuringLang/DynamicPPL.jl?branch=master)
[![Codecov](https://codecov.io/gh/TuringLang/DynamicPPL.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/TuringLang/DynamicPPL.jl)
[![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle)
[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor%27s%20Guide-blueviolet)](https://colprac.sciml.ai/)
[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor%27s%20Guide-blueviolet)](https://docs.sciml.ai/ColPrac/stable/)

*A domain-specific language and backend for probabilistic programming, used by [Turing.jl](https://github.com/TuringLang/Turing.jl).*

Expand All @@ -20,24 +20,15 @@ DynamicPPL is the part of Turing.jl that deals with defining, running, and manip
- A rich contextual dispatch system allowing for tailored behaviour during model execution.
- A user-friendly syntax for probabilistic queries.

Information on how to use the DynamicPPL frontend to build Bayesian models can be found on the [Turing website](https://turing.ml/). Tutorials explaining how to use the backend can be found [alongside the documentation](https://turinglang.github.io/DynamicPPL.jl/stable/). More information can be found in our paper [DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models](https://arxiv.org/pdf/2002.02702.pdf).
Information on how to use the DynamicPPL frontend to build Bayesian models can be found on the [Turing website](https://turinglang.org/). Tutorials explaining how to use the backend can be found [alongside the documentation](https://turinglang.github.io/DynamicPPL.jl/stable/). More information can be found in our paper [DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models](https://arxiv.org/pdf/2002.02702.pdf).

## Do you want to contribute?

If you feel you have some relevant skills and are interested in contributing, please get in touch! You can find us in the #turing channel on the [Julia Slack](https://julialang.org/slack/) or [Discourse](discourse.julialang.org). If you're having any problems, please open a Github issue, even if the problem seems small (like help figuring out an error message). Every issue you open helps us improve the library!

### Contributor's Guide

This project follows the [![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor%27s%20Guide-blueviolet)](https://colprac.sciml.ai/), apart from the following slight variation:

- The master branch contains the most recent release at any point in time. All non-breaking changes (bug fixes etc.) are merged directly into master and a new patch version is released immediately.
- A separate dev branch contains all breaking changes, and is merged into master when a minor version release happens.

For instance, suppose we are currently on version 0.13.5.

- If someone produces a bug fix, it is merged directly into master and bumps the version to 0.13.6. This change is also merged into dev so that it remains up-to-date with master.
- If someone is working on a new feature that is not breaking (performance-related, fancy new syntax that is backwards-compatible etc.), the same happens.
- New breaking changes are merged into dev until a release is ready to go, at which point dev is merged into master and version 0.14 is released.
This project follows the [![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor%27s%20Guide-blueviolet)](https://docs.sciml.ai/ColPrac/stable/).

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