Update README (#659)

* Fix Turing website link

* Update contributing section

Author: penelopeysm

README.md

@@ -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).*
 
@@ -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/).
 
 ### Merge Queue