Default branch change

Author: penelopeysm

.github/workflows/publish.yml

@@ -3,7 +3,7 @@ name: Render Docs Website
 on:
   push:
     branches:
-      - master
+      - main
       - backport-v0.*
   workflow_dispatch: # manual trigger for testing
 

.github/workflows/resolve_manifest.yml

@@ -1,4 +1,4 @@
-# This action runs Pkg.instantiate() and Pkg.resolve() every time the master
+# This action runs Pkg.instantiate() and Pkg.resolve() every time the main
 # branch is pushed to. If this leads to a change in the Manifest.toml file, it
 # will open a PR to update the Manifest.toml file. This ensures that the
 # contents of the Manifest in the repository are consistent with the contents
@@ -11,7 +11,7 @@ name: Resolve Manifest
 on:
   push:
     branches:
-      - master
+      - main
   workflow_dispatch:
 
 jobs:

.github/workflows/version_check.yml

@@ -1,7 +1,7 @@
 # This action checks that the minor versions of Turing.jl specified in the
 # Project.toml, _quarto.yml, and Manifest.toml files are consistent.
 #
-# For pushes to master or PRs to master, it additionally also checks that the
+# For pushes to main or PRs to main, it additionally also checks that the
 # version specified in Manifest.toml matches the latest release on GitHub.
 # 
 # If any discrepancies are observed, it will open a PR to fix them.
@@ -10,11 +10,11 @@ name: Check Turing.jl version consistency
 on:
   push:
     branches:
-      - master
+      - main
       - backport-*
   pull_request:
     branches:
-      - master
+      - main
       - backport-*
   workflow_dispatch:
 
@@ -27,9 +27,9 @@ jobs:
       pull-requests: write
 
     env:
-      # Determine whether the target branch is master (i.e. this is a push to
-      # master or a PR to master).
-      TARGET_IS_MASTER: ${{ (github.event_name == 'push' && github.ref_name == 'master') || (github.event_name == 'pull_request' && github.base_ref == 'master') }}
+      # Determine whether the target branch is main (i.e. this is a push to
+      # main or a PR to main).
+      TARGET_IS_MAIN: ${{ (github.event_name == 'push' && github.ref_name == 'main') || (github.event_name == 'pull_request' && github.base_ref == 'main') }}
       IS_PR_FROM_FORK: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.repo.fork }}
       # Disable precompilation as it takes a long time and is not needed for this workflow
       JULIA_PKG_PRECOMPILE_AUTO: 0
@@ -46,7 +46,7 @@ jobs:
           echo github.event_name: ${{ github.event_name }}
           echo github.ref_name: ${{ github.ref_name }}
           echo github.base_ref: ${{ github.base_ref }}
-          echo TARGET_IS_MASTER: ${{ env.TARGET_IS_MASTER }}
+          echo TARGET_IS_MAIN: ${{ env.TARGET_IS_MAIN }}
           echo IS_PR_FROM_FORK: ${{ env.IS_PR_FROM_FORK }}
 
       - name: Check version consistency
@@ -55,7 +55,7 @@ jobs:
 
       - name: Create a PR with suggested changes
         id: create_pr
-        if: always() && steps.version_check.outcome == 'failure' && env.TARGET_IS_MASTER && (! env.IS_PR_FROM_FORK)
+        if: always() && steps.version_check.outcome == 'failure' && env.TARGET_IS_MAIN && (! env.IS_PR_FROM_FORK)
         uses: peter-evans/create-pull-request@v6
         with:
           base: ${{ github.event_name == 'pull_request' && github.head_ref || github.ref_name }}

README.md

@@ -1,8 +1,8 @@
 # Documentation and Tutorials for Turing.jl
 
 This repository is part of [Turing.jl's](https://turinglang.org/) website (i.e. `https://turinglang.org/docs/`). It contains the Turing.jl documentation and tutorials. 
-- The `master` branch contains the quarto source 
-- The `gh-pages` branch contains the `html` version of these documents compiled from the `master` branch.
+- The `main` branch contains the quarto source 
+- The `gh-pages` branch contains the `html` version of these documents compiled from the `main` branch.
 
 ## Local development
 

_quarto.yml

@@ -116,7 +116,7 @@ website:
     background: "#073c44"
     left: |
       Turing is created by <a href="http://mlg.eng.cam.ac.uk/hong/" target="_blank">Hong Ge</a>, and lovingly maintained by the <a href="https://github.com/TuringLang/Turing.jl/graphs/contributors" target="_blank">core team</a> of volunteers. <br>
-      The contents of this website are © 2024 under the terms of the <a href="https://github.com/TuringLang/Turing.jl/blob/master/LICENCE" target="_blank">MIT License</a>.
+      The contents of this website are © 2024 under the terms of the <a href="https://github.com/TuringLang/Turing.jl/blob/main/LICENCE" target="_blank">MIT License</a>.
 
     right:
       - icon: twitter
@@ -129,7 +129,7 @@ website:
   back-to-top-navigation: true
   repo-url: https://github.com/TuringLang/docs
   repo-actions: [edit, issue]
-  repo-branch: master
+  repo-branch: main
   repo-link-target: _blank
   page-navigation: true
 

assets/scripts/changelog.sh

@@ -1,4 +1,4 @@
-url="https://raw.githubusercontent.com/TuringLang/Turing.jl/master/HISTORY.md"
+url="https://raw.githubusercontent.com/TuringLang/Turing.jl/main/HISTORY.md"
 
 changelog_content=$(curl -s "$url")
 

developers/inference/abstractmcmc-turing/index.qmd

@@ -37,7 +37,7 @@ chn = sample(mod, alg, n_samples, progress=false)
 
 The function `sample` is part of the AbstractMCMC interface. As explained in the [interface guide]({{<meta using-turing-interface>}}), building a sampling method that can be used by `sample` consists in overloading the structs and functions in `AbstractMCMC`. The interface guide also gives a standalone example of their implementation, [`AdvancedMH.jl`]().
 
-Turing sampling methods (most of which are written [here](https://github.com/TuringLang/Turing.jl/tree/master/src/mcmc)) also implement `AbstractMCMC`. Turing defines a particular architecture for `AbstractMCMC` implementations, that enables working with models defined by the `@model` macro, and uses DynamicPPL as a backend. The goal of this page is to describe this architecture, and how you would go about implementing your own sampling method in Turing, using Importance Sampling as an example. I don't go into all the details: for instance, I don't address selectors or parallelism.
+Turing sampling methods (most of which are written [here](https://github.com/TuringLang/Turing.jl/tree/main/src/mcmc)) also implement `AbstractMCMC`. Turing defines a particular architecture for `AbstractMCMC` implementations, that enables working with models defined by the `@model` macro, and uses DynamicPPL as a backend. The goal of this page is to describe this architecture, and how you would go about implementing your own sampling method in Turing, using Importance Sampling as an example. I don't go into all the details: for instance, I don't address selectors or parallelism.
 
 First, we explain how Importance Sampling works in the abstract. Consider the model defined in the first code block. Mathematically, it can be written:
 
@@ -86,7 +86,7 @@ mod = gdemo(1.5, 2)
 
 creates an instance `mod` of the struct `Model`, which corresponds to the observations of a value of `1.5` for `x`, and a value of `2` for `y`.
 
-This is all handled by DynamicPPL, more specifically [here](https://github.com/TuringLang/DynamicPPL.jl/blob/master/src/model.jl). I will return to how models are used to inform sampling algorithms [below](#assumeobserve).
+This is all handled by DynamicPPL, more specifically [here](https://github.com/TuringLang/DynamicPPL.jl/blob/main/src/model.jl). I will return to how models are used to inform sampling algorithms [below](#assumeobserve).
 
 ### Algorithms
 
@@ -217,7 +217,7 @@ A lot of the things here are method-specific. However, Turing also has some func
 - its value: $\theta$
 - log of the joint probability of the observed data and this sample: `lp`
 
-`Inference.jl` [defines](https://github.com/TuringLang/Turing.jl/blob/master/src/inference/Inference.jl#L103) a struct `Transition`, which corresponds to this default situation
+`Inference.jl` [defines](https://github.com/TuringLang/Turing.jl/blob/main/src/inference/Inference.jl#L103) a struct `Transition`, which corresponds to this default situation
 
 ```{julia}
 #| eval: false
@@ -227,7 +227,7 @@ struct Transition{T,F<:AbstractFloat}
 end
 ```
 
-It also [contains](https://github.com/TuringLang/Turing.jl/blob/master/src/inference/Inference.jl#L108) a constructor that builds an instance of `Transition` from an instance `spl` of `Sampler`: $\theta$ is `spl.state.vi` converted to a `namedtuple`, and `lp` is `getlogp(spl.state.vi)`. `is.jl` uses this default constructor at the end of the `step!` function [here](https://github.com/TuringLang/Turing.jl/blob/master/src/inference/is.jl#L58).
+It also [contains](https://github.com/TuringLang/Turing.jl/blob/main/src/inference/Inference.jl#L108) a constructor that builds an instance of `Transition` from an instance `spl` of `Sampler`: $\theta$ is `spl.state.vi` converted to a `namedtuple`, and `lp` is `getlogp(spl.state.vi)`. `is.jl` uses this default constructor at the end of the `step!` function [here](https://github.com/TuringLang/Turing.jl/blob/main/src/inference/is.jl#L58).
 
 ### How `sample` works