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.github/workflows/Benchmarking.yml

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steps:
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- name: Checkout Repository
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uses: actions/checkout@v4
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uses: actions/checkout@v5
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with:
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ref: ${{ github.event.pull_request.head.sha }}
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.github/workflows/CI.yml

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- Group2
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steps:
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- uses: actions/checkout@v4
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- uses: actions/checkout@v5
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- uses: julia-actions/setup-julia@v2
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with:

.github/workflows/DocTest.yml

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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- uses: actions/checkout@v5
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- uses: julia-actions/setup-julia@v2
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with:

.github/workflows/DocsNav.yml

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- name: Checkout gh-pages branch
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uses: actions/checkout@v4
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uses: actions/checkout@v5
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with:
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ref: gh-pages
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.github/workflows/DocsPreviewCleanup.yml

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runs-on: ubuntu-latest
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steps:
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- name: Checkout gh-pages branch
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uses: actions/checkout@v4
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uses: actions/checkout@v5
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with:
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ref: gh-pages
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- name: Delete preview and history + push changes

.github/workflows/Enzyme.yml

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enzyme:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- uses: actions/checkout@v5
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- uses: julia-actions/setup-julia@v2
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with:

.github/workflows/IntegrationTest.yml

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- {user: TuringLang, repo: Turing.jl}
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steps:
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- uses: actions/checkout@v4
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- uses: actions/checkout@v5
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- uses: julia-actions/setup-julia@v2
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with:
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version: 1
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- uses: julia-actions/julia-buildpkg@v1
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- name: Clone Downstream
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uses: actions/checkout@v4
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uses: actions/checkout@v5
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with:
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repository: ${{ matrix.package.user }}/${{ matrix.package.repo }}
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path: downstream

.github/workflows/JuliaPre.yml

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- Group2
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- uses: actions/checkout@v5
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- uses: julia-actions/setup-julia@v2
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with:
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version: 'pre' # pre-release

HISTORY.md

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The `varname_leaves` and `varname_and_value_leaves` functions have been moved to AbstractPPL.jl.
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Their behaviour is otherwise identical.
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[...]
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## 0.37.2
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Make the `resume_from` keyword work for multiple-chain (parallel) sampling as well.
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Prior to this version, it was silently ignored.
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Note that to get the correct behaviour you also need to have a recent version of MCMCChains (v7.2.1).
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## 0.37.1
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src/sampler.jl

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)
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end
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function AbstractMCMC.sample(
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rng::Random.AbstractRNG,
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model::Model,
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sampler::Sampler,
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parallel::AbstractMCMC.AbstractMCMCEnsemble,
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N::Integer,
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nchains::Integer;
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chain_type=default_chain_type(sampler),
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resume_from=nothing,
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initial_state=loadstate(resume_from),
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kwargs...,
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)
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return AbstractMCMC.mcmcsample(
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rng, model, sampler, parallel, N, nchains; chain_type, initial_state, kwargs...
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)
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end
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# initial step: general interface for resuming and
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function AbstractMCMC.step(
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rng::Random.AbstractRNG, model::Model, spl::Sampler; initial_params=nothing, kwargs...

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