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chore(deps): update Turing requirement from 0.42, 0.43, 0.44, 0.45 to 0.42, 0.43, 0.44, 0.45, 0.46#209

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chore(deps): update Turing requirement from 0.42, 0.43, 0.44, 0.45 to 0.42, 0.43, 0.44, 0.45, 0.46#209
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Updates the requirements on Turing to permit the latest version.

Release notes

Sourced from Turing's releases.

v0.46.0

Turing v0.46.0

Diff since v0.45.0

Breaking changes

DynamicPPL 0.42

Turing.jl v0.46 brings with it all the underlying changes in DynamicPPL 0.42. Most notably, gradient preparation and evaluation now go through AbstractPPL's prepare / value_and_gradient!! interface. This is an internal change and does not affect sampling results. Please see the DynamicPPL changelog for full details, and the AdvancedVI section below for the user-facing changes in this release.

AdvancedVI 0.7

Turing.jl v0.46 also brings in the changes in AdvancedVI 0.7. Please see the AdvancedVI changelog for full details; the changes most pertinent to users of vi are:

  • AutoReverseDiff(; compile=true) is no longer supported for VI, and is rejected with an ArgumentError, as compiled tapes can silently produce incorrect gradients when reused across optimisation steps. Use AutoReverseDiff(; compile=false), or a different reverse-mode backend such as AutoMooncake(), instead.
  • vi with KLMinScoreGradDescent now optimises in unconstrained (linked) space, making it consistent with the other KLMin... algorithms. If you use it with a model that has constrained parameters, results may differ slightly from previous releases.

Other changes

DifferentiationInterface removed as a direct dependency

informationmatrix (and hence vcov) now computes its Hessian through AbstractPPL's second-order interface instead of DifferentiationInterface. There is no change in behaviour for users.

Performance of nested submodels

DynamicPPL 0.42.1 fixes a type-inference failure that made nested submodels (a x ~ to_submodel(...) statement inside a model that is itself used as a submodel) many times slower to evaluate and differentiate; see TuringLang/Turing.jl#2844.

MCMCChains extension fix

Turing v0.45.0 was accidentally released without declaring the TuringMCMCChainsExt package extension, meaning that the extension did not load when MCMCChains was imported. This broke some MCMCChains-specific functionality, such as loadstate (for resuming sampling from a previous chain) and the post-sampling divergence warnings for Hamiltonian samplers; this is now fixed.

Merged pull requests:

... (truncated)

Changelog

Sourced from Turing's changelog.

0.46.0

Breaking changes

DynamicPPL 0.42

Turing.jl v0.46 brings with it all the underlying changes in DynamicPPL 0.42. Most notably, gradient preparation and evaluation now go through AbstractPPL's prepare / value_and_gradient!! interface. This is an internal change and does not affect sampling results. Please see the DynamicPPL changelog for full details, and the AdvancedVI section below for the user-facing changes in this release.

AdvancedVI 0.7

Turing.jl v0.46 also brings in the changes in AdvancedVI 0.7. Please see the AdvancedVI changelog for full details; the changes most pertinent to users of vi are:

  • AutoReverseDiff(; compile=true) is no longer supported for VI, and is rejected with an ArgumentError, as compiled tapes can silently produce incorrect gradients when reused across optimisation steps. Use AutoReverseDiff(; compile=false), or a different reverse-mode backend such as AutoMooncake(), instead.
  • vi with KLMinScoreGradDescent now optimises in unconstrained (linked) space, making it consistent with the other KLMin... algorithms. If you use it with a model that has constrained parameters, results may differ slightly from previous releases.

Other changes

DifferentiationInterface removed as a direct dependency

informationmatrix (and hence vcov) now computes its Hessian through AbstractPPL's second-order interface instead of DifferentiationInterface. There is no change in behaviour for users.

Performance of nested submodels

DynamicPPL 0.42.1 fixes a type-inference failure that made nested submodels (a x ~ to_submodel(...) statement inside a model that is itself used as a submodel) many times slower to evaluate and differentiate; see TuringLang/Turing.jl#2844.

MCMCChains extension fix

Turing v0.45.0 was accidentally released without declaring the TuringMCMCChainsExt package extension, meaning that the extension did not load when MCMCChains was imported. This broke some MCMCChains-specific functionality, such as loadstate (for resuming sampling from a previous chain) and the post-sampling divergence warnings for Hamiltonian samplers; this is now fixed.

0.45.0

Breaking changes

Make FlexiChains the default chain type for MCMC sampling.

MCMCChains is still fully supported: you can specify chain_type=MCMCChains.Chains in the sample function to use it instead. However, it is no longer loaded as a dependency of Turing and re-exported (it is now an extension). That means that if you were previously importing MCMCChains via Turing, you will now have to import it directly.

0.44.5

Allow users to disable the post-sample hook by passing verbose=false keyword argument to sample.

... (truncated)

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… 0.42, 0.43, 0.44, 0.45, 0.46

Updates the requirements on [Turing](https://github.com/TuringLang/Turing.jl) to permit the latest version.
- [Release notes](https://github.com/TuringLang/Turing.jl/releases)
- [Changelog](https://github.com/TuringLang/Turing.jl/blob/main/HISTORY.md)
- [Commits](https://github.com/TuringLang/Turing.jl/commits/v0.46.0)

---
updated-dependencies:
- dependency-name: Turing
  dependency-version: 0.46.0
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot dependabot Bot added dependencies Pull requests that update a dependency file julia Pull requests that update julia code labels Jul 18, 2026
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Codecov Report

✅ All modified and coverable lines are covered by tests.

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ad-enzyme-forward 0.00% <ø> (ø)
ad-forwarddiff 21.98% <ø> (-0.25%) ⬇️
ad-mooncake-forward 0.00% <ø> (ø)
ad-mooncake-reverse 22.03% <ø> (-0.25%) ⬇️
ad-reversediff 21.98% <ø> (-0.25%) ⬇️
unit 85.88% <ø> (+0.09%) ⬆️

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Try this Pull Request!

Option 1: Julia Package Manager

Open Julia and type:

import Pkg
Pkg.activate(temp=true)
Pkg.add(url="https://github.com/EpiAware/ComposedDistributions.jl", rev="dependabot/julia/Turing-0.42-and-0.43-and-0.44-and-0.45-and-0.46")
using ComposedDistributions

Option 2: Local Checkout

If you have the repo locally:

git checkout dependabot/julia/Turing-0.42-and-0.43-and-0.44-and-0.45-and-0.46
julia --project=. -e "using Pkg; Pkg.instantiate()"

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📖 Documentation preview is ready!

View the docs for this PR at: https://EpiAware.github.io/ComposedDistributions.jl/previews/PR209/

This preview will be updated automatically when you push new commits.

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