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Bump the all-julia-packages group across 2 directories with 4 updates - #41

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Updates the requirements on OrderedCollections, FlexiChains, Turing and DynamicPPL to permit the latest version.
Updates OrderedCollections to 2.0.1

Release notes

Sourced from OrderedCollections's releases.

v2.0.1

OrderedCollections v2.0.1

Diff since v2.0.0

Merged pull requests:

Commits
  • ee78af1 Bump version from 2.0.0 to 2.0.1 (#170)
  • 304d5ed avoid rehashing in read-only operations (and fix some bugs) (#169)
  • d5d76c7 Merge pull request #164 from JuliaCollections/ox/v2
  • 68ab740 Remove depreacations for v2.0 release
  • 7bb0847 Bump version from 1.8.1 to 1.8.2
  • 6af78d8 OrderedDict: improve performance for non-concrete values and keys (#166)
  • 1eaa980 Bump actions/checkout from 5 to 6 (#157)
  • 7be2211 Bump actions/checkout from 4 to 5 (#156)
  • a25bffa Add Integer type annotation to sizehint! overloads (#155)
  • a64f186 issorted(ld; byvalue=true) for LittleDicts (#151)
  • Additional commits viewable in compare view

Updates FlexiChains to 0.6.23

Release notes

Sourced from FlexiChains's releases.

v0.6.23

FlexiChains v0.6.23

Diff since v0.6.22

The plotting functions pushforward_hist, pushforward_continuous, and pushforward_discrete now use Makie's default figure size instead of that determined by setup_figure_and_layout().

Merged pull requests:

Changelog

Sourced from FlexiChains's changelog.

0.6.23

The plotting functions pushforward_hist, pushforward_continuous, and pushforward_discrete now use Makie's default figure size instead of that determined by setup_figure_and_layout().

0.6.22

When pretty-printing a FlexiChain, the size of array-valued parameters is now shown in the summary table.

0.6.21

Added FlexiChains.transform_values which maps a function over the values stored in a FlexiChain, returning a new FlexiChain with the transformed values. Please see the docs for more information and example usecases.

0.6.20

Added Makie implementations of pushforward plots, following the examples in Michael Betancourt's visualisation tools. These allow you to visualise the way in which posterior uncertainty is propagated through a function. Full worked examples of these are given in the docs.

0.6.19

Improved the internal structure of VNChains constructed from Arrays, such that rand(chn) returns DynamicPPL.ParamsWithStats or DynamicPPL.VarNamedTuple (but unfortunately, only if DynamicPPL is loaded, since those types are defined there).

0.6.18

Re-export Begin and End from DimensionalData.jl, to make indexing into FlexiChains easier.

0.6.17

Added the exported alias SymChain for FlexiChain{Symbol}.

0.6.16

Added FlexiChains.Makie.ridgeline and FlexiChains.Makie.forestplot for creating ridgeline and forest plots with a Makie backend. Note that these functions do not have FlexiChains.mridgeline and FlexiChains.mforestplot aliases!

0.6.15

Added compatibility with OrderedCollections@2.

0.6.14

Added compatibility with AbstractPPL@0.15 and DynamicPPL@0.42.

0.6.13

Moved plotting functions into the FlexiChains.Plots and FlexiChains.Makie submodules.

What used to be called FlexiChains.traceplot (the Plots.jl backend) is now FlexiChains.Plots.traceplot, and what used to be called FlexiChains.mtraceplot (the Makie.jl backend) is now FlexiChains.Makie.traceplot.

... (truncated)

Commits

Updates Turing to 0.45.0

Release notes

Sourced from Turing's releases.

v0.45.0

Turing v0.45.0

Diff since v0.44.5

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.

Merged pull requests:

Closed issues:

  • Rework sample() call stack to use LogDensityFunction (#2555)
Changelog

Sourced from Turing's changelog.

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.

0.44.4

Add post-sampling warning message when there are divergent transitions with HMC, NUTS or HMCDA.

0.44.3

Add compatibility with SciMLBase v3.

0.44.2

Fix a bug in v0.44 where extra keyword arguments passed to vi (e.g. callback) would cause Turing to error.

0.44.1

Re-export pointwise_logdensities and pointwise_prior_logdensities from DynamicPPL.

0.44.0

Breaking changes

Variational inference interface

The VI interface in Turing has been modified to make it more interoperable with the rest of Turing.

  • The arguments to vi(...) are slightly different: instead of specifying a q_init argument (the initial variational approximation), you now directly pass a function that constructs this for you. For example, instead of

    q_init = q_meanfield_gaussian(model)
vi(model, q_init, n_iters)

    you would now do

    vi(model, q_meanfield_gaussian, n_iters)

... (truncated)

Commits

Updates DynamicPPL to 0.42.1

Release notes

Sourced from DynamicPPL's releases.

v0.42.1

DynamicPPL v0.42.1

Diff since v0.42.0

Closed issues:

  • Separate Mooncake tests into its own test suite (#1352)
  • DynamicPPL 0.41.8 vs 0.42.0: posteriordb-bench Comparison (#1412)
Changelog

Sourced from DynamicPPL's changelog.

0.42.1

Fixed a type-inference failure that made nested submodels (a ~ to_submodel(...) statement inside a model that is itself evaluated as a submodel) very slow.

Previously, evaluating a submodel recursed through the shared _evaluate!!(::Model, ::AbstractVarInfo) method. Each level of nesting adds another context layer to the Model type, which tripped Julia's type-inference recursion limit: from the first level of nesting onwards the return type was inferred as Any and evaluation fell back to runtime dispatch, slowing down both primal and gradient evaluation (the primal slowdown was roughly 15x, growing with nesting depth). Submodel evaluation now calls the model function directly, keeping nested submodels type-stable. See Turing.jl#2844.

0.42.0

LogDensityFunction now performs AD preparation through AbstractPPL's prepare / value_and_gradient!! interface instead of calling DifferentiationInterface directly. Internally this removes the _use_closure heuristic and the explicit DI.Constant plumbing; the choice between closure and constants now lives in AbstractPPL.

logdensity_at has been renamed to logdensity_internal. The old name is kept as a const alias so external callers do not break.

LogDensityAt is now a deprecation shim that emits a warning and returns an AbstractPPL.Evaluators.VectorEvaluator whose call forwards to logdensity_internal. New code should call AbstractPPL.prepare(logdensity_internal, x; context=...) directly.

Breaking changes

DifferentiationInterface is no longer a hard dependency of DynamicPPL. With AbstractPPL 0.15.2, the following backends now have native AbstractPPL extensions and only need the concrete AD package loaded:

  • AutoForwardDiff — load ForwardDiff
  • AutoMooncake, AutoMooncakeForward — load Mooncake

For other DI-routed backends like AutoReverseDiff, users must load DifferentiationInterface together with the concrete AD package:

using DynamicPPL, ADTypes, DifferentiationInterface, ReverseDiff
ldf = LogDensityFunction(model; adtype=AutoReverseDiff())

For distributed sampling the same packages must be loaded on every worker.

Compatibility bounds bumped:

  • AbstractPPL 0.14 → 0.15
  • Bijectors 0.15.17 → 0.16

The integration test suites for MarginalLogDensities, ReverseDiff, and Enzyme now live in their own environments under test/ext/DynamicPPL*Ext/ and run as separate CI jobs.

0.41.8

Override MarginalLogDensities.optimize_marginal! for LogDensityFunctionWrapper so the underlying OptimizationProblem is rebuilt with the current non-marginalised parameters on each call, rather than reusing a stale problem.

0.41.7

Enable usage of ComponentVectors on the left-hand side of tilde-statements.

Accessing a nonexistent variable in a VarNamedTuple now throws a KeyError with the original VarName, instead of an opaque type NamedTuple has no field ... error.

0.41.6

Add a factorize::Bool keyword argument for pointwise_logdensities(model, values), which controls whether pointwise logdensities for factorisable distributions (e.g. MvNormal, product_distribution, etc.) are returned as a single log-density for the whole distribution, or as an array of log-densities for each factor.

... (truncated)

Commits
  • 0a1499f Fix type-inference failure for nested submodels (#1427)
  • b310eec skip codecov upload on Dependabot PRs (#1425)
  • 173defb Bump codecov/codecov-action from 6 to 7 in the all-github-actions-packages gr...
  • b887a35 Bump the all-github-actions-packages group with 2 updates (#1421)
  • a81ff4d Remove CompatHelper and update Dependabot config (#1420)
  • 9347d11 Update HISTORY.md for 0.42.0 (#1408)
  • a115fa8 Use AbstractPPL AD interface (#1363)
  • d2052a1 Benchmark improvements (#1396)
  • 9e5ae37 Bump patch version to 0.41.8 (#1397)
  • 90a74c3 Override optimize_marginal! for LogDensityFunctionWrapper (#1395)
  • Additional commits viewable in compare view

Updates FlexiChains to 0.6.23

Release notes

Sourced from FlexiChains's releases.

v0.6.23

FlexiChains v0.6.23

Diff since v0.6.22

The plotting functions pushforward_hist, pushforward_continuous, and pushforward_discrete now use Makie's default figure size instead of that determined by setup_figure_and_layout().

Merged pull requests:

Changelog

Sourced from FlexiChains's changelog.

0.6.23

The plotting functions pushforward_hist, pushforward_continuous, and pushforward_discrete now use Makie's default figure size instead of that determined by setup_figure_and_layout().

0.6.22

When pretty-printing a FlexiChain, the size of array-valued parameters is now shown in the summary table.

0.6.21

Added FlexiChains.transform_values which maps a function over the values stored in a FlexiChain, returning a new FlexiChain with the transformed values. Please see the docs for more information and example usecases.

0.6.20

Added Makie implementations of pushforward plots, following the examples in Michael Betancourt's visualisation tools. These allow you to visualise the way in which posterior uncertainty is propagated through a function. Full worked examples of these are given in the docs.

0.6.19

Improved the internal structure of VNChains constructed from Arrays, such that rand(chn) returns DynamicPPL.ParamsWithStats or DynamicPPL.VarNamedTuple (but unfortunately, only if DynamicPPL is loaded, since those types are defined there).

0.6.18

Re-export Begin and End from DimensionalData.jl, to make indexing into FlexiChains easier.

0.6.17

Added the exported alias SymChain for FlexiChain{Symbol}.

0.6.16

Added FlexiChains.Makie.ridgeline and FlexiChains.Makie.forestplot for creating ridgeline and forest plots with a Makie backend. Note that these functions do not have FlexiChains.mridgeline and FlexiChains.mforestplot aliases!

0.6.15

Added compatibility with OrderedCollections@2.

0.6.14

Added compatibility with AbstractPPL@0.15 and DynamicPPL@0.42.

0.6.13

Moved plotting functions into the FlexiChains.Plots and FlexiChains.Makie submodules.

What used to be called FlexiChains.traceplot (the Plots.jl backend) is now FlexiChains.Plots.traceplot, and what used to be called FlexiChains.mtraceplot (the Makie.jl backend) is now FlexiChains.Makie.traceplot.

... (truncated)

Commits

Updates Turing to 0.45.0

Release notes

Sourced from Turing's releases.

v0.45.0

Turing v0.45.0

Diff since v0.44.5

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.

Merged pull requests:

Closed issues:

  • Rework sample() call stack to use LogDensityFunction (#2555)
Changelog

Sourced from Turing's changelog.

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.

0.44.4

Add post-sampling warning message when there are divergent transitions with HMC, NUTS or HMCDA.

0.44.3

Add compatibility with SciMLBase v3.

0.44.2

Fix a bug in v0.44 where extra keyword arguments passed to vi (e.g. callback) would cause Turing to error.

0.44.1

Re-export pointwise_logdensities and pointwise_prior_logdensities from DynamicPPL.

0.44.0

Breaking changes

Variational inference interface

The VI interface in Turing has been modified to make it more interoperable with the rest of Turing.

  • The arguments to vi(...) are slightly different: instead of specifying a q_init argument (the initial variational approximation), you now directly pass a function that constructs this for you. For example, instead of

    q_init = q_meanfield_gaussian(model)
vi(model, q_init, n_iters)

    you would now do

    vi(model, q_meanfield_gaussian, n_iters)

... (truncated)

Commits

Updates DynamicPPL to 0.42.1

Release notes

Sourced from DynamicPPL's releases.

v0.42.1

DynamicPPL v0.42.1

Diff since v0.42.0

Closed issues:

  • Separate Mooncake tests into its own test suite (#1352)
  • DynamicPPL 0.41.8 vs 0.42.0: posteriordb-bench Comparison (#1412)
Changelog

Sourced from DynamicPPL's changelog.

0.42.1

Fixed a type-inference failure that made nested submodels (a ~ to_submodel(...) statement inside a model that is itself evaluated as a submodel) very slow.

Previously, evaluating a submodel recursed through the shared _evaluate!!(::Model, ::AbstractVarInfo) method. Each level of nesting adds another context layer to the Model type, which tripped Julia's type-inference recursion limit: from the first level of nesting onwards the return type was inferred as Any and evaluation fell back to runtime dispatch, slowing down both primal and gradient evaluation (the primal slowdown was roughly 15x, growing with nesting depth). Submodel evaluation now calls the model function directly, keeping nested submodels type-stable. See Turing.jl#2844.

0.42.0

LogDensityFunction now performs AD preparation through AbstractPPL's prepare / value_and_gradient!! interface instead of calling DifferentiationInterface directly. Internally this removes the _use_closure heuristic and the explicit DI.Constant plumbing; the choice between closure and constants now lives in AbstractPPL.

logdensity_at has been renamed to logdensity_internal. The old name is kept as a const alias so external callers do not break.

LogDensityAt is now a deprecation shim that emits a warning and returns an AbstractPPL.Evaluators.VectorEvaluator whose call forwards to logdensity_internal. New code should call AbstractPPL.prepare(logdensity_internal, x; context=...) directly.

Breaking changes

DifferentiationInterface is no longer a hard dependency of DynamicPPL. With AbstractPPL 0.15.2, the following backends now have native AbstractPPL extensions and only need the concrete AD package loaded:

  • AutoForwardDiff — load ForwardDiff
  • AutoMooncake, AutoMooncakeForward — load Mooncake

For other DI-routed backends like AutoReverseDiff, users must load DifferentiationInterface together with the concrete AD package:

using DynamicPPL, ADTypes, DifferentiationInterface, ReverseDiff
ldf = LogDensityFunction(model; adtype=AutoReverseDiff())

For distributed sampling the same packages must be loaded on every worker.

Compatibility bounds bumped:

  • AbstractPPL 0.14 → 0.15
  • Bijectors 0.15.17 → 0.16

The integration test suites for MarginalLogDensities, ReverseDiff, and Enzyme now live in their own environments under test/ext/DynamicPPL*Ext/ and run as separate CI jobs.

0.41.8

Override MarginalLogDensities.optimize_marginal! for LogDensityFunctionWrapper so the underlying OptimizationProblem is rebuilt with the current non-marginalised parameters on each call, rather than reusing a stale problem.

0.41.7

Enable usage of ComponentVectors on the left-hand side of tilde-statements.

Accessing a nonexistent variable in a VarNamedTuple now throws a KeyError with the original VarName, instead of an opaque type NamedTuple has no field ... error.

0.41.6

Add a factorize::Bool keyword argument for pointwise_logdensities(model, values), which controls whether pointwise logdensities for factorisable distributions (e.g. MvNormal, product_distribution, etc.) are returned as a single log-density for the whole distribution, or as an array of log-densities for each factor.

... (truncated)

Commits
  • 0a1499f Fix type-inference failure for nested submodels (#1427)
  • b310eec skip codecov upload on Dependabot PRs (#1425)
  • 173defb Bump codecov/codecov-action from 6 to 7 in the all-github-actions-packages gr...
  • b887a35 Bump the all-github-actions-packages group with 2 updates (#1421)
  • a81ff4d Remove CompatHelper and update Dependabot config (#1420)
  • 9347d11 Update HISTORY.md for 0.42.0 (#1408)
  • a115fa8 Use AbstractPPL AD interface (#1363)
  • d2052a1 Benchmark improvements (#1396)
  • 9e5ae37 Bump patch version to 0.41.8 (#1397)
  • 90a74c3 Override optimize_marginal! for LogDensityFunctionWrapper (#1395)
  • Additional commits viewable in compare view

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@dependabot
Bump the all-julia-packages group across 2 directories with 4 updates
064cb30 
Updates the requirements on [OrderedCollections](https://github.com/JuliaCollections/OrderedCollections.jl), [FlexiChains](https://github.com/penelopeysm/FlexiChains.jl), [Turing](https://github.com/TuringLang/Turing.jl) and [DynamicPPL](https://github.com/TuringLang/DynamicPPL.jl) to permit the latest version.

Updates `OrderedCollections` to 2.0.1
- [Release notes](https://github.com/JuliaCollections/OrderedCollections.jl/releases)
- [Commits](JuliaCollections/OrderedCollections.jl@v1.0.0...v2.0.1)

Updates `FlexiChains` to 0.6.23
- [Release notes](https://github.com/penelopeysm/FlexiChains.jl/releases)
- [Changelog](https://github.com/penelopeysm/FlexiChains.jl/blob/main/HISTORY.md)
- [Commits](JuliaBayes/FlexiChains.jl@v0.4.0...v0.6.23)

Updates `Turing` to 0.45.0
- [Release notes](https://github.com/TuringLang/Turing.jl/releases)
- [Changelog](https://github.com/TuringLang/Turing.jl/blob/main/HISTORY.md)
- [Commits](TuringLang/Turing.jl@v0.43.0...v0.45.0)

Updates `DynamicPPL` to 0.42.1
- [Release notes](https://github.com/TuringLang/DynamicPPL.jl/releases)
- [Changelog](https://github.com/TuringLang/DynamicPPL.jl/blob/main/HISTORY.md)
- [Commits](TuringLang/DynamicPPL.jl@v0.40.0...v0.42.1)

Updates `FlexiChains` to 0.6.23
- [Release notes](https://github.com/penelopeysm/FlexiChains.jl/releases)
- [Changelog](https://github.com/penelopeysm/FlexiChains.jl/blob/main/HISTORY.md)
- [Commits](JuliaBayes/FlexiChains.jl@v0.4.0...v0.6.23)

Updates `Turing` to 0.45.0
- [Release notes](https://github.com/TuringLang/Turing.jl/releases)
- [Changelog](https://github.com/TuringLang/Turing.jl/blob/main/HISTORY.md)
- [Commits](TuringLang/Turing.jl@v0.43.0...v0.45.0)

Updates `DynamicPPL` to 0.42.1
- [Release notes](https://github.com/TuringLang/DynamicPPL.jl/releases)
- [Changelog](https://github.com/TuringLang/DynamicPPL.jl/blob/main/HISTORY.md)
- [Commits](TuringLang/DynamicPPL.jl@v0.40.0...v0.42.1)

---
updated-dependencies:
- dependency-name: OrderedCollections
  dependency-version: 2.0.1
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: FlexiChains
  dependency-version: 0.6.23
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: Turing
  dependency-version: 0.45.0
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: DynamicPPL
  dependency-version: 0.42.1
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: FlexiChains
  dependency-version: 0.6.23
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: Turing
  dependency-version: 0.45.0
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: DynamicPPL
  dependency-version: 0.42.1
  dependency-type: direct:production
  dependency-group: all-julia-packages
...

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@dependabot dependabot Bot added dependencies Pull requests that update a dependency file julia Pull requests that update julia code labels Jul 6, 2026
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BayesianWorkflow.jl documentation for PR #41 is available at:
https://JuliaBayes.github.io/BayesianWorkflow.jl/previews/PR41/

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