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Bump the all-julia-packages group across 2 directories with 3 updates - #19

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Bump the all-julia-packages group across 2 directories with 3 updates#19
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Updates the requirements on FlexiChains, Turing and DynamicPPL to permit the latest version.
Updates FlexiChains to 0.5.9

Release notes

Sourced from FlexiChains's releases.

v0.5.9

FlexiChains v0.5.9

Diff since v0.5.8

Support the factorize=true keyword argument for DynamicPPL.pointwise_logdensities and PosteriorStats.loo.

Merged pull requests:

Closed issues:

  • More Plots! (#41)
  • Handle single indices (?) (#51)
  • resolve plotting function name conflict (#67)
Changelog

Sourced from FlexiChains's changelog.

0.5.9

Support the factorize=true keyword argument for DynamicPPL.pointwise_logdensities and PosteriorStats.loo.

0.5.8

Add compatibility with DynamicPPL 0.41 and Turing 0.44.

0.5.7

Improve pretty-printing of FlexiChain and FlexiSummary objects.

0.5.6

Implemented a Tables.jl interface for FlexiChains, along with Wide and Long wrappers to control the output format.

Made the previously internal function FlexiChains.get_name public. This function unwraps the Parameter or Extra wrapper from a key.

0.5.5

Added a new keyword argument stack to getindex on FlexiChain. For array-valued parameters, chain[key, stack=true] will now return a stacked DimArray instead of a DimArray{AbstractArray}. Conversely, chain[key, stack=false] will return a DimArray{Array} as before.

The same keyword argument can be used for indexing into FlexiSummary as well, with exactly the same implications.

The default value for stack is false, except for the case where the parameter is a DimArray of DimArrays, in which case the stacking happens by default, but with a deprecation warning. In a future version this automatic stacking will be disabled, and to stack DimArrays you will have to explicitly set stack=true.

0.5.4

Added a method, DimensionalData.DimArray(::FlexiChain, kwargs...), to convert a FlexiChain into a 3D DimArray with dimensions (iters, chains, parameters). A method for Base.Array has also been added, which is the same but drops the dimensional metadata.

Implemented PosteriorStats.loo for chains of log-likelihoods, as well as Turing.jl models. Please see the documentation for more information about the PosteriorStats integration.

0.5.3

Added the FlexiChains.rankplot and FlexiChains.mrankplot functions for creating rank plots with Plots.jl and Makie.jl backends.

Also fixed a bug where plotfunc(chn, :) errored (for all plotting functions).

0.5.2

Added a method MCMCDiagnosticTools.bfmi(chn::FlexiChain, energy_key) to compute the Bayesian fraction of missing information (BFMI) for a chain.

0.5.1

... (truncated)

Commits

Updates Turing to 0.44.3

Release notes

Sourced from Turing's releases.

v0.44.3

Turing v0.44.3

Diff since v0.44.2

Add compatibility with SciMLBase v3.

Merged pull requests:

Changelog

Sourced from Turing's changelog.

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)

  • The return value of vi is now a VIResult struct (please see the documentation for information), which bundles the previous return values together in a more cohesive way. Most importantly, you can now call rand([rng,] result::VIResult) to obtain new samples from the variational approximation. This returns a VarNamedTuple of raw values, which can be used directly in all other Turing interfaces without any further wrangling. (In contrast, the previous return value of rand(q) would yield a vector of transformed parameters.)

Internally, the VI interface has been reworked to directly use DynamicPPL.LogDensityFunction instead of relying on a transformed distribution from Bijectors.jl.

Gibbs sampler interface

This section is only relevant if you are writing a sampler that is intended to be directly used as a component sampler in Turing's Gibbs sampler. (If Gibbs calls your sampler via Turing's externalsampler interface, this section does not apply to you.)

Turing's Gibbs sampler has been reworked in this release to fix a number of correctness and performance issues. The main change is that the Gibbs state carries a VarNamedTuple of raw values, instead of a VarInfo of vectorised (transformed) parameters. This fixes correctness issues that arise with value-dependent transformations, and also leads to much reduced overhead when sampling with Gibbs: see TuringLang/Turing.jl#2803 for some representative benchmarks.

In Turing v0.43, you would have to define two methods

... (truncated)

Commits

Updates DynamicPPL to 0.41.6

Release notes

Sourced from DynamicPPL's releases.

v0.41.6

DynamicPPL v0.41.6

Diff since v0.41.5

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. The same argument is also added to pointwise_loglikelihoods and pointwise_prior_logdensities.

Merged pull requests:

Closed issues:

  • Computing pointwise log-likelihoods without factorizing the likelihood (#1038)
Changelog

Sourced from DynamicPPL's changelog.

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. The same argument is also added to pointwise_loglikelihoods and pointwise_prior_logdensities.

0.41.5

Make sure that DynamicPPL.TestUtils.AD.run_ad(...; verbose=false) truly silences all messages.

0.41.4

Improve docs (in particular, make sure that outer constructors of structs also appear in the built docs).

0.41.3

Add a lower-level constructor for LogDensityFunction which directly takes a VNT of RangeAndTransforms plus a sample vectorised input. This is only intended for use in Turing: users should not need to use this directly.

All other constructors are still available and unchanged in behaviour.

To facilitate the functionality needed for Turing, this also adds more accessor functions for LogDensityFunction, namely get_all_ranges_and_transforms, get_sample_input_vector.

0.41.2

Export the accessor functions get_values(::VarInfo) and get_logdensity_callable(::LogDensityFunction), so that users do not need to access internal fields of these types directly.

0.41.1

Fix a missing interpolation in the DynamicPPL compiler which would cause errors if DynamicPPL was not loaded explicitly by the user.

Also fixes a bug with predict(::Model, ::MCMCChains.Chains) where an error would be thrown if unnecessary parameters were removed from the chain before calling predict.

0.41.0

Breaking changes

Unification of transformed values

Previously, there were separate types UntransformedValue, VectorValue, and LinkedVectorValue, which were all subtypes of AbstractTransformedValue. The abstract type has been removed, and all of these have been unified in a single TransformedValue struct, which wraps the (maybe transformed) value, plus an AbstractTransform that describes the inverse transformation (to get back to the raw value).

Concretely,

  • UntransformedValue(val) is now TransformedValue(val, NoTransform())
  • VectorValue(vec, tfm) is now TransformedValue(vec, Unlink())
  • LinkedVectorValue(vec, tfm) is now TransformedValue(vec, DynamicLink())

Note that this means for VectorValue and LinkedVectorValue, the transform is no longer stored on the value itself. This means that given one of these values, you cannot access the raw value without knowing the distribution from which it was sampled.

... (truncated)

Commits
  • 7d85366 fix backwards compat just in case (#1375)
  • 81fe531 Implement factorised pointwise probabilities (#1331)
  • 4201c6c don't print messages in run_ad when verbose=false (#1372)
  • de25f13 format (#1371)
  • e7e9855 Improve NamedDist docstring with fields documentation (#1370)
  • a4c4ae5 fix docs (#1369)
  • 81a245a Add more LDF accessor functions that Turing requires (#1366)
  • 10380bf Add accessor functions for necessary types (#1362)
  • f1c5ed3 Fix interpolation error in macro + predict on MCMCChains indexing bug (#1360)
  • 5f244a8 CompatHelper: add new compat entry for MarginalLogDensities at version 0.4 fo...
  • Additional commits viewable in compare view

Updates FlexiChains to 0.5.9

Release notes

Sourced from FlexiChains's releases.

v0.5.9

FlexiChains v0.5.9

Diff since v0.5.8

Support the factorize=true keyword argument for DynamicPPL.pointwise_logdensities and PosteriorStats.loo.

Merged pull requests:

Closed issues:

  • More Plots! (#41)
  • Handle single indices (?) (#51)
  • resolve plotting function name conflict (#67)
Changelog

Sourced from FlexiChains's changelog.

0.5.9

Support the factorize=true keyword argument for DynamicPPL.pointwise_logdensities and PosteriorStats.loo.

0.5.8

Add compatibility with DynamicPPL 0.41 and Turing 0.44.

0.5.7

Improve pretty-printing of FlexiChain and FlexiSummary objects.

0.5.6

Implemented a Tables.jl interface for FlexiChains, along with Wide and Long wrappers to control the output format.

Made the previously internal function FlexiChains.get_name public. This function unwraps the Parameter or Extra wrapper from a key.

0.5.5

Added a new keyword argument stack to getindex on FlexiChain. For array-valued parameters, chain[key, stack=true] will now return a stacked DimArray instead of a DimArray{AbstractArray}. Conversely, chain[key, stack=false] will return a DimArray{Array} as before.

The same keyword argument can be used for indexing into FlexiSummary as well, with exactly the same implications.

The default value for stack is false, except for the case where the parameter is a DimArray of DimArrays, in which case the stacking happens by default, but with a deprecation warning. In a future version this automatic stacking will be disabled, and to stack DimArrays you will have to explicitly set stack=true.

0.5.4

Added a method, DimensionalData.DimArray(::FlexiChain, kwargs...), to convert a FlexiChain into a 3D DimArray with dimensions (iters, chains, parameters). A method for Base.Array has also been added, which is the same but drops the dimensional metadata.

Implemented PosteriorStats.loo for chains of log-likelihoods, as well as Turing.jl models. Please see the documentation for more information about the PosteriorStats integration.

0.5.3

Added the FlexiChains.rankplot and FlexiChains.mrankplot functions for creating rank plots with Plots.jl and Makie.jl backends.

Also fixed a bug where plotfunc(chn, :) errored (for all plotting functions).

0.5.2

Added a method MCMCDiagnosticTools.bfmi(chn::FlexiChain, energy_key) to compute the Bayesian fraction of missing information (BFMI) for a chain.

0.5.1

... (truncated)

Commits

Updates Turing to 0.44.3

Release notes

Sourced from Turing's releases.

v0.44.3

Turing v0.44.3

Diff since v0.44.2

Add compatibility with SciMLBase v3.

Merged pull requests:

Changelog

Sourced from Turing's changelog.

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)

  • The return value of vi is now a VIResult struct (please see the documentation for information), which bundles the previous return values together in a more cohesive way. Most importantly, you can now call rand([rng,] result::VIResult) to obtain new samples from the variational approximation. This returns a VarNamedTuple of raw values, which can be used directly in all other Turing interfaces without any further wrangling. (In contrast, the previous return value of rand(q) would yield a vector of transformed parameters.)

Internally, the VI interface has been reworked to directly use DynamicPPL.LogDensityFunction instead of relying on a transformed distribution from Bijectors.jl.

Gibbs sampler interface

This section is only relevant if you are writing a sampler that is intended to be directly used as a component sampler in Turing's Gibbs sampler. (If Gibbs calls your sampler via Turing's externalsampler interface, this section does not apply to you.)

Turing's Gibbs sampler has been reworked in this release to fix a number of correctness and performance issues. The main change is that the Gibbs state carries a VarNamedTuple of raw values, instead of a VarInfo of vectorised (transformed) parameters. This fixes correctness issues that arise with value-dependent transformations, and also leads to much reduced overhead when sampling with Gibbs: see TuringLang/Turing.jl#2803 for some representative benchmarks.

In Turing v0.43, you would have to define two methods

... (truncated)

Commits

Updates DynamicPPL to 0.41.6

Release notes

Sourced from DynamicPPL's releases.

v0.41.6

DynamicPPL v0.41.6

Diff since v0.41.5

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. The same argument is also added to pointwise_loglikelihoods and pointwise_prior_logdensities.

Merged pull requests:

Closed issues:

  • Computing pointwise log-likelihoods without factorizing the likelihood (#1038)
Changelog

Sourced from DynamicPPL's changelog.

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. The same argument is also added to pointwise_loglikelihoods and pointwise_prior_logdensities.

0.41.5

Make sure that DynamicPPL.TestUtils.AD.run_ad(...; verbose=false) truly silences all messages.

0.41.4

Improve docs (in particular, make sure that outer constructors of structs also appear in the built docs).

0.41.3

Add a lower-level constructor for LogDensityFunction which directly takes a VNT of RangeAndTransforms plus a sample vectorised input. This is only intended for use in Turing: users should not need to use this directly.

All other constructors are still available and unchanged in behaviour.

To facilitate the functionality needed for Turing, this also adds more accessor functions for LogDensityFunction, namely get_all_ranges_and_transforms, get_sample_input_vector.

0.41.2

Export the accessor functions get_values(::VarInfo) and get_logdensity_callable(::LogDensityFunction), so that users do not need to access internal fields of these types directly.

0.41.1

Fix a missing interpolation in the DynamicPPL compiler which would cause errors if DynamicPPL was not loaded explicitly by the user.

Also fixes a bug with predict(::Model, ::MCMCChains.Chains) where an error would be thrown if unnecessary parameters were removed from the chain before calling predict.

0.41.0

Breaking changes

Unification of transformed values

Previously, there were separate types UntransformedValue, VectorValue, and LinkedVectorValue, which were all subtypes of AbstractTransformedValue. The abstract type has been removed, and all of these have been unified in a single TransformedValue struct, which wraps the (maybe transformed) value, plus an AbstractTransform that describes the inverse transformation (to get back to the raw value).

Concretely,

  • UntransformedValue(val) is now TransformedValue(val, NoTransform())
  • VectorValue(vec, tfm) is now TransformedValue(vec, Unlink())
  • LinkedVectorValue(vec, tfm) is now TransformedValue(vec, DynamicLink())

Note that this means for VectorValue and LinkedVectorValue, the transform is no longer stored on the value itself. This means that given one of these values, you cannot access the raw value without knowing the distribution from which it was sampled.

... (truncated)

Commits
  • 7d85366 fix backwards compat just in case (#1375)
  • 81fe531 Implement factorised pointwise probabilities (#1331)
  • 4201c6c don't print messages in run_ad when verbose=false (#1372)
  • de25f13 format (#1371)
  • e7e9855 Improve NamedDist docstring with fields documentation (#1370)
  • a4c4ae5 fix docs (#1369)
  • 81a245a Add more LDF accessor functions that Turing requires (#1366)
  • 10380bf Add accessor functions for necessary types (#1362)
  • f1c5ed3 Fix interpolation error in macro + predict on MCMCChains indexing bug (#1360)
  • 5f244a8 CompatHelper: add new compat entry for MarginalLogDensities at version 0.4 fo...
  • Additional commits viewable in compare view

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@dependabot
Bump the all-julia-packages group across 2 directories with 3 updates
b671b3e 
Updates the requirements on [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 `FlexiChains` to 0.5.9
- [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...v0.5.9)

Updates `Turing` to 0.44.3
- [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...v0.44.3)

Updates `DynamicPPL` to 0.41.6
- [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...v0.41.6)

Updates `FlexiChains` to 0.5.9
- [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...v0.5.9)

Updates `Turing` to 0.44.3
- [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...v0.44.3)

Updates `DynamicPPL` to 0.41.6
- [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...v0.41.6)

---
updated-dependencies:
- dependency-name: FlexiChains
  dependency-version: 0.5.9
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: Turing
  dependency-version: 0.44.3
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: DynamicPPL
  dependency-version: 0.41.6
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: FlexiChains
  dependency-version: 0.5.9
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: Turing
  dependency-version: 0.44.3
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: DynamicPPL
  dependency-version: 0.41.6
  dependency-type: direct:production
  dependency-group: all-julia-packages
...

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 Apr 29, 2026
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BayesianWorkflow.jl documentation for PR #19 is available at:
https://JuliaBayes.github.io/BayesianWorkflow.jl/previews/PR19/

@dependabot @github

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Superseded by #20.

@dependabot dependabot Bot closed this Apr 30, 2026
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dependabot Bot deleted the dependabot/julia/all-julia-packages-3dc5efc936 branch April 30, 2026 17:40
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