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Bump the all-julia-packages group across 2 directories with 3 updates - #17

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Bump the all-julia-packages group across 2 directories with 3 updates#17
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Updates the requirements on FlexiChains, Turing and DynamicPPL to permit the latest version.
Updates FlexiChains to 0.5.8

Release notes

Sourced from FlexiChains's releases.

v0.5.8

FlexiChains v0.5.8

Diff since v0.5.7

Add compatibility with DynamicPPL 0.41 and Turing 0.44.

Changelog

Sourced from FlexiChains's changelog.

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

Added the divergences and divergences_kwargs keyword arguments to pairplot to additionally superimpose a scatter plot of divergent transitions, as well as a keyword argument args that allows passing extra positional arguments to pairplot.

0.5.0

... (truncated)

Commits

Updates Turing to 0.44.2

Release notes

Sourced from Turing's releases.

v0.44.2

Turing v0.44.2

Diff since v0.44.1

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

Merged pull requests:

Changelog

Sourced from Turing's changelog.

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

  • Turing.Inference.get_varinfo(::MyState) -> returns a VarInfo of values from your sampler's state

  • Turing.Inference.setparams_varinfo!!(::DynamicPPL.Model, ::MySampler, ::MyState, params::AbstractVarInfo) -> uses a VarInfo of values to update your sampler's state

... (truncated)

Commits

Updates DynamicPPL to 0.41.3

Release notes

Sourced from DynamicPPL's releases.

v0.41.3

DynamicPPL v0.41.3

Diff since v0.41.2

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.

Merged pull requests:

Changelog

Sourced from DynamicPPL's changelog.

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.

The reason why this is done is that the transform may in principle change between model executions. This can happen if the prior distribution of a variable depends on the value of another variable. Previously, in DynamicPPL, we always made sure to recompute the transform during model evaluation; however, this was not enforced by the data structure. The current implementation makes it impossible to accidentally use an outdated transform, and is therefore more robust.

The function DynamicPPL.get_raw_value(::TransformedValue[, ::Distribution]) has been added to simplify the extraction of the raw value from a TransformedValue. The distribution argument is only needed if the transform is DynamicLink or Unlink.

Addition of FixedTransform

The above unification allows us to introduce a new transform subtype, FixedTransform{F}, which wraps a known function F that is assumed to always be static, allowing the transform to be cached and reused across model executions. This should only be used when it is known ahead of time that the transform will never change between model executions. It is the user's responsibility to ensure that this is the case.

... (truncated)

Commits
  • 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...
  • 0a05b4c CompatHelper: add new compat entry for ADTypes at version 1 for package docs,...
  • 4559bb4 CompatHelper: add new compat entry for Chairmarks at version 1 for package do...
  • 6595674 [breaking] v0.41 (#1286)
  • ff55ee8 Fix UnlinkSome bug (#1355)
  • c636aaa Add conversion from matrix of VNTs to MCMCChains (#1351)
  • b223b83 Avoid deepcopy in RawValueAccumulator for views (#1350)
  • Additional commits viewable in compare view

Updates FlexiChains to 0.5.8

Release notes

Sourced from FlexiChains's releases.

v0.5.8

FlexiChains v0.5.8

Diff since v0.5.7

Add compatibility with DynamicPPL 0.41 and Turing 0.44.

Changelog

Sourced from FlexiChains's changelog.

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

Added the divergences and divergences_kwargs keyword arguments to pairplot to additionally superimpose a scatter plot of divergent transitions, as well as a keyword argument args that allows passing extra positional arguments to pairplot.

0.5.0

... (truncated)

Commits

Updates Turing to 0.44.2

Release notes

Sourced from Turing's releases.

v0.44.2

Turing v0.44.2

Diff since v0.44.1

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

Merged pull requests:

Changelog

Sourced from Turing's changelog.

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

  • Turing.Inference.get_varinfo(::MyState) -> returns a VarInfo of values from your sampler's state

  • Turing.Inference.setparams_varinfo!!(::DynamicPPL.Model, ::MySampler, ::MyState, params::AbstractVarInfo) -> uses a VarInfo of values to update your sampler's state

... (truncated)

Commits

Updates DynamicPPL to 0.41.3

Release notes

Sourced from DynamicPPL's releases.

v0.41.3

DynamicPPL v0.41.3

Diff since v0.41.2

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.

Merged pull requests:

Changelog

Sourced from DynamicPPL's changelog.

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.

The reason why this is done is that the transform may in principle change between model executions. This can happen if the prior distribution of a variable depends on the value of another variable. Previously, in DynamicPPL, we always made sure to recompute the transform during model evaluation; however, this was not enforced by the data structure. The current implementation makes it impossible to accidentally use an outdated transform, and is therefore more robust.

The function DynamicPPL.get_raw_value(::TransformedValue[, ::Distribution]) has been added to simplify the extraction of the raw value from a TransformedValue. The distribution argument is only needed if the transform is DynamicLink or Unlink.

Addition of FixedTransform

The above unification allows us to introduce a new transform subtype, FixedTransform{F}, which wraps a known function F that is assumed to always be static, allowing the transform to be cached and reused across model executions. This should only be used when it is known ahead of time that the transform will never change between model executions. It is the user's responsibility to ensure that this is the case.

... (truncated)

Commits
  • 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...
  • 0a05b4c CompatHelper: add new compat entry for ADTypes at version 1 for package docs,...
  • 4559bb4 CompatHelper: add new compat entry for Chairmarks at version 1 for package do...
  • 6595674 [breaking] v0.41 (#1286)
  • ff55ee8 Fix UnlinkSome bug (#1355)
  • c636aaa Add conversion from matrix of VNTs to MCMCChains (#1351)
  • b223b83 Avoid deepcopy in RawValueAccumulator for views (#1350)
  • Additional commits viewable in compare view

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@dependabot
Bump the all-julia-packages group across 2 directories with 3 updates
d70d150 
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 0.5.8
- [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...v0.5.8)

Updates `Turing` to 0.44.2
- [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...v0.44.2)

Updates `DynamicPPL` to 0.41.3
- [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...v0.41.3)

Updates `FlexiChains` to 0.5.8
- [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...v0.5.8)

Updates `Turing` to 0.44.2
- [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...v0.44.2)

Updates `DynamicPPL` to 0.41.3
- [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...v0.41.3)

---
updated-dependencies:
- dependency-name: FlexiChains
  dependency-version: 0.5.8
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: Turing
  dependency-version: 0.44.2
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: DynamicPPL
  dependency-version: 0.41.3
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: FlexiChains
  dependency-version: 0.5.8
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: Turing
  dependency-version: 0.44.2
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: DynamicPPL
  dependency-version: 0.41.3
  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 23, 2026
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BayesianWorkflow.jl documentation for PR #17 is available at:
https://JuliaBayes.github.io/BayesianWorkflow.jl/previews/PR17/

@dependabot @github

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Superseded by #18.

@dependabot dependabot Bot closed this Apr 27, 2026
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dependabot Bot deleted the dependabot/julia/all-julia-packages-55c93ee8c2 branch April 27, 2026 21:40
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