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Bump the all-julia-packages group across 2 directories with 3 updates - #29

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Bump the all-julia-packages group across 2 directories with 3 updates#29
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Updates the requirements on FlexiChains, Turing and DynamicPPL to permit the latest version.
Updates FlexiChains to 0.6.12

Release notes

Sourced from FlexiChains's releases.

v0.6.12

FlexiChains v0.6.12

Diff since v0.6.11

Added Base.merge(fs1::FlexiSummary, fs2::FlexiSummary) to merge two summaries together.

The resulting FlexiSummary will have keys that are the union of the keys of fs1 and fs2, as well as statistics that are the union of the statistics of fs1 and fs2.

In general, the value for a given key and statistic in the merged summary will be taken from fs2 if it exists, and from fs1 otherwise. For key/statistic combinations that don't exist in either summary, the value will be missing.

For the most part, it is expected that the two summaries being merged will have either the same keys (in which case this amounts to concatenation of statistics), or the same statistics (i.e., concatenation of keys).

Merged pull requests:

Closed issues:

  • Add forest plot (caterpillar plot) (#127)
  • Add ridgeline plot (#128)
  • Pin GHA workflows to commit hashes (#210)
  • merge on FlexiSummary (#227)
Changelog

Sourced from FlexiChains's changelog.

0.6.12

Added Base.merge(fs1::FlexiSummary, fs2::FlexiSummary) to merge two summaries together.

The resulting FlexiSummary will have keys that are the union of the keys of fs1 and fs2, as well as statistics that are the union of the statistics of fs1 and fs2.

In general, the value for a given key and statistic in the merged summary will be taken from fs2 if it exists, and from fs1 otherwise. For key/statistic combinations that don't exist in either summary, the value will be missing.

For the most part, it is expected that the two summaries being merged will have either the same keys (in which case this amounts to concatenation of statistics), or the same statistics (i.e., concatenation of keys).

0.6.11

Added the split_interval keyword argument to PosteriorStats.hdi(::FlexiChain) and PosteriorStats.eti(::FlexiChain), which allows you to specify that the returned interval should be split into its lower and upper bounds as separate statistics.

0.6.10

Implemented Makie-backed FlexiChains.mmeanplot and FlexiChains.mautocorplot.

0.6.9

Implemented Plots.violin for FlexiChains.

0.6.8

Implemented a Tables.jl interface for Wide(::FlexiSummary). Unlike Wide(::FlexiChain), where each parameter is given a different column, for a FlexiSummary each statistic is given a different column, and the parameters are split up into rows.

FlexiSummary itself also has a default Tables.jl interface which simply delegates to Wide.

0.6.7

Add an InferenceObjects.jl extension, which allows conversion of a FlexiChain into an InferenceObjects.InferenceData via InferenceObjects.convert_to_inference_data(chain::FlexiChain).

Add a method Base.Symbol(::ParameterOrExtra), which converts the name of the parameter (or extra) to a Symbol.

0.6.6

Add an additional constructor to convert an array of iters x chains x params into a FlexiChain. Please see the documentation for more information.

0.6.5

Minor improvements to documentation.

0.6.4

Extend the maximum width for the type column in the summary table.

0.6.3

... (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 0.41.8

Release notes

Sourced from DynamicPPL's releases.

v0.41.8

DynamicPPL v0.41.8

Diff since v0.41.7

Merged pull requests:

Closed issues:

  • Benchmark comments are duplciated (#1389)
Changelog

Sourced from DynamicPPL's changelog.

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. 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).

... (truncated)

Commits

Updates FlexiChains to 0.6.12

Release notes

Sourced from FlexiChains's releases.

v0.6.12

FlexiChains v0.6.12

Diff since v0.6.11

Added Base.merge(fs1::FlexiSummary, fs2::FlexiSummary) to merge two summaries together.

The resulting FlexiSummary will have keys that are the union of the keys of fs1 and fs2, as well as statistics that are the union of the statistics of fs1 and fs2.

In general, the value for a given key and statistic in the merged summary will be taken from fs2 if it exists, and from fs1 otherwise. For key/statistic combinations that don't exist in either summary, the value will be missing.

For the most part, it is expected that the two summaries being merged will have either the same keys (in which case this amounts to concatenation of statistics), or the same statistics (i.e., concatenation of keys).

Merged pull requests:

Closed issues:

  • Add forest plot (caterpillar plot) (#127)
  • Add ridgeline plot (#128)
  • Pin GHA workflows to commit hashes (#210)
  • merge on FlexiSummary (#227)
Changelog

Sourced from FlexiChains's changelog.

0.6.12

Added Base.merge(fs1::FlexiSummary, fs2::FlexiSummary) to merge two summaries together.

The resulting FlexiSummary will have keys that are the union of the keys of fs1 and fs2, as well as statistics that are the union of the statistics of fs1 and fs2.

In general, the value for a given key and statistic in the merged summary will be taken from fs2 if it exists, and from fs1 otherwise. For key/statistic combinations that don't exist in either summary, the value will be missing.

For the most part, it is expected that the two summaries being merged will have either the same keys (in which case this amounts to concatenation of statistics), or the same statistics (i.e., concatenation of keys).

0.6.11

Added the split_interval keyword argument to PosteriorStats.hdi(::FlexiChain) and PosteriorStats.eti(::FlexiChain), which allows you to specify that the returned interval should be split into its lower and upper bounds as separate statistics.

0.6.10

Implemented Makie-backed FlexiChains.mmeanplot and FlexiChains.mautocorplot.

0.6.9

Implemented Plots.violin for FlexiChains.

0.6.8

Implemented a Tables.jl interface for Wide(::FlexiSummary). Unlike Wide(::FlexiChain), where each parameter is given a different column, for a FlexiSummary each statistic is given a different column, and the parameters are split up into rows.

FlexiSummary itself also has a default Tables.jl interface which simply delegates to Wide.

0.6.7

Add an InferenceObjects.jl extension, which allows conversion of a FlexiChain into an InferenceObjects.InferenceData via InferenceObjects.convert_to_inference_data(chain::FlexiChain).

Add a method Base.Symbol(::ParameterOrExtra), which converts the name of the parameter (or extra) to a Symbol.

0.6.6

Add an additional constructor to convert an array of iters x chains x params into a FlexiChain. Please see the documentation for more information.

0.6.5

Minor improvements to documentation.

0.6.4

Extend the maximum width for the type column in the summary table.

0.6.3

... (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 0.41.8

Release notes

Sourced from DynamicPPL's releases.

v0.41.8

DynamicPPL v0.41.8

Diff since v0.41.7

Merged pull requests:

Closed issues:

  • Benchmark comments are duplciated (#1389)
Changelog

Sourced from DynamicPPL's changelog.

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. 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).

... (truncated)

Commits

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@dependabot
Bump the all-julia-packages group across 2 directories with 3 updates
b0af193 
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 0.6.12
- [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...v0.6.12)

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 0.41.8
- [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...v0.41.8)

Updates `FlexiChains` to 0.6.12
- [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...v0.6.12)

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 0.41.8
- [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...v0.41.8)

---
updated-dependencies:
- dependency-name: FlexiChains
  dependency-version: 0.6.12
  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: 0.41.8
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: FlexiChains
  dependency-version: 0.6.12
  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: 0.41.8
  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 May 20, 2026
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BayesianWorkflow.jl documentation for PR #29 is available at:
https://JuliaBayes.github.io/BayesianWorkflow.jl/previews/PR29/

@dependabot @github

dependabot Bot commented on behalf of github May 26, 2026

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Superseded by #30.

@dependabot dependabot Bot closed this May 26, 2026
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dependabot Bot deleted the dependabot/julia/all-julia-packages-479c8d522e branch May 26, 2026 23:23
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