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Bump the all-julia-packages group across 1 directory with 3 updates#1510

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Bump the all-julia-packages group across 1 directory with 3 updates#1510
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Updates the requirements on LinearSolve, Turing and DataInterpolations to permit the latest version.
Updates LinearSolve to 5.1.0

Commits

Updates Turing to 0.46.0

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)

Commits

Updates DataInterpolations to 9.0.2

Release notes

Sourced from DataInterpolations's releases.

v9.0.2

DataInterpolations v9.0.2

Diff since v8.10.0

Merged pull requests:

Closed issues:

  • QuadraticSpline edge-case crashes: n=2 BoundsError, Rational t starting at 0, duplicate knots (#542)
Changelog

Sourced from DataInterpolations's changelog.

DataInterpolations v9 Release Notes

Breaking changes

  • The deprecated RegularizationTools extension and the RegularizationSmooth interpolation type have been removed. RegularizationTools was deprecated and capped Optim to ≤ 1; removing it restores support for Optim v2.

  • The assume_linear_t constructor keyword and the DataInterpolations.looks_linear utility have been removed. Knot-vector structure is now probed once at construction through FindFirstFunctions.SearchProperties(t) and cached on every interpolation as A.t_props; uniformly-spaced knots are detected exactly and automatically. To override the probe, pass the new search_properties keyword accepted by every constructor, e.g. LinearInterpolation(u, t; search_properties = FindFirstFunctions.SearchProperties(t; is_uniform = true)).

New features

  • Every interpolation constructor accepts a search_properties::Union{Nothing, FindFirstFunctions.SearchProperties} keyword. The default nothing probes t at construction; passing a pre-built SearchProperties skips the probe (useful when constructing many interpolations over the same knot vector).

  • Knot search is dispatched through FindFirstFunctions.Auto(t) resolved at construction: uniformly-spaced knots (any AbstractRange, or vectors detected as exactly uniform) use a closed-form O(1) lookup; short non-uniform knot vectors use a linear scan; everything else keeps the previous bracketed gallop.

  • LinearInterpolation with uniformly-spaced knots and floating-point values takes a statically-dispatched fast path (closed-form index + lerp, verified against the live knots) — 5-10x faster per query on uniform grids.

  • QuadraticSpline construction is now O(n) instead of O(n^2) (running locator in quadratic_spline_params), e.g. ~870x faster at 100k points.

DataInterpolations v5 Release Notes

Breaking changes

  • AbstractInterpolation is not a subtype of AbstractVector anymore. This was needed for previous versions of ModelingToolkit.jl to represent splines as vectors.

  • Indexing overloads for AbstractInterpolation and the type parameter associated with it are removed. For example - A is an interpolation object:

    • Doing A[i] will error. Use A.u[i].
    • size(A) will error. Use size(A.u) or size(A.t).

  • Removed deprecated bindings for ZeroSpline which is the same as ConstantInterpolation.

DataInterpolations v6 Release Notes

Breaking changes

  • SciML/DataInterpolations.jl#274 introduced caching of parameters for interpolations (released in v5.3) and also introduced a field safetycopy which was a boolean flag to create a copy of the data as the parameters would be invalid if data is mutated. This was removed in SciML/DataInterpolations.jl#315 to introduce cache_parameters which made it explicit if a user wants to opt in for parameter caching or not.
Commits

@dependabot dependabot Bot added dependencies Pull requests that update a dependency file julia Pull requests that update julia code labels Jul 14, 2026
@dependabot
dependabot Bot force-pushed the dependabot/julia/docs/all-julia-packages-de26bdca6b branch 2 times, most recently from b591482 to 0853439 Compare July 23, 2026 00:27
@dependabot
Bump the all-julia-packages group across 1 directory with 3 updates
74a3174 
Updates the requirements on [LinearSolve](https://github.com/SciML/LinearSolve.jl), [Turing](https://github.com/TuringLang/Turing.jl) and [DataInterpolations](https://github.com/SciML/DataInterpolations.jl) to permit the latest version.

Updates `LinearSolve` to 5.1.0
- [Release notes](https://github.com/SciML/LinearSolve.jl/releases)
- [Commits](https://github.com/SciML/LinearSolve.jl/commits)

Updates `Turing` to 0.46.0
- [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)

Updates `DataInterpolations` to 9.0.2
- [Release notes](https://github.com/SciML/DataInterpolations.jl/releases)
- [Changelog](https://github.com/SciML/DataInterpolations.jl/blob/master/NEWS.md)
- [Commits](https://github.com/SciML/DataInterpolations.jl/commits/v9.0.2)

---
updated-dependencies:
- dependency-name: DataInterpolations
  dependency-version: 9.0.2
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: LinearSolve
  dependency-version: 5.0.0
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: Turing
  dependency-version: 0.46.0
  dependency-type: direct:production
  dependency-group: all-julia-packages
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot
dependabot Bot force-pushed the dependabot/julia/docs/all-julia-packages-de26bdca6b branch from 0853439 to 74a3174 Compare July 24, 2026 00:27
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