Skip to content

Bump the all-julia-packages group across 2 directories with 2 updates - #184

Open
dependabot[bot] wants to merge 1 commit into
mainfrom
dependabot/julia/GeneralisedFilters/all-julia-packages-a3807fa5cf
Open

Bump the all-julia-packages group across 2 directories with 2 updates#184
dependabot[bot] wants to merge 1 commit into
mainfrom
dependabot/julia/GeneralisedFilters/all-julia-packages-a3807fa5cf

Conversation

@dependabot

@dependabot dependabot Bot commented on behalf of github Jul 19, 2026 •
edited
Loading

Copy link
Copy Markdown
Contributor

Updates the requirements on Turing and DynamicPPL to permit the latest version.
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
  • 98e9773 Release 0.46.0 (#2846)
  • cdded58 Bump actions/checkout from 6 to 7 in the all-github-actions-packages group (#...
  • abde704 Bump codecov/codecov-action from 6 to 7 in the all-github-actions-packages gr...
  • f6f9d13 skip codecov upload on Dependabot PRs (#2843)
  • 56793a9 CompatHelper: bump compat for DynamicPPL to 0.42, (keep existing compat) (#2832)
  • 00e8f1d Bump the all-github-actions-packages group with 4 updates (#2839)
  • 602a69f Replace CompatHelper with Dependabot (#2838)
  • 9876626 CompatHelper: bump compat for OrderedCollections to 2, (keep existing compat)...
  • 211a3ec CompatHelper: bump compat for StatsFuns to 2 for package test, (keep existing...
  • e98c291 CompatHelper: bump compat for AbstractPPL to 0.15 for package test, (keep exi...
  • Additional commits viewable in compare view

Updates DynamicPPL to 0.42.2

Release notes

Sourced from DynamicPPL's releases.

v0.42.2

DynamicPPL v0.42.2

Diff since v0.42.1

Widened the Mooncake compat bound to 0.4.147, 0.5, 0.6. Mooncake 0.6 is a breaking release (forward-mode redesign), but the reverse-mode rule API DynamicPPLMooncakeExt uses (Mooncake.@zero_derivative) and the prepared-cache API it relies on via AbstractPPL are unchanged, so no code changes were needed.

Merged pull requests:

Closed issues:

  • Introduce an API submodule to make clear what APIs are planned to be supported for programmatic access of models (#656)
  • Are models always "constant" (from DI's perspective)? (#856)
Changelog

Sourced from DynamicPPL's changelog.

0.42.2

Widened the Mooncake compat bound to 0.4.147, 0.5, 0.6. Mooncake 0.6 is a breaking release (forward-mode redesign), but the reverse-mode rule API DynamicPPLMooncakeExt uses (Mooncake.@zero_derivative) and the prepared-cache API it relies on via AbstractPPL are unchanged, so no code changes were needed.

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.

... (truncated)

Commits
  • 439073e Release 0.42.2: HISTORY.md entry and missed Mooncake compat (#1437)
  • 551ab58 Bump the all-julia-packages group across 7 directories with 12 updates (#1435)
  • a49099c Allow Mooncake 0.6 (#1436)
  • 5989b33 Add documentation on partial specification of a multivariate variable (#2239)...
  • d7e84ce Bump actions/checkout from 6 to 7 in the all-github-actions-packages group (#...
  • 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)
  • Additional commits viewable in compare view

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
  • 98e9773 Release 0.46.0 (#2846)
  • cdded58 Bump actions/checkout from 6 to 7 in the all-github-actions-packages group (#...
  • abde704 Bump codecov/codecov-action from 6 to 7 in the all-github-actions-packages gr...
  • f6f9d13 skip codecov upload on Dependabot PRs (#2843)
  • 56793a9 CompatHelper: bump compat for DynamicPPL to 0.42, (keep existing compat) (#2832)
  • 00e8f1d Bump the all-github-actions-packages group with 4 updates (#2839)
  • 602a69f Replace CompatHelper with Dependabot (#2838)
  • 9876626 CompatHelper: bump compat for OrderedCollections to 2, (keep existing compat)...
  • 211a3ec CompatHelper: bump compat for StatsFuns to 2 for package test, (keep existing...
  • e98c291 CompatHelper: bump compat for AbstractPPL to 0.15 for package test, (keep exi...
  • Additional commits viewable in compare view

Updates DynamicPPL to 0.42.2

Release notes

Sourced from DynamicPPL's releases.

v0.42.2

DynamicPPL v0.42.2

Diff since v0.42.1

Widened the Mooncake compat bound to 0.4.147, 0.5, 0.6. Mooncake 0.6 is a breaking release (forward-mode redesign), but the reverse-mode rule API DynamicPPLMooncakeExt uses (Mooncake.@zero_derivative) and the prepared-cache API it relies on via AbstractPPL are unchanged, so no code changes were needed.

Merged pull requests:

Closed issues:

  • Introduce an API submodule to make clear what APIs are planned to be supported for programmatic access of models (#656)
  • Are models always "constant" (from DI's perspective)? (#856)
Changelog

Sourced from DynamicPPL's changelog.

0.42.2

Widened the Mooncake compat bound to 0.4.147, 0.5, 0.6. Mooncake 0.6 is a breaking release (forward-mode redesign), but the reverse-mode rule API DynamicPPLMooncakeExt uses (Mooncake.@zero_derivative) and the prepared-cache API it relies on via AbstractPPL are unchanged, so no code changes were needed.

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.

... (truncated)

Commits
  • 439073e Release 0.42.2: HISTORY.md entry and missed Mooncake compat (#1437)
  • 551ab58 Bump the all-julia-packages group across 7 directories with 12 updates (#1435)
  • a49099c Allow Mooncake 0.6 (#1436)
  • 5989b33 Add documentation on partial specification of a multivariate variable (#2239)...
  • d7e84ce Bump actions/checkout from 6 to 7 in the all-github-actions-packages group (#...
  • 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)
  • Additional commits viewable in compare view

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

  • @dependabot rebase will rebase this PR
  • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
  • @dependabot show <dependency name> ignore conditions will show all of the ignore conditions of the specified dependency
  • @dependabot ignore <dependency name> major version will close this group update PR and stop Dependabot creating any more for the specific dependency's major version (unless you unignore this specific dependency's major version or upgrade to it yourself)
  • @dependabot ignore <dependency name> minor version will close this group update PR and stop Dependabot creating any more for the specific dependency's minor version (unless you unignore this specific dependency's minor version or upgrade to it yourself)
  • @dependabot ignore <dependency name> will close this group update PR and stop Dependabot creating any more for the specific dependency (unless you unignore this specific dependency or upgrade to it yourself)
  • @dependabot unignore <dependency name> will remove all of the ignore conditions of the specified dependency
  • @dependabot unignore <dependency name> <ignore condition> will remove the ignore condition of the specified dependency and ignore conditions

@dependabot
Bump the all-julia-packages group across 2 directories with 2 updates
1a375f7 
Updates the requirements on [Turing](https://github.com/TuringLang/Turing.jl) and [DynamicPPL](https://github.com/TuringLang/DynamicPPL.jl) to permit the latest version.

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](TuringLang/Turing.jl@v0.43.0...v0.46.0)

Updates `DynamicPPL` to 0.42.2
- [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...v0.42.2)

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](TuringLang/Turing.jl@v0.43.0...v0.46.0)

Updates `DynamicPPL` to 0.42.2
- [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...v0.42.2)

---
updated-dependencies:
- dependency-name: Turing
  dependency-version: 0.46.0
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: DynamicPPL
  dependency-version: 0.42.2
  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
- dependency-name: DynamicPPL
  dependency-version: 0.42.2
  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 Jul 19, 2026
@github-actions

github-actions Bot commented Jul 19, 2026

Copy link
Copy Markdown
Contributor

SSMProblems.jl/SSMProblems documentation for PR #184 is available at:
https://TuringLang.github.io/SSMProblems.jl/SSMProblems/previews/PR184/

@github-actions

github-actions Bot commented Jul 19, 2026

Copy link
Copy Markdown
Contributor

SSMProblems.jl/GeneralisedFilters documentation for PR #184 is available at:
https://TuringLang.github.io/SSMProblems.jl/GeneralisedFilters/previews/PR184/

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Reviewers

No reviews

Assignees

No one assigned

Labels

dependencies Pull requests that update a dependency file julia Pull requests that update julia code

Projects

None yet

Milestone

No milestone

Development

Successfully merging this pull request may close these issues.

0 participants