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

[dependabot]

Updates the requirements on NonlinearSolveFirstOrder, Turing and StatsPlots to permit the latest version.
Updates NonlinearSolveFirstOrder to 2.0.0

Release notes

Sourced from NonlinearSolveFirstOrder's releases.

SimpleNonlinearSolve-v2.0.0

SimpleNonlinearSolve SimpleNonlinearSolve-v2.0.0

Diff since SimpleNonlinearSolve-v1.12.2

This release has been identified as a backport. Automated changelogs for backports tend to be wildly incorrect. Therefore, the list of issues and pull requests is hidden.

Commits

• 6e373b5 Bump version from 1.12.0 to 2.0.0
• e41ff6b Apply suggestion from @​ChrisRackauckas
• 358b006 Add tutorial on bound constraints for nonlinear problems
• b5f79df fixup! Add support for transforming bounded parameters into unbounded ones
• f94b51b fixup! Add support for transforming bounded parameters into unbounded ones
• 0d6d676 fixup! Add support for transforming bounded parameters into unbounded ones
• 0c54543 fixup! Add support for transforming bounded parameters into unbounded ones
• f901afb fixup! Add support for transforming bounded parameters into unbounded ones
• 7c84dd3 fixup! Add support for transforming bounded parameters into unbounded ones
• 13d9059 Add support for transforming bounded parameters into unbounded ones
• Additional commits viewable in compare view

Updates Turing to 0.43.3

Release notes

Sourced from Turing's releases.

v0.43.3

Turing v0.43.3

Diff since v0.43.2

Unify parameter initialisation for HMC and external samplers. External samplers (like HMC) now attempt multiple times to generate valid initial parameters, instead of just taking the first set of parameters.

Re-exports set_logprob_type! from DynamicPPL to allow users to control the base log-probability type used when evaluating Turing models. For example, calling set_logprob_type!(Float32) will mean that Turing will use Float32 for log-probability calculations, only promoting if there is something in the model that causes it to be (e.g. a distribution that returns Float64 log-probabilities). Note that this is a compile-time preference: for it to take effect you will have to restart your Julia session after calling set_logprob_type!.

Furthermore, note that sampler support for non-Float64 log-probabilities is currently limited. Although DynamicPPL promises not promote float types unnecessarily, many samplers, including HMC and NUTS, still use Float64 internally and thus will cause log-probabilities and parameters to be promoted to Float64, even if the model itself uses Float32.

Merged pull requests:

• Unify HMC + externalsampler + DynamicHMC parameter initialisation; re-export DynamicPPL.set_logprob_type! (#2794) (@​penelopeysm)

Closed issues:

• New Turing domain: please visit turinglang.org (#2041)
• Support for Float32 (#2212)
• Including VarNames in sampler states, etc. (#2511)
• Modifying an array input as part of struct and passing it to a softmax throws a conversion MethodError when done with ForwardDiff (#2516)
• Gibbs when a variable has no sampler (#2536)
• Dispatch error on loglikelihood when sampling from arraydist of multivariate distributions (#2549)
• Remove MH in favour of externalsampler(MH) (#2593)
• Segfault on test mcmc/Inference (#2655)
• Stop using squash commits? (#2700)
• External sampler should try harder at generating initial params (#2739)
• Remove IS 'sampler' (maybe also SGLD + SGHMC)? (#2767)
• replace ParticleMCMCContext with init strategy (#2768)
• Export Model as GraphViz, Mermaid, NetworkX and probably Cytoscape.js (#2782)
• Add convenience functionality for general Bayesian workflow methods (#2785)
• densify VNT inside ModeResult? (#2786)
• initial_params in sample won't allow me to change a variable like t[1] (#2792)

Changelog

Sourced from Turing's changelog.

0.43.3

Unify parameter initialisation for HMC and external samplers. External samplers (like HMC) now attempt multiple times to generate valid initial parameters, instead of just taking the first set of parameters.

Re-exports set_logprob_type! from DynamicPPL to allow users to control the base log-probability type used when evaluating Turing models. For example, calling set_logprob_type!(Float32) will mean that Turing will use Float32 for log-probability calculations, only promoting if there is something in the model that causes it to be (e.g. a distribution that returns Float64 log-probabilities). Note that this is a compile-time preference: for it to take effect you will have to restart your Julia session after calling set_logprob_type!.

Furthermore, note that sampler support for non-Float64 log-probabilities is currently limited. Although DynamicPPL promises not promote float types unnecessarily, many samplers, including HMC and NUTS, still use Float64 internally and thus will cause log-probabilities and parameters to be promoted to Float64, even if the model itself uses Float32.

0.43.2

Throw an ArgumentError when a Gibbs sampler is missing component samplers for any variable in the model. This check can be bypassed by passing check_model=false to sample.

0.43.1

Ignore discard_initial and thinning for SMC samplers to prevent a BoundsError when these arguments are provided.

0.43.0

DynamicPPL 0.40 and VarNamedTuple

DynamicPPL v0.40 includes a major overhaul of Turing's internal data structures. Most notably, cases where we might once have used Dict{VarName} or NamedTuple have all been replaced with a single data structure, called VarNamedTuple.

This provides substantial benefits in terms of robustness and performance.

However, it does place some constraints on Turing models, and introduces some breaking changes to the user interface. Specifically, the types of containers that can include random variables are now more limited: if x[i] ~ dist is a random variable, then x must obey the following criteria:

• They must be AbstractArrays. Dicts and other containers are currently unsupported (we have an issue to track this). If you really need this functionality, please open an issue and let us know; we can try to make it a priority.

  @model function f()
      # Allowed
      x = Array{Float64}(undef, 1)
      x[1] ~ Normal()
  # Forbidden
  x = Dict{Int,Float64}()
  return x[1] ~ Normal()
  
  end

• They must not be resized between calls to ~. The following is forbidden (you should initialise x to the correct size before the loop):

... (truncated)

Commits

• f86999b Unify HMC + externalsampler + DynamicHMC parameter initialisation; re-export ...
• 838ae6a Throw an error when Gibbs sampler is missing components for any variable (#2788)
• 33e1a22 Ignore discard_initial and thinning for SMC (#2784)
• 2211373 Twiddle API docs
• 26c0b49 Fix docs
• 94ec347 JuliaRegistrator really wants me to say 'breaking'
• 84ad63a Remove temporary optim doc in anticipation of releasing
• 158b2a0 breaking - v0.43 (#2733)
• 184d592 Remove Random.seed! from test suite, use Xoshiro/StableRNG instead (#2779)
• 7072ff1 Mark all methods with DynamicPPL.Model as produceable (#2780)
• Additional commits viewable in compare view

Updates StatsPlots to 0.15.8

Changelog

Sourced from StatsPlots's changelog.

StatsPlots.jl NEWS

0.10

• Rename package from StatPlots to StatsPlots

0.7 current version

0.7.3

• fixed out of bound error with violin and boxplot
• fixed title location in corrplot
• better handling of NaN and Inf
• recipe for hclust dendrogram (clustering visualization)
• dataviewer recipe for interactive GUIs

0.7.2

• fix stack overflow with @df and begin ... end blocks
• avoid recomputing data unnecessarily in @df

0.7.1

• remove Loess dependency
• fix hygien macro issue in @df
• add curly bracket syntax for automatic naming of groups
• add cols() to select all columns

0.7.0

• remove DataFrames dependency
• improve tick handling in correlation plots
• add support for discrete distributions
• add automatic legend with @df
• allow passing columns of a data table programmatically with cols

0.6.0

• deprecate the plot(df, :x, :y) syntax
• complete the removal of groupederror
• remove shadederror
• suppress axis labels in marginalhist

0.5.1

• remove groupederror, as that is now in it's own package
• add qqnorm and qqplot
• fix 2d density plots

0.5.0

• major reconfiguring of the support for tables:
  • change the syntax to @df mydataframe plot(:a, :b)
  • allows using DataFrames automatically in user recipes

... (truncated)

Commits

• 9db7d09 bump DS and Interp compat, + StatsPlots version (#569)
• 44200aa Merge pull request #564 from Jonas-a-Zimmermann/PermuteInsteadOfOrientation
• 1ef19e9 Update README.md
• 1c80878 Changed Orientation to Permute
• 7cfe9c1 Update README.md
• e34f65e Bump codecov/codecov-action from 3 to 4 (#548)
• bb65b7c bump Project.toml
• b1311cc Merge pull request #546 from JuliaPlots/compathelper/new_version/2023-11-25-0...
• 710d428 CompatHelper: bump compat for Interpolations to 0.15, (keep existing compat)
• 15fc66b Bump actions/checkout from 3 to 4 (#542)
• Additional commits viewable in compare view

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