TuringLang · penelopeysm · Mar 18, 2025 · Jan 21, 2025 · Jan 21, 2025 · Jan 21, 2025
diff --git a/HISTORY.md b/HISTORY.md
@@ -23,6 +23,22 @@ Turing.jl v0.37 uses DynamicPPL v0.35, which brings with it several breaking cha
 
 For more details about all of the above, see the changelog of DynamicPPL [here](https://github.com/TuringLang/DynamicPPL.jl/releases/tag/v0.35.0).
 
+### Export list
+
+Turing.jl's export list has been cleaned up a fair bit. This affects what is imported into your namespace when you do an unqualified `using Turing`. You may need to import things more explicitly than before.
+
+  - The `DynamicPPL` and `AbstractMCMC` modules are no longer exported. You will need to `import DynamicPPL` or `using DynamicPPL: DynamicPPL` (likewise `AbstractMCMC`) yourself, which in turn means that they have to be made available in your project environment.
+
+  - `@logprob_str` and `@prob_str` have been removed following a long deprecation period.
+  - We no longer re-export everything from `Bijectors` and `Libtask`. To get around this, add `using Bijectors` or `using Libtask` at the top of your script (but we recommend using more selective imports).
+
+      + We no longer export `Bijectors.ordered`. If you were using `ordered`, even Bijectors does not (currently) export this. You will have to manually import it with `using Bijectors: ordered`.
+
+On the other hand, we have added a few more exports:
+
+  - `DynamicPPL.returned` and `DynamicPPL.prefix` are exported (for use with submodels).
+  - `LinearAlgebra.I` is exported for convenience.
+
 # Release 0.36.0
 
 ## Breaking changes

diff --git a/Project.toml b/Project.toml
@@ -58,7 +58,7 @@ BangBang = "0.4.2"
 Bijectors = "0.14, 0.15"
 Compat = "4.15.0"
 DataStructures = "0.18"
-Distributions = "0.23.3, 0.24, 0.25"
+Distributions = "0.25.77"
 DistributionsAD = "0.6"
 DocStringExtensions = "0.8, 0.9"
 DynamicHMC = "3.4"

diff --git a/docs/Project.toml b/docs/Project.toml
@@ -1,7 +1,4 @@
 [deps]
-Bijectors = "76274a88-744f-5084-9051-94815aaf08c4"
-Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 DocumenterInterLinks = "d12716ef-a0f6-4df4-a9f1-a5a34e75c656"
-DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8"
 Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
diff --git a/docs/make.jl b/docs/make.jl
@@ -1,35 +1,32 @@
 using Documenter
 using Turing
-# Need to import Distributions and Bijectors to generate docs for functions
-# from those packages.
-using Distributions
-using Bijectors
-using DynamicPPL
 
 using DocumenterInterLinks
 
 links = InterLinks(
     "DynamicPPL" => "https://turinglang.org/DynamicPPL.jl/stable/objects.inv",
-    "AbstractPPL" => "https://turinglang.org/AbstractPPL.jl/dev/objects.inv",
+    "AbstractPPL" => "https://turinglang.org/AbstractPPL.jl/stable/objects.inv",
+    "LinearAlgebra" => "https://docs.julialang.org/en/v1/objects.inv",
+    "AbstractMCMC" => "https://turinglang.org/AbstractMCMC.jl/stable/objects.inv",
     "ADTypes" => "https://sciml.github.io/ADTypes.jl/stable/objects.inv",
     "AdvancedVI" => "https://turinglang.org/AdvancedVI.jl/v0.2.8/objects.inv",
     "DistributionsAD" => "https://turinglang.org/DistributionsAD.jl/stable/objects.inv",
+    "OrderedCollections" => "https://juliacollections.github.io/OrderedCollections.jl/stable/objects.inv",
 )
 
 # Doctest setup
 DocMeta.setdocmeta!(Turing, :DocTestSetup, :(using Turing); recursive=true)
 
 makedocs(;
     sitename="Turing",
-    modules=[Turing, Distributions, Bijectors],
+    modules=[Turing],
     pages=[
         "Home" => "index.md",
         "API" => "api.md",
         "Submodule APIs" =>
             ["Inference" => "api/Inference.md", "Optimisation" => "api/Optimisation.md"],
     ],
     checkdocs=:exports,
-    # checkdocs_ignored_modules=[Turing, Distributions, DynamicPPL, AbstractPPL, Bijectors],
     doctest=false,
     warnonly=true,
     plugins=[links],

diff --git a/docs/src/api.md b/docs/src/api.md
@@ -6,15 +6,13 @@ Turing.jl directly re-exports the entire public API of the following packages:
 
   - [Distributions.jl](https://juliastats.org/Distributions.jl)
   - [MCMCChains.jl](https://turinglang.org/MCMCChains.jl)
-  - [AbstractMCMC.jl](https://turinglang.org/AbstractMCMC.jl)
-  - [Bijectors.jl](https://turinglang.org/Bijectors.jl)
-  - [Libtask.jl](https://github.com/TuringLang/Libtask.jl)
 
 Please see the individual packages for their documentation.
 
 ## Individual exports and re-exports
 
-**All** of the following symbols are exported unqualified by Turing, even though the documentation suggests that many of them are qualified.
+In this API documentation, for the sake of clarity, we have listed the module that actually defines each of the exported symbols.
+Note, however, that **all** of the following symbols are exported unqualified by Turing.
 That means, for example, you can just write
 
 ```julia
@@ -37,17 +35,22 @@ even though [`Prior()`](@ref) is actually defined in the `Turing.Inference` modu
 
 ### Modelling
 
-| Exported symbol | Documentation                       | Description                                  |
-|:--------------- |:----------------------------------- |:-------------------------------------------- |
-| `@model`        | [`DynamicPPL.@model`](@extref)      | Define a probabilistic model                 |
-| `@varname`      | [`AbstractPPL.@varname`](@extref)   | Generate a `VarName` from a Julia expression |
-| `to_submodel`   | [`DynamicPPL.to_submodel`](@extref) | Define a submodel                            |
+| Exported symbol      | Documentation                              | Description                                                                                  |
+|:-------------------- |:------------------------------------------ |:-------------------------------------------------------------------------------------------- |
+| `@model`             | [`DynamicPPL.@model`](@extref)             | Define a probabilistic model                                                                 |
+| `@varname`           | [`AbstractPPL.@varname`](@extref)          | Generate a `VarName` from a Julia expression                                                 |
+| `to_submodel`        | [`DynamicPPL.to_submodel`](@extref)        | Define a submodel                                                                            |
+| `prefix`             | [`DynamicPPL.prefix`](@extref)             | Prefix all variable names in a model with a given symbol                                     |
+| `LogDensityFunction` | [`DynamicPPL.LogDensityFunction`](@extref) | A struct containing all information about how to evaluate a model. Mostly for advanced users |
 
 ### Inference
 
-| Exported symbol | Documentation                                                                                    | Description         |
-|:--------------- |:------------------------------------------------------------------------------------------------ |:------------------- |
-| `sample`        | [`StatsBase.sample`](https://turinglang.org/AbstractMCMC.jl/stable/api/#Sampling-a-single-chain) | Sample from a model |
+| Exported symbol   | Documentation                                                                                    | Description                        |
+|:----------------- |:------------------------------------------------------------------------------------------------ |:---------------------------------- |
+| `sample`          | [`StatsBase.sample`](https://turinglang.org/AbstractMCMC.jl/stable/api/#Sampling-a-single-chain) | Sample from a model                |
+| `MCMCThreads`     | [`AbstractMCMC.MCMCThreads`](@extref)                                                            | Run MCMC using multiple threads    |
+| `MCMCDistributed` | [`AbstractMCMC.MCMCDistributed`](@extref)                                                        | Run MCMC using multiple processes  |
+| `MCMCSerial`      | [`AbstractMCMC.MCMCSerial`](@extref)                                                             | Run MCMC using without parallelism |
 
 ### Samplers
 
@@ -68,6 +71,7 @@ even though [`Prior()`](@ref) is actually defined in the `Turing.Inference` modu
 | `SMC`                | [`Turing.Inference.SMC`](@ref)                | Sequential Monte Carlo                                              |
 | `PG`                 | [`Turing.Inference.PG`](@ref)                 | Particle Gibbs                                                      |
 | `CSMC`               | [`Turing.Inference.CSMC`](@ref)               | The same as PG                                                      |
+| `RepeatSampler`      | [`Turing.Inference.RepeatSampler`](@ref)      | A sampler that runs multiple times on the same variable             |
 | `externalsampler`    | [`Turing.Inference.externalsampler`](@ref)    | Wrap an external sampler for use in Turing                          |
 
 ### Variational inference
@@ -108,52 +112,37 @@ OrderedLogistic
 LogPoisson
 ```
 
-`BernoulliLogit` is part of Distributions.jl since version 0.25.77.
-If you are using an older version of Distributions where this isn't defined, Turing will export the same distribution.
-
-```@docs
-Distributions.BernoulliLogit
-```
-
 ### Tools to work with distributions
 
 | Exported symbol | Documentation                          | Description                                                    |
 |:--------------- |:-------------------------------------- |:-------------------------------------------------------------- |
+| `I`             | [`LinearAlgebra.I`](@extref)           | Identity matrix                                                |
 | `filldist`      | [`DistributionsAD.filldist`](@extref)  | Create a product distribution from a distribution and integers |
 | `arraydist`     | [`DistributionsAD.arraydist`](@extref) | Create a product distribution from an array of distributions   |
 | `NamedDist`     | [`DynamicPPL.NamedDist`](@extref)      | A distribution that carries the name of the variable           |
 
 ### Predictions
 
-```@docs
-DynamicPPL.predict
-```
+| Exported symbol | Documentation                                                                     | Description                                             |
+|:--------------- |:--------------------------------------------------------------------------------- |:------------------------------------------------------- |
+| `predict`       | [`StatsAPI.predict`](https://turinglang.org/DynamicPPL.jl/stable/api/#Predicting) | Generate samples from posterior predictive distribution |
 
 ### Querying model probabilities and quantities
 
 Please see the [generated quantities](https://turinglang.org/docs/tutorials/usage-generated-quantities/) and [probability interface](https://turinglang.org/docs/tutorials/usage-probability-interface/) guides for more information.
 
-| Exported symbol            | Documentation                                                                                                                     | Description                                                     |
-|:-------------------------- |:--------------------------------------------------------------------------------------------------------------------------------- |:--------------------------------------------------------------- |
-| `generated_quantities`     | [`DynamicPPL.generated_quantities`](@extref)                                                                                      | Calculate additional quantities defined in a model              |
-| `pointwise_loglikelihoods` | [`DynamicPPL.pointwise_loglikelihoods`](@extref)                                                                                  | Compute log likelihoods for each sample in a chain              |
-| `logprior`                 | [`DynamicPPL.logprior`](@extref)                                                                                                  | Compute log prior probability                                   |
-| `logjoint`                 | [`DynamicPPL.logjoint`](@extref)                                                                                                  | Compute log joint probability                                   |
-| `LogDensityFunction`       | [`DynamicPPL.LogDensityFunction`](@extref)                                                                                        | Wrap a Turing model to satisfy LogDensityFunctions.jl interface |
-| `condition`                | [`AbstractPPL.condition`](@extref)                                                                                                | Condition a model on data                                       |
-| `decondition`              | [`AbstractPPL.decondition`](@extref)                                                                                              | Remove conditioning on data                                     |
-| `conditioned`              | [`DynamicPPL.conditioned`](@extref)                                                                                               | Return the conditioned values of a model                        |
-| `fix`                      | [`DynamicPPL.fix`](@extref)                                                                                                       | Fix the value of a variable                                     |
-| `unfix`                    | [`DynamicPPL.unfix`](@extref)                                                                                                     | Unfix the value of a variable                                   |
-| `OrderedDict`              | [`OrderedCollections.OrderedDict`](https://juliacollections.github.io/OrderedCollections.jl/dev/ordered_containers/#OrderedDicts) | An ordered dictionary                                           |
-
-### Extra re-exports from Bijectors
-
-Note that Bijectors itself does not export `ordered`.
-
-```@docs
-Bijectors.ordered
-```
+| Exported symbol            | Documentation                                                                                                                | Description                                        |
+|:-------------------------- |:---------------------------------------------------------------------------------------------------------------------------- |:-------------------------------------------------- |
+| `returned`                 | [`DynamicPPL.returned`](https://turinglang.org/DynamicPPL.jl/stable/api/#DynamicPPL.returned-Tuple%7BModel,%20NamedTuple%7D) | Calculate additional quantities defined in a model |
+| `pointwise_loglikelihoods` | [`DynamicPPL.pointwise_loglikelihoods`](@extref)                                                                             | Compute log likelihoods for each sample in a chain |
+| `logprior`                 | [`DynamicPPL.logprior`](@extref)                                                                                             | Compute log prior probability                      |
+| `logjoint`                 | [`DynamicPPL.logjoint`](@extref)                                                                                             | Compute log joint probability                      |
+| `condition`                | [`AbstractPPL.condition`](@extref)                                                                                           | Condition a model on data                          |
+| `decondition`              | [`AbstractPPL.decondition`](@extref)                                                                                         | Remove conditioning on data                        |
+| `conditioned`              | [`DynamicPPL.conditioned`](@extref)                                                                                          | Return the conditioned values of a model           |
+| `fix`                      | [`DynamicPPL.fix`](@extref)                                                                                                  | Fix the value of a variable                        |
+| `unfix`                    | [`DynamicPPL.unfix`](@extref)                                                                                                | Unfix the value of a variable                      |
+| `OrderedDict`              | [`OrderedCollections.OrderedDict`](@extref)                                                                                  | An ordered dictionary                              |
 
 ### Point estimates
 

diff --git a/src/Turing.jl b/src/Turing.jl
@@ -4,22 +4,24 @@ using Reexport, ForwardDiff
 using DistributionsAD, Bijectors, StatsFuns, SpecialFunctions
 using Statistics, LinearAlgebra
 using Libtask
-@reexport using Distributions, MCMCChains, Libtask, AbstractMCMC, Bijectors
+@reexport using Distributions, MCMCChains
 using Compat: pkgversion
 
 using AdvancedVI: AdvancedVI
-using DynamicPPL: DynamicPPL, LogDensityFunction
+using DynamicPPL: DynamicPPL
 import DynamicPPL: NoDist, NamedDist
 using LogDensityProblems: LogDensityProblems
 using NamedArrays: NamedArrays
 using Accessors: Accessors
 using StatsAPI: StatsAPI
 using StatsBase: StatsBase
+using AbstractMCMC
 
 using Accessors: Accessors
 
 using Printf: Printf
 using Random: Random
+using LinearAlgebra: I
 
 using ADTypes: ADTypes
 
@@ -64,75 +66,91 @@ include("deprecated.jl") # to be removed in the next minor version release
 using DynamicPPL:
     pointwise_loglikelihoods,
     generated_quantities,
+    returned,
     logprior,
     logjoint,
     condition,
     decondition,
     fix,
     unfix,
     conditioned,
-    to_submodel
+    to_submodel,
+    LogDensityFunction
 using StatsBase: predict
-using Bijectors: ordered
 using OrderedCollections: OrderedDict
 
 # Turing essentials - modelling macros and inference algorithms
-export @model,                 # modelling
+export
+    # DEPRECATED
+    @submodel,
+    generated_quantities,
+    # Modelling - AbstractPPL and DynamicPPL
+    @model,
     @varname,
-    @submodel,  # Deprecated
     to_submodel,
-    DynamicPPL,
-    Prior,                  # Sampling from the prior
-    MH,                     # classic sampling
+    prefix,
+    LogDensityFunction,
+    # Sampling - AbstractMCMC
+    sample,
+    MCMCThreads,
+    MCMCDistributed,
+    MCMCSerial,
+    # Samplers - Turing.Inference
+    Prior,
+    MH,
     Emcee,
     ESS,
     Gibbs,
-    HMC,                    # Hamiltonian-like sampling
+    HMC,
     SGLD,
     SGHMC,
+    PolynomialStepsize,
     HMCDA,
     NUTS,
-    PolynomialStepsize,
-    IS,                     # particle-based sampling
+    IS,
     SMC,
-    CSMC,
     PG,
+    CSMC,
     RepeatSampler,
-    vi,                     # variational inference
-    ADVI,
-    sample,                 # inference
-    @logprob_str,  # TODO: Remove, see https://github.com/TuringLang/DynamicPPL.jl/issues/356
-    @prob_str,     # TODO: Remove, see https://github.com/TuringLang/DynamicPPL.jl/issues/356
     externalsampler,
-    AutoForwardDiff,        # ADTypes
+    # Variational inference - AdvancedVI
+    vi,
+    ADVI,
+    # ADTypes
+    AutoForwardDiff,
     AutoReverseDiff,
     AutoMooncake,
-    setprogress!,           # debugging
+    # Debugging - Turing
+    setprogress!,
+    # Distributions
     Flat,
     FlatPos,
     BinomialLogit,
-    BernoulliLogit,         # Part of Distributions >= 0.25.77
     OrderedLogistic,
     LogPoisson,
-    filldist,
-    arraydist,
-    NamedDist,              # Exports from DynamicPPL
+    # Tools to work with Distributions
+    I,  # LinearAlgebra
+    filldist,  # DistributionsAD
+    arraydist,  # DistributionsAD
+    NamedDist,  # DynamicPPL
+    # Predictions - DynamicPPL
     predict,
+    # Querying model probabilities - DynamicPPL
+    returned,
     pointwise_loglikelihoods,
-    generated_quantities,
     logprior,
+    loglikelihood,
     logjoint,
-    LogDensityFunction,
     condition,
     decondition,
+    conditioned,
     fix,
     unfix,
-    conditioned,
-    OrderedDict,
-    ordered,                # Exports from Bijectors
+    OrderedDict, # OrderedCollections
+    # Point estimates - Turing.Optimisation
+    # The MAP and MLE exports are only needed for the Optim.jl interface.
     maximum_a_posteriori,
     maximum_likelihood,
-    # The MAP and MLE exports are only needed for the Optim.jl interface.
     MAP,
     MLE