TuringLang · ArpanC6 · May 9, 2026 · May 9, 2026 · May 9, 2026 · May 9, 2026
diff --git a/AGENTS.md b/AGENTS.md
@@ -28,7 +28,7 @@ DynamicPPL builds on AbstractPPL.jl for shared PPL interfaces such as `VarName`,
   - `VarName` (AbstractPPL): address for model variables, including nested fields/indices.
   - `VarNamedTuple` (`src/varnamedtuple.jl`): named-tuple-like parameter storage keyed by `VarName`.
   - `LogDensityFunction` (`src/logdensityfunction.jl`): bridge from named parameters to flat `AbstractVector{<:Real}` for samplers, optimisers, and AD via LogDensityProblems.jl.
-  - `ext/`: `DynamicPPLForwardDiffExt`, `DynamicPPLMooncakeExt`, `DynamicPPLReverseDiffExt`, `DynamicPPLEnzymeCoreExt`, `DynamicPPLComponentArraysExt`, `DynamicPPLMCMCChainsExt`, and `DynamicPPLMarginalLogDensitiesExt`.
+  - `ext/`: `DynamicPPLForwardDiffExt`, `DynamicPPLMooncakeExt`, `DynamicPPLReverseDiffExt`, `DynamicPPLEnzymeCoreExt`, `DynamicPPLComponentArraysExt`, and `DynamicPPLMCMCChainsExt`.
   - `DynamicPPL.TestUtils`: analytical test models (`logprior_true`, `loglikelihood_true`, etc.), `run_ad`, `ADResult`.
 
 ## DynamicPPL Invariants

diff --git a/Project.toml b/Project.toml
@@ -35,7 +35,6 @@ EnzymeCore = "f151be2c-9106-41f4-ab19-57ee4f262869"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 KernelAbstractions = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
-MarginalLogDensities = "f0c3360a-fb8d-11e9-1194-5521fd7ee392"
 Mooncake = "da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"
 ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
 
@@ -44,7 +43,6 @@ DynamicPPLComponentArraysExt = ["ComponentArrays"]
 DynamicPPLEnzymeCoreExt = ["EnzymeCore"]
 DynamicPPLForwardDiffExt = ["ForwardDiff"]
 DynamicPPLMCMCChainsExt = ["MCMCChains"]
-DynamicPPLMarginalLogDensitiesExt = ["MarginalLogDensities"]
 DynamicPPLMooncakeExt = ["Mooncake", "DifferentiationInterface"]
 DynamicPPLReverseDiffExt = ["ReverseDiff"]
 
@@ -71,7 +69,6 @@ LinearAlgebra = "1.6"
 LogDensityProblems = "2"
 MCMCChains = "6, 7"
 MacroTools = "0.5.6"
-MarginalLogDensities = "0.4.3"
 Mooncake = "0.4.147, 0.5"
 OrderedCollections = "1"
 PartitionedDistributions = "0.0.1"

diff --git a/docs/make.jl b/docs/make.jl
@@ -11,7 +11,6 @@ using AbstractPPL
 using Distributions
 using DocumenterMermaid
 using MCMCChains
-using MarginalLogDensities
 using AbstractMCMC: AbstractMCMC
 using Random
 
@@ -20,7 +19,6 @@ DocMeta.setdocmeta!(
     DynamicPPL, :DocTestSetup, :(using DynamicPPL, MCMCChains); recursive=true
 )
 # Need this to document a method which uses a type inside the extension
-DPPLMLDExt = Base.get_extension(DynamicPPL, :DynamicPPLMarginalLogDensitiesExt)
 
 links = InterLinks("AbstractPPL" => "https://turinglang.org/AbstractPPL.jl/stable/")
 
@@ -31,11 +29,7 @@ makedocs(;
     format=Documenter.HTML(;
         size_threshold=2^10 * 400, mathengine=Documenter.HTMLWriter.MathJax3()
     ),
-    modules=[
-        DynamicPPL,
-        Base.get_extension(DynamicPPL, :DynamicPPLMCMCChainsExt),
-        Base.get_extension(DynamicPPL, :DynamicPPLMarginalLogDensitiesExt),
-    ],
+    modules=[DynamicPPL, Base.get_extension(DynamicPPL, :DynamicPPLMCMCChainsExt)],
     pages=[
         "Home" => "index.md",
         "Contributor onboarding" => "onboarding.md",

diff --git a/docs/src/api.md b/docs/src/api.md
@@ -158,18 +158,6 @@ When using `predict` with `MCMCChains.Chains`, you can control which variables a
   - `include_all=false` (default): Include only newly predicted variables
   - `include_all=true`: Include both parameters from the original chain and predicted variables
 
-## Marginalisation
-
-DynamicPPL provides the `marginalize` function to marginalise out variables from a model.
-This requires `MarginalLogDensities.jl` to be loaded in your environment.
-
-```@docs
-marginalize
-```
-
-A `MarginalLogDensity` object acts as a function which maps non-marginalised parameter values to a marginal log-probability.
-To retrieve a VarInfo object from it, you can use [`InitFromVector`](@ref).
-
 ## Models within models
 
 One can include models and call another model inside the model function with `left ~ to_submodel(model)`.

diff --git a/docs/src/init.md b/docs/src/init.md
@@ -24,7 +24,6 @@ InitFromParams(::ParamsWithStats, ::Union{Nothing,AbstractInitStrategy})
 InitFromUniform
 InitFromVector
 InitFromVector(::AbstractVector{<:Real}, ::LogDensityFunction)
-InitFromVector(::MarginalLogDensities.MarginalLogDensity{<:DPPLMLDExt.LogDensityFunctionWrapper}, ::Union{AbstractVector,Nothing})
 ```
 
 However, sometimes you will need to implement your own initialisation strategy.

diff --git a/ext/DynamicPPLMarginalLogDensitiesExt.jl b/ext/DynamicPPLMarginalLogDensitiesExt.jl
diff --git a/src/DynamicPPL.jl b/src/DynamicPPL.jl
@@ -211,7 +211,6 @@ export AbstractVarInfo,
     @addlogprob!,
     check_model,
     set_logprob_type!,
-    marginalize,
     # Deprecated.
     generated_quantities,
     typed_identity
@@ -282,29 +281,8 @@ using .DebugUtils
 include("test_utils.jl")
 
 include("deprecated.jl")
-
-# Extended in MarginalLogDensitiesExt
+# Extended in MarginalLogDensities.jl
 function marginalize end
-if isdefined(Base.Experimental, :register_error_hint)
-    function __init__()
-        # Same for MarginalLogDensities.jl
-        Base.Experimental.register_error_hint(MethodError) do io, exc, argtypes, _
-            requires_mld =
-                exc.f === DynamicPPL.marginalize &&
-                length(argtypes) == 2 &&
-                argtypes[1] <: Model &&
-                argtypes[2] <: AbstractVector{<:Union{Symbol,<:VarName}}
-            if requires_mld
-                printstyled(
-                    io,
-                    "\n\n    `$(exc.f)` requires MarginalLogDensities.jl to be loaded.\n    Please run `using MarginalLogDensities` before calling `$(exc.f)`.\n";
-                    color=:cyan,
-                    bold=true,
-                )
-            end
-        end
-    end
-end
 
 # Standard tag: Improves stacktraces
 # Ref: https://www.stochasticlifestyle.com/improved-forwarddiff-jl-stacktraces-with-package-tags/

diff --git a/test/Project.toml b/test/Project.toml
@@ -19,7 +19,6 @@ InvertedIndices = "41ab1584-1d38-5bbf-9106-f11c6c58b48f"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
-MarginalLogDensities = "f0c3360a-fb8d-11e9-1194-5521fd7ee392"
 Mooncake = "da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"
 OffsetArrays = "6fe1bfb0-de20-5000-8ca7-80f57d26f881"
 OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
@@ -50,7 +49,6 @@ ForwardDiff = "0.10.12, 1"
 InvertedIndices = "1"
 LogDensityProblems = "2"
 MCMCChains = "7.2.1"
-MarginalLogDensities = "0.4"
 Mooncake = "0.4, 0.5"
 OffsetArrays = "1"
 OrderedCollections = "1"

diff --git a/test/compiler.jl b/test/compiler.jl
@@ -372,12 +372,14 @@ end
             x .~ [Normal(), Normal()]
             return x
         end
-        expected_error = ArgumentError("""
-            As of v0.35, DynamicPPL does not allow arrays of distributions in `.~`. \
-            Please use `product_distribution` instead, or write a loop if necessary. \
-            See https://github.com/TuringLang/DynamicPPL.jl/releases/tag/v0.35.0 for more \
-            details.\
-            """)
+        expected_error = ArgumentError(
+            """
+As of v0.35, DynamicPPL does not allow arrays of distributions in `.~`. \
+Please use `product_distribution` instead, or write a loop if necessary. \
+See https://github.com/TuringLang/DynamicPPL.jl/releases/tag/v0.35.0 for more \
+details.\
+"""
+        )
         @test_throws expected_error (vector_dot_tilde()(); true)
     end