diff --git a/Project.toml b/Project.toml
index fe3b64f..cc44faf 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,15 +1,12 @@
 name = "SliceSampling"
 uuid = "43f4d3e8-9711-4a8c-bd1b-03ac73a255cf"
-version = "0.7.4"
+version = "0.7.5"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
-Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
-FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
-LogDensityProblemsAD = "996a588d-648d-4e1f-a8f0-a84b347e47b1"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 
@@ -21,14 +18,10 @@ SliceSamplingTuringExt = ["Turing"]
 
 [compat]
 AbstractMCMC = "4, 5"
-Accessors = "0.1"
 Distributions = "0.25"
-FillArrays = "1"
 LinearAlgebra = "1"
 LogDensityProblems = "2"
-LogDensityProblemsAD = "1"
 Random = "1"
-Requires = "1"
 Turing = "0.37, 0.38"
 julia = "1.10"
 
diff --git a/ext/SliceSamplingTuringExt.jl b/ext/SliceSamplingTuringExt.jl
index 784f50c..713a04d 100644
--- a/ext/SliceSamplingTuringExt.jl
+++ b/ext/SliceSamplingTuringExt.jl
@@ -2,12 +2,10 @@
 module SliceSamplingTuringExt
 
 if isdefined(Base, :get_extension)
-    using LogDensityProblemsAD
     using Random
     using SliceSampling
     using Turing
 else
-    using ..LogDensityProblemsAD
     using ..Random
     using ..SliceSampling
     using ..Turing
diff --git a/src/SliceSampling.jl b/src/SliceSampling.jl
index a795a16..c43875c 100644
--- a/src/SliceSampling.jl
+++ b/src/SliceSampling.jl
@@ -2,17 +2,11 @@
 module SliceSampling
 
 using AbstractMCMC
-using Accessors
 using Distributions
-using FillArrays
 using LinearAlgebra
 using LogDensityProblems
 using Random
 
-# The following is necessary because Turing wraps all models with
-# LogDensityProblemsAD by default. So we need access to these types.
-using LogDensityProblemsAD
-
 # reexports
 using AbstractMCMC: sample, MCMCThreads, MCMCDistributed, MCMCSerial
 export sample, MCMCThreads, MCMCDistributed, MCMCSerial
@@ -59,14 +53,6 @@ function initial_sample(::Random.AbstractRNG, ::Any)
     )
 end
 
-# If target is from `LogDensityProblemsAD`, unwrap target before calling `initial_sample`.
-# This is necessary since Turing wraps `DynamicPPL.Model`s when passed to an `externalsampler`.
-function initial_sample(
-    rng::Random.AbstractRNG, wrap::LogDensityProblemsAD.ADGradientWrapper
-)
-    return initial_sample(rng, parent(wrap))
-end
-
 function exceeded_max_prop(max_prop::Int)
     return error(
         "Exceeded maximum number of proposal $(max_prop), ",
@@ -116,18 +102,4 @@ include("multivariate/latent.jl")
 export GibbsPolarSlice
 include("multivariate/gibbspolar.jl")
 
-# Turing Compatibility
-
-if !isdefined(Base, :get_extension)
-    using Requires
-end
-
-@static if !isdefined(Base, :get_extension)
-    function __init__()
-        @require Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" include(
-            "../ext/SliceSamplingTuringExt.jl"
-        )
-    end
-end
-
 end
diff --git a/src/multivariate/randpermgibbs.jl b/src/multivariate/randpermgibbs.jl
index 84bc100..05b4f77 100644
--- a/src/multivariate/randpermgibbs.jl
+++ b/src/multivariate/randpermgibbs.jl
@@ -38,7 +38,8 @@ end
 
 function LogDensityProblems.logdensity(gibbs::GibbsTarget, θi)
     (; model, idx, θ) = gibbs
-    return LogDensityProblems.logdensity(model, (@set θ[idx] = θi))
+    θ[idx] = θi
+    return LogDensityProblems.logdensity(model, θ)
 end
 
 function AbstractMCMC.step(
@@ -73,7 +74,7 @@ function AbstractMCMC.step(
     unislices       = if sampler.unislice isa AbstractVector
         sampler.unislice
     else
-        Fill(sampler.unislice, d)
+        fill(sampler.unislice, d)
     end
 
     props = zeros(Int, d)