diff --git a/Project.toml b/Project.toml
index 1871588..76f1ebb 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,6 +1,6 @@
 name = "SliceSampling"
 uuid = "43f4d3e8-9711-4a8c-bd1b-03ac73a255cf"
-version = "0.7.1"
+version = "0.7.2"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
diff --git a/docs/src/gibbs_polar.md b/docs/src/gibbs_polar.md
index d87a4d1..8372708 100644
--- a/docs/src/gibbs_polar.md
+++ b/docs/src/gibbs_polar.md
@@ -2,7 +2,7 @@
 # [Gibbsian Polar Slice Sampling](@id polar)
 
 ## Introduction
-Gibbsian polar slice sampling (GPSS) is a recent vector-valued slice sampling algorithm proposed by P. Schär, M. Habeck, and D. Rudolf[^SHR2023].
+Gibbsian polar slice sampling (GPSS) is a multivariate slice sampling algorithm proposed by P. Schär, M. Habeck, and D. Rudolf[^SHR2023].
 It is an computationally efficient variant of polar slice sampler previously proposed by Roberts and Rosenthal[^RR2002].
 Unlike other slice sampling algorithms, it operates a Gibbs sampler over polar coordinates, reminiscent of the elliptical slice sampler (ESS).
 Due to the involvement of polar coordinates, GPSS only works reliably on more than one dimension.
diff --git a/src/multivariate/randpermgibbs.jl b/src/multivariate/randpermgibbs.jl
index c8ba434..84bc100 100644
--- a/src/multivariate/randpermgibbs.jl
+++ b/src/multivariate/randpermgibbs.jl
@@ -25,9 +25,7 @@ struct GibbsState{T<:Transition}
     transition::T
 end
 
-function AbstractMCMC.setparams!!(
-    model::AbstractMCMC.LogDensityModel, state::GibbsState, params
-)
+function AbstractMCMC.setparams!!(model::AbstractMCMC.LogDensityModel, ::GibbsState, params)
     lp = LogDensityProblems.logdensity(model.logdensity, params)
     return GibbsState(Transition(params, lp, NamedTuple()))
 end
diff --git a/test/multivariate.jl b/test/multivariate.jl
index 5e081ae..67c8ccb 100644
--- a/test/multivariate.jl
+++ b/test/multivariate.jl
@@ -59,7 +59,7 @@ function LogDensityProblems.dimension(model::MultiModel)
 end
 
 @testset "multivariate samplers" begin
-    model = MultiModel(1.0, 1.0, [0.0])
+    model = MultiModel(3.0, 3.0, [0.0])
     @testset for sampler in [
         # Vector-valued windows
         RandPermGibbs(Slice.(fill(1, LogDensityProblems.dimension(model)))),
@@ -74,14 +74,12 @@ end
         HitAndRun(SliceSteppingOut(1)),
         HitAndRun(SliceDoublingOut(1)),
 
-        # Latent slice sampling
+        # Multivariate slice samplers
         LatentSlice(5),
-
-        # Gibbsian polar slice sampling
         GibbsPolarSlice(100),
     ]
         @testset "initial_params" begin
-            model  = MultiModel(1.0, 1.0, [0.0])
+            model  = MultiModel(3.0, 3.0, [0.0])
             θ, y   = MCMCTesting.sample_joint(Random.default_rng(), model)
             model′ = AbstractMCMC.LogDensityModel(@set model.y = y)
 
@@ -92,7 +90,7 @@ end
 
         @testset "initial_sample" begin
             rng = StableRNG(1)
-            model = MultiModel(1.0, 1.0, [0.0])
+            model = MultiModel(3.0, 3.0, [0.0])
             θ0 = SliceSampling.initial_sample(rng, model)
 
             rng = StableRNG(1)
@@ -101,7 +99,7 @@ end
         end
 
         @testset "determinism" begin
-            model  = MultiModel(1.0, 1.0, [0.0])
+            model  = MultiModel(3.0, 3.0, [0.0])
             θ, y   = MCMCTesting.sample_joint(Random.default_rng(), model)
             model′ = AbstractMCMC.LogDensityModel(@set model.y = y)
 
@@ -140,7 +138,7 @@ end
             n_mcmc_thin      = 10
             test             = ExactRankTest(n_samples, n_mcmc_steps, n_mcmc_thin)
 
-            model   = MultiModel(1.0, 1.0, [0.0])
+            model   = MultiModel(3.0, 3.0, [0.0])
             subject = TestSubject(model, sampler)
             @test seqmcmctest(test, subject, 0.001, n_pvalue_samples; show_progress=false)
         end
diff --git a/test/turing.jl b/test/turing.jl
index 032fa76..f6c2e89 100644
--- a/test/turing.jl
+++ b/test/turing.jl
@@ -4,7 +4,8 @@
         s ~ InverseGamma(2, 3)
         m ~ Normal(0, sqrt(s))
         1.5 ~ Normal(m, sqrt(s))
-        return 2.0 ~ Normal(m, sqrt(s))
+        2.0 ~ Normal(m, sqrt(s))
+        return nothing
     end
 
     n_samples = 1000