Fix mode of LKJCholesky and define mean(::LKJCholesky)

Project.toml

@@ -1,7 +1,7 @@
 name = "Distributions"
 uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
 authors = ["JuliaStats"]
-version = "0.25.116"
+version = "0.25.117"
 
 [deps]
 AliasTables = "66dad0bd-aa9a-41b7-9441-69ab47430ed8"

src/cholesky/lkjcholesky.jl

@@ -109,11 +109,20 @@ function insupport(d::LKJCholesky, R::LinearAlgebra.Cholesky)
     return true
 end
 
-function StatsBase.mode(d::LKJCholesky)
+function StatsBase.mean(d::LKJCholesky)
     factors = Matrix{eltype(d)}(LinearAlgebra.I, size(d))
     return LinearAlgebra.Cholesky(factors, d.uplo, 0)
 end
 
+function mode(d::LKJCholesky; check_args::Bool=true)
+    @check_args(
+        LKJCholesky,
+        @setup(η = d.η),
+        (η, η > 1, "mode is defined only when η > 1."),
+    )
+    return mean(d)
+end
+
 StatsBase.params(d::LKJCholesky) = (d.d, d.η, d.uplo)
 
 @inline partype(::LKJCholesky{T}) where {T <: Real} = T

test/cholesky/lkjcholesky.jl

@@ -124,14 +124,25 @@ using FiniteDifferences
     end
 
     @testset "properties" begin
-        @testset for p in (4, 5), η in (2, 3.5), uplo in ('L', 'U')
+        @testset for p in (4, 5), η in (0.5, 2, 3.5), uplo in ('L', 'U')
             d = LKJCholesky(p, η, uplo)
             @test d.d == p
             @test size(d) == (p, p)
             @test Distributions.params(d) == (d.d, d.η, d.uplo)
             @test partype(d) <: Float64
 
-            m = mode(d)
+            if η > 1
+                m = mode(d)
+                @test m isa Cholesky{eltype(d)}
+                @test Matrix(m) ≈ I
+            else
+                @test_throws "DomainError with $η:\nLKJCholesky: mode is defined only when η > 1." mode(d)
+            end
+            m = mode(d; check_args = false)
+            @test m isa Cholesky{eltype(d)}
+            @test Matrix(m) ≈ I
+
+            m = mean(d)
             @test m isa Cholesky{eltype(d)}
             @test Matrix(m) ≈ I
         end