diff --git a/src/weights.jl b/src/weights.jl
index cf535d408..d17ec9bde 100644
--- a/src/weights.jl
+++ b/src/weights.jl
@@ -682,6 +682,32 @@ function mean(A::AbstractArray, w::UnitWeights; dims::Union{Colon,Int}=:)
     return mean(A, dims=dims)
 end
 
+"""
+    mean(f, A::AbstractArray, w::AbstractWeights)
+
+Compute the weighted mean of array `A`, after transforming it'S
+contents with the function `f`, with weight vector `w` (of type
+`AbstractWeights`).
+
+# Examples
+```julia
+n = 20
+x = rand(n)
+w = rand(n)
+mean(√, x, weights(w))
+```
+"""
+function mean(f, A::AbstractArray, w::AbstractWeights)
+    return sum(Broadcast.instantiate(Broadcast.broadcasted(A, w) do a_i, wg
+        return f(a_i) * wg
+    end)) / sum(w)
+end
+
+function mean(f, A::AbstractArray, w::UnitWeights)
+    length(A) != length(w) && throw(DimensionMismatch("Inconsistent array dimension."))
+    return mean(f, A)
+end
+
 ##### Weighted quantile #####
 
 """
diff --git a/test/weights.jl b/test/weights.jl
index 52142efd8..e80ca96a0 100644
--- a/test/weights.jl
+++ b/test/weights.jl
@@ -270,6 +270,27 @@ end
         @test mean(a, f(wt), dims=3) ≈ sum(a.*reshape(wt, 1, 1, length(wt)), dims=3)/sum(wt)
         @test_throws ErrorException mean(a, f(wt), dims=4)
     end
+
+    @test mean(√, [1:3;], f([1.0, 1.0, 0.5])) ≈ 1.3120956
+    @test mean(√, 1:3, f([1.0, 1.0, 0.5]))    ≈ 1.3120956
+    @test mean(√, [1 + 2im, 4 + 5im], f([1.0, 0.5])) ≈ 1.60824421 + 0.88948688im
+
+    @test mean(log, [1:3;], f([1.0, 1.0, 0.5])) ≈ 0.49698133
+    @test mean(log, 1:3, f([1.0, 1.0, 0.5])) ≈ 0.49698133
+    @test mean(log, [1 + 2im, 4 + 5im], f([1.0, 0.5])) ≈ 1.155407982 + 1.03678427im
+
+    @test mean(x -> x^2, [1:3;], f([1.0, 1.0, 0.5])) ≈ 3.8
+    @test mean(x -> x^2, 1:3, f([1.0, 1.0, 0.5])) ≈ 3.8
+    @test mean(x -> x^2, [1 + 2im, 4 + 5im], f([1.0, 0.5])) ≈ -5.0 + 16.0im
+
+    c = 1.0:9.0
+    w = UnitWeights{Float64}(9)
+    @test mean(√, c, w) ≈ sum(sqrt.(c)) / length(c)
+    @test_throws DimensionMismatch mean(√, c, UnitWeights{Float64}(6))
+    @test mean(log, c, w) ≈ sum(log.(c)) / length(c)
+    @test_throws DimensionMismatch mean(log, c, UnitWeights{Float64}(6))
+    @test mean(x -> x^2, c, w) ≈ sum(c.^2) / length(c)
+    @test_throws DimensionMismatch mean(x -> x^2, c, UnitWeights{Float64}(6))
 end
 
 @testset "Quantile fweights" begin