updates docs and add tests for middle on non-reals

src/Statistics.jl

@@ -733,14 +733,14 @@ cor(x::AbstractVecOrMat, y::AbstractVecOrMat; dims::Int=1) =
 Compute the middle of a scalar value, which is equivalent to `x` itself, but of the type of `middle(x, x)` for consistency.
 """
 middle(x::Union{Bool,Int8,Int16,Int32,Int64,Int128,UInt8,UInt16,UInt32,UInt64,UInt128}) = Float64(x)
-# Specialized functions for real types allow for improved performance
+# Specialized functions for number types allow for improved performance
 middle(x::AbstractFloat) = x
 middle(x::Number) = (x + zero(x)) / 1
 
 """
     middle(x, y)
 
-Compute the middle of two reals `x` and `y`, which is
+Compute the middle of two numbers `x` and `y`, which is
 equivalent in both value and type to computing their mean (`(x + y) / 2`).
 """
 middle(x::Number, y::Number) = x/2 + y/2

test/runtests.jl

@@ -14,6 +14,9 @@ Random.seed!(123)
     @test middle(1:8) === 4.5
     @test middle([1:8;]) === 4.5
 
+    @test middle(5.0 + 2.0im, 2.0 + 3.0im) == 3.5 + 2.5im
+    @test middle(5.0 + 2.0im) == 5.0 + 2.0im
+
     # ensure type-correctness
     for T in [Bool,Int8,Int16,Int32,Int64,Int128,UInt8,UInt16,UInt32,UInt64,UInt128,Float16,Float32,Float64]
         @test middle(one(T)) === middle(one(T), one(T))