Review changes

Author: yha

src/cov.jl

@@ -123,6 +123,11 @@ Compute the Pearson correlation matrix of `X` along the dimension
 cor(x::AbstractMatrix, w::AbstractWeights, dims::Int=1) =
     corm(x, mean(x, w, dims=dims), w, dims)
 
+function mean_and_cov(x::AbstractVector; corrected::Bool=true)
+    m = mean(x)
+    return m, covm(x, m, corrected=corrected)
+end
+
 function mean_and_cov(x::AbstractMatrix, dims::Int=1; corrected::Bool=true)
     m = mean(x, dims=dims)
     return m, covm(x, m, dims, corrected=corrected)
@@ -133,11 +138,6 @@ function mean_and_cov(x::AbstractMatrix, wv::AbstractWeights, dims::Int=1;
     return m, cov(x, wv, dims; corrected=depcheck(:mean_and_cov, corrected))
 end
 
-function mean_and_cov(x::AbstractVector; corrected::Bool=true)
-    m = mean(x)
-    return m, covm(x, m, corrected=corrected)
-end
-
 """
     cov2cor(C, s)
 

test/cov.jl

@@ -137,7 +137,6 @@ sparse_array = sprandn(3, 8, 0.2)
             cor1 = cor(X, wv1, 1)
             cor2 = cor(X, wv2, 2)
 
-
             @testset "cov2cor" begin
                 @test cov2cor(cov(X, dims = 1), std(X, dims = 1)) ≈ₙ cor(X, dims = 1)
                 @test cov2cor(cov(X, dims = 2), std(X, dims = 2)) ≈ₙ cor(X, dims = 2)