Merge pull request #856 from itsdebartha/master

Optional argument for `variation()`

src/scalarstats.jl

@@ -404,14 +404,17 @@ span(x) = ((a, b) = extrema(x); a:b)
 
 # Variation coefficient: std / mean
 """
-    variation(x, m=mean(x))
+    variation(x, m=mean(x); corrected=true)
 
 Return the coefficient of variation of collection `x`, optionally specifying
-a precomputed mean `m`. The coefficient of variation is the ratio of the
-standard deviation to the mean.
-"""
-variation(x, m) = stdm(x, m) / m
-variation(x) = ((m, s) = mean_and_std(x); s/m)
+a precomputed mean `m`, and the optional correction parameter `corrected`.
+The coefficient of variation is the ratio of the
+standard deviation to the mean. If `corrected` is `false`,
+then `std` is calculated with denominator `n`. Else, the `std` is
+calculated with denominator `n-1`.
+"""
+variation(x, m; corrected::Bool=true) = stdm(x, m; corrected=corrected) / m
+variation(x; corrected::Bool=true) = ((m, s) = mean_and_std(x; corrected=corrected); s/m)
 
 # Standard error of the mean: std / sqrt(len)
 # Code taken from var in the Statistics stdlib module

test/scalarstats.jl

@@ -163,7 +163,13 @@ z2 = [8. 2. 3. 1.; 24. 10. -1. -1.; 20. 12. 1. -2.]
 @test span(skipmissing([1, missing, 5, missing])) == 1:5
 
 @test variation([1:5;]) ≈ 0.527046276694730
+@test variation([1:5;]; corrected=false) ≈ 0.471404520791032
 @test variation(skipmissing([missing; 1:5; missing])) ≈ 0.527046276694730
+@test isnan(variation(1))
+@test variation(1; corrected=false) == 0
+# Possibly deprecated
+@test variation([1:5;],4) ≈ 0.4841229182759271
+@test variation([1:5;],4; corrected=false) ≈ 0.4330127018922193
 
 @test @inferred(sem([1:5;])) ≈ 0.707106781186548
 @test @inferred(sem(skipmissing([missing; 1:5; missing]))) ≈ 0.707106781186548