Mention alg selection

Project.toml

@@ -1,7 +1,7 @@
 name = "AlphaStableDistributions"
 uuid = "f20549b4-2d50-407f-863c-cdd202ba59a3"
 authors = ["Fredrik Bagge Carlson", "Too Yuen Min"]
-version = "0.1.2"
+version = "1.0.0"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"

README.md

@@ -21,7 +21,7 @@ AlphaStable{Float64}(α=1.5, β=0.0, scale=1.0, location=0.0)
 
 julia> s = rand(d1, 100000);
 
-julia> d2 = fit(AlphaStable, s)
+julia> d2 = fit(AlphaStable, s, alg=QuickSort) # See ThreadsX.QuickSort for a threaded algorithm
 
 AlphaStable{Float64}(α=1.4748701622930906, β=0.0, scale=1.006340087707924, location=-0.0036724481641865715)
 

src/AlphaStableDistributions.jl

@@ -54,10 +54,11 @@ const _ena = [
 ]
 
 """
+    fit(d::Type{<:AlphaStable}, x; alg=QuickSort)
+
 Fit a symmetric α stable distribution to data.
 
-:param x: data
-:returns: (α, c, δ)
+returns `AlphaStable`
 
 α, c and δ are the characteristic exponent, scale parameter
 (dispersion^1/α) and location parameter respectively.
@@ -66,11 +67,11 @@ Fit a symmetric α stable distribution to data.
 c is computed based on Fama & Roll (1971) fractile.
 δ is the 50% trimmed mean of the sample.
 """
-function Distributions.fit(d::Type{<:AlphaStable}, x, alg=QuickSort)
+function Distributions.fit(d::Type{<:AlphaStable}, x; alg=QuickSort)
     sx = sort(x, alg=alg)
-    δ = mean(@view(sx[end÷4:(3*end)÷4]))
-    p = quantile.(Ref(sx), (0.05, 0.25, 0.28, 0.72, 0.75, 0.95), sorted=true)
-    c = (p[4]-p[3])/1.654
+    δ  = mean(@view(sx[end÷4:(3*end)÷4]))
+    p  = quantile.(Ref(sx), (0.05, 0.25, 0.28, 0.72, 0.75, 0.95), sorted=true)
+    c  = (p[4]-p[3])/1.654
     an = (p[6]-p[1])/(p[5]-p[2])
     if an < 2.4388
         α = 2.

test/runtests.jl

@@ -86,3 +86,9 @@ end
 # using Cthulhu
 # d = AlphaSubGaussian(n=96)
 # @descend_code_warntype rand(Random.GLOBAL_RNG, d)
+# 
+# using AlphaStableDistributions
+# d1 = AlphaStable(α=1.5)
+# s = rand(d1, 100000)
+# using ThreadsX
+# @btime fit($AlphaStable, $s, $ThreadsX.MergeSort)