Use Rmath instead of private StatsFuns.RFunctions submodule

Project.toml

@@ -1,7 +1,7 @@
 name = "Distributions"
 uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
 authors = ["JuliaStats"]
-version = "0.25.122"
+version = "0.25.123"
 
 [deps]
 AliasTables = "66dad0bd-aa9a-41b7-9441-69ab47430ed8"
@@ -13,6 +13,7 @@ PDMats = "90014a1f-27ba-587c-ab20-58faa44d9150"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 QuadGK = "1fd47b50-473d-5c70-9696-f719f8f3bcdc"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Rmath = "79098fc4-a85e-5d69-aa6a-4863f24498fa"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsAPI = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
@@ -48,14 +49,15 @@ PDMats = "0.11.35"
 Printf = "<0.0.1, 1"
 QuadGK = "2"
 Random = "<0.0.1, 1"
+Rmath = "0.9"
 SparseArrays = "<0.0.1, 1"
 SpecialFunctions = "1.2, 2"
 StableRNGs = "1"
 StaticArrays = "1"
 Statistics = "1"
 StatsAPI = "1.6"
 StatsBase = "0.32, 0.33, 0.34"
-StatsFuns = "0.9.15, 1"
+StatsFuns = "1.5.2"
 Test = "<0.0.1, 1"
 julia = "1.10"
 

src/Distributions.jl

@@ -28,6 +28,7 @@ using SpecialFunctions
 using Base.MathConstants: eulergamma
 
 import AliasTables
+import Rmath
 
 export
     # re-export Statistics

src/common.jl

@@ -502,7 +502,7 @@ Get the probability of failure.
 """
 failprob(d::DiscreteUnivariateDistribution)
 
-# Temporary fix to handle RFunctions dependencies
+# Temporary fix to handle Rmath dependencies
 """
     @rand_rdist(::Distribution)
 

src/univariate/continuous/noncentralbeta.jl

@@ -39,7 +39,7 @@ partype(::NoncentralBeta{T}) where {T} = T
 
 @_delegate_statsfuns NoncentralBeta nbeta α β λ
 
-# TODO: remove RFunctions dependency once NoncentralChisq has its removed
+# TODO: remove Rmath dependency once NoncentralChisq has its removed
 @rand_rdist(NoncentralBeta)
 
 function rand(d::NoncentralBeta)

src/univariate/continuous/noncentralchisq.jl

@@ -79,6 +79,6 @@ end
 
 @_delegate_statsfuns NoncentralChisq nchisq ν λ
 
-# TODO: remove RFunctions dependency
+# TODO: remove Rmath dependency
 @rand_rdist(NoncentralChisq)
-rand(d::NoncentralChisq) = StatsFuns.RFunctions.nchisqrand(d.ν, d.λ)
+rand(d::NoncentralChisq) = Rmath.rnchisq(d.ν, d.λ)

src/univariate/continuous/noncentralf.jl

@@ -56,7 +56,7 @@ function rand(rng::AbstractRNG, d::NoncentralF)
     r1 / r2
 end
 
-# TODO: remove RFunctions dependency once NoncentralChisq has its removed
+# TODO: remove Rmath dependency once NoncentralChisq has its removed
 @rand_rdist(NoncentralF)
 function rand(d::NoncentralF)
     r1 = rand(NoncentralChisq(d.ν1,d.λ)) / d.ν1

src/univariate/discrete/hypergeometric.jl

@@ -84,11 +84,10 @@ entropy(d::Hypergeometric) = entropy(map(Base.Fix1(pdf, d), support(d)))
 
 ## sampling
 
-# TODO: remove RFunctions dependency. Implement:
+# TODO: remove Rmath dependency. Implement:
 #   V. Kachitvichyanukul & B. Schmeiser
 #   "Computer generation of hypergeometric random variates"
 #   Journal of Statistical Computation and Simulation, 22(2):127-145
 #   doi:10.1080/00949658508810839
 @rand_rdist(Hypergeometric)
-rand(d::Hypergeometric) =
-    convert(Int, StatsFuns.RFunctions.hyperrand(d.ns, d.nf, d.n))
+rand(d::Hypergeometric) = convert(Int, Rmath.rhyper(d.ns, d.nf, d.n))