[Internals] Simplify \rho for Clayton and InvGaussian (#331)

Author: lrnv

src/Generator/ClaytonGenerator.jl

@@ -81,25 +81,8 @@ function Distributions._logpdf(C::ClaytonCopula{d,TG}, u) where {d,TG<:ClaytonGe
     S1==d-1 && return eltype(u)(-Inf)
     return log(θ + 1) * (d - 1) - (θ + 1) * S2 + (-1 / θ - d) * log(S1 - d + 1)
 end
-### only for test...
-@inline function _C_clayton(u::Float64, v::Float64, θ::Real)
-    s = u^(-θ) + v^(-θ) - 1
-    if θ < 0
-        return (s <= 0) ? 0.0 : s^(-1/θ)   # soporte recortado para θ<0
-    else
-        return s^(-1/θ)                    # para θ>0 siempre s≥1
-    end
-end
-# Spearman (vía CDF) — with _safett Integral
-function ρ(G::ClaytonGenerator; rtol=1e-8, atol=1e-10)
-    θ = float(G.θ)
-    θ ≤ -1 && throw(ArgumentError("Para Clayton: θ > -1."))
-    iszero(θ) && return 0.0
-    I = HCubature.hcubature(x -> _C_clayton(x[1], x[2], θ),
-                            [0.0,0.0], [1.0,1.0];
-                            rtol=rtol, atol=atol)[1]
-    return 12I - 3
-end
+
+ρ(G::ClaytonGenerator) = @invoke ρ(ArchimedeanCopula(2, G)::Copula)
 
 # Inverse ρ → θ for Clayton (without trimming to [0,1])
 function ρ⁻¹(::Type{<:ClaytonGenerator}, ρ̂; atol=1e-10)

src/Generator/InvGaussianGenerator.jl

@@ -141,21 +141,12 @@ function τ⁻¹(::Type{<:InvGaussianGenerator}, τ)
 end
 frailty(G::InvGaussianGenerator) = Distributions.InverseGaussian(G.θ,1)
 
-
-function _rho_invgaussian(θ; rtol=1e-7, atol=1e-9, maxevals=10^6)
-    θeff = clamp(θ, 1e-12, Inf)
-    Cθ   = Copulas.ArchimedeanCopula(2, InvGaussianGenerator(θeff))
-    f(x) = _cdf(Cθ, (x[1], x[2]))  # <- tu _cdf
-    I = HCubature.hcubature(f, (0.0,0.0), (1.0,1.0); rtol=rtol, atol=atol, maxevals=maxevals)[1]
-    return 12I - 3
-end
-
-ρ(G::InvGaussianGenerator) = _rho_invgaussian(G.θ)
+ρ(G::InvGaussianGenerator) = @invoke ρ(ArchimedeanCopula(2, G)::Copula)
 function ρ⁻¹(::Type{<:InvGaussianGenerator}, rho)
     # Numerically inverts _rho_invgaussian using Brent's method.
     # Spearman's rho for InvGaussian: [0, 1/2)
     rho ≤ 0 && return zero(rho)
     rho ≥ log(2) && return Inf * rho
-    xhat = Roots.find_zero(x -> _rho_invgaussian(-log(x)) - rho, (0, 1))
+    xhat = Roots.find_zero(x -> ρ(InvGaussianGenerator(-log(x))) - rho, (0, 1))
     return -log(xhat)
 end