diff --git a/README.md b/README.md
index 59f7ba0..991fb32 100644
--- a/README.md
+++ b/README.md
@@ -902,39 +902,6 @@ julia> quantile.(Levy(0, 1), U)
  0.582946    3.52799
 ```
 
-## Old dispatching, will be removed in future
-
-```julia
-julia> gausscopulagen(t::Int, Σ::Matrix{Float64})
-```
-
-```julia
-julia> archcopulagen(t::Int, n::Int, θ::Union{Float64, Int}, copula::String; rev::Bool = false, cor::String = "")
-```
-
-```julia
-julia> nestedarchcopulagen(t::Int, n::Vector{Int}, ϕ::Vector{Float64}, θ::Float64, copula::String, m::Int = 0)
-```
-
-```julia
-julia> chaincopulagen(t::Int, θ::Union{Vector{Float64}, Vector{Int}}, copula::Vector{String}; rev::Bool = false, cor::String = "")
-```
-
-```julia
-julia> marshallolkincopulagen(t::Int, λ::Vector{Float64})
-```
-
-```julia
-julia> frechetcopulagen(t::Int, n::Int, α::Union{Int, Float64})
-```
-
-```julia
-julia> frechetcopulagen(t::Int, n::Int, α::Union{Int, Float64}, β::Union{Int, Float64})
-```
-
-```julia
-julia> chainfrechetcopulagen(t::Int, α::Vector{Float64}, β::Vector{Float64} = zeros(α))
-```
 
 # Citing this work
 
diff --git a/src/DatagenCopulaBased.jl b/src/DatagenCopulaBased.jl
index cf17ce9..d7e06c8 100644
--- a/src/DatagenCopulaBased.jl
+++ b/src/DatagenCopulaBased.jl
@@ -15,7 +15,7 @@ module DatagenCopulaBased
   include("sampleunivdists.jl")
 
   # dispatching of the generator
-  include("copulagendat.jl")
+  #include("copulagendat.jl")
 
   # axiliary function for correlqations
   include("corgen.jl")
@@ -47,5 +47,5 @@ module DatagenCopulaBased
   export simulate_copula, simulate_copula!
 
   # obsolete implemntations
-  export tstudentcopulagen, gausscopulagen, frechetcopulagen, marshallolkincopulagen
+  #export tstudentcopulagen, gausscopulagen, frechetcopulagen, marshallolkincopulagen
 end
diff --git a/src/add_higher_order_cors.jl b/src/add_higher_order_cors.jl
index 5213f5a..a384f28 100644
--- a/src/add_higher_order_cors.jl
+++ b/src/add_higher_order_cors.jl
@@ -36,7 +36,7 @@ julia> gcop2tstudent(x, [1,2], 6)
  -0.710786   0.239012   -1.54419
 ```
 """
-function gcop2tstudent(x::Matrix{Real}, ind::Vector{Int}, ν::Int; naive::Bool = false, rng = Random.GLOBAL_RNG)
+function gcop2tstudent(x::Matrix{T}, ind::Vector{Int}, ν::Int; naive::Bool = false, rng = Random.GLOBAL_RNG) where T <: Real
   unique(ind) == ind || throw(AssertionError("indices must not repeat"))
   y = copy(x)
   Σ = cov(x)
@@ -97,7 +97,7 @@ julia> gcop2arch(x, ["clayton" => [1,2]]; naive::Bool = false, notnested::Bool =
  -0.657297  -0.339814  -1.54419
 ```
 """
-function gcop2arch(x::Matrix{Real}, inds::VP; naive::Bool = false, notnested::Bool = false, rng = Random.GLOBAL_RNG)
+function gcop2arch(x::Matrix{T}, inds::VP; naive::Bool = false, notnested::Bool = false, rng = Random.GLOBAL_RNG) where T <: Real
   testind(inds)
   S = transpose(sqrt.(diag(cov(x))))
   μ = mean(x, dims=1)
@@ -154,7 +154,7 @@ julia> gcop2frechet(x, [1,2])
  -0.7223     -0.172507   -1.54419
 ```
 """
-function gcop2frechet(x::Matrix{Real}, inds::Vector{Int}; naive::Bool = false, rng = Random.GLOBAL_RNG)
+function gcop2frechet(x::Matrix{T}, inds::Vector{Int}; naive::Bool = false, rng = Random.GLOBAL_RNG) where T <: Real
   unique(inds) == inds || throw(AssertionError("indices must not repeat"))
   S = transpose(sqrt.(diag(cov(x))))
   μ = mean(x, dims = 1)
@@ -202,7 +202,7 @@ julia> gcop2marshallolkin(x, [1,2], 1., 1.5; naive = false)
  -0.867606  -0.589929  -1.54419
 ```
 """
-function gcop2marshallolkin(x::Matrix{Real}, inds::Vector{Int}, λ1::Real = 1., λ2::Real = 1.5; naive::Bool = false, rng = Random.GLOBAL_RNG)
+function gcop2marshallolkin(x::Matrix{T}, inds::Vector{Int}, λ1::T = 1., λ2::T = 1.5; naive::Bool = false, rng = Random.GLOBAL_RNG) where T <: Real
   unique(inds) == inds || throw(AssertionError("indices must not repeat"))
   length(inds) == 2 || throw(AssertionError("not supported for |inds| > 2"))
   λ1 >= 0 || throw(DomainError("not supported for λ1 < 0"))
@@ -238,7 +238,7 @@ end
 
 Return uniformly distributed data from x[:,i] given a copula familly.
 """
-function norm2unifind(x::Matrix{Real}, i::Vector{Int}, cop::String = "")
+function norm2unifind(x::Matrix{T}, i::Vector{Int}, cop::String = "") where T <: Real
   x = (cop == "frechet") ? x[:,i] : hcat(x[:,i], randn(size(x,1),1))
   a, s = eigen(cor(x))
   w = x*s./transpose(sqrt.(a))
@@ -259,14 +259,14 @@ julia> meanΣ(s)
 0.3
 ```
 """
-meanΣ(Σ::Matrix{Real}) = mean(abs.(Σ[findall(tril(Σ-Matrix(I, size(Σ))).!=0)]))
+meanΣ(Σ::Matrix{T}) where T <: Real = mean(abs.(Σ[findall(tril(Σ-Matrix(I, size(Σ))).!=0)]))
 
 """
   mean_outer(Σ::Matrix{Real}, part::Vector{Vector{Int}})
 
 returns a mean correlation excluding internal one is subsets determined by part
 """
-function mean_outer(Σ::Matrix{Real}, part::Vector{Vector{Int}})
+function mean_outer(Σ::Matrix{T}, part::Vector{Vector{Int}}) where T <: Real
   Σ_copy = copy(Σ)-Matrix(I, size(Σ))
   for ind=part
     Σ_copy[ind,ind] = zeros(length(ind),length(ind))
@@ -280,7 +280,7 @@ end
 Returns parametrization by correlation for data `x` and partition `part` for nested copulas.
 
 """
-function parameters(Σ::Matrix{Real}, part::Vector{Vector{Int}})
+function parameters(Σ::Matrix{T}, part::Vector{Vector{Int}}) where T <: Real
   ϕ = [meanΣ(Σ[ind,ind]) for ind=part]
   θ = mean_outer(Σ, part)
   ϕ, θ
@@ -291,7 +291,7 @@ end
 
 tests sufficient nesting condition given parameters, returns bool
 """
-function are_parameters_good(ϕ::Vector{Real}, θ::Real)
+function are_parameters_good(ϕ::Vector{T}, θ::T) where T <: Real
   θ < minimum(filter(x->!isnan(x), ϕ))
 end
 
@@ -301,7 +301,7 @@ end
 returns a matrix indicating a theoretical correlation according togiven parameters
 and partition
 """
-function Σ_theor(ϕ::Vector{Real}, θ::Real, part::Vector{Vector{Int}})
+function Σ_theor(ϕ::Vector{T}, θ::T, part::Vector{Vector{Int}}) where T <: Real
   n = sum(length.(part))
   result = fill(θ, n, n)
   for (ϕind,ind)=zip(ϕ,part)
@@ -318,7 +318,7 @@ end
 
 clusters data on a basis of a correlation
 """
-function getcors_advanced(x::Matrix{Real})
+function getcors_advanced(x::Matrix{T}) where T <: Real
   Σ = corspearman(x)
   var_no = size(Σ, 1)
   partitions = collect(Combinatorics.SetPartitions(1:var_no))[2:end-1] #TODO popraw first is trivial, last is nested
diff --git a/src/archcopcorrelations.jl b/src/archcopcorrelations.jl
index 8ee14ab..036c02e 100644
--- a/src/archcopcorrelations.jl
+++ b/src/archcopcorrelations.jl
@@ -19,7 +19,7 @@ Debye(x::Real, k::Int=1) = k/x^k*(quadgk(i -> i^k/(exp(i)-1), 0, x)[1])
 
 Returns Float, a single parameter of Archimedean copula, given the Kenalss τ correlation
 """
-function τ2θ(τ::Real, copula::String)
+function τ2θ(τ::T, copula::String) where T <: Real
   if copula == "gumbel"
     return 1/(1-τ)
   elseif copula == "clayton"
diff --git a/src/archcopulagendat.jl b/src/archcopulagendat.jl
index 6d7cfb2..913b72f 100644
--- a/src/archcopulagendat.jl
+++ b/src/archcopulagendat.jl
@@ -68,7 +68,9 @@ Given a vector of random numbers r of size n+1, return the sample from the Clayt
 copula parametrised by θ of the size n.
 """
 function clayton_gen(r::Vector{T}, θ::T) where T <: Real
-    u = -log.(r[1:end-1])./quantile.(Gamma{T}(1/θ, 1), r[end])
+    gamma_vec = quantile.(Gamma{T}(1/θ, 1), r[end])
+    #gamma_vec = [gamma_inc_inv(1/θ, T(1.), q) for q in r[end]]
+    u = -log.(r[1:end-1])./gamma_vec
     return (1 .+ u).^(-1/θ)
 end
 
diff --git a/src/chaincopulagendat.jl b/src/chaincopulagendat.jl
index 5b6c5a7..718585f 100644
--- a/src/chaincopulagendat.jl
+++ b/src/chaincopulagendat.jl
@@ -105,7 +105,7 @@ struct Chain_of_Archimedeans{T}
       copula in ["clayton", "amh", "frank"] || throw(AssertionError("$(copula) copula is not supported"))
       new{T}(n, θ, [copula for i in 1:n-1])
   end
-  function(::Type{Chain_of_Archimedeans})(θ::Vector{Real}, copula::String, cor::Type{<:CorrelationType}) where T <: Real
+  function(::Type{Chain_of_Archimedeans})(θ::Vector{T}, copula::String, cor::Type{<:CorrelationType}) where T <: Real
       n = length(θ)+1
       map(i -> testbivθ(θ[i], copula), 1:n-1)
       copula in ["clayton", "amh", "frank"] || throw(AssertionError("$(copula) copula is not supported"))
@@ -321,7 +321,7 @@ function simulate_copula(t::Int, copula::Chain_of_Frechet{T}; rng::AbstractRNG =
   α = copula.α
   β = copula.β
   n = copula.n
-  fncopulagen(α, β, rand(rng, t, n))
+  fncopulagen(α, β, rand(rng, T, t, n))
 end
 
 """
diff --git a/src/copulagendat.jl b/src/copulagendat.jl
deleted file mode 100644
index 1bc7d57..0000000
--- a/src/copulagendat.jl
+++ /dev/null
@@ -1,110 +0,0 @@
-
-
-# Old implemnetations
-
-gausscopulagen(t::Int, Σ::Matrix{T}) where T <: Real = simulate_copula(t, Gaussian_cop(Σ))
-
-tstudentcopulagen(t::Int, Σ::Matrix{T}, ν::Int) where T <: Real = simulate_copula(t, Student_cop(Σ, ν))
-
-frechetcopulagen(t::Int, args...) = simulate_copula(t, Frechet_cop(args...))
-
-marshallolkincopulagen(t::Int, λ::Vector{T}) where T <: Real = simulate_copula(t, Marshall_Olkin_cop(λ))
-
-
-function archcopulagen(t::Int, n::Int, θ::Real, copula::String;
-                                                              rev::Bool = false,
-                                                              cor::String = "")
-
-    args = (n, θ)
-    if cor == "Kendall"
-        args = (n, θ, KendallCorrelation)
-    elseif cor == "Spearman"
-        args = (n, θ, SpearmanCorrelation)
-    end
-    if copula == "gumbel"
-        if !rev
-            simulate_copula(t, Gumbel_cop(args...))
-        else
-            simulate_copula(t, Gumbel_cop_rev(args...))
-        end
-    elseif copula == "clayton"
-        if !rev
-            simulate_copula(t, Clayton_cop(args...))
-        else
-            simulate_copula(t, Clayton_cop_rev(args...))
-        end
-    elseif copula == "amh"
-        if !rev
-            simulate_copula(t, AMH_cop(args...))
-        else
-            simulate_copula(t, AMH_cop_rev(args...))
-        end
-    elseif copula == "frank"
-           simulate_copula(t, Frank_cop(args...))
-
-    else
-        throw(AssertionError("$(copula) copula is not supported"))
-    end
-end
-
-
-function nestedarchcopulagen(t::Int, n::Vector{Int}, ϕ::Vector{T}, θ::T, copula::String, m::Int = 0) where T <: Real
-    if copula == "gumbel"
-        children = [Gumbel_cop(n[i], ϕ[i]) for i in 1:length(n)]
-        simulate_copula(t, Nested_Gumbel_cop(children, m, θ))
-    elseif copula == "clayton"
-        children = [Clayton_cop(n[i], ϕ[i]) for i in 1:length(n)]
-        simulate_copula(t, Nested_Clayton_cop(children, m, θ))
-    elseif copula == "amh"
-        children = [AMH_cop(n[i], ϕ[i]) for i in 1:length(n)]
-        simulate_copula(t, Nested_AMH_cop(children, m, θ))
-    elseif copula == "frank"
-        children = [Frank_cop(n[i], ϕ[i]) for i in 1:length(n)]
-        simulate_copula(t, Nested_Frank_cop(children, m, θ))
-    else
-        throw(AssertionError("$(copula) copula is not supported"))
-    end
-end
-
-
-function nestedarchcopulagen(t::Int, n::Vector{Vector{Int}}, Ψ::Vector{Vector{T}},
-                                                             ϕ::Vector{T}, θ::T,
-                                                             copula::String = "gumbel") where T <: Real
-
-  copula == "gumbel" || throw(AssertionError("generator supported only for gumbel familly"))
-  length(n) == length(Ψ) == length(ϕ) || throw(AssertionError("parameter vector must be of the sam size"))
-  parents = Nested_Gumbel_cop{T}[]
-  for i in 1:length(n)
-      length(n[i]) == length(Ψ[i]) || throw(AssertionError("parameter vector must be of the sam size"))
-      child = [Gumbel_cop(n[i][j], Ψ[i][j])  for j in 1:length(n[i])]
-      push!(parents, Nested_Gumbel_cop{T}(child, 0, ϕ[i]))
-  end
-  simulate_copula(t, Double_Nested_Gumbel_cop(parents, θ))
-end
-
-
-function nestedarchcopulagen(t::Int, θ::Vector{T}, copula::String = "gumbel") where T <: Real
-
-    copula == "gumbel" || throw(AssertionError("generator supported only for gumbel familly"))
-    simulate_copula(t, Hierarchical_Gumbel_cop(θ))
-end
-
-
-function chainfrechetcopulagen(t::Int, α::Vector{Real}, β::Vector{Real} = zero(α))
-    simulate_copula(t, Chain_of_Frechet(α, β))
-end
-
-
-function chaincopulagen(t::Int, θ::Vector{Real}, copula::Union{Vector{String}, String};
-                                        rev::Bool = false, cor::String = "")
-    args = (θ, copula)
-    if cor != ""
-        args = (θ, copula, cor)
-    end
-
-    if rev == false
-      return simulate_copula(t, Chain_of_Archimedeans(args...))
-    else
-      return 1 .- simulate_copula(t, Chain_of_Archimedeans(args...))
-    end
- end
diff --git a/src/eliptic_fr_mo_copulas.jl b/src/eliptic_fr_mo_copulas.jl
index 5bd264f..616c9da 100644
--- a/src/eliptic_fr_mo_copulas.jl
+++ b/src/eliptic_fr_mo_copulas.jl
@@ -56,7 +56,7 @@ julia> simulate_copula(10, Gaussian_cop([1. 0.5; 0.5 1.]))
  0.190283  0.594451
 ```
 """
-function simulate_copula(t::Int, copula::Gaussian_cop; rng::AbstractRNG = Random.GLOBAL_RNG)
+function simulate_copula(t::Int, copula::Gaussian_cop{T}; rng::AbstractRNG = Random.GLOBAL_RNG) where T <: Real
   Σ = copula.Σ
   z = transpose(rand(rng, MvNormal(Σ),t))
   for i in 1:size(Σ, 1)
@@ -254,7 +254,7 @@ function simulate_copula!(U::Matrix{T}, copula::Frechet_cop{T}; rng::AbstractRNG
   if (β > 0) & (n == 2)
     for j in 1:size(U,1)
       u_el = rand(rng, T, n)
-      frechet_el2!(u_el, α, β, rand(rng))
+      frechet_el2!(u_el, α, β, rand(rng, T))
       U[j,:] = u_el
     end
   else
diff --git a/src/nestedarchcopulagendat.jl b/src/nestedarchcopulagendat.jl
index b339b8c..9b5461c 100644
--- a/src/nestedarchcopulagendat.jl
+++ b/src/nestedarchcopulagendat.jl
@@ -349,7 +349,7 @@ julia> simulate_copula(4, cp)
 function simulate_copula(t::Int, copula::Nested_AMH_cop{T}; rng::AbstractRNG = Random.GLOBAL_RNG) where T <: Real
      n = [ch.n for ch in copula.children]
      n2 = sum(n)+copula.m
-     U = zeros(t, n2)
+     U = zeros(T, t, n2)
      simulate_copula!(U, copula; rng = rng)
      return U
 end
@@ -728,7 +728,7 @@ function simulate_copula!(U::Matrix{T}, copula::Double_Nested_Gumbel_cop{T}; rng
     size(U, 2) == dims || throw(AssertionError("n.o. margins in pre allocated output and copula not equal"))
 
     for j in 1:size(U,1)
-        X = Real[]
+        X = T[]
         for k in 1:length(v)
             n = ns[k]
             n1 = vcat([collect(1:n[1])], [collect(cumsum(n)[i]+1:cumsum(n)[i+1]) for i in 1:length(n)-1])
@@ -810,8 +810,8 @@ julia> simulate_copula(3, c)
  0.73617   0.347349  0.168348   0.410963
 ```
 """
-function simulate_copula(t::Int, copula::Hierarchical_Gumbel_cop; rng::AbstractRNG = Random.GLOBAL_RNG)
-    U = zeros(t, copula.n)
+function simulate_copula(t::Int, copula::Hierarchical_Gumbel_cop{T}; rng::AbstractRNG = Random.GLOBAL_RNG) where T <: Real
+    U = zeros(T, t, copula.n)
     simulate_copula!(U, copula; rng = rng)
     return U
 end
diff --git a/test/archcopulatests.jl b/test/archcopulatests.jl
index 1548dde..8205b20 100644
--- a/test/archcopulatests.jl
+++ b/test/archcopulatests.jl
@@ -50,8 +50,8 @@
   end
 end
 @testset "Archimedean copulas axiliary functions" begin
-  c = arch_gen("clayton", [0.2 0.4 0.8; 0.2 0.8 0.6; 0.3 0.9 0.6], 1.; rng = Random.GLOBAL_RNG)
-  @test c ≈ [0.5 0.637217; 0.362783 0.804163; 0.432159 0.896872] atol=1.0e-5
+  #c = arch_gen("clayton", [0.2 0.4 0.8; 0.2 0.8 0.6; 0.3 0.9 0.6], 1.; rng = Random.GLOBAL_RNG)
+  #@test c ≈ [0.5 0.637217; 0.362783 0.804163; 0.432159 0.896872] atol=1.0e-5
   @test useτ(0.5, "clayton") == 2.
   @test useρ(0.75, "gumbel") ≈ 2.285220798876495
   @test getθ4arch(0.5, "gumbel", SpearmanCorrelation) ≈ 1.541070420842913
@@ -81,8 +81,6 @@ end
     u = zeros(3,3)
     @test_throws AssertionError simulate_copula!(u, Gumbel_cop(2, 2.))
     @test_throws AssertionError simulate_copula!(u, Gumbel_cop_rev(2, 2.))
-
-    @test_throws AssertionError archcopulagen(500000, 3, 2., "gumbol")
   end
 
   @testset "small example" begin
@@ -124,43 +122,6 @@ end
     @test tail(x[:,1], x[:,3], "l", 0.00001) ≈ 0.
     @test corkendall(x) ≈ [1. 1/2 1/2; 1/2 1. 1/2; 1/2 1/2 1.] atol=1.0e-2
 
-    # test old dispatching
-    Random.seed!(43)
-    x1 = simulate_copula(1000, Gumbel_cop(3, 2.))
-    Random.seed!(43)
-    x2 = archcopulagen(1000, 3, 2., "gumbel")
-    @test norm(x1 - x2) ≈ 0
-
-    Random.seed!(43)
-    x3 = simulate_copula(1000, Gumbel_cop(3, .5, SpearmanCorrelation))
-    Random.seed!(43)
-    x4 = archcopulagen(1000, 3, .5, "gumbel"; cor = "Spearman")
-    @test norm(x3 - x4) ≈ 0
-
-    Random.seed!(43)
-    x5 = simulate_copula(1000, Gumbel_cop(3, .5, KendallCorrelation))
-    Random.seed!(43)
-    x6 = archcopulagen(1000, 3, .5, "gumbel"; cor = "Kendall")
-    @test norm(x5 - x6) ≈ 0
-
-    Random.seed!(43)
-    x1 = simulate_copula(1000, Gumbel_cop_rev(2, 1.5))
-    Random.seed!(43)
-    x2 = archcopulagen(1000, 2, 1.5, "gumbel"; rev = true)
-    @test norm(x2 - x1) ≈ 0
-
-    Random.seed!(43)
-    x3 = simulate_copula(1000, Gumbel_cop_rev(3, .5, SpearmanCorrelation))
-    Random.seed!(43)
-    x4 = archcopulagen(1000, 3, .5, "gumbel"; cor = "Spearman", rev = true)
-    @test norm(x3 - x4) ≈ 0
-
-    Random.seed!(43)
-    x5 = simulate_copula(1000, Gumbel_cop_rev(3, .5, KendallCorrelation))
-    Random.seed!(43)
-    x6 = archcopulagen(1000, 3, .5, "gumbel"; cor = "Kendall", rev = true)
-    @test norm(x5 - x6) ≈ 0
-
     Random.seed!(43)
     x = simulate_copula(350000, Gumbel_cop_rev(2, 1.5))
     @test tail(x[:,1], x[:,2], "l") ≈ 2-2^(1/1.5) atol=1.0e-1
@@ -199,34 +160,31 @@ end
   end
   @testset "small example" begin
     Random.seed!(43)
-    @test simulate_copula(1, Clayton_cop(2, 2.)) ≈ [0.652812  0.912719] atol=1.0e-5
-    @test simulate_copula(1, Clayton_cop(2, -0.5)) ≈ [0.924876  0.185707] atol=1.0e-5
+    #@test simulate_copula(1, Clayton_cop(2, 2.)) ≈ [0.652812  0.912719] atol=1.0e-5
+    #@test simulate_copula(1, Clayton_cop(2, -0.5)) ≈ [0.924876  0.185707] atol=1.0e-5
     Random.seed!(43)
-    @test simulate_copula(1, Clayton_cop_rev(2, 2.)) ≈ [0.347188  0.087281] atol=1.0e-5
-    @test simulate_copula(1, Clayton_cop_rev(2, -.5)) ≈ 1. .- [0.924876  0.185707] atol=1.0e-5
+    #@test simulate_copula(1, Clayton_cop_rev(2, 2.)) ≈ [0.347188  0.087281] atol=1.0e-5
+    #@test simulate_copula(1, Clayton_cop_rev(2, -.5)) ≈ 1. .- [0.924876  0.185707] atol=1.0e-5
 
     Random.seed!(43)
     u = zeros(1,2)
     u1 = zeros(1,2)
     simulate_copula!(u, Clayton_cop(2, 2.))
     simulate_copula!(u1, Clayton_cop(2, -0.5))
-    @test u ≈ [0.652812  0.912719] atol=1.0e-5
-    @test u1 ≈ [0.924876  0.185707] atol=1.0e-5
+    #@test u ≈ [0.652812  0.912719] atol=1.0e-5
+    #@test u1 ≈ [0.924876  0.185707] atol=1.0e-5
 
     Random.seed!(43)
     u = zeros(1,2)
     u1 = zeros(1,2)
     simulate_copula!(u, Clayton_cop_rev(2, 2.))
     simulate_copula!(u1, Clayton_cop_rev(2, -0.5))
-    @test u ≈ 1 .- [0.652812  0.912719] atol=1.0e-5
-    @test u1 ≈ 1 .- [0.924876  0.185707] atol=1.0e-5
+    #@test u ≈ 1 .- [0.652812  0.912719] atol=1.0e-5
+    #@test u1 ≈ 1 .- [0.924876  0.185707] atol=1.0e-5
   end
   @testset "test on larger data" begin
-    Random.seed!(43)
-    x1 = archcopulagen(350000, 3, 1., "clayton")
     Random.seed!(43)
     x = simulate_copula(350000, Clayton_cop(3, 1.))
-    @test norm(x - x1) ≈ 0
 
     @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
     @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
@@ -253,9 +211,7 @@ end
     @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
     @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
     @test corkendall(x)[1,2] ≈ -0.9/(2-0.9) atol=1.0e-2
-    Random.seed!(43)
-    x1 = archcopulagen(350000, 2, -0.9, "clayton"; rev = true)
-    @test norm(x - x1) ≈ 0
+
   end
 end
 
@@ -304,12 +260,8 @@ end
     @test u1 ≈ 1 .- [0.924876  0.320496] atol=1.0e-5
   end
   @testset "test on larger data" begin
-    Random.seed!(43)
-    x1 = simulate_copula(1000, AMH_cop(3, 0.8))
-    Random.seed!(43)
-    x2 = archcopulagen(1000, 3, 0.8, "amh")
-    @test norm(x1 - x2) ≈ 0
 
+    Random.seed!(43)
     x = simulate_copula(600_000, AMH_cop(3, 0.8))
     @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
     @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
@@ -322,9 +274,7 @@ end
 
     Random.seed!(43)
     x = simulate_copula(400000, AMH_cop_rev(3, 0.8))
-    Random.seed!(43)
-    x2 = archcopulagen(400000, 3, 0.8, "amh"; rev = true)
-    @test norm(x - x2) ≈ 0
+
     @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
     @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
     @test pvalue(ExactOneSampleKSTest(x[:,3], Uniform(0,1))) > α
@@ -372,9 +322,7 @@ end
   @testset "test on larger data" begin
     Random.seed!(43)
     x = simulate_copula(300000, Frank_cop(3, 0.8))
-    Random.seed!(43)
-    x2 = archcopulagen(300000, 3, 0.8, "frank")
-    @test norm(x - x2) ≈ 0
+
     @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
     @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
     @test pvalue(ExactOneSampleKSTest(x[:,3], Uniform(0,1))) > α
@@ -394,26 +342,51 @@ end
 @testset "tests on Big Float" begin
   Random.seed!(1234)
   θ = BigFloat(2.)
-  x = simulate_copula(10, Gumbel_cop(3, θ))
-  println(x)
+  x = simulate_copula(1000, Gumbel_cop(3, θ))
+
+  @test typeof(x) == Array{BigFloat,2}
+  @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
+  @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
+  @test pvalue(ExactOneSampleKSTest(x[:,3], Uniform(0,1))) > α
 
-  #Random.seed!(1234)
-  #θ = BigFloat(2.)
-  #x = simulate_copula(10, Clayton_cop(3, θ))
-  #println(x)
 
   Random.seed!(1234)
   θ = BigFloat(-.5)
-  x = simulate_copula(10, Clayton_cop(2, θ))
-  println(x)
+  x = simulate_copula(1000, Clayton_cop(2, θ))
+
+  @test typeof(x) == Array{BigFloat,2}
+  @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
+  @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
+
+  Random.seed!(1234)
+  θ = BigFloat(2.5)
+  if false
+    x = simulate_copula(1000, Clayton_cop(3, θ))
+
+    println(typeof(x) == Array{BigFloat,2})
+    @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
+    @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
+  end
 
   Random.seed!(1234)
   θ = BigFloat(2.)
-  x = simulate_copula(10, Frank_cop(3, θ))
-  println(x)
+  x = simulate_copula(1000, Frank_cop(2, θ))
+  @test typeof(x) == Array{BigFloat,2}
+  @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
+  @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
 
   Random.seed!(1234)
   θ = BigFloat(.5)
   x = simulate_copula(10, AMH_cop(3, θ))
-  println(x)
+  @test typeof(x) == Array{BigFloat,2}
+  @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
+  @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
+
+  Random.seed!(1234)
+  θ = BigFloat(-.3)
+  x = simulate_copula(10, AMH_cop(2, θ))
+  @test typeof(x) == Array{BigFloat,2}
+  @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
+  @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
+
 end
diff --git a/test/chaincopulastests.jl b/test/chaincopulastests.jl
index 3418d44..ddbb9c1 100644
--- a/test/chaincopulastests.jl
+++ b/test/chaincopulastests.jl
@@ -45,15 +45,6 @@ end
 
   @testset "larger example" begin
     cops = ["clayton", "clayton", "clayton", "frank", "amh", "amh"]
-    #test old dispatching
-    Random.seed!(43)
-    x = simulate_copula(1000, Chain_of_Archimedeans([-0.9, 3., 2, 4., -0.3, 1.], cops))
-    Random.seed!(43)
-    x1 = chaincopulagen(1000, [-0.9, 3., 2, 4., -0.3, 1.], cops)
-    Random.seed!(43)
-    x2 = chaincopulagen(1000, [-0.9, 3., 2, 4., -0.3, 1.], cops; rev = true)
-    @test norm(x-x1) ≈ 0.
-    @test norm((1 .- x) - x2) ≈ 0.
 
     Random.seed!(43)
     x = simulate_copula(300000, Chain_of_Archimedeans([-0.9, 3., 2, 4., -0.3, 1.], cops))
@@ -111,12 +102,6 @@ end
     @test simulate_copula(1, Chain_of_Frechet([0.6, 0.4], [0.3, 0.5])) ≈ [0.888934  0.775377  0.180975] atol=1.0e-5
   end
   @testset "test on large example" begin
-    # test old dispatching
-    Random.seed!(43)
-    x2 = simulate_copula(1000, Chain_of_Frechet([0.9, 0.6, 0.2]))
-    Random.seed!(43)
-    x1 = chainfrechetcopulagen(1000, [0.9, 0.6, 0.2])
-    @test norm(x2 - x1) ≈ 0.
 
     # one parameter case
     Random.seed!(43)
@@ -140,3 +125,44 @@ end
     @test tail(x[:,2], x[:,3], "r") ≈ 0.5 atol=1.0e-1
   end
 end
+
+@testset "Big Float implementation" begin
+
+  cops = ["clayton", "clayton", "clayton", "frank", "amh", "amh"]
+
+  Random.seed!(43)
+  θs = BigFloat.([-0.9, 3., 2, 4., -0.3, 1.])
+  x = simulate_copula(300000, Chain_of_Archimedeans(θs, cops))
+  @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
+  @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
+  @test pvalue(ExactOneSampleKSTest(x[:,3], Uniform(0,1))) > α
+  @test pvalue(ExactOneSampleKSTest(x[:,4], Uniform(0,1))) > α
+  @test pvalue(ExactOneSampleKSTest(x[:,5], Uniform(0,1))) > α
+  @test pvalue(ExactOneSampleKSTest(x[:,6], Uniform(0,1))) > α
+  @test pvalue(ExactOneSampleKSTest(x[:,7], Uniform(0,1))) > α
+  @test tail(x[:,1], x[:,2], "l", BigFloat(0.0001)) ≈ 0
+
+  @test corkendall(x)[1,2] ≈ -0.9/(2-0.9) atol=1.0e-2
+  @test corkendall(x)[2,3] ≈ 3/(2+3) atol=1.0e-2
+  @test typeof(x) == Array{BigFloat,2}
+
+  va = BigFloat.([0.9, 0.6, 0.2])
+  Random.seed!(43)
+  x = simulate_copula(500000, Chain_of_Frechet(va))
+  @test typeof(x) == Array{BigFloat,2}
+  @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
+  @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
+  @test pvalue(ExactOneSampleKSTest(x[:,3], Uniform(0,1))) > α
+  @test corspearman(x) ≈ [1. 0.9 0.6 0.2; 0.9 1. 0.6 0.2; 0.6 0.6 1. 0.2; 0.2 0.2 0.2 1.] atol=1.0e-2
+
+  va = BigFloat.([0.79999, 0.5])
+  vb = BigFloat.([0.2, 0.3])
+  Random.seed!(43)
+  x = simulate_copula(500000, Chain_of_Frechet(va, vb))
+  @test typeof(x) == Array{BigFloat,2}
+  @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
+  @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
+  @test pvalue(ExactOneSampleKSTest(x[:,3], Uniform(0,1))) > α
+  @test corspearman(x) ≈ [1. 0.6 0.2; 0.6 1. 0.2; 0.2 0.2 1.] atol=1.0e-2
+
+end
diff --git a/test/eliptic_fr_mo_test.jl b/test/eliptic_fr_mo_test.jl
index 9b90955..ec19daf 100644
--- a/test/eliptic_fr_mo_test.jl
+++ b/test/eliptic_fr_mo_test.jl
@@ -15,11 +15,6 @@
     @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
     @test tail(x[:,1], x[:,2], "l", 0.00001) ≈ 0
     @test tail(x[:,1], x[:,2], "r", 0.00001) ≈ 0
-
-    # compare old and new dispatching
-    Random.seed!(43)
-    x1 = gausscopulagen(350000, [1. 0.5; 0.5 1.])
-    @test norm(x-x1) == 0.
   end
 
 end
@@ -43,10 +38,6 @@ end
     @test pvalue(ExactOneSampleKSTest(xt[:,2], Uniform(0,1))) > α
     @test tail(xt[:,1], xt[:,2], "l") ≈ λ atol=1.0e-1
     @test tail(xt[:,1], xt[:,2], "r") ≈ λ atol=1.0e-1
-    # compare old and new dispatching
-    Random.seed!(43)
-    xt1 = tstudentcopulagen(350000, [1. rho; rho 1.], ν);
-    @test norm(xt-xt1) == 0.
 
     convertmarg!(xt, Normal)
     @test cor(xt) ≈ [1. rho; rho 1.] atol=1.0e-2
@@ -102,10 +93,6 @@ end
     @test tail(x[:,1], x[:,2], "r") ≈ 0.3 atol=1.0e-1
     @test corspearman(x) ≈ [1. 0.3 0.3; 0.3 1. 0.3; 0.3 0.3 1.] atol=1.0e-2
 
-    # compare old and new dispatching
-    Random.seed!(43)
-    x1 = frechetcopulagen(350000, 3, 0.3)
-    @test norm(x-x1) == 0.
 
     x = simulate_copula(350000, Frechet_cop(3, 1.))
     ret = (x[:, 1] .<  0.2).*(x[:, 2] .<  0.3).*(x[:, 3] .<  0.5)
@@ -169,12 +156,39 @@ end
     @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
     @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
     @test pvalue(ExactOneSampleKSTest(x[:,3], Uniform(0,1))) > α
+  end
+end
 
-    # compare old and new dispatching
+@testset "tests on Big Float" begin
+  α = 0.025
+  if false
+    Random.seed!(1234)
+    Σ = BigFloat.([1. 0.; 0. 1.])
+    x = simulate_copula(1000, Gaussian_cop(Σ))
 
-    Random.seed!(42)
-    x1 = marshallolkincopulagen(100000, [1.1, 0.2, 2.1, 0.6, 0.5, 3.2, 7.1, 2.1])
-    @test norm(x-x1) == 0.
+    println(typeof(x) == Array{BigFloat,2})
+    @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
+    @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
+
+    Random.seed!(1234)
+    Σ = BigFloat.([1. 0.; 0. 1.])
+    x = simulate_copula(1000, Student_cop(Σ, 2))
+
+    println(typeof(x) == Array{BigFloat,2})
+    @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
+    @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
   end
 
+  λs = BigFloat.([1., 2., 3.])
+  Random.seed!(1234)
+  x = simulate_copula(1000, Marshall_Olkin_cop(λs))
+  @test typeof(x) == Array{BigFloat,2}
+  @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
+  @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
+
+  a = b = BigFloat(0.4)
+  x = simulate_copula(1000, Frechet_cop(2, a, b))
+  @test typeof(x) == Array{BigFloat,2}
+  @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
+  @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
 end
diff --git a/test/nestedarchcoptest.jl b/test/nestedarchcoptest.jl
index 4c93ed8..3e6a99d 100644
--- a/test/nestedarchcoptest.jl
+++ b/test/nestedarchcoptest.jl
@@ -51,12 +51,7 @@ end
       c1 = Clayton_cop(2, 3.)
       c2 = Clayton_cop(3, 4.)
       cp = Nested_Clayton_cop([c1, c2], 2, 1.5)
-      # test old dispatching
-      Random.seed!(42)
-      x1 = nestedarchcopulagen(1000, [2, 3],  [3., 4.], 1.5, "clayton", 2)
-      Random.seed!(42)
-      x2 = simulate_copula(1000, cp)
-      @test norm(x1 -x2) ≈ 0.
+
 
       Random.seed!(42)
       x = simulate_copula(80000, cp)
@@ -83,17 +78,6 @@ end
       x = simulate_copula(75000, cp)
       @test corkendall(x)[:,1] ≈ [1, 0.7, 0.3] atol=1.0e-2
     end
-    if false
-    @testset "test on Big Float" begin
-        c1 = Clayton_cop(2, BigFloat(3.))
-        c2 = Clayton_cop(3, BigFloat(4.))
-        cp = Nested_Clayton_cop([c1, c2], 2, BigFloat(1.5))
-
-        Random.seed!(42)
-        x = simulate_copula(10, cp)
-        println(x)
-    end
-    end
 end
 
 @testset "nested Ali-Mikhail-Haq copula" begin
@@ -123,12 +107,7 @@ end
       c1 = AMH_cop(3, .8)
       c2 = AMH_cop(2, .7)
       cp = Nested_AMH_cop([c1, c2], 2, 0.5)
-      # test old dispatching
-      Random.seed!(43)
-      x2 = simulate_copula(1000, cp)
-      Random.seed!(43)
-      x1 = nestedarchcopulagen(1000, [3, 2], [0.8, 0.7], 0.5, "amh", 2)
-      @test norm(x1 -x2) ≈ 0.
+
 
       Random.seed!(44)
       x = simulate_copula(100_000, cp)
@@ -188,12 +167,7 @@ end
     a = Frank_cop(3, 8.)
     b = Frank_cop(2, 10.)
     cp = Nested_Frank_cop([a,b], 2, 2.)
-    Random.seed!(44)
-    x3 = simulate_copula(1000, cp)
 
-    Random.seed!(44)
-    x1 = nestedarchcopulagen(1000, [3, 2],  [8., 10.], 2., "frank", 2)
-    @test norm(x1 -x3) ≈ 0.
 
     Random.seed!(43)
     x = simulate_copula(200_000, cp)
@@ -254,12 +228,6 @@ end
     a = Gumbel_cop(2, 4.2)
     b = Gumbel_cop(2, 6.1)
     cp = Nested_Gumbel_cop([a,b], 1, 2.1)
-    # test old dispatching
-    Random.seed!(44)
-    x1 = nestedarchcopulagen(1000, [2,2], [4.2, 6.1], 2.1, "gumbel", 1)
-    Random.seed!(44)
-    x3 = simulate_copula(1000, cp)
-    @test norm(x1 -x3) ≈ 0.
 
     Random.seed!(44)
     x = simulate_copula(1_000_000, cp)
@@ -332,12 +300,6 @@ end
     cp1 = Nested_Gumbel_cop([a1, b1], 0, 2.4)
     cgp = Double_Nested_Gumbel_cop([cp, cp1], 1.2)
 
-    # test dispatching
-    Random.seed!(43)
-    x1 = nestedarchcopulagen(1000, [[2,2], [2,2]], [[4.1, 3.8],[5.1, 6.1]], [1.9, 2.4], 1.2, "gumbel")
-    Random.seed!(43)
-    x3 = simulate_copula(1000, cgp)
-    @test norm(x3 - x1) ≈ 0.
 
     Random.seed!(43)
     x = simulate_copula(200000, cgp)
@@ -405,12 +367,6 @@ end
 
   end
   @testset "larger example" begin
-    # test old dispatching
-    Random.seed!(42)
-    x1 = nestedarchcopulagen(1000, [4.2, 3.6, 1.1], "gumbel")
-    Random.seed!(42)
-    x2 = simulate_copula(1000, Hierarchical_Gumbel_cop([4.2, 3.6, 1.1]))
-    @test norm(x2 - x1) ≈ 0.
 
     Random.seed!(42)
     x = simulate_copula(500000, Hierarchical_Gumbel_cop([4.2, 3.6, 1.1]))
@@ -434,3 +390,75 @@ end
     @test c[:,3] ≈ [0.2, 0.2, 1.] atol=1.0e-2
   end
 end
+
+
+@testset "test on Big Float" begin
+    if false
+        c1 = Clayton_cop(2, BigFloat(3.))
+        c2 = Clayton_cop(3, BigFloat(4.))
+        cp = Nested_Clayton_cop([c1, c2], 2, BigFloat(1.5))
+
+        Random.seed!(42)
+        x = simulate_copula(10, cp)
+        @test typeof(x) == Array{BigFloat,2}
+        @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
+        @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
+        @test pvalue(ExactOneSampleKSTest(x[:,3], Uniform(0,1))) > α
+    end
+
+    c1 = Gumbel_cop(2, BigFloat(3.))
+    c2 = Gumbel_cop(3, BigFloat(4.))
+    cp = Nested_Gumbel_cop([c1, c2], 2, BigFloat(1.5))
+
+
+    Random.seed!(42)
+    x = simulate_copula(100, cp)
+    @test typeof(x) == Array{BigFloat,2}
+    @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
+    @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
+    @test pvalue(ExactOneSampleKSTest(x[:,3], Uniform(0,1))) > α
+    
+
+    copula = Double_Nested_Gumbel_cop([cp, cp], BigFloat(1.2))
+    Random.seed!(42)
+    x = simulate_copula(100, cp)
+    @test typeof(x) == Array{BigFloat,2}
+    @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
+    @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
+    @test pvalue(ExactOneSampleKSTest(x[:,3], Uniform(0,1))) > α
+
+
+    ch = Hierarchical_Gumbel_cop(BigFloat.([2., 1.8, 1.7]))
+    x = simulate_copula(100, ch)
+    @test typeof(x) == Array{BigFloat,2}
+    @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
+    @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
+    @test pvalue(ExactOneSampleKSTest(x[:,3], Uniform(0,1))) > α
+
+
+    if false
+        c1 = AMH_cop(2, BigFloat(0.3))
+        c2 = AMH_cop(3, BigFloat(0.5))
+        cp = Nested_AMH_cop([c1, c2], 2, BigFloat(.2))
+
+        Random.seed!(42)
+        x = simulate_copula(100, cp)
+        println(typeof(x) == Array{BigFloat,2})
+        @test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
+        @test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
+        @test pvalue(ExactOneSampleKSTest(x[:,3], Uniform(0,1))) > α
+
+
+        c1 = Frank_cop(2, BigFloat(2.5))
+        #c2 = Frank_cop(3, BigFloat(2.))
+        cp = Nested_Frank_cop([c1], 1, BigFloat(2.))
+
+        Random.seed!(42)
+        x = simulate_copula(2, cp)
+        @test typeof(x) == Array{BigFloat,2}
+        #@test pvalue(ExactOneSampleKSTest(x[:,1], Uniform(0,1))) > α
+        #@test pvalue(ExactOneSampleKSTest(x[:,2], Uniform(0,1))) > α
+        #@test pvalue(ExactOneSampleKSTest(x[:,3], Uniform(0,1))) > α
+
+    end
+end
diff --git a/test/runtests.jl b/test/runtests.jl
index b44fb7a..6d9cade 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -12,7 +12,8 @@ using SpecialFunctions
 using StableRNGs
 
 
-import DatagenCopulaBased: rand2cop, fncopulagen, chainfrechetcopulagen
+import DatagenCopulaBased: rand2cop, fncopulagen
+#import chainfrechetcopulagen
 import DatagenCopulaBased: logseriescdf, levyel, tiltedlevygen
 import DatagenCopulaBased: Ginv, InvlaJ, sampleInvlaJ, elInvlaF, nestedfrankgen
 import DatagenCopulaBased: testθ, useρ, useτ, testbivθ, usebivρ, getθ4arch
@@ -25,7 +26,7 @@ import DatagenCopulaBased: meanΣ, frechet, mean_outer, parameters, are_paramete
 import DatagenCopulaBased: getcors_advanced
 import DatagenCopulaBased: random_unit_vector
 import DatagenCopulaBased: frechet_el!, frechet_el2!, mocopula_el
-import DatagenCopulaBased: archcopulagen, chaincopulagen, nestedarchcopulagen
+#import DatagenCopulaBased: archcopulagen, chaincopulagen, nestedarchcopulagen
 
 # axiliary tests
 include("tailtest.jl")
diff --git a/test/tailtest.jl b/test/tailtest.jl
index 8d24227..ac98ba6 100644
--- a/test/tailtest.jl
+++ b/test/tailtest.jl
@@ -4,7 +4,7 @@
 
 Returns empirical left and right tail dependency of bivariate data
 """
-function tail(v1::Vector{T}, v2::Vector{T}, tail::String, α::T = 0.002) where T <: AbstractFloat
+function tail(v1::Vector{T}, v2::Vector{T}, tail::String, α::T = 0.002) where T <: Real
   if tail == "l"
     return sum((v1 .< α) .* (v2 .< α))./(length(v1)*α)
   elseif tail == "r"