#

using Distributions
using Base.Test

dg = Gamma(1,1)

d_s = EdgeworthSum(dg,10)
d_m = EdgeworthMean(dg,10)
d_z = EdgeworthZ(dg,10)

dg_s = Gamma(10,1)
dg_m = Gamma(10,0.1)
dg_za = Gamma(10,1/std(dg_s))

for i = 0.01:0.01:0.99

    q = quantile(dg_s,i)
    @test isapprox(quantile(d_s, i)     , q          , atol=0.02)
    @test isapprox(cquantile(d_s, 1 - i), q          , atol=0.02)
    @test isapprox(cdf(d_s, q)          , i          , atol=0.002)
    @test isapprox(ccdf(d_s, q)         , 1 - i      , atol=0.002)
    @test isapprox(pdf(d_s, q)          , pdf(dg_s,q), atol=0.005)

    q = quantile(dg_m,i)
    @test isapprox(quantile(d_m, i)     , q          , atol=0.01)
    @test isapprox(cquantile(d_m, 1 - i), q          , atol=0.01)
    @test isapprox(cdf(d_m, q)          , i          , atol=0.002)
    @test isapprox(ccdf(d_m, q)         , 1 - i      , atol=0.002)
    @test isapprox(pdf(d_m, q)          , pdf(dg_m,q), atol=0.05)

    q = quantile(dg_za,i) - mean(dg_za)
    @test isapprox(quantile(d_z, i)     , q                       , atol=0.01)
    @test isapprox(cquantile(d_z, 1 - i), q                       , atol=0.01)
    @test isapprox(cdf(d_z, q)          , i                       , atol=0.002)
    @test isapprox(ccdf(d_z, q)         , 1 - i                   , atol=0.002)
    @test isapprox(pdf(d_z, q)          , pdf(dg_za,q+mean(dg_za)), atol=0.02)

end



