======================================

> ## Does the Gumbel family seem to be a good choice (statistic "Sn")?
> gofCopula(gumbelCopula(), x)
========Warning in fitCopula.ml(copula, u = data, method = method, start = start,  :
  possible convergence problem: optim() gave code=52
=====Warning in fitCopula.ml(copula, u = data, method = method, start = start,  :
  possible convergence problem: optim() gave code=52
======================Warning in fitCopula.ml(copula, u = data, method = method, start = start,  :
  possible convergence problem: optim() gave code=52
==============================================================

        Parametric bootstrap-based goodness-of-fit test of Gumbel copula, dim. d = 2, with
        'method'="Sn", 'estim.method'="mpl":

data:  x
statistic = 0.244, parameter = 2.09, p-value = 5e-04

> ## With "SnC", really s..l..o..w.. --- with "SnB", *EVEN* slower
> gofCopula(gumbelCopula(), x, method = "SnC", trafo.method = "cCopula")
==============================================================================================Warning in fitCopula.ml(copula, u = data, method = method, start = start,  :
  possible convergence problem: optim() gave code=52
===

        Parametric bootstrap-based goodness-of-fit test of Gumbel copula, dim. d = 2, with
        'method'="SnC", 'estim.method'="mpl", 'trafo.method'="cCopula":

data:  x
statistic = 0.565, parameter = 2.09, p-value = 5e-04

> ## What about the Clayton family?
> gofCopula(claytonCopula(), x)
=================================================================================================

        Parametric bootstrap-based goodness-of-fit test of Clayton copula, dim. d = 2, with
        'method'="Sn", 'estim.method'="mpl":

data:  x
statistic = 0.0163, parameter = 3.24, p-value = 0.44

>
> ## Similar with a different estimation method
> gofCopula(gumbelCopula (), x, estim.method="itau")
=================================================================================================

        Parametric bootstrap-based goodness-of-fit test of Gumbel copula, dim. d = 2, with
        'method'="Sn", 'estim.method'="itau":

data:  x
statistic = 0.163, parameter = 2.58, p-value = 5e-04

> gofCopula(claytonCopula(), x, estim.method="itau")
=================================================================================================

        Parametric bootstrap-based goodness-of-fit test of Clayton copula, dim. d = 2, with
        'method'="Sn", 'estim.method'="itau":

data:  x
statistic = 0.0169, parameter = 3.17, p-value = 0.31

>
>
> ## A three-dimensional example  ------------------------------------
> x <- rCopula(200, tCopula(c(0.5, 0.6, 0.7), dim = 3, dispstr = "un"))
>