The *Galambos copula* (Joe, 2014, p. 174) is

*\mathbf{C}_{Θ}(u,v) = \mathbf{GL}(u,v) = uv\,\mathrm{exp}\{[x^{-Θ} + y^{-Θ}]^{-1/Θ}\}\mbox{,}*

where *Θ \in [0, ∞)*, *x = -\log(u)*, and *y = -\log(v)*. As *Θ \rightarrow 0^{+}*, the copula limits to *independence* (*\mathbf{Π}*; `P`

) and as *Θ \rightarrow ∞*, the copula limits to perfect association (*\mathbf{M}*; `M`

).
The copula here is a *bivariate extreme value copula* (*BEV*), and the parameter *Θ* requires numerical methods.

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`u` |
Nonexceedance probability |

`v` |
Nonexceedance probability |

`para` |
A vector (single element) of parameters—the |

`...` |
Additional arguments to pass. |

Value(s) for the copula are returned.

W.H. Asquith

Joe, H., 2014, Dependence modeling with copulas: Boca Raton, CRC Press, 462 p.

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