psi(copula, s)
iPsi(copula, u, ...)
diPsi(copula, u, degree=1, log=FALSE, ...)
A(copula, w)
dAdu(copula, w)
"copula"
. log
of the
absolute derivative is desired. Note that the derivatives
of $psi$ alternate in sign.psi()
and iPsi()
are, respectively, the generator
function $\psi()$ and its inverse $\psi^(-1)$ for
an Archimedean copula, see pnacopula
for definition and
more details. diPsi()
computes (currently only the first two) derivatives of
iPsi()
($= \psi^(-1)$).
A()
, the Pickands dependence function, can be seen as the
generator function of an extreme-value copula. For instance, in the
bivariate case, we have the following result (see, e.g., Gudendorf and
Segers 2009):
A bivariate copula $C$ is an extreme-value copula if and only if $$C(u,v) = (uv)^{A(\log(v) / \log(uv))}, \qquad (u,v) \in (0,1]^2 \setminus \{(1,1)\},$$ where $A: [0,1] -> [1/2, 1]$ is convex and satisfies $max(t,1-t) <= a(t)="" <="1$" for="" all="" $t="" in="" [0,1]$.="" the="" $d$-variate="" case,="" there="" is="" a="" similar="" characterization,="" except="" that="" this="" time,="" pickands="" dependence="" function="" $a$="" defined="" on="" $d$-dimensional="" unit="" simplex.<="" p="">
dAdu()
returns a data.frame containing the 1st and 2nd
derivative of A()
.
An
.
## List the available methods (and their definitions):
showMethods("A")
showMethods("iPsi", incl=TRUE)
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