rFFrank

rFJoe

theta0

theta1

method switch for <code>rFFrank</code>: if <code>theta0</code> >
    <code>rej</code> a rejection from Joe's family (Sibuya distribution) is
    applied (otherwise, a logarithmic envelope is used).

parameter $alpha = theta0/theta1$ in $(0,1]$ for
    <code>rFJoe</code>.

alpha


  Generate a vector of variates $V ~ F$ from the distribution
  function $F$ with Laplace-Stieltjes transform
  $$(1-(1-\exp(-t)(1-e^{-\theta_1}))^\alpha)/(1-e^{-\theta_0}),
  $$
  for Frank, or
  $$1-(1-\exp(-t))^\alpha,$$ for Joe, respectively,
  where $theta0$ and $theta1$ denote two parameters
  of Frank (that is, $theta0,theta1
    in (0,Inf)$) and Joe (that is, $
    theta0,theta1 in [1,Inf)$) satisfying
  $theta0 

distribution

rFFrankJoe

Classes (S4) of commonly used elliptical, Archimedean, extreme
value and some more copula families.  Methods for density, distribution,
random number generation, bivariate dependence measures, perspective and
contour plots.  Fitting copula models including variance estimates.
Independence and serial (univariate and multivariate) independence tests,
and other copula related tests. Empirical copula and multivariate CDF.
Goodness-of-fit tests for copulas based on multipliers, the parametric
bootstrap with several transformation options.
Merged former package 'nacopula' for nested Archimedean copulas: Efficient
sampling algorithms, various estimators, goodness-of-fit tests and related
tools and special functions.

Martin Maechler

copula

Multivariate Dependence with Copulas

rFFrankJoe function


  Generate a vector of variates $V ~ F$ from the distribution
  function $F$ with Laplace-Stieltjes transform
  $$(1-(1-\exp(-t)(1-e^{-\theta_1}))^\alpha)/(1-e^{-\theta_0}),
  $$
  for Frank, or
  $$1-(1-\exp(-t))^\alpha,$$ for Joe, respectively,
  where $theta0$ and $theta1$ denote two parameters
  of Frank (that is, $theta0,theta1
    in (0,Inf)$) and Joe (that is, $
    theta0,theta1 in [1,Inf)$) satisfying
  $theta0 <= theta1$
  and $alpha=theta0/theta1$.


rFFrankJoe: Sampling Distribution F for Frank and Joe

Description

Usage

Arguments

Value

Details

See Also

Examples