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\author{Marius Hofert\thanks{The author (Willis Research Fellow) thanks Willis Re
    for financial support while this work was completed.} \\ETH Zurich
 \And
 Martin M\"achler \\ ETH Zurich}
\title{Nested Archimedean Copulas Meet \R --- The \pkg{nacopula} Package}
\def\mythanks{a version of this paper, for \pkg{nacopula} 0.4\_4, has been published
    in JSS, \url{http://www.jstatsoft.org/v39/i09}.}
%% for pretty printing and a nice hypersummary also set:
\Plainauthor{Marius Hofert, Martin M\"achler} %% comma-separated
\Plaintitle{Nested Archimedean Copulas Meet R---The nacopula Package} %% without formatting
% \Shorttitle{}
%
%\VignetteIndexEntry{Nested Archimedean Copulas Meet R}% << shown on CRAN page!
%\VignetteDepends{copula}
%\VignetteDepends{lattice}
\SweaveOpts{engine=R,eps=FALSE, pdf=FALSE,grdevice=pdfDB,width=7,height=4,strip.white=true,keep.source=TRUE}

%% an abstract and keywords
\Abstract{
  The package \pkg{copula} (formerly \pkg{nacopula}) provides procedures for constructing
  nested Archimedean copulas in any dimensions and with any kind of nesting
  structure, generating vectors of random variates from the constructed
  objects, computing function values and probabilities of falling into
  hypercubes, as well as evaluation of characteristics such as Kendall's
  tau and the tail-dependence coefficients.
%% provides the families of Ali-Mikhail-Haq, Clayton, Frank, Gumbel, and Joe
%% also  *outer power* versions of our families
  As by-products, algorithms for
  various distributions, including exponentially tilted stable and Sibuya
  distributions, are implemented. Detailed examples are given.
}

\Keywords{Archimedean copulas, nested Archimedean copulas, sampling algorithms,
 Kendall's tau, tail-dependence coefficients, exponentially tilted stable
 distribution, \R}
\Plainkeywords{Archimedean copulas, nested Archimedean copulas, sampling
 algorithms, Kendall's tau, tail-dependence coefficients, exponentially tilted
 stable distribution, R} %% without formatting
%% at least one keyword must be supplied

%% publication information
%% NOTE: Typically, this can be left commented and will be filled out by the technical editor
%% \Volume{13}
%% \Issue{9}
%% \Month{September}
%% \Year{2004}
%% \Submitdate{2004-09-29}
%% \Acceptdate{2004-09-29}

%% The address of (at least) one author should be given
%% in the following format:
\Address{
	Marius Hofert\\
	Department of Mathematics\\
	ETH Zurich\\
	8092 Zurich, Switzerland\\
	E-mail: \email{marius.hofert@math.ethz.ch}\\
	URL: \url{http://www.math.ethz.ch/~hofertj/}
	\par\bigskip
	Martin M\"achler\\
	Seminar f\"ur Statistik, HG G~16\\
	ETH Zurich\\
	8092 Zurich, Switzerland\\
	E-mail: \email{maechler@stat.math.ethz.ch}\\
	URL: \url{http://stat.ethz.ch/people/maechler}
}
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\usepackage{mathtools}%fix amsmath deficiencies
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\usepackage{tabularx}%for special table environment (tabularx-table)
\usepackage{booktabs}%for table layout

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\setlength{\heavyrulewidth}{0.4pt}%sets width of toprule and bottomrule in booktabs-tables
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\setlength{\cmidrulewidth}{0.4pt}%sets width of cmidrule in booktabs-tables

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\newcommand*{\IR}{\mathbb{R}}
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\newcommand*{\IP}{\Prob}
\newcommand*{\IE}{\E}
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\newcommand*{\vp}{\varphi}
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\newcommand*{\psiD}{\psi^\prime}
\newcommand*{\psii}{{\psi^{-1}}}
\newcommand*{\psiis}[1]{{\psi_{#1}^{-1}}}
\renewcommand*{\L}{\mathcal{L}}
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\newcommand*{\LS}{\mathcal{LS}}
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\newcommand{\tr}{\ensuremath{^\mathsf{T}}}% or  ^{\intercal}
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\newcommand*{\Geo}{\operatorname*{Geo}}
\newcommand*{\Exp}{\operatorname*{Exp}}
\newcommand*{\Sibuya}{\operatorname*{Sibuya}}
\newcommand*{\Log}{\operatorname*{Log}}
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\begin{document}
%% include your article here, just as usual
%% Note that you should use the \pkg{}, \proglang{} and \code{} commands.
% \section[About Java]{About \proglang{Java}}
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%% Note: These are explained in '?RweaveLatex' :
<<preliminaries, echo=FALSE, results=hide>>=
pdfDB <- function(name, width, height, ...)
{
    grDevices::pdf(paste0(name, ".pdf"), ## "DB":             vvvvvvvvvvvvvvvv
                   width=width, height=height, onefile=FALSE, useDingbats=TRUE)
}
op.orig <-
options(width = 70,
        ## SweaveHooks= list(fig=function() par(mar=c(5.1, 4.1, 1.1, 2.1))),
        useFancyQuotes = FALSE, prompt="R> ", continue="+  ")
Sys.setenv(LANGUAGE = "en")
if(.Platform$OS.type != "windows")
  Sys.setlocale("LC_MESSAGES","C")
## if(Sys.getenv("USER") == "maechler")# take CRAN's version, not development one:
##    require("copula", lib="~/R/Pkgs/CRAN_lib")
@
\section[Introduction]{Introduction \small~\footnote{\mythanks}}
A \textit{copula} is a multivariate distribution function with standard
uniform univariate margins. Standard references for an introduction
are \citet{joe1997} or \citet{nelsen2007}.

\citet{sklar1959} shows that for any multivariate
distribution function $H$ with margins $F_j$, $j\in\{1,\dots,d\}$, there exists
a copula $C$ such that
\begin{align}
	H(x_1,\dots,x_d)=C(F_1(x_1),\dots,F_d(x_d)),\ \bm{x}\in\IR^d.\label{sklar}
\end{align}
Conversely, given a copula $C$ and arbitrary univariate distribution functions $F_j$,
$j\in\{1,\dots,d\}$, $H$ defined by (\ref{sklar}) is a distribution function
with marginals $F_j$, $j\in\{1,\dots,d\}$. On one hand, Sklar's Theorem
tells us that we can decompose any given multivariate distribution function into
its margins and a copula. By this decomposition, copulas allow us to study
multivariate distributions functions independently of the margins. This is of
particular interest in statistics. On the other hand, Sklar's Theorem provides a
tool for constructing large classes of multivariate distributions and is
therefore often used for sampling multivariate distributions via
copulas. This is indispensable for
many applications in the areas of statistics and finance. For sampling the
multivariate distribution $H$ it suffices to sample the common dependence
structure, given by the copula $C$, and to transform the obtained variates to
the correct margins $F_j$, $j\in\{1,\dots,d\}$. Since this transformation is
usually easy to achieve (simply apply the \textit{generalized inverse}
$F_j^-(y)=\inf\{x\in\IR:F_j(x)\ge y\}$ corresponding to $F_j$,
$j\in\{1,\dots,d\}$, with the convention that $\inf\emptyset=\infty$), sampling
from $H$ usually boils down to sampling the copula $C$ under consideration.

Alongside \textit{elliptical copulas}, i.e., the copulas arising from elliptical
 distributions via Sklar's Theorem (see, e.g.,
 \citet{embrechtslindskogmcneil2003}), Archimedean copulas play an important
 role  in practical applications. In contrast to elliptical ones, Archimedean
 copulas are given explicitly in terms of a generator. They are able to
 capture different kinds of tail dependencies, e.g., only upper tail dependence
 and no lower tail dependence or both lower and upper tail dependence but of
 different magnitude. With the algorithm of \citet{marshallolkin1988},
 Archimedean copulas are usually easy to sample. Their functional symmetry
 (in $u_j$, $j=1,\dots,d$), also referred to as \textit{exchangeability},
 however, is often considered to be a drawback, e.g., in risk-management
 applications where the considered portfolios are typically high-dimensional. To
 circumvent exchangeability, Archimedean copulas can be nested within each other
 under certain conditions. The resulting copulas are referred to as
``nested Archimedean copulas'' and allow to model hierarchical dependence
 structures.

The \R\ package \pkg{copula} (formerly \pkg{nacopula}) implements several functions for working with
Archimedean and nested Archimedean copulas. In contrast to other \R\ packages
dealing with Archimedean copulas, e.g., \pkg{copula} (\cite{copula1, copula2}) or
\pkg{fCopulae} (\cite{fCopulae}),
particular focus is put on nested Archimedean copulas. The \R\ package
\pkg{nacopula} had been the first \R\ package dealing with these functions,
and is now part of the \R\ package \pkg{copula}.
It is available from the Comprehensive R Archive Network at
\url{https://CRAN.R-project.org/package=copula}.

\section{Archimedean copulas}
\subsection{Archimedean copulas and their properties}
An \textit{Archimedean generator}, or simply \textit{generator}, is a
continuous, decreasing function $\psi:[0,\infty]\to[0,1]$ which satisfies
$\psi(0)=1$, $\psi(\infty):=\lim_{t\to\infty}\psi(t)=0$, and which is strictly
decreasing on $[0,\inf\{t:\psi(t)=0\}]$. A $d$-dimensional copula is called
\textit{Archimedean} if it is of the form
\begin{align}
  C(\bm{u};\psi)=\psi(\psii(u_1)+\dots+\psii(u_d)),\ \bm{u}\in[0,1]^d,\label{ac}
\end{align}
for some generator $\psi$ with inverse $\psii:[0,1]\to[0,\infty]$, where
$\psii(0)=\inf\{t:\psi(t)=0\}$. \citet{mcneilneslehova2009} show that a
 generator defines a $d$-dimensional Archimedean copula if and only if $\psi$ is
 $d$\textit{-monotone}, i.e., $\psi$ is continuous on $[0,\infty]$, admits
 derivatives up to the order $d-2$ satisfying
 $\smash[t]{(-1)^k\frac{d^k}{dt^k}\psi(t)\ge 0}$ for all $k\in\{0,\dots,d-2\}$,
 $t\in(0,\infty)$, and $\smash[t]{(-1)^{d-2}\frac{d^{d-2}}{dt^{d-2}}\psi(t)}$ is
 decreasing and convex on $(0,\infty)$. A necessary and sufficient condition for
an Archimedean generator $\psi$ to generate a proper copula in all
dimensions $d$ is that $\psi$ is \textit{completely monotone}, i.e.,
$(-1)^k\psi^{(k)}(t)\ge0$ for all $t\in(0,\infty)$ and $k\in\IN_0$, see
\citet{kimberling1974} in the context of t-norms or
\citet[p.\ 54]{hofert2010c} for a reworking in terms of copulas. Unless
 otherwise stated, we assume $\psi$ to be completely monotone in what follows.
 Finally, let us note that the most simple dependence model, namely,
 independence, is provided by $\psi(t)=\exp(-t)$, with $\psii(t)=-\log(t)$, and
 corresponding \textit{independence} copula $C({\bm u})=\prod_{j=1}^d u_j$.

The class of all completely monotone Archimedean generators is denoted by
$\Psi_\infty$ in what follows. Bernstein's Theorem, see, e.g.,
\citet[p.\ 439]{feller1971}, shows that this class coincides with the class of
Laplace-Stieltjes transforms of distribution functions $F$ on the positive
real line, where the Laplace-Stieltjes transform of $F$, also known as the
\textit{Laplace transform of the distribution}, % <- hier kein $F$ (MM)
is defined as
\begin{align*}
  \LS[F](t):=\int_0^\infty\exp(-tx)\,dF(x),\ t\in[0,\infty).
\end{align*}
For a $\psi\in\Psi_\infty$, we hence have the relation
\begin{align*}
  \psi=\LS[F],\ \text{or, equivalently,}\ F=\LSi[\psi].
\end{align*}
for a distribution function $F$ on the positive real line.

Note that the distribution function $F$ is known for virtually all commonly
used Archimedean generators, see, e.g., \citet[p.\ 62]{hofert2010c}. The
package \pkg{copula} currently provides the most widely used families of
Ali-Mikhail-Haq, Clayton, Frank, Gumbel, and Joe; see Table~\ref{tab:cop-families-psi}
for the generators and their corresponding distribution functions. Except
for Clayton's family, where we use a slightly simpler generator, these
generators are the ones given in \citet[pp.\ 116]{nelsen2007}. Also note that for the families of Ali-Mikhail-Haq, Frank, and Joe, $F$ is discrete with support $\IN$. In this case, $\psi$ is of the form $\psi(t)=\sum_{k=1}^\infty p_k\exp(-kt)$, $t\in[0,\infty]$, where $(p_k)_{k=1}^\infty$ denotes the probability mass function corresponding to $F$.

These Archimedean copula families are provided as \code{"acopula"} \R\
objects (for more information about the statistical software \R, see \citet[2.11.1]{r}), containing as slots the corresponding generator
$\psi$, \code{psi}, its inverse $\psii$, \code{iPsi},
and the ``sampler'', i.e., random number generator for $V\sim F$, as \code{V0}:
<<acopula-family>>=
require(copula)
ls("package:copula", pattern = "^cop[A-Z]")
copClayton
copClayton@psi
copClayton@iPsi # the inverse of psi(), psi^{-1}
copClayton@V0     # "sampler" for  V ~ F()
@
The majority of slots of such copula objects are functions, encoding properties
of that copula family. In what follows, some of these functions are presented.

\subsubsection{Sampling Archimedean copulas}
From a mixture representation with respect to $F$, the following algorithm
may be derived for sampling Archimedean copulas, see
\citet{marshallolkin1988}.
\begin{algorithm}[\citet{marshallolkin1988}]
  \myskipalgo
  \linespread{1.22}\normalfont
  \begin{tabbing}
    \hspace{8mm} \= \hspace{4mm} \= \kill
    (1) \> \textbf{sample} $V\sim F = \LSi[\psi]$\\
    (2)	\> \textbf{sample} $R_j\sim\Exp(1)$, $j\in\{1,\dots,d\}$\\
    (3)	\> \textbf{set} $U_j=\psi(R_j/V)$, $j\in\{1,\dots,d\}$\\
    (4) \> \textbf{return} $\bm{U}=(U_1,\dots,U_d)\tr$
  \end{tabbing}
\end{algorithm}
In order for this algorithm to be easily applied, we need to know how to
sample the distribution functions $F=\LSi[\psi]$. For the families of
Ali-Mikhail-Haq, Clayton, Frank, Gumbel, and Joe, see
Table~\ref{tab:cop-families-psi}.
\begin{table}[htbp]
  \centering
  \begin{tabularx}{\textwidth}{@{\extracolsep{\fill}}Xc>{\hspace{-2mm}}c>{\hspace{-8mm}}c}
    \toprule
    \multicolumn{1}{c}{Family}&\multicolumn{1}{c}{Parameter}&\multicolumn{1}{c}{$\psi(t)$}&\multicolumn{1}{c}{$V\sim F=\LSi[\psi]$}\\
    \midrule
    Ali-Mikhail-Haq&$\theta\in[0,1)$&$(1-\theta)/(\exp(t)-\theta)$&$\Geo(1-\theta)$\\
    Clayton&$\theta\in(0,\infty)$&$(1+t)^{-{1/\theta}}$&$\Gamma(1/\theta,1)$\\
    Frank&$\theta\in(0,\infty)$&$-\log(1-(1-e^{-\theta})\exp(-t))/\theta$&$\Log(1-e^{-\theta})$\\
    Gumbel&$\theta\in[1,\infty)$&$\exp(-t^{1/\theta})$&$\S(1/\theta,1,\cos^\theta(\pi/(2\theta)),\I_{\{\theta=1\}};1)$\\
    Joe&$\theta\in[1,\infty)$&$1-(1-\exp(-t))^{1/\theta}$&$\Sibuya(1/\theta)$\\
    \bottomrule
  \end{tabularx}
  \caption{Commonly used one-parameter Archimedean generators.}
  \label{tab:cop-families-psi}
\end{table}

For the family of Ali-Mikhail-Haq, $\Geo(p)$ denotes a \textit{geometric
  distribution} with success probability $p\in(0,1]$ and probability mass function
$p_k=p(1-p)^{k-1}$ at $k\in\IN$ (which in \R\ is \code{dgeom(}$k$\code{, }$p$\code{)}).
%
For Clayton's family, $\Gamma(\alpha,\beta)$ denotes the \textit{gamma
  distribution} with shape $\alpha\in(0,\infty)$, rate
$\beta\in(0,\infty)$, and density
$f(x)=\beta^\alpha x^{\alpha-1}\exp(-\beta x)/\Gamma(\alpha)$, $x\in(0,\infty)$
(provided in \R\ by \code{[dpqr]gamma(., shape=}$\alpha$\code{, rate=}$\beta$\code{)}).
%
For the family of Frank, $\Log(p)$ denotes a
\textit{logarithmic distribution} with parameter $p\in(0,1)$ and mass
function $p_k=p^k/(-k\log(1-p))$ at $k\in\IN$. For sampling this
distribution, we provide \code{rlog(., }$p$\code{)}, using the algorithm
``LK'' of \citet{kemp1981}.
%
For Gumbel's family, $F$ corresponds to a $S(\alpha, \beta, \gamma, \delta; 1)$, i.e., a \textit{stable}, distribution with characteristic function
\begin{align}
	\phi(t)=\exp\bigl(i\delta t-\gamma^\alpha\lvert t\rvert^\alpha(1-i\beta\sgn(t)w(t,\alpha))\bigr),\ t\in\IR,
\end{align}
where
\begin{align*}
	w(t,\alpha)=\begin{cases}
			\tan(\alpha\pi/2),&\alpha\neq1,\\
			-2\log(\lvert t\rvert)/\pi,&\alpha=1,
		\end{cases}
\end{align*}
see, e.g., \citet[p.\ 8]{nolan2009} for this ``1-parameterization''. For
sampling from $S(.)$, we provide
\code{rstable1(., }$\alpha$\code{, }$\beta$\code{, }$\gamma$\code{, }$\delta$\code{, 1)},
having implemented an algorithm for sampling stable distributions according
to the ideas presented in \citet{chambersmallowsstuck1976}, and
improving on previous implementations in \R.
%
For the family of Joe, $\Sibuya(\alpha)$ denotes a \textit{Sibuya distribution}
 with probability mass function $p_k=\binom{\alpha}{k}(-1)^{k-1}$ at $k\in\IN$,
 where $\alpha\in(0,1]$; $\binom{\alpha}{k}=\alpha(\alpha-1)\dots(\alpha-k+1)/k!$
 denotes the (generalized) binomial coefficient, which is implemented in \R\ via
 \code{choose(}$\alpha$\code{, }$k$\code{)}. This distribution can be sampled
 via the \R{} function \code{rSibuya(., }$\alpha$\code{)} which we implemented
 based on an algorithm presented in \citet{hofert2011a}.

\subsubsection{The rank-correlation coefficient Kendall's tau}
	In practical applications, it is often desirable to measure the degree of
	dependence between random variables by a real number, a generalized
	 correlation. Such measures are referred to as \textit{measures of
	 association} and are usually studied for in the bivariate case, i.e., for
	 pairs of random variables. One such measure is Kendall's tau. For a
	 bivariate vector of continuously distributed random variables
	 $(X_1,X_2)\tr$, \textit{Kendall's tau} is defined by
	\begin{align*}
	  \tau&=\IE[\sign((X_1-X_1^\prime)(X_2-X_2^\prime))]\\
		&=\IP((X_1-X_1^\prime)(X_2-X_2^\prime)>0)-\IP((X_1-X_1^\prime)(X_2-X_2^\prime)<0),
	\end{align*}
	where $(X_1^\prime,X_2^\prime)\tr$ is an independent and identically
distributed copy of $(X_1,X_2)\tr$ and
$\sign(x)=\I_{(0,\infty)}(x)-\I_{(-\infty,0)}(x)$ denotes the signum function
(as \R's \code{sign(x)}). Since Kendall's tau equals the probability of
 concordance minus the probability of discordance, it is a measure of
 concordance, see, e.g., \citet{scarsini1984}. Informally, it measures, as a
 number in $[-1,1]$, the probability with which large values of one variable are
 associated with large values of the other. If $C$ is a bivariate Archimedean
 copula generated by a twice continuously differentiable generator $\psi$ with
 $\psi(t)>0$, $t\in[0,\infty)$, Kendall's tau can be represented in semi-closed
 form as
\begin{align*}
  \tau= 1 + 4\int_0^1\frac{\psii(t)}{(\psii(t))^\prime}\,dt =
        1 - 4\int_0^\infty t(\psiD(t))^2\,dt,
\end{align*}
see \citet[p.\ 91]{joe1997}. For the Archimedean families of
Ali-Mikhail-Haq, Clayton, and Gumbel, this integral can be evaluated
explicitly, for Frank's family it involves the \textit{Debye function of order
 one}, i.e., $D_1(\theta)=\frac 1 \theta \int_0^\theta t/(e^t-1)\,dt$, and for
 Joe's family, it is given as a series, see Table~\ref{table2}.

\subsubsection{Tail-dependence coefficients}
Another notion of association is tail dependence. Tail dependence measures the
probability that one random variable takes on values in its tail, given the
other one takes on values in its tail. To be more precise, if $X_j\sim F_j$,
$j\in\{1,2\}$, are continuously distributed random variables, the
\textit{lower tail-dependence coefficient}, respectively the
\textit{upper tail-dependence coefficient}, of $X_1$ and $X_2$ are defined as
\begin{align*}
  \lambda_l=\lim_{t\downarrow0}\IP(X_2\le F_2^-(t)\,\vert\,X_1\le  F_1^-(t)), \quad
  \lambda_u=\lim_{t\uparrow1}  \IP(X_2 >  F_2^-(t)\,\vert\,X_1 > F_1^-(t)),
\end{align*}
provided that the limits exist. These measures of association can be
expressed in terms of the copula $C$ corresponding to $(X_1,X_2)\tr$. If $C$
is a bivariate Archimedean copula generated by $\psi$ with $\psi(t)>0$,
$t\in[0,\infty)$, then
\begin{align*}
  \lambda_l=\lim_{t\to\infty}\frac{\psi(2t)}{\psi(t)}        =  2\lim_{t\to\infty}\frac{\psiD(2t)}{\psiD(t)},
  \quad
  \lambda_u=2-\lim_{t\downarrow0}\frac{1-\psi(2t)}{1-\psi(t)} = 2-2\lim_{t\downarrow0}\frac{\psiD(2t)}{\psiD(t)},
\end{align*}
where the equalities involving derivatives are obtained by l'H\^opital's rule
and therefore the corresponding assumptions are required to hold. For the
implemented Archimedean families, these limits can easily be found and are
given in Table~\ref{table2}.
\begin{table}[htbp]
  \centering
  \begin{tabularx}{\textwidth}{@{\extracolsep{\fill}}Xcccc}
    \toprule
    \multicolumn{1}{c}{Family}&\multicolumn{1}{c}{Parameter}&\multicolumn{1}{c}{$\tau$}&\multicolumn{1}{c}{$\lambda_l$}&\multicolumn{1}{c}{$\lambda_u$}\\
    \midrule
    Ali-Mikhail-Haq& $\theta\in[0,1)$      &$1-2(\theta+(1-\theta)^2\log(1-\theta))/(3\theta^2)$ & 0 & 0\\
    Clayton        & $\theta\in(0,\infty)$ &$\theta/(\theta+2)$		&$2^{-1/\theta}$ & 0\\
    Frank          & $\theta\in(0,\infty)$ &$1+4(D_1(\theta)-1)/\theta$	& 0	& 0\\
    Gumbel         & $\theta\in[1,\infty)$ &$(\theta-1)/\theta$		& 0	&$2-2^{1/\theta}$\\
    Joe            & $\theta\in[1,\infty)$ &$1-4\sum_{k=1}^\infty 1/(k(\theta k+2)(\theta(k-1)+2))$
								& 0	&$2-2^{1/\theta}$\\
    \bottomrule
  \end{tabularx}
  \caption{Kendall's tau and tail-dependence coefficients for commonly used one-parameter Archimedean generators.}
  \label{table2}
\end{table}

\subsection{A three-dimensional Joe copula}
As an example, we define a three-dimensional Joe copula with
parameter chosen such that Kendall's tau (for the corresponding bivariate
marginal copula of the Archimedean type) equals 0.5; this is achieved with the
function \code{iTau}, which computes the parameter $\theta$ such that
Kendall's tau equals 0.5.
<<ex1-definition>>=
(theta <- copJoe@iTau(0.5))
C3joe.5 <- onacopula("Joe", C(theta, 1:3))
@
The internal structure of this object%
\footnote{Note that we use a parametric nested Archimedean copula (see
  below), without any nesting, and corresponding \code{*nacopula()}
  functions, since they generalize the present non-nested case.}
\ is
<<ex1-str>>=
str(C3joe.5)  # str[ucture] of object
@
%NO more!\pagebreak
Figure \ref{fig:AC_Joe} visualizes 500 vectors of random variates (each in $[0,1]^3$)
from this copula with a scatter-plot matrix.  The \code{splom2()} function
is a version of standard's \pkg{lattice} (\cite{lattice}) \code{splom()}, with the addition
of using nice labels $U_1, U_2, \dots, U_d$ by default.
<<ex1-U3>>=
require(lattice)
set.seed(1)
dim(U3 <- rnacopula(500, C3joe.5))
@
\begin{figure}[h!]
\centering
<<ex1-splom-def, eval=false>>=
splom2(U3, cex = 0.4)
<<ex1-splom, echo=FALSE, fig=TRUE, height=5, keep.source=false>>=
## NB: 'keep.source=false' is workaround-a-bug-in-R-devel-(2.13.x)--- and  2 x more "splom"
print(
<<ex1-splom-def>>
     )
@
\setcapwidth{\textwidth}%
  \caption{500 vectors of random variates following a trivariate Joe copula with (pairwise) Kendall's tau equal to 0.5.}
  \label{fig:AC_Joe}
\end{figure}

The matrix of pairwise sample versions of Kendall's tau corresponding to the
 generated data is given by
<<ex1-explore1>>=
round(cor(U3, method="kendall"), 3)
@
Note that the entries are close to 0.5 which is the chosen population version of
 Kendall's tau for the simulated data.

Next, let us evaluate this Joe copula at $(0.5,0.5,0.5)\tr$ and $(0.99,0.99,0.99)\tr$.
<<ex1-explore2>>=
c(pnacopula(C3joe.5, c(.5, .5, .5)),
  pnacopula(C3joe.5, c(.99,.99,.99)))
@

Now let us answer the question what the probability is for $\bm{U}$ to fall in
the cube $(0.8,1]^3$.
<<ex1-explore3>>=
prob(C3joe.5, c(.8, .8, .8), c(1, 1, 1))
@

Finally, the lower and upper tail-dependence coefficients for this copula can be
obtained as follows.
<<ex2-explore4>>=
c(copJoe@lambdaL(theta),
  copJoe@lambdaU(theta))
@

\section{Nested Archimedean copulas}
\subsection{Construction}
In contrast to elliptical copulas, Archimedean copulas can capture different
tail dependencies, i.e., $\lambda_l\neq\lambda_u$. Further, they are given
explicitly, which facilitates computing probabilities for such dependence
models. However, the exchangeability inherent in Archimedean copulas implies
that all margins of the same dimension are equal. For modeling purposes, this
becomes an increasingly strong assumption in the dimension. Asymmetries, i.e.,
more realistic dependencies, can be modeled by a hierarchical structure of
Archimedean copulas, obtained by plugging in Archimedean copulas into each
other. In practical applications, these hierarchical structures are
often naturally motivated, e.g., by different macroeconomic effects, political
decisions, or consumer trends affecting the components of a random vector.

A $d$-dimensional copula $C$ is called \textit{nested Archimedean} if it is an
Archimedean copula with arguments possibly replaced by other nested Archimedean
copulas. If $C$ is given recursively by (\ref{ac}) for $d=2$ and, up to
permutation of the arguments, for $d\ge 3$, by
\begin{align}
  C(u_1,\dots,u_d;\psi_0,\dots,\psi_{d-2})=\psi_0\bigl(\psiis{0}(u_1)+
  \psiis{0}(C(u_2,\dots,u_d;\psi_{1},\dots,\psi_{d-2}))\bigr),\label{nac}
\end{align}
then $C$ is called \textit{fully nested Archimedean copula} with
$d-1$ \textit{nesting levels} or \textit{hierarchies}. Otherwise, $C$ is
called \textit{partially nested Archimedean copula}. Fully and partially
nested Archimedean copulas are summarized as \textit{nested} (or
\textit{hierarchical}) \textit{Archimedean copulas}.

Note that the structure of a nested Archimedean copula can be depicted by a
tree. For example, the three-dimensional nested Archimedean copula of Type
(\ref{nac}) involving the generators $\psi_0$ and $\psi_1$, which is given by
\begin{align*}
	C(u_1,u_2,u_3)=C(u_1,C(u_2,u_3;\psi_1);\psi_0),
\end{align*}
can be depicted by the tree structure as given in Figure~\ref{fig:NAC_3d}.
\begin{figure}[htbp]
  \centering
  \begin{tikzpicture}[
		    grow=south,
		    level 1/.style={sibling distance=32mm,level distance=10mm},
		    level 2/.style={sibling distance=18mm,level distance=10mm},
		    edge from parent/.style={draw,shorten >=1.4mm,shorten <=1.4mm},
		    every node/.style={draw,rectangle,rounded corners,inner sep=0mm},
				mynodestyle/.style={minimum width=16mm,minimum height=6.5mm},
				myleafstyle/.style={minimum width=7mm,minimum height=5mm}
		    ]
			\node[mynodestyle]{$C(\cdot\,;\psi_0)$}
			   %left
			    child{
			       node[myleafstyle]{$u_1$}
			    }
			   %right
			    child{
			      node[mynodestyle]{$C(\cdot\,;\psi_1)$}{
			      	%left
							child{
								node[myleafstyle]{$u_2$}
							}
							%right
							child{
								node[myleafstyle]{$u_3$}
							}
			      }
			    };
  \end{tikzpicture}
  \setcapwidth{\textwidth}%
  \caption{Tree structure of a three-dimensional fully nested Archimedean copula.}
  \label{fig:NAC_3d}
\end{figure}
Due to this tree representation, we refer to the outermost Archimedean copula
(generated by $\psi_0$) as \textit{root copula}. Further, we call an Archimedean
copula \textit{parent copula} if it has at least one nested Archimedean copula
as one of its components in the tree structure of a nested Archimedean copula.
We refer to these components as \textit{child copulas}. For example, the
three-dimensional nested Archimedean copula as given in Figure~\ref{fig:NAC_3d}
has parent copula $C(\cdot\,;\psi_0)$ (which is also the root copula here) with
child copula $C(\cdot\,;\psi_1)$; as another example, $C(\cdot\,;\psi_1)$ in
Figure~\ref{fig:NAC_9d} is the parent copula of the child copula
$C(\cdot\,;\psi_2)$ and $C(\cdot\,;\psi_0)$ is the parent of
$C(\cdot\,;\psi_1)$.

In \R, because of the recursive tree structure, a powerful approach is to
use a recursive class definition: In the \pkg{copula} package, we
define the \code{nacopula} (\textbf{n}ested \textbf{a}rchimedean
\textbf{copula}) class, with three components (\code{slots}),
\begin{Schunk}
%% manual cut & paste from ../../R/AllClass.R :
\begin{Sinput}
setClass("nacopula",
	 representation(copula    = "acopula",
                        comp      = "integer",  # from 1:d -- of length in [0,d]
                        childCops = "list"      # of nacopulas, possibly empty
                       ),
         validity = function(object) {
            ......
             if(!all("nacopula" == sapply(object@childCops, class)))
                 return("All 'childCops' elements must be 'nacopula' objects")
            ......
         })
\end{Sinput}
\end{Schunk}
i.e., by its root copula (slot \code{@copula}, an \code{"acopula"} object),
a vector of indices of its ``direct components'' (slot \code{ @comp = 1 }
for $u_1$ in the example), and a list of child copulas (slot \code{@childCops}).
The \code{childCops} list is the tool to contain sub branches of the ``tree''
of nested copulas, and will be empty, i.e., \code{list()}, for the final
nodes (referred to as \textit{leaves}) of the tree. In the example, it will be
of length one, containing a (non-nested) bivariate copula for the components
$u_2$ and $u_3$.

The class \code{outer\_nacopula} is a version of the same class (i.e., it
``contains'' \code{nacopula} without any further slots), just with
stricter validity checking, namely requiring that all components from all
child copulas are exactly the set $\{1,2,\dots,d\}$.
\begin{Schunk}
\begin{Sinput}
setClass("outer_nacopula", contains = "nacopula",
         validity = function(object) {
             ## *Extra* checks in addition to those of "nacopula"
             .......
        })
\end{Sinput}
\end{Schunk}
The \code{onacopula()} function\footnote{In addition to \code{onacopula()},
  there's \code{onacopulaL()} (``L'' for \textbf{L}ist) which requires a
  more formal specification of the nesting structure by a \code{list}.}
allows to specify (outer) \code{nacopula}s with convenient specification of
the nesting structure and parameters for each copula, using a ``formula''-like
notation $C(\theta, c(i_1,\dots,i_c),{}$\code{list(}$C(..),\dots,C(..)$\code{)}$)$ similar
to the mathematical notation above; for more, see its help page.
%%\\
For example, (one parametrization of) the three-dimensional example from
Figure~\ref{fig:NAC_3d} is
<<NAC_3d-ex>>=
( C3 <- onacopula("A", C(0.2, 1, C(0.8, 2:3))) )
@
This is a shortened form of the following.
<<NAC_3d-ex2>>=
stopifnot(identical(C3,
      onacopula("A", C(0.2, 1, list(C(0.8, 2:3, list()))))
))
@

The recursive definition (\ref{nac}) of nested Archimedean copulas not only leads to
recursive class definitions in \R, but also to recursive functions for
computations involving such ``nacopulas''.
All the following functions and methods (from our package \code{copula})
are defined recursively, typically using
\code{lapply(x@childCops,} \textit{<fun>}\code{)}:
The utilities \code{dim()}, \code{allComp()}, \code{printNacopula()} (which is the hidden
\code{show()} method), and the principal functions
\code{pnacopula()} and \code{rncopula()} (via recursive utility \code{rnchild()}).
As a simple example of these, \code{pnacopula(x, u)} simply evaluates the
(recursive) Formula~(\ref{nac}), recursively applying itself to its child copulas:
%% This is not as nice (wrong indentation; lost comments):
% <<pnacopula-def>>=
% pnacopula
% @
%% as this --- cut & paste from ../../R/nacopula.R :
\begin{Schunk}
\begin{Sinput}
pnacopula <- function(x,u) {
    stopifnot(is.numeric(u), 0 <= u, u <= 1, length(u) >= dim(x)) # can be larger
    C <- x@copula
    th <- C@theta
    ## use u[j] for the direct components 'comp':
    C@psi(sum(unlist(lapply(u[x@comp], C@iPsi, theta=th)),
              C@iPsi(unlist(lapply(x@childCops, pnacopula, u = u)),
                       theta=th)),
          theta=th)
}
\end{Sinput}
\end{Schunk}

In order for (\ref{nac}) to be indeed a proper copula,
\citet[p.\ 88]{joe1997} and \citet{mcneil2008} present the
\textit{sufficient nesting condition} that $\psiis{i}\circ\psi_{j}$ is
completely monotone for all nodes (with parent $i$ and child $j$) appearing in a
nested Archimedean copula.
%%
This condition can be derived from a mixture representation of $C$ based on
the distribution functions $F_0=\LSi[\psi_0]$ and
$F_{ij}=\LSi[\psi_{ij}(\cdot\,;V_0)]$ where
%$\psi_{ij}(t;x)=\exp\bigl(-x\psiis{i}(\psi_{j}(t))\bigr)$,
\begin{equation*}
  \psi_{ij}(t;x)=\exp\bigl(-x\psiis{i}(\psi_{j}(t))\bigr),\ t\in[0,\infty],\ x\in(0,\infty).
\end{equation*}
The sufficient nesting condition is often easily verified if all generators
appearing in the nested structure come from the same parametric family. For each
of the implemented Archimedean families of Ali-Mikhail-Haq, Clayton, Frank,
Gumbel, and Joe, two generators $\psi_i,\psi_j$ of the same family with
corresponding parameters $\theta_i,\theta_j$, respectively, fulfill the
sufficient nesting condition if $\theta_i\le\theta_j$; equivalently, if $\tau_i\le\tau_j$ for the corresponding Kendall's tau. Verifying the
sufficient nesting condition if $\psi_i$ and $\psi_j$ belong to different
Archimedean families is usually more complicated, see, \citet{hofert2010c}. Such
combinations of Archimedean families are therefore currently not implemented in
the \R\ package \pkg{copula}.

\subsection{Sampling}
If the sufficient nesting condition is fulfilled for all %% appearing
nodes in the nested Archimedean structure, the
following algorithm, based on proposals by \citet{mcneil2008} and \citet{hofert2011a}, may be derived for sampling nested Archimedean copulas.
\begin{algorithm}
  Let $C$ be a nested Archimedean copula with root copula $C_0$ generated by
  $\psi_0$. Let $\bm{U}$ be a vector of the same dimension as $C$.
  \myskipalgotext
  \linespread{1.22}\normalfont
  \begin{tabbing}
	\hspace{8mm} \= \hspace{4mm} \= \kill
	(1) \> \textbf{sample} $V_0\sim F_0=\LSi[\psi_0]$\\
	(2) \> \textbf{for} all components $u$ of $C_0$ that are nested
	 Archimedean copulas \textbf{do} \{\\
	(3) \>\> \textbf{set} $C_1$ with generator $\psi_1$ to the nested
	 Archimedean copula $u$\\
	(4) \>\> \textbf{sample} $V_{01}\sim F_{01}=\LSi[\psi_{01}(\cdot\,;V_0)]$\\
	(5) \>\> \textbf{set} $C_0:=C_1$, $\psi_0:=\psi_1$, and $V_0:=V_{01}$ and \textbf{continue} with (2)\\
	(6) \> \}\\
	(7) \> \textbf{for} all other components $u$ of $C_0$ \textbf{do} \{\\
	(8) \>\> \textbf{sample} $R\sim\Exp(1)$\\
	(9) \>\> \textbf{set} the component of $\bm{U}$ corresponding to $u$
	 to $\psi_0(R/V_0)$\\
	(10) \> \}\\
	(11) \> \textbf{return} $\bm{U}$
   \end{tabbing}
\end{algorithm}
Note that for sampling nested Archimedean copulas when all generators
involved belong to the same parametric family, it suffices to know how to
sample
\begin{align*}
  V_0\sim F_0=\LSi[\psi_0],\quad F_{01}=\LSi[\psi_{01}(\cdot\,;V_0)]
\end{align*}
as all distribution functions $F_{ij}$ take the same form as $F_{01}$, only
the parameters may differ.
In our \R\ package \pkg{copula} (formerly \pkg{nacopula}), the supported Archimedean family
objects therefore provide the three slots \code{V0}, \code{nestConstr}, and \code{V01},
all functions. \code{nestConstr}, is a
\code{function(}$\theta_0$\code{, }$\theta_1$\code{)} which returns \code{TRUE}
if the sufficient nesting condition is fulfilled and \code{V0} and \code{V01}
are random number generating functions, generating
$V$ from Table~\ref{tab:cop-families-psi} and
$V_{01}$ from Table~\ref{tab:nestedAC-V01}, respectively. For example, for
Ali-Mikhail-Haq,
<<AMH-V01>>=
copAMH@nestConstr
copAMH@V01
@
Sampling strategies for $F_0$ and $F_{01}$ for
many known Archimedean generators are presented in \citet{hofert2008},
\citet{hofert2010c}, and \citet{hofert2011a}. For the families of
Ali-Mikhail-Haq, Clayton, Frank, Gumbel, and Joe, Table~\ref{tab:nestedAC-V01}
summarizes stochastic representations for $V_0$ and $V_{01}$.
\begin{table}[htbp]
  \centering
  \begin{tabularx}{\textwidth}{@{\extracolsep{\fill}}Xcc}
	\toprule
	\multicolumn{1}{c}{Family}&\multicolumn{1}{c}{Nesting}
        &$V_{01}\sim F_{01}=\LSi[\psi_{01}(\cdot\,;V_0)],\ \alpha=\theta_0/\theta_1$\\
	\midrule
	Ali-Mikhail-Haq&$\theta_0\le\theta_1$&$V_0+\NB(V_0,(1-\theta_1)/(1-\theta_0))$\\
	Clayton&$\theta_0\le\theta_1$&$\tS(\alpha,1,(\cos(\alpha\pi/2)V_0)^{1/\alpha},V_0\I_{\{\alpha=1\}},\I_{\{\alpha\neq1\}};1)$\\
	Frank&$\theta_0\le\theta_1$&$\sum_{l=1}^{V_0}V_l,\ V_l\sim p_k=(\theta_1/(1-e^{-\theta_0}))kp_k^{\Sibuya(\alpha)}p_k^{\Log(1-e^{-\theta_1})}$\\
	Gumbel&$\theta_0\le\theta_1$&$\S(\alpha,1,(\cos(\alpha\pi/2)V_0)^{1/\alpha},V_0\I_{\{\alpha=1\}};1)$\\
	Joe&$\theta_0\le\theta_1$&$\sum_{l=1}^{V_0}V_l,\ V_l\sim\Sibuya(\alpha)$\\
	\bottomrule
   \end{tabularx}
   \caption{Sufficient nesting conditions and stochastic representations for
 				$V_{01}$.}
   \label{tab:nestedAC-V01}
\end{table}

First note that for nested Archimedean copulas based on generators belonging to
the same Archimedean family, all implemented families indeed lead to proper
copulas if the generators on a more nested (inner) level have larger parameter
values than the ones on a lower (outer) level. This is equivalent to saying that
Kendall's tau for a pair of random variables having a bivariate Archimedean
copula which resides on a deeper nesting level as margin has to be larger
than or equal to the one for a pair of random variables having an Archimedean
marginal copula residing on a lower nesting level.
Slightly more informally, the inner (or lower) nested components $u_j$ are
more correlated than the outer (``higher up'') ones.

Some comments on the distributions $F_{01}$ of $V_{01}$ for the
different implemented families: For the family of Ali-Mikhail-Haq, $V_{01}$
admits the stochastic representation $V_0+X$, where $X$ follows a negative
binomial distribution with parameters as given in Table~\ref{tab:nestedAC-V01}.
The parameterization is $\NB(r,p)$, $r\in(0,\infty)$, $p\in(0,1)$, with mass
function $p_k=\binom{k+r-1}{r-1}p^r(1-p)^k$, $k\in\IN_0$, which in \R{} is
\code{dnbinom(}$k$\code{, size=}$r$\code{, prob=}$p$\code{)}.
%% dnbinom(x, size, prob, mu, log = FALSE)

For Clayton's family, $F_{01}$ can be interpreted as a special case (take
$h=1$, $\alpha=\theta_0/\theta_1$) of the \textit{exponentially tilted stable
  distribution}
\begin{align}
  \tS(\alpha,1,(\cos(\alpha\pi/2)V_0)^{1/\alpha},V_0\I_{\{\alpha=1\}},h\I_{\{\alpha\neq1\}};1)\label{exptilted}
\end{align}
with $\alpha\in(0,1]$, $h\in[0,\infty)$, $V_0\in(0,\infty)$, and
corresponding Laplace-Stieltjes transform
\begin{align*}
  \psi(t)=\exp\bigl(-V_0((h+t)^{\alpha}-h^{\alpha})\bigr),\ t\in[0,\infty].
\end{align*}
\citet{hofert2011a} suggested a fast rejection algorithm for sampling this
distribution. \citet{devroye2009} suggested an algorithm for
sampling the exponentially tilted stable distribution
\begin{align*}
  \tS(\alpha,1,\cos(\alpha\pi/2)^{1/\alpha},\I_{\{\alpha=1\}},\lambda\I_{\{\alpha\neq1\}};1)
\end{align*}
with $\alpha\in(0,1]$, $\lambda\in[0,\infty)$, and corresponding
Laplace-Stieltjes transform
\begin{align*}
  \psi(t)=\exp\bigl(-((\lambda+t)^{\alpha}-\lambda^{\alpha})\bigr),\ t\in[0,\infty].
\end{align*}
One can easily check that by setting $\lambda=hV_0^{1/\alpha}$ and
generating $V_\lambda$ with the algorithm of \citet{devroye2009}, the
random variable $V_{01}$ from $F_{01}$ as given by (\ref{exptilted}) can be
obtained via $V_{01}=V_0^{1/\alpha}V_\lambda$. Therefore, the distribution as
given in (\ref{exptilted}) may be sampled by either the algorithm of
\citet{devroye2009} or the one of \citet{hofert2011a}. The former
author reports that the complexity of his algorithm is bounded, the latter
author shows that the complexity of his algorithm is $\O(V_{0}h^{\alpha})$.
We implemented both algorithms for sampling (\ref{exptilted}) in the package
\pkg{nacopula} (now \pkg{copula}) and decide for each drawn $V_0$ which method is to be
applied. As a simple rule, investigated by several parameter combinations,
the default chooses the method of \citet{hofert2011a} if
$V_{0}h^{\alpha}<4$ and the one of \citet{devroye2009} otherwise. As
mentioned before, note that for sampling nested Clayton copulas, we have
$h=1$. Further, $\IE[V_0]=1/\theta_0$. Hence, in the mean, the algorithm of
\citet{hofert2011a} is more often applied if $\theta_0>1/4$,
equivalently, if Kendall's tau for the bivariate Archimedean copula
generated by $\psi_0$ is greater than $1/9$.

For the family of Gumbel, $\psi_{01}(t;V_0)=\exp(-V_0t^\alpha)$,
$\alpha=\theta_0/\theta_1$, hence, see Table~\ref{tab:cop-families-psi},
 $F_{01}$ corresponds to the stable distribution as given in
 Table~\ref{tab:nestedAC-V01}. For Joe's family, $V_{01}$ can be represented as
 a $V_0$-fold sum of independent and identically Sibuya distributed random
 variables $V_k$, $k\in\{1,\dots,V_0\}$, which can be sampled with the \R{}
 function \code{rSibuya(., }$\alpha$\code{)} we provide. For large $V_0$ an
 approximation based on a stable distribution may be used, see
 \citet{hofert2011a}. Finally, Frank's family is more complicated. We refer to
 the source code of the package \pkg{copula} and references therein for more
details.

\subsection{A nine-dimensional nested Clayton copula}
In this example, we consider a nine-dimensional partially nested Clayton
copula $C$ of the form
\begin{align*}
 C(\bm{u})=C(u_3,u_6,u_1,C(u_9,u_2,u_7,u_5,C(u_8,u_4;\psi_2);\psi_1);\psi_0)
\end{align*}
with tree structure depicted in Figure~\ref{fig:NAC_9d}.
\begin{figure}[htbp]
 \centering
 \begin{tikzpicture}[
	grow=south,
  	level 1/.style={sibling distance=22mm,level distance=11mm},
  	level 2/.style={sibling distance=19mm,level distance=11mm},
  	level 3/.style={sibling distance=16mm,level distance=11mm},
  	edge from parent/.style={draw,shorten >=1.4mm,shorten <=1.4mm},
  	every node/.style={draw,rectangle,rounded corners,inner sep=0mm},
	mynodestyle/.style={minimum width=16mm,minimum height=6.5mm},
	myleafstyle/.style={minimum width=7mm,minimum height=5mm}
  ]
\node[mynodestyle]{$C(\cdot\,;\psi_0)$}
  %child 0,1
  child{
     node[myleafstyle]{$u_3$}
   }
 %child 0,2
  child{
     node[myleafstyle]{$u_6$}
   }
 %child 0,3
  child{
     node[myleafstyle]{$u_1$}
   }
  %child 0,4
   child{
     node[mynodestyle]{$C(\cdot\,;\psi_1)$}{
     	%child 1,1
			child{
				node[myleafstyle]{$u_9$}
			}
			%child 1,2
			child{
				node[myleafstyle]{$u_2$}
			}
			%child 1,3
			child{
				node[myleafstyle]{$u_7$}
			}
			%child 1,4
			child{
				node[myleafstyle]{$u_5$}
			}
			%child 1,5
			child{
				node[mynodestyle]{$C(\cdot\,;\psi_2)$}
				%child 2,1
				child{
					node[myleafstyle]{$u_8$}
				}
				%child 2,2
				child{
					node[myleafstyle]{$u_4$}
				}
			}
     }
   };
\end{tikzpicture}
\setcapwidth{\textwidth}%
\caption{Tree structure of the nine-dimensional partially nested Archimedean
copula $C$.}
\label{fig:NAC_9d}
\end{figure}
Such a copula can be defined as follows, where we choose the parameters of
$\psi_0$, $\psi_1$, and $\psi_2$ such that the corresponding Kendall's tau are
0.2, 0.5, and 0.8, respectively.
<<ex2-definition>>=
theta0 <- copClayton@iTau(0.2)
theta1 <- copClayton@iTau(0.5)
theta2 <- copClayton@iTau(0.8)
c(theta0, theta1, theta2)
C_9_clayton <- onacopula("Clayton",
                         C(theta0, c(3,6,1),
                           C(theta1, c(9,2,7,5),
                             C(theta2, c(8,4)))))
C_9_clayton
@

Figure \ref{fig:NAC_Clayton} visualizes 500 sampled random vectors (each in $[0,1]^9$) from this copula
(this involves our efficient procedure for exponentially tilted stable
distributions) with a scatter-plot matrix. For plotting the data, we re-order the columns according to the same strength of dependence.
<<U9-prepare-splom>>=
set.seed(1)
U9 <- rnacopula(500, C_9_clayton)
j <- allComp(C_9_clayton)# copula component "numbers": 1:9 but in "correct order"
(vnames <- do.call(expression,
                   lapply(j, function(i) substitute( U[I], list(I=0+i)))))
@
\begin{figure}[hbp!]
\centering
<<ex2-splom-def, eval=FALSE>>=
splom2(U9[, j], varnames= vnames, cex = 0.4, pscales = 0)
<<ex2-splom, echo=FALSE, fig=TRUE, height=7, keep.source=false>>=
print(
<<ex2-splom-def>>
      )
@
\setcapwidth{\textwidth}%
  \caption{500 vectors of random variates following a nine-dimensional nested Clayton copula with three different levels of dependence.}
  \label{fig:NAC_Clayton}
\end{figure}

The population version of Kendall's tau for $(U_4,U_5)\tr$ is 0.5. Let us check
if the sample version of Kendall's tau is close to this value.
<<ex2-explore1>>=
round(cor(U9[,9],U9[,7], method="kendall"), 3)
@

Evaluating this nine-dimensional nested Clayton copula at
$(0.5,\dots,0.5)\tr$ and near the upper corner $(0.99,\dots,0.99)\tr$ leads to
the following results.
<<ex2-explore2>>=
c(pnacopula(C_9_clayton, rep(.5,9)),
  pnacopula(C_9_clayton, rep(.99,9)))
@

The probability mass in the cube  $(0.8, 1]^9$ can be determined as follows.
<<ex2-explore3>>=
prob(C_9_clayton, rep(.8,9), rep(1,9))
@

Finally, let us find the different lower and upper tail-dependence coefficients
appearing on the different levels of this nested Archimedean copula.
<<ex2-explore4>>=
c(copClayton@lambdaL(theta0),
  copClayton@lambdaU(theta0))
c(copClayton@lambdaL(theta1),
  copClayton@lambdaU(theta1))
c(copClayton@lambdaL(theta2),
  copClayton@lambdaU(theta2))
@

\section{Outer power Archimedean copulas}
For an Archimedean generator $\psi\in\Psi_\infty$, $\psi(t^{1/\theta})$ is also a
valid generator in $\Psi_\infty$ for all $\theta\in[1,\infty)$, see, e.g.,
\citet[p.\ 441]{feller1971}. The resulting copulas are
referred to as \textit{outer power Archimedean copulas}. Note that two
parametric Archimedean generators $\psi(t^{1/\theta_0})$, $\theta_0\in[1,\infty)$, and
$\psi(t^{1/\theta_1})$, $\theta_1\in[1,\infty)$, of this type, constructed with the
same ``base'' generator $\psi(t)$, $t\in[0,\infty)$, fulfill the sufficient nesting condition if $\theta_0\le\theta_1$. Therefore, one can build so-called
\textit{nested outer power Archimedean copulas}.
\citet{hofert2011a} derives some results for
these copulas, including instructions for sampling the corresponding random
variables $V_0$ and $V_{01}$, as well as an explicit formula for Kendall's tau
in terms of the Kendall's tau of the copula generated by the base generator
$\psi$. Further, note that if the tail-dependence coefficients exist, they are
greater than or equal to the ones corresponding to the copula generated by the
base generator. For the Archimedean families implemented in the package
\pkg{copula}, these can all be computed explicitly.

The goal of this section is to show how one might work with outer power
Archimedean copulas with the package \pkg{copula}.
For now, we use the outer power transformation \code{opower()}
which generates an outer power family based on the provided copula family
\code{copbase} with corresponding parameter \code{thetabase}.
<<outerpower-def>>=
str(opower)
@
We believe that it is both more natural and flexible
to work with copula families that are generalizations of our current
families, each including the power as an extra parameter (such that
\code{theta}, i.e., $\theta$, will become 2-dimensional), rather than
using the \code{opower()} construction below. This section therefore should be
considered mainly as an outlook to further features of the package
\pkg{copula}, where this transformation is not required anymore for working
with outer power Archimedean copulas.

Using this transformation, we define a valid outer power Clayton copula with
base generator of Clayton's type and with base parameter such that Kendall's tau
equals 0.5.
%% MM: chosing name "opow.Clayton"  to make it "stand out":
<<opwer-def2>>=
thetabase <- copClayton@iTau(.5)
(opow.Clayton <- opower(copClayton, thetabase))
@

Based on this copula generator, we would like to define and sample a
three-dimensional fully nested outer power Clayton copula with parameters
such that Kendall's tau are $2/3$ and $0.75$.
%% onacopula() now works since Martin implemented it on 2010-06-26  :
<<opwer-def3>>=
theta0 <- opow.Clayton@iTau(2/3) # will be 1.5
theta1 <- opow.Clayton@iTau(.75) # will be 2
opC3 <- onacopula(opow.Clayton, C(theta0, 1, C(theta1, c(2,3))))
@

Now we sample 500 random variates from this copula and visualize the generated
data with a scatter-plot matrix, see Figure \ref{fig:NAC_opClayton}. In contrast
to Clayton copulas, note that outer power Clayton copulas allow for both lower
and upper tail dependence.
%% dim(U3 <- rnacopula(500, opC3))
<<U3-ex>>=
U3 <- rnacopula(500, opC3) ; stopifnot(dim(U3) == c(500,3))
@
\begin{figure}[h!]
\centering
<<opower-splom-def, eval=false>>=
splom2(U3, cex = 0.4)
<<opower-splom, echo=FALSE, fig=TRUE, height=5, keep.source=false>>=
print(
<<opower-splom-def>>
      )
@
  \setcapwidth{\textwidth}%
  \caption{500 vectors of random variates following a trivariate nested outer
power Clayton copula.}
  \label{fig:NAC_opClayton}
\end{figure}

Further, we can compare the population and sample versions of Kendall's tau for
the generated data. The $(1,2)$- and $(1,3)$-entry of the matrix of
pairwise sample versions of Kendall's tau should be close to $2/3$, the
$(2,3)$-entry should be close to $0.75$.
<<opower-explore1>>=
round(cor(U3, method="kendall"), 3)
@

The different lower and upper tail-dependence coefficients for this copula can
be obtained as follows.
<<opower-explore2>>=
rbind(th0 =
      c(L = opow.Clayton@lambdaL(theta0),
        U = opow.Clayton@lambdaU(theta0)),
      th1 =
      c(L = opow.Clayton@lambdaL(theta1),
        U = opow.Clayton@lambdaU(theta1)))
@

%%   =======

\section{Session Information}

<<sessionInfo, results=tex>>=
toLatex(sessionInfo())
@
%%
%% packageDescription("copula")[c("Version", "LastChanged")]
%% Now,  packageDescription("copula")[c("Date", "Revision")]  is too long,
%% ----> we "cheat" as follows:
<<copula-version, eval=FALSE>>=
my.strsplit(  packageDescription("copula")[c("Date", "Revision")]  )
@
<<copula-version,echo=FALSE>>=
pd <- lapply(packageDescription("copula")[c("Date", "Revision")],
             function(ch) gsub("\\$",'', ch))
cat(pd[["Revision"]], "-- ", sub("^Date: +", '', pd[["Date"]]), "\n", sep="")
<<finalizing, echo=FALSE>>=
options(op.orig)
@

\section{Conclusion}
The package \pkg{copula} (formerly \pkg{nacopula}) allows us to easily construct and work with nested
Archimedean copulas. First and foremost, fast sampling algorithms for these copulas
are implemented. As a by-product, the package also provides related mathematical
and random number generating functions, e.g., efficient
sampling algorithms for exponentially tilted stable and Sibuya distributions.
Further features include the evaluation of nested Archimedean copulas, as well
as computing probabilities of a random vector falling into a given hypercube.
Concerning measures of association, Kendall's tau and the tail-dependence
coefficients are implemented. Currently supported Archimedean families include
the well-known families of Ali-Mikhail-Haq, Clayton, Frank, Gumbel, and
Joe. Each can be used in generalized form via ``outer power'' transformations.

\bibliography{nacopula}

\end{document}
