\documentclass[article,nojss]{jss}
%% NOTA BENE: More definitions --> further down
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%
\author{Martin M\"achler \\ ETH Zurich%
\\ May 2011 -- July 2012 {\tiny (\LaTeX'ed \today)}%---- for now
}
\title{Numerically Stable Frank Copula Functions via Multiprecision:
  \proglang{R} Package \pkg{Rmpfr}}
% \def\mythanks{a version of this paper, for \pkg{copula} 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{Martin M\"achler} %% comma-separated
\Plaintitle{Numerically Stable Frank Copula Functions via Multiprecision:
  R Package 'Rmpfr'}
\Shorttitle{Numerically Stable Frank via Multiprecision in R}
%
%\VignetteIndexEntry{Numerically stable Frank Copulas via Multiprecision (Rmpfr)}
%\VignetteDepends{copula}
%\VignetteDepends{Rmpfr}
\SweaveOpts{engine=R,eps=FALSE,pdf=TRUE,width=7,height=5,strip.white=true,keep.source=TRUE}

%% an abstract and keywords
\Abstract{
  The package \pkg{copula} (formerly \texttt{nacopula}) has provided
  functionality for Archimedean copulas, one of them the ``Frank copula''.
  Recently, explicit formulas for the density of those copulas have allowed
  for maximum likelihood estimation in high (e.g., $d=150)$) dimensions,
  (\cite{hofertmaechlermcneil2012a}).

  However for non-small dimensions, the evaluation of these densities is
  numerically challenging, and in some cases difficult.  Here, we use high
  precision arithmetic from MPFR, via R package \pkg{Rmpfr} in order to get
  accurate values for the diagonal density of the Frank copula.

  Subsequently, judiciously analysing and circumventing the numerical
  infelicities of the ``traditional'' evaluation, we gain accurate values
  even with traditional (double precision) arithmetic.
}

\Keywords{Archimedean copulas, Frank Copula, Multiple Precision, R, MPFR, Rmpfr}
%% 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{
	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}
}
%% It is also possible to add a telephone and fax number
%% before the e-mail in the following format:
%% Telephone: +43/1/31336-5053
%% Fax: +43/1/31336-734

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\usepackage[american]{babel}%for American English
\usepackage{amsmath}%sophisticated mathematical formulas with amstex (includes \text{})
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\usepackage{enumitem}%for automatic numbering of new enumerate environments

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\newcommand*{\R}{\proglang{R}}%{\textsf{R}}
%% Marius' commands
\newcommand*{\eps}{\varepsilon}
%NON-STANDARD{bbm}:\newcommand*{\I}{\mathbbm{1}}
% \newcommand*{\I}{\mathbf{1}}
% \newcommand*{\IN}{\mathbb{N}}
% \newcommand*{\IK}{\mathbb{K}}
% \newcommand*{\IR}{\mathbb{R}}
% \newcommand*{\IC}{\mathbb{C}}
% \newcommand*{\IP}{\Prob}
% \newcommand*{\IE}{\E}
% \newcommand*{\V}{\operatorname*{V}}
%- \abs{ab}  -->  | ab |   ``absolut Betrag''
        \newcommand{\abs}[1]    {\left| #1 \right|}
\newcommand*{\Li}[1]{\mathrm{Li}_{#1}}% polylog / polylogarithm
% \renewcommand*{\S}{\operatorname*{S}}
% \newcommand*{\tS}{\operatorname*{\tilde{S}}}
% \newcommand*{\ran}{\operatorname*{ran}}
%\newcommand*{\sgn}{\operatorname*{sgn}}
\DeclareMathOperator{\sign}{sign}
\newcommand*{\psiD}{\psi^\prime}
\newcommand*{\psii}{{\psi^{-1}}}
\newcommand*{\psiis}[1]{{\psi_{#1}^{-1}}}
% \renewcommand*{\L}{\mathcal{L}}
% \newcommand*{\Li}{\mathcal{L}^{-1}}
% \newcommand*{\LS}{\mathcal{LS}}
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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}}
%% Note: If there is markup in \(sub)section, then it has to be escape as above.
%% Note: These are explained in '?RweaveLatex' :
<<preliminaries, echo=FALSE, results=hide>>=
op.orig <-
options(width = 75,
        SweaveHooks= list(fig=function() par(mar=c(5.1, 4.1, 1.1, 2.1))),
        useFancyQuotes = FALSE,
        ## for JSS, but otherwise MM does not like it:
        ## prompt="R> ",
        continue="  ")# 2 (or 3) blanks: use same length as 'prompt'
copDDir <- system.file('doc', package='copula')

Sys.setenv(LANGUAGE = "en")
if(.Platform$OS.type != "windows")
  Sys.setlocale("LC_MESSAGES","C")
@
%\section[Introduction]{Introduction \small~\footnote{\mythanks}}
%\section{Introduction}
\section{The diagonal density of Frank's copula}

The ``diagonal density'' of a copula $C(\cdot)$ is the density of
$\max(U_1,U_2,\dots,U_d)$, where \linebreak[4]
$\bm U = (U_1,U_2,\dots,U_d)\tr \sim C$.
The (cumulative) distribution function, by definition,
\begin{equation}
  F^D(u) := P\left[ \max(U_1,U_2,\dots,U_d) \le u \right] =
            P\left[U_1\le u, U_2,\le u, \dots, U_d\le u\right] = C(u,u,\dots,u),
                \label{eq:diagF}
\end{equation}
evaluates the copula only on the diagonal and is
therefore called \emph{``the diagonal of $C$''}.
Its density $f^D(u) := \frac{d}{du}F^D(u)$, is therefore called the diagonal density of $C$.
For Archimedean copulas, i.e., where
\begin{align}
  C(\bm{u})=C(\bm{u};\psi)=\psi(\psii(u_1)+\dots+\psii(u_d)),\ \bm{u}\in[0,1]^d,\label{ac}
\end{align}
the diagonal density is
\begin{eqnarray}
  f^D(u) &=& \frac{d}{du}F^D(u) = \frac{d}{du}\psi\left(\sum\nolimits_{j=1}^d \psii(u)\right) =
  \frac{d}{du}\psi(d\cdot \psii(u)) =  \nonumber\\
   &=& \psi^\prime\!\left(d\cdot \psii(u)\right) \cdot d \cdot \frac{d}{du} \psii(u)\nonumber\\
   &=& d\cdot \psi^\prime\!\left(d\cdot \psii(u)\right) \cdot \left[\psii\right]^\prime(u).
  \label{eq:fD}
\end{eqnarray}

For this reason, the \pkg{copula} package's \code{dDiag()} function for
computing the diagonal density $f^D(u)$ makes use of the following
%% ==> lines 261 ff from ~/R/Pkgs/copula/R/estimation.R
%%                       ================================
<<nacopula-dDiagA, eval=FALSE>>=
copula:::dDiagA
<<nacopula-dDiagA-show,echo=FALSE>>=
writeLines(head(capture.output(print(
<<nacopula-dDiagA>>
                     )), -1))
@
where the three functions
\begin{align}
\mathtt{absdPsi(t,thet)} &= \bigl|\psi_\theta^\prime(t)\bigr|,\label{def:absdPsi}\\
\mathtt{iPsi(u,thet)} &= \psi_\theta^{-1}(u), \ \mathrm{and}\label{def:iPsi}\\
\mathtt{absdiPsi(u,thet)} &= \bigl|[\psi_\theta^{-1}]^\prime(u) \bigr| \label{def:absdiPsi}
\end{align}
are all provided by the slots of the corresponding Archimedean copula family.

For the following explorations,  we need a definition of \code{dDiagA} which
is more flexible as it does not work with the copula family object but gets the three
functions as arguments,
<<my-dDiagA>>=
dDiagA <- function(u, th, d, iPsi, absdPsi, absdiPsi, log = FALSE) {
    stopifnot(is.finite(th), d > 0, is.function(iPsi),
              is.function(absdPsi), is.function(absdiPsi))
    if(log) {
        log(d) + absdPsi(d*iPsi(u,th), th, log = TRUE) +
            absdiPsi(u, th, log = TRUE)
    } else {
        d* absdPsi(d*iPsi(u,th), th) * absdiPsi(u,th)
    }
}
@
Now, for the Frank copula (\cite{Hofert:Maechler:2010:JSSOBK:v39i09}),
\begin{eqnarray}
  \psi_\theta(t) &=& - \frac{1}{\theta}\log\left(1 - (1 - e^{-\theta})e^{-t}\right),
  \label{eq:psiFrank}
  \ \ \ \theta > 0,
  \ \ \mathrm{\ \ hence,}
  \\
  \psi_\theta^{-1}(u) &=& - \log\left(\frac{e^{-u\theta} - 1}{e^{-\theta} - 1}\right),
  \label{eq:iPsiFrank}
  \ \ \mathrm{\ \ and}
  \\
  %% absdPsi(t,thet)
  (-1)^k {\psi_\theta}^{(k)}(t) = \bigl|{\psi_\theta}^{(k)}(t)\bigr| &=&
  \frac{1}{\theta} \Li{k-1}((1-e^{-\theta})e^{-t}),
  \label{eq:absdPsi}
  \ \ \ \ \ \ \mathrm{and}
  \\
  %% absdiPsi.1(u,theta)
  \bigl|[\psi_\theta^{-1}]^\prime(u) \bigr| &=& \theta/(e^{u\cdot\theta} - 1),
  \label{eq:iPsiD1}
\end{eqnarray}
where $\Li{s}(z)$ is the \emph{polylogarithm of order} $s$ at $z$, defined
as (analytic continuation of) $\sum_{k=1}^\infty \frac{z^k}{k^s}$. When $s
= -n$, $n\in\mathbb{N}$, one has
\begin{equation}
  \label{eq:Li-n}
  \Li{-n}(z) = \left(z \cdot \frac{\partial}{\partial z} \right)^n \frac{z}{1-z},
\end{equation}
and we note that here, only the first derivative is needed,
$ - {\psi_\theta}^\prime(t) = \left|{\psi_\theta}^\prime(t)\right|$, and hence only
\begin{equation}
  \mathtt{polylog(z,\: s=0)} = \Li{0}(z) = z / (1 - z). \label{eq:polylog-0}
\end{equation}

First note that numerically, $e^{-a} - 1$ suffers from cancellation when $0
< a \ll 1 $, and the \R\ (and C) function \code{expm1(-a)} is advisably used
instead of \code{exp(-a) - 1}.
For this reason, in \pkg{copula}, I had replaced the original \code{iPsi.0(u, th)}
for $\psi_\theta^{-1}(u)$ by \code{iPsi.1()}, making use of \code{expm1()}.
These and the derivative (\ref{eq:iPsiD1})
originally were
<<def-orig-psi-func>>=
iPsi.0 <- function(u,theta) -log( (exp(-theta*u)-1) / (exp(-theta)-1) )
iPsi.1 <- function(u,theta) -log(expm1(-u*theta) / expm1(-theta))
absdiPsi.1 <- function(u, theta, log = FALSE)
  if(log) log(theta)-log(expm1(u*theta)) else theta/expm1(u*theta)
@
and the general $k$-th derivative (\ref{eq:absdPsi}), simplified, for $k= 1$
(\code{degree = 1}),
<<def-orig-absdPsi>>=
require("copula")# for polylog()
absdPsi.1 <- function(t, theta, log=FALSE) {
  p <- -expm1(-theta)
  Li. <- polylog(log(p) - t, s = 0, log=log,
                 method="negI-s-Eulerian", is.log.z=TRUE)
  if(log) Li. - log(theta) else Li. / theta
}
@
where we however now assume that the \code{polylog()} function for
\code{s=0} would basically use the direct formula (\ref{eq:polylog-0}),
such that we use
<<simpler-absdPsi>>=
absdPsi.2 <- function(t, theta, log=FALSE) {
  w <- log(-expm1(-theta)) - t
  Li. <- if(log) w - log(-expm1(w)) else -exp(w)/expm1(w)
  if(log) Li. - log(theta) else Li. / theta
}
@

\section{Computing the ``diagonal MLE''}

The most important use of the diagonal density is to compute the ``diagonal
maximum likelihood estimator'', \code{dmle}, ${\hat\theta}^D$ which for a sample of observations
$\bm u_1,\bm u_2,\ldots, \bm u_n$, ($\bm u_i \in [0,1]^d$) is defined as
minimizer of the negative log-likelihood,
\begin{align}
  {\hat\theta}^D &= \arg\min_\theta - l(\theta; \bm u_1,\ldots,\bm u_n),\ \ \mathrm{where}\\
  l(\theta; \bm u_1,\ldots,\bm u_n) &= \sum_{i=1}^n \log f^D(\tilde u_i)\ \ \mathrm{and}\\
  \tilde{u_i} &= \max_{j=1,\dots,d} u_{i,j}.
  \label{eq:dmle}
\end{align}
In our exploration of the \code{dmle} estimator, we found cases with
numerical problems, at first already in evaluating the logarithm of the diagonal density
$\log f^D = \log f^D_\theta(u)$\code{= dDiag(u, theta, *, log=TRUE)}
for non-small $\theta$ and ``large'' $u$, i.e., $u \approx 1$:
<<dDiag-probl-big-theta, fig=TRUE>>=
curve(dDiagA(x, th = 38, d = 2, iPsi = iPsi.0,
             absdPsi=absdPsi.1, absdiPsi=absdiPsi.1, log = TRUE),
      ylab = "dDiagA(x, theta= 38, *, log=TRUE)",
      0, 1, col = 4, n = 1000)
## and using the slightly better   iPsi.1  does not help here:
curve(dDiagA(x, th = 38, d = 2, iPsi = iPsi.1,
             absdPsi=absdPsi.2, absdiPsi=absdiPsi.1, log = TRUE),
      add = TRUE, col = 2, n=1000)
legend("bottom", c("iPsi.0()","iPsi.1()"),col=c(4,2), lty=1, bty="n")
@

However, it's not hard to see that indeed our initial computation of Frank's
$\psiis{\theta}$, i.e, (\ref{eq:iPsiFrank}),
$\psiis{\theta}(u) = -\log\left(\frac{1-e^{-u\theta}}{1-e^{-\theta}}\right)$,
suffers from ``division cancellation'' for ``large'' $\theta$ ($\theta=38$
in ex.) when computed directly with
\code{iPsi.0()}, see above, and that the improvement of using
\code{expm1(-t)} instead of \code{exp(-t) - 1}, as used in
\code{iPsi.1()}, see above, helps only for $t\approx 0$.
However, we can rewrite $\psiis{\theta}$ as
\begin{equation}
  \label{eq:psii:log:1}
  \psiis{\theta}(u) = -\log\left(1 -\frac{e^{-u\theta}-e^{-\theta}}{1-e^{-\theta}}\right),
\end{equation}
which when using \code{log1p(e)} for $\log(1+e)$ is much better numerically:
<<dDiag-ok-big-theta, fig=TRUE>>=
iPsi.2 <- function(u,theta) -log1p((exp(-u*theta)-exp(-theta)) / expm1(-theta))
curve(dDiagA(x, th = 38, d = 2, iPsi = iPsi.2,
             absdPsi=absdPsi.2, absdiPsi=absdiPsi.1, log = TRUE),
      ylab = "dDiagA(x, theta= 38, *, log=TRUE)",
      0, 1, col = 4, n = 1000)
## previously
curve(dDiagA(x, th = 38, d = 2, iPsi = iPsi.1,
             absdPsi=absdPsi.2, absdiPsi=absdiPsi.1, log = TRUE),
      add = TRUE, col = "darkgray", lwd=2, lty=3, n=1000)
@

Unfortunately, this is not enough to get numerically stable evaluations of
the negative log-likelihood $l()$:
<<ex-U-data>>=
d <- 5
(theta <- copFrank@iTau(tau = 0.75))
cop <- onacopulaL("Frank", list(theta, 1:d))
set.seed(1); for(l in 1:4) U <- rnacopula(n = 100, cop)
U. <- sort(apply(U, 1, max)) # build the max
<<negLogL>>=
mlogL <- function(theta)
    -sum(dDiagA(U., theta, d=d, iPsi = iPsi.2,
                absdPsi=absdPsi.2, absdiPsi=absdiPsi.1,
                log = TRUE))
@
Now, plot this negative log likelihood function in an interval $\theta$,
close to what is proposed \pkg{copula}'s \code{initOpt()}
function, defining a utility function that we'll reuse later:
<<llog-theta-plot, fig=true>>=
p.mlogL <- function(th, mlogL, col= "red2", lwd = 1, lty = 1,
                    add= FALSE) {
  stopifnot(is.numeric(th), is.function(mlogL))
  nll <- vapply(th, mlogL, 0.)
  if(add) lines(nll ~ th, col=col, lwd=lwd, lty=lty)
  else plot(nll ~ th, xlab=expression(theta),
            ylab = expression(- logLik(theta, .)),
            type = "l", col=col, lwd=lwd, lty=lty)
  invisible(nll) # return invisibly
}
thet <- seq(11, 99, by = 1/4)
p.mlogL(thet, mlogL)

require("Rmpfr")## compute the same with *high* accuracy ...
## using three different precisions:
MPrecBits <- c(160, 128, 96)
mkNm <- function(bits) sprintf("%03d.bits", bits)
## As it takes a while, cache the result:
fnam <- sprintf("mlogL_mpfr_%s.rda", Sys.info()[["machine"]])
if (!file.exists(fn <- file.path(copDDir,fnam))) {
  print(system.time(
    nllMP <- lapply(MPrecBits, function(pBit) {
        nlM <- thM <- mpfr(thet, precBits = pBit)
        ## (vapply() does not work for "Rmpfr":)
        for(i in seq_along(thet)) nlM[i] <- mlogL(thM[i])
        nlM
    })
  )) ## 91.226 0.013 91.506 [nb-mm icore 5]
  names(nllMP) <- mkNm(MPrecBits)
  copSrcDDir <- if(Sys.getenv("USER") == "maechler")
      '~/R/Pkgs/copula/inst/doc' else ""
  if(file.exists(copSrcDDir))# <<- only for certain users; not on CRAN etc
      save(nllMP, file = file.path(copSrcDDir, fnam))
} else load(fn)

colB <- c("blue3","violetred4","tan3")
ltyB <- c(5:3)
lwdB <- c(2,2,2)

for(i in seq_along(nllMP)) {
    lines(thet, as.numeric(nllMP[[i]]),
          col=colB[i], lty = ltyB[i], lwd = lwdB[i])
}
leg <- c("double prec.", sprintf("mpfr(*, precBits = %d)", MPrecBits))
legend("top", leg,
       col= c("red3",colB), lty=c(1, ltyB), lwd=c(1,lwdB), bty="n")
@
So, clearly, high-precision computations can solve the
numerical problems, if the precision is high enough. E.g., for $\theta = 100$,
it needs more than 128 bits precision.

Let us look at the phenomenon in more details now.
The flesh in the \code{mlogL()} computation is (up to the constant
$\log(d)$, $d=5$), only the sum of the two terms
<<empty-prompt, echo=FALSE>>=
op <- options(prompt = " ")
<<mlogL_2terms, eval=FALSE>>=
   absdPsi(d*iPsi(u,th), th, log = TRUE) +
   absdiPsi(u,          th, log = TRUE)
@
currently, with the three functions
<<3f, eval=false>>=
iPsi = iPsi.2; absdPsi = absdPsi.2; absdiPsi = absdiPsi.1
<<reset-prompt, echo=FALSE>>=
options(op)
@
where we have already tried to ensure that the \code{iPsi()} function is
ok, but now can confirm it, e.g., for $\theta = 50$.  Further note, that using
high-precision arithmetic, we can also ``partially afford'' to use the
simplistic \code{iPsi.0()} function instead of the more stable
\code{iPsi.2()} one:
<<check-iPsi>>=
stopifnot(all.equal(iPsi.2(U.,  50 ),
                    iPsi.2(U., mpfr(50, 96))),
          all.equal(iPsi.0(U., mpfr(50, 200)),
            pI.U <- iPsi.2(U., mpfr(50, 200)), tol=1e-50) )
@
However, we can observe dramatic differences in \code{absdPsi.2()} ($=
|\psi^\prime(.)|$):
<<plot-absdPsi-2, fig=TRUE>>=
psD.n <-            absdPsi.2(as.numeric(pI.U), 40)
psD.M <- as.numeric(absdPsi.2(pI.U,        mpfr(40, 200)))
matplot(U., cbind(psD.n, psD.M), type="l", log="y")
legend("top", c("double prec.", "mpfr(*, precBits = 200)"),
       col= 1:2, lty=1:2, bty="n")
@
where we see a very large difference (note the \emph{$\log$} scale!) for
$u \approx 1$, i.e., for very small \linebreak[3]
\code{pI.U= iPsi.2(U., theta)}$ = \psi_\theta^{-1}(u)$.
<<plot-absdPsi-zero, fig=TRUE>>=
u0 <- 2^-(100:1)
psD.n <-            absdPsi.2(u0, 40)
psD.M <- as.numeric(absdPsi.2(u0, mpfr(40, 200)))
matplot(u0, cbind(psD.n, psD.M), type="l", log="xy")
legend("top", c("double prec.", "mpfr(*, precBits = 200)"),
       col= 1:2, lty=1:2, bty="n")
@
Further investigation shows that the culprit is really the use of
\code{log(-expm1(-theta))} inside \code{absdPsi.2()} which underflows for
large theta, and hence should be replaced by the generally accurate
\code{log1mexp()}, see \citet{maechler2012}.
%%   M\"achler, M. (2012) Accurately Computing $\log(1-\exp(-\lvert a\rvert))$,
%%  https://cran.r-project.org/web/packages/Rmpfr/vignettes/log1mexp-note.pdf
%% (MM: ~/R/Pkgs/Rmpfr/inst/doc/log1mexp-note.Rnw + ~/R/MM/NUMERICS/log1-exp.R)
<<log1mexp>>=
log1mexp <- function(a) # accurately compute log(1-exp(-a))
{
    stopifnot(a >= 0)
    r <- a
    tst <- a <= log(2)
    r[ tst] <- log(-expm1(-a[ tst]))
    r[!tst] <- log1p(-exp(-a[!tst]))
    r
}
@
so that we rather compute $|\psi^\prime(.)|$ via
<<absdPsi.3>>=
absdPsi.3 <- function(t, theta, log=FALSE) {
  w <- log1mexp(theta) - t
  Li. <- if(log) w - log1mexp(-w) else -exp(w)/expm1(w)
  if(log) Li. - log(theta) else Li. / theta
}
@
Does this already solve the ``diagonal likelihood'' problem?
We investigate via the graphic
<<nlogL-2-plot, fig=TRUE>>=
p.mlogL(th = seq(11, 99, by = 1/4),
        mlogL = (mlogL2 <- function(theta)
                 -sum(dDiagA(U., theta, d=d, iPsi = iPsi.2,
                            absdPsi = absdPsi.3, absdiPsi=absdiPsi.1,
                            log = TRUE))), lwd = 2)
@
Yes, indeed, using a numerically stable version for \code{absdPsi()}
did solve the numerical problem of computing, the ``diagonal likelihood'',
and hence the \code{dmle()} (``diagonal MLE'').

Well, if we really insist, there are more problems,
but probably not really practical:
<<llog-theta-plot-2,fig=true>>=
thet <- 9:1000
nll <- p.mlogL(thet, mlogL = mlogL2, lwd=2)
(th0 <- thet[i0 <- max(which(is.finite(nll)))])
abline(v = th0, col="red2", lty="15", lwd=2)
@

where we see that for $\theta > \Sexpr{th0}$, \code{mlogL()} is not finite,
e.g.,
<<dDiag-large-thet>>=
dDiagA(0.999, 715, d = d, iPsi = iPsi.2, absdPsi = absdPsi.3,
                          absdiPsi = absdiPsi.1, log = TRUE)
@
which after closer inspection is from  the \code{absdiPsi(u, th, log =
TRUE)} part of \code{dDiagA()} and that, see (\ref{def:absdiPsi}), uses
\code{log(theta)-log(expm1(u*theta))},
where clearly already \code{expm1(u*theta)) = expm1(0.999 * 715)} numerically overflows to \code{Inf}:
<<psiID1-large-thet>>=
absdiPsi.1(0.999, th = 715, log=TRUE)
@
However, as $\log(\mathrm{expm1}(y)) = \log(e^y -1) = \log(e^y(1 - e^{-y}))
= y + \log(1 - e^{-y})$, the ``numerical stable'' solution is to replace
\code{log(expm1(y))} by \code{y + log1mexp(y)}, such that we will use
<<def-absdiPsi-2>>=
absdiPsi.2 <- function(u, theta, log = FALSE)
  if(log) log(theta)- {y <- u*theta; y + log1mexp(y)} else theta/expm1(u*theta)
@
Unfortunately, this improves the situation for large $\theta$ only slightly:
<<llog-theta-plot-3, fig=TRUE>>=
plot(nll ~ thet, xlab=expression(theta),
     ylab = expression(- logLik(theta, .)),
     type = "l", col="red2", lwd=2)
abline(v = th0, col="red2", lty="15", lwd=2)
nll3 <- p.mlogL(thet, mlogL = function(theta)
                 -sum(dDiagA(U., theta, d=d, iPsi= iPsi.2, absdPsi= absdPsi.3,
                             absdiPsi = absdiPsi.2, log = TRUE)),
                col = "blue2", lwd=3, lty=2, add = TRUE)
nll3[thet == 800]
@
where we can see, that this time, the numerical overflow to $-\infty$
happens in \code{absdPsi(d*iPsi(u,th), th, log = TRUE)}, as
\code{iPsi(u,th) = iPsi(0.999, th=800)}$=$\Sexpr{format(iPsi.2(u=0.999,th=800))}
underflows to zero --- by necessity: the correct value is smaller than the
smallest representable double precision number:
<<true-iPsi-small>>=
pI <- iPsi.2(u=0.999, th= mpfr(800, 200))
cat(sapply(list(pI, .Machine$double.xmin),
           format, digits = 7), "\n")
@
The only solution here is to allow passing the \emph{logarithm}
$\log(t)$ instead of $t$ to \code{absdPsi()}, i.e., we start computing
\code{log(iPsi(.))} directly (evading the underflow to 0 there).

\dots \dots to be continued.


\section{Session Information}

<<sessionInfo, results=tex>>=
toLatex(sessionInfo())
<<finalizing, echo=FALSE>>=
options(op.orig)
@

\section{Conclusion}

\bibliography{nacopula}% Rmpfr

\end{document}
