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%-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
\DeclareMathOperator{\logIp}{log1p}% R & C function log1p(x):= log(1+x) accurately
\DeclareMathOperator{\logIpmx}{log1pmx}% log1pmx(x) = log(1+x)-x accurately
%%---------------------------------------------------------------
%%---------------------------------------------------------------

%\VignetteIndexEntry{log1pmx, bd0, stirlerr - Probability Computations in R}
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%%%------------------------------------------------------------

\bibliographystyle{apalike}
\begin{document}

\title{log1pmx(), bd0(), stirlerr() -- Computing Poisson, Binomial, Gamma Probabilities in \R}
\author{Martin M\"achler\\ Seminar f\"ur Statistik \\ ETH Zurich}
\date{April 2021 ff {\footnotesize%\tiny
    (\LaTeX'ed \today)}}
\maketitle

\begin{abstract} %maybe  \normalsize
  The auxiliary function $\logIpmx()$ (``log 1 \textbf{p}lus \textbf{m}inus x''),
  had been introduced when R's \code{pgamma()} (incomplete $\Gamma$
  function) had been numerically improved by Morten Welinder's contribution
  to  % ~/R/D/r-devel/R/src/nmath/pgamma.c
  \myHref{https://bugs.R-project.org/show\_bug.cgi?id=7307\#c6}{R's \PR{7307}, in Jan.\ 2005},
  it is mathematically defined as $\logIpmx(x) := \log(1+x) - x$
  and for numerical evaluation, suffers from two levels of cancellations for small $x$, i.e.,
  using $\mathrm{log1p(x)}$ for $\log(1+x)$ is not sufficient.

  In 2000 already, Catherine Loader's contributions for more accurate computation of
  binomial, Poisson and negative binomial probabilities, \cite{LoaC2000}, had introduced
  auxiliary functions   \code{bd0()} and \code{stirlerr()}, see below.

  Much later, in \myHref{https://bugs.r-project.org/show\_bug.cgi?id=15628}{%
    R's \PR{15628}, in Jan.\ 2014},
  Welinder noticed that in spite of Loader's improvements, Poisson probabilities were not
  perfectly accurate (only ca.~13 accurate digits instead of $15.6 \approx \log_{10}(2^{52})$),
  relating the problem to somewhat imperfect computations in \code{bd0()},
  which he proposed to address using  \code{log1pmx()} on one hand, and
  additionally addressing cancellation by using \emph{two} double precision
  numbers to store the result (his proposal of an \code{ebd0()} function).

  Here, I address the problem of providing more accurate \code{bd0()} (and
  \code{stirlerr()} as well), applying Welinder's proposal to use
  $\logIpmx()$, but otherwise diverging from the proposal.
\end{abstract}

%% place holder, so emacs uses polymode+R
<<require, echo=FALSE>>=
require(DPQ)
@

%% Morten Welinder's early bug fixes and improvements for pgamma --- all
%% building on  C. Loader's

\section{Introduction}
According to \R{}'s reference documentation, \code{help(dbinom)},
%% >>> ~/R/D/r-devel/R/src/library/stats/man/Binomial.Rd
the binomial (point-mass) probabilities of the binomial distribution with
\code{size} $= n$ and \code{prob} $= p$ has ``density'' (point probabilities)
\begin{align*}
  p(x) := p(x; n,p) := {n \choose x} {p}^{x} {(1-p)}^{n-x}
\end{align*}
for \(x = 0, \dots, n\), and these are (in \R function \code{dbinom()})
computed via Loader's algorithm (\cite{LoaC2000}) which had improved accuracy
considerably, also for R's internal \verb|dpois_raw()| function which is
used further directly in % ~/R/D/r-devel/R/src/nmath/dpois_raw.grep
\code{dpois()}, \code{dnbinom()}, \code{dgamma()}, the non-central
\code{dbeta()} and \code{dchisq()} and even the \emph{cumulative}
$\Gamma()$ probabilities \code{pgamma()} and hence indirectly e.g., for
cumulative central and non-central chisquare probabilities (\code{pchisq()}).

\citeauthor{LoaC2000} noticed that for large $n$, the usual way to compute $p(x; n,p)$ via
its logarithm $\log(p(x; n, p)) = \log(n!) - \log(x!) - \log((n - x)!) + x
\log(p) + (n - x) \log(1 - p)$ was inaccurate, even when
accurate $\log \Gamma(x) = $ \code{lgamma(x)} values are available to get
$\log(x!) = \log\Gamma(x+1)$, e.g., for $x=10^6$, $n=2\times 10^6$,
$p=1/2$, about 7 digits accuracy were lost from cancellation (in
substraction of the log factorials).

Instead, she wrote
\begin{align}
  \label{eq:p-D}
  p(x; n, p) = p(x; n, \frac{x}{n}) \cdot e^{-D(x;n,p)},
\end{align}
where the ``Deviance'' $D(.)$ is defined as
\begin{align}
  \label{eq:D-def}
  D(x;n,p) &= \log p(x; n, \frac{x}{n}) - \log p(x; n, p) \nonumber\\
           &= x\log\big(\frac{x}{n p}\big) + (n-x) \log \big(\frac{n-x}{n(1-p)}\big),
\end{align}
and to avoid cancellation, $D()$ has to be computed somewhat differently,
namely -- correcting notation wrt the original -- using a
\emph{two}-argument version $D_0()$:
\begin{align}
  \label{eq:D-D0}
  D(x;n,p) &= n p \tilde D_0\big(\frac{ x }{n p}\big) +
               n q \tilde D_0\big(\frac{n-x}{n q}\big) \nonumber \\
           &= D_0(x, n p) + D_0(n-x, n q),
\end{align}
where $q := 1-p$ and
\begin{align}
  \label{eq:tD0-def}
 \tilde D_0(r) &:= r\log(r) + 1-r \ \ \ \ \textrm{and} \\
  D_0(x, M) &:= M \cdot \tilde D_0(x/M)   \label{eq:D0-def} \\
            &= M \cdot \Big(\frac{x}{M}\log\big(\frac x M\big) + 1 - \frac x M\Big)
             = x \log\big(\frac x M\big) + M - x \label{eq:D0-is}
\end{align}
Note that since $\lim_{x \downarrow 0} x \log x = 0$, setting
\begin{align}
  \label{eq:D0-x0}
  \tilde D_0(0) &:= 1 \ \mathrm{and} \\
    D_0(0, M)   & := M \tilde D_0(0) = M \cdot 1 = M \nonumber
\end{align}
defines $D_0(x,M)$ for all $x \ge 0$, $M > 0$.

The careful C function implementation of $D_0(x, M)$ is called \code{bd0(x, np)}
in Loader's C code and now R's Mathlib ((lib)Rmath) at
\url{https://svn.r-project.org/R/trunk/src/nmath/bd0.c},
mirrored, e.g., at
\myHref{https://github.com/wch/r-source/blob/trunk/src/nmath/bd0.c}{Winston Chen's github mirror}.
In 2014, Morten Welinder suggested in
\myHref{https://bugs.r-project.org/show\_bug.cgi?id=15628}{R's \PR{15628}}
that the current \code{bd0()} implementation is still inaccurate in some
regions (mostly \emph{not} in the one it has been carefully implemented to
be accurate, i.e., when $x \approx M$) notably for computing Poisson
probabilities, \code{dpois()} in R; see more below.

\bigskip

Evaluating of $p(x; n,p)$ in (\ref{eq:p-D}), in addition to
$D(x; n,p)$ in (\ref{eq:D-D0}) also needs
$p(x; n, \frac{x}{n})$ where in turn, the Stirling De Moivre series is
used:
\begin{align}
  \label{eq:Stirling}
  \log n!   &= \frac 1 2 \log(2\pi n) + n \log(n) - n + \delta(n),
              \quad\textrm{where the ``Stirling error'' } \delta(n) \ \mathrm{ is} \\
  \delta(n) &:= \log n! - \frac 1 2 \log(2\pi n) - n \log(n) + n = \label{eq:deltaStirling}\\
            & = \frac 1{12 n} - \frac 1{360 n^3} + \frac 1{1260 n^5}
              - \frac 1{1680 n^7} + \frac 1{1188 n^9} + O(n^{-11}).
\end{align}
See appendix~\ref{C:stirlerr} how $\delta(n) \equiv$\code{stirlerr(n)} is
computed and implemented in the C code of R, and can be improved.


Note that for the binomial, $x$ is an integer in $\{0, 1, \dots, n\}$ and $M=n p \ge 0$,
but the formulas (\ref{eq:D0-def}), (\ref{eq:D0-is}) for $D_0(x,M)$ apply and are needed, e.g., for \code{pgamma()} computations for general non-negative ($x, M >
0$) where even $x = 0$ is well defined, see (\ref{eq:D0-x0}) above.

Further, \citeauthor{LoaC2000} showed, such a saddle point approach is needed for Poisson
probabilities, as well, where
\begin{align}
  \label{eq:Pois}
      p_\lambda(x) &= e^{-\lambda} \frac{\lambda^x}{x!} \\
 \log p_\lambda(x) &= -\lambda + x \log\lambda % - \log{x!} "re-expressed":
        \underbrace{- \log(x!)}_{\log(1/\sqrt{2\pi x}) - (x\log x - x + \delta(x))}
				 \nonumber\\
                  &= \log\frac{1}{\sqrt{2\pi x}} - x \log\frac{x}{\lambda} +
                    x - \lambda - \delta(x),
\end{align}
is re-expressed using $\delta(x)$ and from (\ref{eq:D0-is}) $D_0(x,\lambda)$ as
\begin{align}   \label{eq:Pois-D0}
  p_\lambda(x) &= %\frac{1}{\sqrt{2\pi x}} e^{-\delta(x) - \lambda \tilde
                % D_0(x/\lambda)} \nonumber \\
               % &=
                 \frac{1}{\sqrt{2\pi x}} e^{-\delta(x) - D_0(x,\lambda)}
\end{align}

\medskip

Also, negative binomial probabilities, \code{dnbinom()}, \dots \dots \dots TODO \dots \dots

\medskip

Even for the $t_\nu$ density, \code{dt()}, \dots \dots \dots
\\ \dots
but there have a direct approximations in package \pkg{DPQ}, currently functions
\code{c\_dt(nu)} and even more promisingly, \code{lb\_chi(nu)}.
 \dots \dots \dots TODO \dots \dots


\medskip

\section{Loader's ``Binomial Deviance'' $D_0(x,M) = $ \code{bd0(x, M)}}\label{sec:bd0}
%% originally got from here ----> ../man/dgamma-utils.Rd

\citeauthor{LoaC2000}'s ``\emph{Binomial Deviance}'' function $D_0(x,M) = $ \code{bd0(x, M)}
has been defined for \(x, M > 0\)
where the limit \(x \to 0\) is allowed (even though not implemented in the original \code{bd0()}),
here repeated from (\ref{eq:D0-def}) :
\begin{align*} %\label{eq:bd0-def-2}
  D_0(x,M)      &:= M \cdot \tilde D_0\bigl(\frac{x}{M}\bigr), \ \ \mathrm{where}\\
  \tilde D_0(u) &:= u \log(u) + 1-u = u(\log(u) - 1) + 1.
\end{align*}

\pagebreak[3]
Note the graph of $\tilde D_0(u)$,
\setkeys{Gin}{width=0.5\textwidth}% set figure width
\begin{center}% \begin{figure}[htb!] \centering\small
<<plot_D0tilde, fig=TRUE, width=4.6, height=4.3, echo=FALSE>>=
par(mar = 0.1 + c(2.5, 3, 0, 0), mgp = c(1.5, 0.6, 0), las=1)
curve(x*log(x)+1-x, 1e-7, 6, n=1001, col=2, lwd=2,
      panel.first=grid(), xlab=quote(u), ylab="")
mtext(quote(tilde(D[0])(u) == ~ u %.%~ log(u)+1-~u), line=-2, col=2)
abline(a = 1-exp(1), b=1, col=adjustcolor(4, 3/4), lwd=1.5, lty=2)
text(5, 2.5, quote(1%.%u - e+1), col=4)
axis(1, at=exp(1), quote(e), tck=0.2, col=4, col.axis=4, lty=3)
@
\end{center}
\vspace*{-1.5ex}
has a double zero at $u=1$, such that for large $M$ and $x \approx M$, i.e.,
$\frac x M \approx 1$, the direct computation of
$D_0(x,M) = M \cdot \tilde D_0\bigl(\frac{x}{M}\bigr)$ is numerically problematic.
Further,
\begin{align}
  \label{eq:bd0-trafo}
  D_0(x,M) & = M \cdot \bigl(\frac{x}{M}(\log(\frac{x}{M}) -1) +1 \bigr) =
             x \log(\frac{x}{M}) - x + M.
\end{align}

We can rewrite this, originally by e-mail from Martyn Plummer, then
also indirectly from Morten Welinder's mentioning of \code{log1pmx()} in his \PR{15628}
notably for the important situation when \(\abs{x-M} \ll M\).
Setting \(t := (x-M)/M\), i.e., \(\abs{t} \ll 1\) for that situation, or equivalently,
\(\frac{x}{M} = 1+t\). Using $t$,
\begin{align}
  \label{eq:def-t}
  t &:= \frac{x-M}{M} \\
  \label{eq:bd0-log1}
  D_0(x,M) % = M \cdot [(t+1)(\log(1+t) - 1) + 1]
           &= \overbrace{M\cdot(1+t)}^{x} \log(1+t) - \overbrace{t \cdot M}^{x-M} =
               M \cdot \big((t+1) \log(1+t) - t \big) =  \nonumber\\
           &=  M \cdot p_1l_1(t),
\end{align}
where
\begin{align}
  \label{eq:p1l1-def}
  p_1l_1(t) &:= (t+1)\log(1+t) - t = \frac{t^2}{2} - \frac{t^3}{6} \pm \cdots,\\
           & = (\log(1+t) - t) + t \cdot \log(1+t) \nonumber \\
           & = \logIpmx(t) + t \cdot \mathrm{log1p}(t) \label{eq:p1l1-log1pmx}
\end{align}
where
\begin{align} \label{eq:log1pmx}
  \logIpmx(x) := \log(1+x) - x \approx - x^2/2 + x^3/3 - x^4/4 \pm \ldots,
\end{align}
and the Taylor series expansions for $\logIpmx(t)$ and $p_1l_1(t)$ are useful for small $\abs{t}$,
\begin{align}
  \label{eq:p1l1-Taylorseries}
  p_1l_1(t) &= \frac{t^2}{2} - \frac{t^3}{6} + \frac{t^4}{12} \pm \cdots =
              \sum_{n=2}^\infty  \frac{(-t)^n}{n(n-1)} =
              \frac{t^2}{2} \sum_{n=2}^\infty \frac{(-t)^{n-2}}{n(n-1)/2} =
              \frac{t^2}{2} \sum_{n=0}^\infty \frac{(-t)^{n}}{{{n+2}\choose 2}} = \\
            &= \frac{t^2}{2}\bigl(1 - t\big(\frac 1 3 - t\big(\frac 1 6 - t\big(\frac{1}{10} -
                                 t\big(\frac{1}{15} - \cdots\big)\big)\big)\big)\bigr),
\end{align}
which we provide in \pkg{DPQ} via function \code{p1l1ser(t, k)} getting the
first $k$ terms, and the corresponding series approximation for
\begin{align}
  \label{eq:D0-by-p1l1-series}
  D_0(x,M) = \lim_{k \to \infty} \mathrm{p1l1ser}\big(\frac{x-M}{M},\ k,\ F = \frac{(x-M)^2}{M}\big) ,
\end{align}
where the approximation of course uses a finite $k$ instead of the limit
\(k \to \infty\).

This Taylor series expansion is useful and nice, but  may not even be needed typically,
as both utility functions $\logIpmx(t)$ and $\mathrm{log1p}(t)$
are available implemented to be fully accurate for small $t$, $t \ll 1$, and
(\ref{eq:p1l1-log1pmx}), indeed, with $t = (x-M)/M$ the evaluation of
\begin{align}
  \label{eq:D0-via-log1pmx}
   D_0(x,M) = M \cdot p_1l_1(t) = M\cdot\bigl(\logIpmx(t) + t \cdot \mathrm{log1p}(t)\bigr),
\end{align}
seems quite accurate already on a wide range of $(x, M)$ values.
%%
%%
%%
%%---------------------------- an "outtake" about p1l1() ----
<<p.l1p1-def, echo=FALSE>>=
p.p1l1 <- function(from, to, ylim=NULL, cS = adjustcolor(6, 1/2),
                   n=1024, do.leg=TRUE) {
    stopifnot(is.numeric(from), is.numeric(to),
              is.character(cS), length(cS) == 1)
    cols <- palette()[c(2,4, 6, 3,5)]; cols[3] <- cS
    c1 <- curve(x*log1p(x), from=from, to=to, n=n,
                col=2, ylab="", ylim=ylim,
                panel.first = abline(h=0, v=0, lty=3, lwd=1/2))
    c2 <- curve(log1pmx(x), add=TRUE, n=n, col=4)
    with(c1, {
        lines(x, y+c2$y, col=cS, lwd=3)
        lines(x, x^2/2          , col=3, lty=2)
        lines(x, x^2/2*(1 - x/3), col=5, lty=4)
    })
    if(do.leg) {
        labexpr <- expression(
            x %.% log1p(x), log1pmx(x),
            p1l1(x) == log1pmx(x) + x %.% log1p(x),
            x^2 / 2, # frac(x^2, 2)  # is too large
            x^2 / 2 %.% (1 - x/3))
        legend("top", labexpr, col = cols,
               lwd=c(1,1,3,1,1), lty=c(1,1,1:2,4), bty="n")
    }
}
@
\setkeys{Gin}{width=1.1\textwidth}% set figure width
\begin{figure}[htb!]%[htbp]
\centering\small
%% sfsmisc::mult.fig(mfcol=c(1,2)) # FAILS with Sweave setup ?!
<<l1p1-curves, fig=TRUE, width=10>>=
par(mfcol=1:2, mar = 0.1 + c(2.5, 3, 1, 2), mgp = c(1.5, 0.6, 0), las=1)
p.p1l1(-7/8, 2, ylim = c(-1,2))
zoomTo <- function(x,y=x, tx,ty){ arrows(x,-y, tx, ty)
                                    text(x,-y, "zoom in", adj=c(1/3,9/8)) }
zoomTo0 <- function(x,y=x) zoomTo(x,y, 0,0)
zoomTo0(.3)
p.p1l1(-1e-4, 1.5e-4, ylim=1e-8*c(-.6, 1), do.leg=FALSE)
@
\caption{\normalsize $p_1l_1(t) =$ \code{p1l1()} and its constituents,
  $x*\logIp(x)$ and $\logIpmx() =$ \code{log1pmx()}, with \R functions from
  our \pkg{DPQ} package.
  On the right, zoomed in 4 and 8 orders of magnitude, where the Taylor
  approximations $x^2/2$ and $x^2/2 - x^3/6$ are visually already perfect.}
\label{fig:p1l1-etc}
\end{figure}

Note that $x*\logIp(x)$ and $\logIpmx()$ have different signs, but also
note that for small $\abs{x}$, are well approximated by $x^2$ and $-x^2/2$,
so their sum $p_1l_1(x) = \logIpmx(x) + x \cdot \logIp(x)$ is approximately
$x^2/2$ \emph{and} numerically computing $x^2 - x^2/2$ should only lose 1
or 2 bits of precision.


%% Also the  ebd0() proposal to improve bd0().

%% NB: "page 2" of ../tests/stirlerr-tst.R  is *only* about bd0(), ebd0() .. !!
%%                 =======================

%% ----> ../man/dgamma-utils.Rd : bd0(), stirlerr()
%%       ======================

\appendix

%% Appendix A:
\section{Accuracy of log1pmx(x) Computations}\label{A:log1pmx}

As we've seen, the ``binomial deviance'' function
$D_0(x,M) = $ \code{bd0(x, M)} is crucial for accurate (saddlepoint)
computations of binomial, Poisson, etc probabilities, and (at the end of section~\ref{sec:bd0}),
one stable way to compute $D_0(x,M)$ is via (\ref{eq:D0-via-log1pmx}), i.e.,
with $t = (x-M)/M$, to compute the sum of two terms
$D_0(x,M) = M\cdot\bigl(\logIpmx(t) + t \cdot \mathrm{log1p}(t)\bigr)$.

Here, we look more closely at the computation of $\logIpmx(x) := \log(1+x) - x$,
at first visualizing the function, notably around $(0, 0)$ where numeric
cancellations happen if no special care is taken.
<<log1pmx-curves, fig=TRUE, width=10>>=
lcurve <- function(Fn, a,b, ylab = "", lwd = 1.5, ...)
    plot(Fn, a,b, n=1001, col=2, ylab=ylab, lwd=lwd, ...,
         panel.last = abline(h=0, v=-1:0, lty=3))

par(mfrow=c(2,2), mar = 0.1 + c(2.5, 3, 1, 2), mgp = c(1.5, 0.6, 0), las=1)
lcurve(log1pmx, -.9999, 7, main=quote(log1pmx(x) == log(1+x)-x))
                            rect(-.1,  log1pmx(-.1  ), .1  , 0); zoomTo0(1/2, 1)
lcurve(log1pmx, -.1,  .1 ); rect(-.01, log1pmx(-.01 ), .01 , 0); zoomTo0(.02, .001)
lcurve(log1pmx, -.01, .01); rect(-.002,log1pmx(-.002), .002, 0); zoomTo0(2e-3,1e-5)
lcurve(function(x) -log1pmx(x), -.002, .002, log="y", yaxt="n") -> l1r
sfsmisc::eaxis(2); abline(v=0, lty=3)
d1r <- cbind(as.data.frame(l1r), y.naive = with(l1r, -(log(1+x)-x)))
c4 <- adjustcolor(4, 1/3)
lines(y.naive ~ x, data=d1r, col=c4, lwd=3, lty=2)
legend("left", legend=expression(- log1pmx(x), -(log(1+x)-x)),
       col=c(palette()[2],c4), lwd=c(1,3), lty=1:2, bty="n")
@

The accuracy of our \code{log1pmx()} is already vastly better than
the naive \(\log(1+x)-x\) computation:

<<log1pmx-naive-error, fig=TRUE, width=10>>=
par(mfrow=1:2, mar = 0.1 + c(2.5, 3, 1, 2), mgp = c(1.5, 0.6, 0), las=1)
d1r[, "relE.naive"] <- with(d1r, sfsmisc::relErrV(y, y.naive))
plot(relE.naive ~ x, data=d1r, type="l", ylim = c(-1,1)*1e-6)
y2 <- 1e-8
rect(-.002, -y2, .002, y2, col=adjustcolor("gray",1/2), border="transparent")
zoomTo(15e-4, 9*y2, 13e-4, -y2)
plot(relE.naive ~ x, data=d1r, type="l", ylim = c(-1,1)*y2); abline(h=0,lty=3)
@
% y2 <- 1e-10
% rect(-.002, -y2, .002, y2, col=adjustcolor("lightgray",1/2), border="gray")
% zoomTo(15e-4, 9*y2, 13e-4, -y2)
% plot(relE.naive ~ x, data=l1r, type="l", ylim = c(-1,1)*y2)

Now, we explore the accuracy achieved with \R's, i.e. Welinder's algorithm,
which uses relatively few terms ao continued-fraction representation of the
Taylor series of $\logIpmx(x)$,
using package \CRANpkg{Rmpfr} and high precision arithmetic.
%%
%% <--> ../man/log1pmx.Rd >> example mostly moved to ../tests/dnbinom-tst.R
%%      ~~~~~~~~~~~~~~~~~                            ======================
see \file{../tests/dnbinom-tst.R}, \texttt{2b:\ \ log1pmx()}.
From there, it seems that the (hardcoded currently in R's \file{pgamma.c} as
\code{ double minLog1Value = -0.79149064 }
could or should (?) be changed to around -0.7 or e.g., -0.66.

In \pkg{DPQ}'s  \code{log1pmx()} it is the argument \code{minL1 = -0.79149064},
there' a switch constant eps2, (hardwired in current \R to \code{1e-2}, i.e., \code{eps2 = 0.02})
to switch from an explicit 5-term formula to the full \code{logcf()} based procedure.
In \pkg{DPQ}, we already use \code{eps2 = 0.01} as default. %
Note that this does \emph{not} influence the choice of \code{minL1} as
long as \code{eps2} (order of 0.01) is far from the range in which we
choose \code{minL1} ($[-0.85, -0.4]$).
\\
((MM: Still:  can we prove that 0.01 is ``uniformly'' better than  0.02  ?? ))

\subsection{Testing \code{dpois\_raw()} / \code{dpois()} Poisson probabilities}
Testing the Poisson probabilities (\sQuote{density})
with several versions of bd0(), ebd0() and
the direct formula where more appropriate (non-log case,
%%  now (R-devel, March 2022) it does *NOT* use ebd0() nor bd0() anymore
%% _iff_   x/lambda <= DBL_EPSILON = 2^-52
%%
%% MM: Mention these already above !!
Look at examples in \file{"../man/dgamma-utils.Rd"}  and then also
\\ % from ../dpois.grep    ^^^^^^^^^^^^^^^^^^^^^^
%%        ~~~~~~~~~~~~~
\verb|/u/maechler/R/MM/NUMERICS/dpq-functions/15628-dpois_raw_accuracy.R| .
%     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##########################


% Appendix B:
\section{Accuracy of $ p_1l_1(t)$ Computations}\label{B:p1l1}

Loader's ``Binomial Deviance'' $D_0(x,M) = $ \code{bd0(x, M)} function can also be re-expressed (mathematically)
%% copy/paste from ../man/p1l1.Rd
  as \(bd0(x,M) = M * p1l1((x-M)/M)\) where we look into providing
  numerically stable formula for \(p1l1(t)\) as its mathematical formula
  \(p1l1(t) = (t+1)\log(1+t) - t\) suffers from cancellation for small \eqn{|t|}, even when
  \code{\link{log1p}(t)} is used instead of \code{log(1+t)}; see the derivations
  (\ref{eq:bd0-log1}),  (\ref{eq:p1l1-def}), and (\ref{eq:log1pmx}) above, and the Taylor series
  expansion (\ref{eq:p1l1-Taylorseries}) which we provide in our \R functions \code{p1l1()}, and \code{p1l1ser},
  respectively.

  Using a hybrid implementation, \code{p1l1()} uses a direct formula, now
  the stable one in \code{p1l1p()}, for $\left| t \right| > c$
  and a series approximation for $\left|t\right| \le c$ for some cutoff $c$.

  NB:  The re-expression via \code{log1pmx()} is almost perfect; it
  fixes the cancellation problem entirely (and exposes the fact that
  \code{log1pmx()}'s internal cutoff seems sub optimal.

TODO --- very unfinished.   \ \  How much more here?

For now, look at the examples in \texttt{?p1l1}, or even run \code{example(p1l1)}.
%%
%% <--> ../man/p1l1.Rd		: p1l1(), p1l1ser(), p1l1p() etc -- plots and more
%%      ==============
% ----------------------------- ../R/dgamma.R
% p1l1p  (t, ...)
% p1l1.  (t)
% p1l1   (t,    F = t^2/2)
% p1l1ser(t, k, F = t^2/2)
% -----------------------------

% Appendix C:
\section{Accuracy of \code{stirlerr(x)}$=\delta(x)$ Computations}\label{C:stirlerr}

Note that the ``Stirling error'', $\delta(x) \equiv$\code{stirlerr(x)},
$\delta(x) := \log x! - \frac 1 2 \log(2\pi x) - x \log(x) + x$ by
Stirling's formula is
$\delta(x) =  \frac 1{12 x} - \frac 1{360 x^3} + \frac 1{1260 x^5}
            - \frac 1{1680 x^7} + \frac 1{1188 x^9} + O(x^{-11})$,
see (\ref{eq:deltaStirling}).

A C code implementation had been provided by
\citeauthor{LoaC2000} and for years now in R's Mathlib at
\url{https://svn.r-project.org/R/trunk/src/nmath/stirlerr.c}.
mirrored, e.g., at \url{https://github.com/wch/r-source/blob/trunk/src/nmath/stirlerr.c}

TODO:

Look at examples in \file{../tests/stirlerr-tst.R}  to show the small
%%                        ~~~~~~~~~~~~~~~~~~~~~~~
accuracy loss with Loader's defaults (for the cut offs of the number of
terms used) and also how we explore improving these defaults to improve accuracy.


\bibliography{R-numerics}% ~/bib/R-numerics.bib  now link --> "here"

\end{document}

