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/*
* AUTHOR
* Catherine Loader, catherine@research.bell-labs.com.
* October 23, 2000 and Feb, 2001.
*
* dnbinom_mu(): Martin Maechler, June 2008
*
* Merge in to R and improvements notably for |x| << size :
* Copyright (C) 2000--2021, The R Core Team
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, a copy is available at
* https://www.R-project.org/Licenses/
*
*
* DESCRIPTION
*
* Computes the negative binomial distribution. For integer n,
* this is probability of x failures before the nth success in a
* sequence of Bernoulli trials. We do not enforce integer n, since
* the distribution is well defined for non-integers,
* and this can be useful for e.g. overdispersed discrete survival times.
*/
#include "nmath.h"
#include "dpq.h"
double dnbinom(double x, double size, double prob, int give_log)
{
#ifdef IEEE_754
if (ISNAN(x) || ISNAN(size) || ISNAN(prob))
return x + size + prob;
#endif
if (prob <= 0 || prob > 1 || size < 0) ML_WARN_return_NAN;
R_D_nonint_check(x);
if (x < 0 || !R_FINITE(x)) return R_D__0;
x = R_forceint(x);
if(x == 0) {
/* limiting case as size approaches zero is point mass at zero */
if(size == 0) return R_D__1;
// size > 0: P(x, ..) = pr^n :
return(give_log ? size*log(prob) : pow(prob, size));
}
if(!R_FINITE(size)) size = DBL_MAX;
if(x < 1e-10 * size) { // instead of dbinom_raw(), use 2 terms of Abramowitz & Stegun (6.1.47)
return R_D_exp(size * log(prob) + x * (log(size) + log1p(-prob))
- lgamma1p(x) + log1p(x*(x-1)/(2*size)));
} else {
/* log( size/(size+x) ) is much less accurate than log1p(- x/(size+x))
for |x| << size (and actually when x < size): */
double p = give_log ? (x < size ? log1p(-x/(size+x)) : log(size/(size+x)))
: size/(size+x),
ans = dbinom_raw(size, x+size, prob, 1-prob, give_log);
return((give_log) ? p + ans : p * ans);
}
}
double dnbinom_mu(double x, double size, double mu, int give_log)
{
/* originally, just set prob := size / (size + mu) and called dbinom_raw(),
* but that suffers from cancellation when mu << size */
#ifdef IEEE_754
if (ISNAN(x) || ISNAN(size) || ISNAN(mu))
return x + size + mu;
#endif
if (mu < 0 || size < 0) ML_WARN_return_NAN;
R_D_nonint_check(x);
if (x < 0 || !R_FINITE(x)) return R_D__0;
/* limiting case as size approaches zero is point mass at zero,
* even if mu is kept constant. limit distribution does not
* have mean mu, though.
*/
if (x == 0 && size == 0) return R_D__1;
x = R_forceint(x);
// FIXME use also for size "almost" Inf because that gives NaN ???
if(!R_FINITE(size)) // limit case: Poisson
return(dpois_raw(x, mu, give_log));
if(x == 0)/* be accurate, both for n << mu, and n >> mu :*/
return R_D_exp(size * (size < mu ? log(size/(size+mu)) : log1p(- mu/(size+mu))));
if(x < 1e-10 * size) { /* don't use dbinom_raw() but MM's formula: */
/* FIXME --- 1e-8 shows problem; rather use algdiv() from ./toms708.c */
double p = (size < mu ? log(size/(1 + size/mu)) : log(mu / (1 + mu/size)));
return R_D_exp(x * p - mu - lgamma1p(x) +
log1p(x*(x-1)/(2*size)));
} else {
/* no unnecessary cancellation inside dbinom_raw, when
x_ = size and n_ = x+size are so close that n_ - x_ loses accuracy
but log( size/(size+x) ) is much less accurate than log1p(- x/(size+x))
for |x| << size (and actually when x < size): */
double p = give_log ? (x < size ? log1p(-x/(size+x)) : log(size/(size+x)))
: size/(size+x),
ans = dbinom_raw(size, x+size, size/(size+mu), mu/(size+mu), give_log);
return((give_log) ? p + ans : p * ans);
}
}