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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_754if (ISNAN(x) || ISNAN(size) || ISNAN(prob))return x + size + prob;#endifif (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_754if (ISNAN(x) || ISNAN(size) || ISNAN(mu))return x + size + mu;#endifif (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: Poissonreturn(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, whenx_ = size and n_ = x+size are so close that n_ - x_ loses accuracybut 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);}}