# Generate Random Inverse Gaussian in R

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Needed to generate draws from an inverse Gaussian today, so I wrote the following Rcpp code:

#include <RcppArmadillo.h> // [[Rcpp::depends(RcppArmadillo)]] using namespace Rcpp; using namespace arma; // [[Rcpp::export]] Col<double> rrinvgauss(int n, double mu, double lambda){ Col<double> random_vector(n); double z,y,x,u; for(int i=0; i<n; ++i){ z=R::rnorm(0,1); y=z*z; x=mu+0.5*mu*mu*y/lambda - 0.5*(mu/lambda)*sqrt(4*mu*lambda*y+mu*mu*y*y); u=R::runif(0,1); if(u <= mu/(mu+x)){ random_vector(i)=x; }else{ random_vector(i)=mu*mu/x; }; } return(random_vector); }

It seems to be faster than existing implementations such as rig from mgcv and rinvgauss from statmod packages.

library(Rcpp) library(RcppArmadillo) library(rbenchmark) library(statmod) library(mgcv) sourceCpp("rrinvgauss.cpp") n=10000 benchmark(rig(n,1,1),rinvgauss(n,1,1),rrinvgauss(n,1,1),replications=100)

rename rrinvgauss as desired.

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