lambda and omega.
  It also allows sampling from the truncated distribution.
  
  [Special Generator] -- Sampling Function: GIG (generalized inverse Gaussian).urgig(n, lambda, omega, lb=1.e-12, ub=Inf)lambda $=\lambda$ and omega $=\omega$
  has a density proportional to 
  $$f(x) \sim x^{\lambda-1}\exp(-(\omega/2)(x+1/x))$$
  for $x \ge 0$, $\lambda > 0$ and $\omega > 0$.  The generation algorithm uses transformed density rejection lb and ub can be used to generate variates from 
  the distribution truncated to the interval (lb,ub).
The generation algorithm works for $\lambda \ge 1$ and $\omega>0$ and for $\lambda>0$ and $\omega \ge 0.5$.
N.L. Johnson, S. Kotz, and N. Balakrishnan (1994): Continuous Univariate Distributions, Volume 1. 2nd edition, John Wiley & Sons, Inc., New York. Chap.15, p.284.
runif and .Random.seed about random number
  generation and unuran  for the UNU.RAN class.## Create a sample of size 1000
x <- urgig(n=1000,lambda=2,omega=3)Run the code above in your browser using DataLab