The R Journal: article published in 2016, volume 8:1

statmod: Probability Calculations for the Inverse Gaussian Distribution
Göknur Giner and Gordon K. Smyth , The R Journal (2016) 8:1, pages 339-351.

Abstract The inverse Gaussian distribution (IGD) is a well known and often used probability dis tribution for which fully reliable numerical algorithms have not been available. We develop fast, reliable basic probability functions (dinvgauss, pinvgauss, qinvgauss and rinvgauss) for the IGD that work for all possible parameter values and which achieve close to full machine accuracy. The most challenging task is to compute quantiles for given cumulative probabilities and we develop a simple but elegant mathematical solution to this problem. We show that Newton’s method for finding the quantiles of a IGD always converges monotonically when started from the mode of the distribution. Simple Taylor series expansions are used to improve accuracy on the log-scale. The IGD probability functions provide the same options and obey the same conventions as do probability functions provided in the stats package.

Received: 2016-01-05; online 2016-07-27
CRAN packages: SuppDists, STAR, statmod
CRAN Task Views implied by cited CRAN packages: Distributions, HighPerformanceComputing, NumericalMathematics

This article is licensed under a Creative Commons Attribution 3.0 Unported license .

@article{RJ-2016-024,
  author = {Göknur Giner and Gordon K. Smyth},
  title = {{statmod: Probability Calculations for the Inverse Gaussian
          Distribution}},
  year = {2016},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2016-024},
  url = {https://doi.org/10.32614/RJ-2016-024},
  pages = {339--351},
  volume = {8},
  number = {1}
}