MultiRNG-package: Multivariate Pseudo-Random Number Generation

In MultiRNG: Multivariate Pseudo-Random Number Generation

Description

This package implements the algorithms described in Demirtas (2004) for pseudo-random number generation of 11 multivariate distributions. The following multivariate distributions are available: Normal, t, Uniform, Bernoulli, Hypergeometric, Beta (Dirichlet), Multinomial, Dirichlet-Multinomial, Laplace, Wishart, and Inverted Wishart.

This package contains 11 main functions and 2 auxiliary functions. The methodology for each random-number generation procedure varies and each distribution has its own function. For multivariate normal, draw.d.variate.normal employs the Cholesky decomposition and a vector of univariate normal draws and for multivariate t, draw.d.variate.t employs the Cholesky decomposition and a vector of univariate normal and chi-squared draws. draw.d.variate.uniform is based on cdf of multivariate normal deviates (Falk, 1999) and draw.correlated.binary generates correlated binary variables using an algorithm developed by Park, Park and Shin (1996) and makes use of the auxiliary function loc.min. draw.multivariate.hypergeometric employs sequential generation of succeeding conditionals which are univariate hypergeometric. Furthermore, draw.dirichlet uses the ratios of gamma variates with a common scale parameter and draw.multinomial generates data via sequential generation of marginals which are binomials. draw.dirichlet.multinomial is a mixture distribution of a multinomial that is a realization of a random variable having a Dirichlet distribution. draw.multivariate.laplace is based on generation of a point s on the d-dimensional sphere and utilizes the auxiliary function generate.point.in.sphere. draw.wishart and draw.inv.wishart employs Wishart variates that follow d-variate normal distribution.

Details

Package:	MultiRNG
Type:	Package
Version:	1.2.4
Date:	2021-03-05
License:	GPL-2 | GPL-3

Author(s)

Hakan Demirtas, Rawan Allozi, Ran Gao

Maintainer: Ran Gao <[e-mail]>

References

Demirtas, H. (2004). Pseudo-random number generation in R for commonly used multivariate distributions. Journal of Modern Applied Statistical Methods, 3(2), 485-497.

Falk, M. (1999). A simple approach to the generation of uniformly distributed random variables with prescribed correlations. Communications in Statistics, Simulation and Computation, 28(3), 785-791.

Park, C. G., Park, T., & Shin D. W. (1996). A simple method for generating correlated binary variates. The American Statistician, 50(4), 306-310.

MultiRNG documentation built on March 6, 2021, 1:06 a.m.