RNGforGPD-package: Generates Univariate and Multivariate Generalized Poisson...
In RNGforGPD: Random Number Generation for Generalized Poisson Distribution

Description Details Author(s) References

This package is about generating univariate and multivariate data that follow the generalized Poisson distribution.There are seven functions in the package: GenUniGpois and GenMVGpois are the data generation functions that simulate univariate and multivariate Poisson variables, respectively; ValidCorrGpois checks the validity of the values of pairwise correlations; ComputeCorrGpois computes the lower and upper correlation bounds of a pairwise correlation between a pair of generalized Poisson variables; CorrNNGpois adjusts the target correlation for a pair of generalized Poisson variables; QuantileGpois computes the quantile of a given generalized Poisson distribution; CmatStarGpois computes an intermediate correlation matrix. To learn more about this package please refer to both the reference manual and the vignette file.

Package:	RNGforGPD
Type:	Package
Version:	1.1.0
Date:	2020-11-17
License:	GPL-2 \| GPL-3

Hesen Li, Ruizhe Chen, Hai Nguyen, Yu-Che Chung, Ran Gao, Hakan Demirtas

Maintainer: Ruizhe Chen <rchen18@uic.edu>

Amatya, A. and Demirtas, H. (2015). Simultaneous generation of multivariate mixed data with Poisson and normal marginals. Journal of Statistical Computation and Simulation, 85(15), 3129-3139.

Amatya, A. and Demirtas, H. (2017). PoisNor: An R package for generation of multivariate data with Poisson and normal marginals. Communications in Statistics - Simulation and Computation, 46(3), 2241-2253.

Demirtas, H. (2017). On accurate and precise generation of generalized Poisson variates. Communications in Statistics - Simulation and Computation, 46(1), 489-499.

Demirtas, H. and Hedeker, D. (2011). A practical way for computing approximate lower and upper correlation bounds. The American Statistician, 65(2), 104-109.

Yahav, I. and Shmueli, G. (2012). On generating multivariate Poisson data in management science applications. Applied Stochastic Models in Business and Industry, 28(1), 91-102.

RNGforGPD documentation built on Nov. 18, 2020, 5:08 p.m.