| RNGforGPD-package {RNGforGPD} | R Documentation |
Generates Univariate and Multivariate Generalized Poisson Variables
Description
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.
Details
| Package: | RNGforGPD |
| Type: | Package |
| Version: | 1.1.0 |
| Date: | 2020-11-17 |
| License: | GPL-2 | GPL-3 |
Author(s)
Hesen Li, Ruizhe Chen, Hai Nguyen, Yu-Che Chung, Ran Gao, Hakan Demirtas
Maintainer: Ruizhe Chen <rchen18@uic.edu>
References
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.