Title: Simultaneous Generation of Multivariate Data with Poisson and
Description: Generates multivariate data with count and continuous variables with a pre-specified correlation matrix. The count and continuous variables are assumed to have Poisson and normal marginals, respectively. The data generation mechanism is a combination of the normal to anything principle and a connection between Poisson and normal correlations in the mixture.

The details of the method are explained in Yahav et al. (2012) <DOI:10.1002/asmb.901>.

Title: Exact and Approximate Implementations of the Poisson Binomial
Authors@R: person("Florian", "Junge", role = c("aut", "cre"), email = "florian.junge@h-da.de")
Description: Implementations of multiple exact and approximate methods as described in Hong (2013) <doi:10.1016/j.csda.2012.10.006> and Biscarri et al. (2018) <doi:10.1016/j.csda.2018.01.007> for computing the probability mass, cumulative distribution and quantile functions, as well as generating random numbers for the Poisson binomial distribution.

Title: Efficient Exact and Approximate Implementations of the Poisson

Title: Efficient Computation of Ordinary and Generalized Poisson
Description: Efficient implementations of multiple exact and approximate methods as described in Hong (2013) <doi:10.1016/j.csda.2012.10.006>, Biscarri, Zhao & Brunner (2018) <doi:10.1016/j.csda.2018.01.007> and Zhang, Hong & Balakrishnan (2018) <doi:10.1080/00949655.2018.1440294> for computing the probability mass, cumulative distribution and quantile functions, as well as generating random numbers for both the ordinary and generalized Poisson binomial distribution.

Packaged: 2020-05-14 15:14:21 UTC; Florian Junge

Packaged: 2020-08-31 11:22:21 UTC; Florian Junge

Packaged: 2020-09-07 07:38:13 UTC; Florian Junge

Packaged: 2020-11-25 07:35:53 UTC; Florian Junge

Packaged: 2020-11-26 10:01:58 UTC; Florian Junge

Packaged: 2021-03-12 09:29:50 UTC; Florian Junge

Packaged: 2021-03-26 14:47:47 UTC; Florian Junge

Authors@R: c(person("Yili Hong", role = c("aut", "cre"),
person("Zhengzhi Lin", role = c("aut","ctb"), email = "zhengzhi@vt.edu"),
person("Yueyao Wang",  role = c("aut","ctb"), email = "yueyao94@vt.edu"),
person("Florian Junge", role = c("aut", "ctb"), email = "florian.junge@h-da.de"))
Description: Implementation of the exact, normal approximation, and simulation-based methods for computing the probability mass function (pmf) and cumulative distribution function (cdf) of the Poisson-Multinomial distribution, together with a random number generator for the distribution. The exact method is based on multi-dimensional fast Fourier transformation (FFT) of the characteristic function of the Poisson-Multinomial distribution. The normal approximation method uses a multivariate normal distribution to approximate the pmf of the distribution based on central limit theorem. The simulation method is based on the law of large numbers. Details about the methods are available in Lin, Wang, and Hong (2022) <arXiv:2201.04237>.