Hakan Demirtas, Rachel Nordgren, Rawan Allozi, Ran Gao

Generation of a chosen number of count, binary, ordinal, and continuous random variables, with specified correlations and marginal properties. The details of the method are explained in Demirtas (2012) <DOI:10.1002/sim.5362>.

Data Generation with Poisson, Binary, Ordinal and Normal
Components
Generation of multiple count, binary, ordinal and normal variables simultaneously given the marginal characteristics and association structure.

Generation of multiple count, binary, ordinal and normal variables
simultaneously given the marginal characteristics and association structure.

Hakan Demirtas, Yiran Hu, Rawan Allozi, Ran Gao

Generation of multiple count, binary, ordinal and normal variables simultaneously given the marginal characteristics and association structure.
The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.

Simultaneous Generation of Count and Continuous Data
This package generates count (assuming Poisson distribution) and continuous data (using Fleishman polynomials) simultaneously.
graphics, grDevices, stats, utils, BB, corpcor, MASS, Matrix

Generation of count (assuming Poisson distribution) and continuous data (using Fleishman polynomials) simultaneously.

Hakan Demirtas, Yaru Shi, Rawan Allozi, Ran Gao

Generation of count (assuming Poisson distribution) and continuous data (using Fleishman polynomials) simultaneously. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.

Simultaneous generation of multivariate data with Poisson and
normal marginals
A package for simulating 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.

Simultaneous Generation of Multivariate Data with Poisson and
Normal Marginals
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.