Depends: R (>= 4.1), stats, methods, ggplot2, inlabru (>= 2.5)
knitr, kableExtra, rmarkdown, spocc, covr

Title: Data Generation with Poisson, Binary and Continuous Components
Description: Generation of multiple count, binary and continuous variables simultaneously
given the marginal characteristics and association structure. Throughout the package,
the word 'Poisson' is used to imply count data under the assumption of Poisson distribution.

The details of the method are explained in Amatya et al. (2015) <DOI:10.1080/00949655.2014.953534>.

Title: Data Generation with Poisson, Binary and Ordinal Components
Description: Generation of multiple count, binary and ordinal variables simultaneously
Depends: corpcor, GenOrd, Matrix, mvtnorm

the word 'Poisson' is used to imply count data under the assumption of Poisson distribution. The details of the method are explained in Amatya, A. and Demirtas, H. (2015) <DOI:10.1080/00949655.2014.953534>.

Title: Generation of up to Four Different Types of Variables
Description: Generation of a chosen number of count, binary, ordinal, and continuous (via Fleishman polynomials) random variables, with specified correlations and marginal properties.
Depends: Matrix, corpcor, MASS, GenOrd, BB

Title: Generation of Up to Four Different Types of Variables
Author: Hakan Demirtas, Rachel Nordgren, Rawan Allozi
Packaged: 2016-05-27 00:11:27 UTC; rawanallozi

Packaged: 2017-01-25 18:27:19 UTC; rawanallozi

Packaged: 2017-03-28 17:07:49 UTC; rawanallozi

Description: Generation of a chosen number of count, binary, ordinal, and continuous random variables, with specified correlations and marginal properties.
Packaged: 2018-01-10 15:30:10 UTC; rawanallozi

Author: Hakan Demirtas, Rachel Nordgren, Rawan Allozi, Ran Gao

Description: 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>.

Title: Data Generation with Poisson, Binary, Ordinal and Normal
Description: Generation of multiple count, binary, ordinal and normal variables simultaneously given the marginal characteristics and association structure.
Depends: Matrix,corpcor, mvtnorm, psych, GenOrd