R (>= 4.1), stats, methods, ggplot2, inlabru (>= 2.5)
testthat (>= 3.0.0), USAboundaries, sf, sn, INLA (>=
21.08.31), rasterVis, ggmap, ggpolypath, RColorBrewer, cowplot,
knitr, kableExtra, rmarkdown, spocc, covr

Data Generation with Poisson, Binary and Continuous Components
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.

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>.

Data Generation with Poisson, Binary and Ordinal Components
Generation of multiple count, binary and ordinal 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.

Generation of multiple count, binary and ordinal 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, A. and Demirtas, H. (2015) <DOI:10.1080/00949655.2014.953534>.

Generation of up to Four Different Types of Variables
Generation of a chosen number of count, binary, ordinal, and continuous (via Fleishman polynomials) random variables, with specified correlations and marginal properties.

Generation of Up to Four Different Types of Variables
Hakan Demirtas, Rachel Nordgren, Rawan Allozi

Generation of a chosen number of count, binary, ordinal, and continuous random variables, with specified correlations and marginal properties.