# Simulation of Correlated Data with Multiple Variable Types

Generate continuous (normal or non-normal), binary, ordinal, and count (Poisson or Negative
Binomial) variables with a specified correlation matrix. It can also produce a single continuous
variable. This package can be used to simulate data sets that mimic real-world situations (i.e.
clinical data sets, plasmodes). All variables are generated from standard normal variables with
an imposed intermediate correlation matrix. Continuous variables are simulated by
specifying mean, variance, skewness, standardized kurtosis, and fifth and sixth standardized
cumulants using either Fleishman's Third-Order () or Headrick's
Fifth-Order () Polynomial Transformation. Binary and
ordinal variables are simulated using a modification of GenOrd's ordsample function. Count
variables are simulated using the inverse cdf method. There are two simulation pathways
which differ primarily according to the calculation of the intermediate correlation matrix.
In Method 1, the intercorrelations involving count variables are determined using a simulation
based, logarithmic correlation correction (adapting Yahav and Shmueli's 2012 method,
). In Method 2, the count variables are treated as ordinal (adapting
Barbiero and Ferrari's 2015 modification of GenOrd, ). There is an
optional error loop that corrects the final correlation matrix to be within a user-specified
precision value of the target matrix. The package also includes functions to calculate
standardized cumulants for theoretical distributions or from real data sets, check if a
target correlation matrix is within the possible correlation bounds (given the distributions
of the simulated variables), summarize results (numerically or graphically), to verify valid
power method pdfs, and to calculate lower standardized kurtosis bounds.