wilcox_R: What the package does (short line)

rmul <-
function(n,p=2,cmat=diag(rep(1,p)),rho=NA,
mar.fun=rnorm,...){
#
# generate n observations from a p-variate dist
# By default, use normal distributions.
#
#To get a g-and-h distribution
# for the marginals, use mar.fun=ghdist.
# Example rmul(30,p=4,rho=.3,mar.fun=ghdist,g=.5,h=.2) will
# generate 30 vectors from a 4-variate distribution where the marginals
# have a g-and-h distribution with g=.5 and h=.2.
#
# This function is similar to ghmul, only here, generate the marginal values
# and then transform the data to have correlation matrix cmat
#
# cmat is the correlation matrix
# if argument
# rho is specified, the correlations are taken to
# have a this common value.
#
# Method (e.g. Browne, M. W. (1968) A comparison of factor analytic
# techniques. Psychometrika, 33, 267-334.
#  Let U'U=R be the Cholesky decomposition of R. Generate independent data
#  from some dist yielding X. Then XU has population correlation matrix R
#
if(!is.na(rho)){
if(abs(rho)>1)stop("rho must be between -1 and 1")
cmat<-matrix(rho,p,p)
diag(cmat)<-1
}
np<-n*p
x<-matrix(mar.fun(np,...),nrow=n,ncol=p)
rmat<-matsqrt(cmat)
x<-x%*%rmat
x
}