wilcox_R: What the package does (short line)

twoDNOV <-
function(x,y,corfun=wincor,alpha=.05,nboot=500,SEED=TRUE,MC=FALSE){
#
# Comparing two robust dependent correlations: Non-overlapping case
# Winsorized correlation is used by default.
#
# Both x and y are assumed to be a matrix with 2 columns
#
#  Compare correlation of x[,1] x[,2] to the correlation between
#   y[,1] and  y[,2] 
#
if(nrow(x)!=nrow(y))stop('x and y have different sample sizes; should be equal')
m1=cbind(x,y)
if(ncol(m1)!=4)stop('Both x and y should have two columns')
m1<-elimna(m1)  # Eliminate rows with missing values
nval=nrow(m1)
x<-m1[,1:2]
y=m1[,3:4]
r12=corfun(x[,1],x[,2])$cor
r13=corfun(y[,1],y[,2])$cor
if(SEED)set.seed(2) # set seed of random number generator so that
#             results can be duplicated.
#
#  If you use corfun=scor, set plotit=F
#
data<-matrix(sample(nrow(y),size=nrow(y)*nboot,replace=TRUE),nrow=nboot)
data=listm(t(data))
if(MC){
library(parallel)
bvec1<-mclapply(data,corbsub,x[,1],x[,2],corfun) 
bvec2<-mclapply(data,corbsub,y[,1],y[,2],corfun) 
}
if(!MC){
bvec1<-lapply(data,corbsub,x[,1],x[,2],corfun) 
bvec2<-lapply(data,corbsub,y[,1],y[,2],corfun) 
}
mat1=matl(bvec1)
mat2=matl(bvec2)
ihi<-floor((1-alpha/2)*nboot+.5)
ilow<-floor((alpha/2)*nboot+.5)
bsort<-sort(mat1-mat2)
ci12<-bsort[ilow]
ci12[2]<-bsort[ihi]
ci12
pv=mean(bsort<0)
pv=2*min(c(pv,1-pv))
list(est.rho1=r12,est.rho2=r13,est.dif=r12-r13,ci=ci12,p.value=pv)
}