wilcox_R: What the package does (short line)

indt <-
function(x,y,nboot=500,flag=1,SEED=TRUE){
#
# Test the hypothesis of independence between x and y by
# testing the hypothesis that the regression surface is a horizontal plane.
# Stute et al. (1998, JASA, 93, 141-149).
#
#  flag=1 gives Kolmogorov-Smirnov test statistic
#  flag=2 gives the Cramer-von Mises test statistic
#  flag=3 causes both test statistics to be reported.
#
#  tr=0 results in the Cramer-von Mises test statistic when flag=2
#      With tr>0, a trimmed version of the test statistic is used.
#
#  Modified Dec 2005.
#
tr=0
#if(tr<0)stop("Amount trimmed must be > 0")
#if(tr>.5)stop("Amount trimmed must be <=.5")
if(SEED)set.seed(2)
x<-as.matrix(x)
# First, eliminate any rows of data with missing values.
temp <- cbind(x, y)
        temp <- elimna(temp)
        pval<-ncol(temp)-1
        x <- temp[,1:pval]
        y <- temp[, pval+1]
x<-as.matrix(x)
mflag<-matrix(NA,nrow=length(y),ncol=length(y))
for (j in 1:length(y)){
for (k in 1:length(y)){
mflag[j,k]<-(sum(x[j,]<=x[k,])==ncol(x))
}
}
# ith row of mflag indicates which rows of the matrix x are less
# than or equal to ith row of x
#
yhat<-mean(y)
res<-y-yhat
print("Taking bootstrap sample, please wait.")
data<-matrix(runif(length(y)*nboot),nrow=nboot)#
data<-(data-.5)*sqrt(12) # standardize the random numbers.
rvalb<-apply(data,1,regts1,yhat,res,mflag,x,tr)
# An n x nboot matrix of R values
rvalb<-rvalb/sqrt(length(y))
dstatb<-apply(abs(rvalb),2,max)
wstatb<-apply(rvalb^2,2,mean,tr=tr)
v<-c(rep(1,length(y)))
rval<-regts1(v,yhat,res,mflag,x,tr=0)
rval<-rval/sqrt(length(y))
dstat<-NA
wstat<-NA
critd<-NA
critw<-NA
p.vald<-NA
p.valw<-NA
if(flag==1 || flag==3){
dstat<-max(abs(rval))
p.vald<-1-sum(dstat>=dstatb)/nboot
}
if(flag==2 || flag==3){
wstat<-mean(rval^2,tr=tr)
p.valw<-1-sum(wstat>=wstatb)/nboot
}
list(dstat=dstat,wstat=wstat,p.value.d=p.vald,p.value.w=p.valw)
}