wilcox_R: What the package does (short line)

cov2med <-
function(x,y=NA,q=.5){
#
# Estimate the covariance between two dependent
# order statistics
# By default, q=.5 meaning that an estimate of
# of covariance is made when a single order statistic
# is used to estimate the median.
# y=NA, function returns squared standard error.
#
if(is.na(y[1]))val<-qse(x,q=q,op=3)^2
if(!is.na(y[1])){
if(sum((x-y)^2)==0)val<-qse(x,q=q,op=3)^2
if(sum((x-y)^2)>0){
n<-length(x)
m<-floor(q*n+.5)
yord<-sort(y)
flag<-(y<=yord[m])
xord<-sort(x)
xq<-xord[m]
yord<-sort(y)
yq<-yord[m]
flag1<-(x<=xq)
flag2<-(y<=yq)
A<-mean(flag1*flag2)
flag1<-(x<=xq)
flag2<-(y>yq)
B<-mean(flag1*flag2)
flag1<-(x>xq)
flag2<-(y<=yq)
C1<-mean(flag1*flag2)
flag1<-(x>xq)
flag2<-(y>yq)
D1<-mean(flag1*flag2)
fx<-akerd(x,pts=xq,plotit=FALSE,pyhat=T)
fy<-akerd(y,pts=yq,plotit=FALSE,pyhat=T)
v1<-(q-1)^2*A
v2<-(q-1)*q*B
v3<-(q-1)*q*C1
v4<-q*q*D1
val<-((v1+v2+v3+v4)/(fx*fy))/n
}}
val
}