wilcox_R: What the package does (short line)

Dancdet <-
function(x1,y1,x2,y2,fr1=1,fr2=1,tr=.2,DIF=TRUE,
alpha=.05,plotit=TRUE,plot.dif=FALSE,pts=NA,sm=FALSE,
pr=TRUE,xout=FALSE,outfun=out,MC=FALSE,
npts=25,p.crit=NULL,nreps=2000,SEED=TRUE,
SCAT=TRUE,xlab='X',ylab='Y',pch1='*',pch2='+',...){
#
#  ANCOVA for dependent groups. 
#
#  Like Dancova, but a more detailed analysis
#  plot.dif=TRUE:  plot difference in the estimates plus a
# confidence band having simultaneous probability coverate 1-alpha
#
# npts = number of  covariate values to be used
#
if(ncol(as.matrix(x1))>1)stop('One covariate only is allowed with this function')
if(ncol(as.matrix(x1))>1)stop('One covariate only is allowed with this function')
if(length(x1)!=length(y1))stop('x1 and y1 have different lengths')
if(length(x1)!=length(x2))stop('x1 and y2 have different lengths')
if(length(x2)!=length(y2))stop('x2 and y2 have different lengths')
if(length(y1)!=length(y2))stop('y1 and y2 have different lengths')
xy=elimna(cbind(x1,y1,x2,y2))
x1=xy[,1]
y1=xy[,2]
x2=xy[,3]
y2=xy[,4]
if(is.null(p.crit))set.seed(2)
res1=ancova(x1,y1,x2,y2,pr=FALSE,plotit=FALSE)$output # Get lowest and covariate
#   values where comparisons can be made. 
pts=seq(res1[1,1],res1[5,1],length.out=npts)
if(is.null(p.crit))p.crit=ancdet.pv(length(y1),length(y2),nreps=nreps,tr=tr,npts=npts,MC=MC) 
if(plot.dif)plotit=FALSE
res=Dancova(x1,y1,x2,y2,fr1=fr1,fr2=fr2,tr=tr,alpha=p.crit,
DIF=DIF,plotit=plotit,pts=pts,SCAT=SCAT)$output
if(plot.dif){
yhat=plot(c(res[,1],res[,1],res[,1]),c(res[,3],res[,6],res[,7]),type='n',xlab=xlab,ylab=ylab)
z1=lplot(res[,1],res[,3],plotit=FALSE,pyhat=T)$yhat
z2=lplot(res[,1],res[,6],plotit=FALSE,pyhat=T)$yhat
z3=lplot(res[,1],res[,7],plotit=FALSE,pyhat=T)$yhat
lines(res[,1],z1)
lines(res[,1],z2,lty=2)
lines(res[,1],z3,lty=2)
}
sig=rep(0,nrow(res))
sig[res[,8]<=p.crit]=1
sig=as.matrix(sig)
dimnames(sig)=list(NULL,'Sig.Dif')
res=cbind(res,sig)
list(p.crit=p.crit,output=res,num.sig=sum(sig),p.crit=p.crit)
}