wilcox_R: What the package does (short line)

pbanovag <-
function(x,alpha=.05,nboot=NA,grp=NA,est=onestep,...){
#
#   Test the hypothesis that J independent groups have
#   equal measures of location using the percentile bootstrap method.
#   (Robust measures of scale can be compared as well.)
#
#   The data are assumed to be stored in x
#   which either has list mode or is a matrix.  In the first case
#   x[[1]] contains the data for the first group, x[[2]] the data
#   for the second group, etc. Length(x)=the number of groups = J.
#   If stored in a matrix, the columns of the matrix correspond
#   to groups.
#
#   est is the measure of location and defaults to a M-estimator
#   ... can be used to set optional arguments associated with est
#
#   The argument grp can be used to analyze a subset of the groups
#   Example: grp=c(1,3,5) would compare groups 1, 3 and 5.
#
#   Missing values are allowed.
#
con<-as.matrix(con)
if(is.matrix(x))x<-listm(x)
if(!is.list(x))stop("Data must be stored in list mode or in matrix mode.")
if(!is.na(sum(grp))){
# Only analyze specified groups.
xx<-list()
for(i in 1:length(grp))xx[[i]]<-x[[grp[1]]]
x<-xx
}
J<-length(x)
tempn<-0
for(j in 1:J){
temp<-x[[j]]
temp<-temp[!is.na(temp)] # Remove missing values.
tempn[j]<-length(temp)
x[[j]]<-temp
}
Jm<-J-1
icl<-ceiling(crit*nboot)
icu<-ceiling((1-crit)*nboot)
con<-matrix(0,J,J-1)
for (j in 1:Jm){
jp<-j+1
con[j,j]<-1
con[jp,j]<-0-1
}
#  Determine nboot if a value was not specified
if(is.na(nboot)){
nboot<-5000
if(J <= 8)nboot<-4000
if(J <= 3)nboot<-2000
}
# Determine critical values
if(alpha==.05){
dvec<-c(.05,.025,.0169,.0127,.0102,.00851,.0073,.00639,.00568,.00511)
if(Jm > 10){
avec<-.05/c(11:Jm)
dvec<-c(dvec,avec)
}}
if(alpha==.01){
dvec<-c(.01,.005,.00334,.00251,.00201,.00167,.00143,.00126,.00112,.00101)
if(Jm > 10){
avec<-.01/c(11:Jm)
dvec<-c(dvec,avec)
}}
if(alpha != .05 && alpha != .01)dvec<-alpha/c(1:Jm)
bvec<-matrix(NA,nrow=J,ncol=nboot)
set.seed(2) # set seed of random number generator so that
#             results can be duplicated.
print("Taking bootstrap samples. Please wait.")
for(j in 1:J){
paste("Working on group ",j)
data<-matrix(sample(x[[j]],size=length(x[[j]])*nboot,replace=TRUE),nrow=nboot)
bvec[j,]<-apply(data,1,est,...) # Bootstrapped trimmed means for jth group
}
test<-NA
for (d in 1:Jm){
dp<-d+1
test[d]<-sum(bvec[d,]>bvec[dp,])/nboot
if(test[d]> .5)test[d]<-1-test[d]
}
test<-(0-1)*sort(-2*test)
sig<-sum((test<dvec[1:Jm]))
if(sig>0)print("Significant result obtained: Reject")
if(sig==0)print("No significant result obtained: Fail to reject")
list(test.vec=test,crit.vec=dvec[1:Jm])
}