wilcox_R: What the package does (short line)

discstep <-
function(x,nboot=500,alpha=.05,SEED=TRUE){
#
#  Step-down multiple comparison procedure for comparing 
#  J independent discrete random variables. 
#  The method is based on a generalization of the Storer--Kim method
#  comparing independent binomials; it can be sensitive to differences
#  not detected by measures of location.
#  
#  x is a matrix with n rows and J columns
#  or it can have list mode
#
if(is.matrix(x) || is.data.frame(x))x=listm(x)
library(mc2d)
if(SEED)set.seed(2)
vals=lapply(x,unique)
vals=sort(elimna(list2vec(vals)))
K=length(unique(vals))
n=lapply(x,length)
n=list2vec(n)
J=length(x)
if(J==2)stop('For 2 groups use disc2com')
if(J>5)stop('Designed for 5 groups or less')
com=modgen(J)
ntest=length(com)
jp1=J+1
com=com[jp1:length(com)]
ntest=length(com)
mout=matrix(NA,nrow=ntest,ncol=3)
dimnames(mout)=list(NULL,c('Groups','p-value','p.crit'))
test=NULL
for(i in 1:ntest){
test[i]=discANOVA.sub(x[com[[i]]])$test #$
nmod=length(com[[i]])-1
temp=c(nmod:0)
mout[i,1]=sum(com[[i]]*10^temp)
}
mout[,3]=alpha
xx=list()
pv=NA
jm2=J-2
mout[,3]=alpha
TB=matrix(NA,nrow=nboot,ncol=ntest)
step1=discANOVA.sub(x)
C1=step1$C1
HT=NULL
for(i in 1:K)HT[i]=mean(C1[i,])
for(ib in 1:nboot){
xx=list()
for(j in 1:J){
temp=rmultinomial(n[j],1,HT)
xx[[j]]=which(temp[1,]==1)
for(i in 2:n[j])xx[[j]][i]=which(temp[i,]==1)
}
for(k in 1:ntest)TB[ib,k]=discANOVA.sub(xx[com[[k]]])$test #$
}
for(k in 1:ntest){
mout[k,2]=1-mean(test[k]>TB[,k])-.5*mean(test[k]==TB[,k])
pnum=length(com[[k]])
pe=1-(1-alpha)^(pnum/J)
if(length(com[[k]])<=jm2)mout[k,3]=pe
}
list(results=mout[nrow(mout):1,])
}