wilcox_R: What the package does (short line)

cidM <-
function(x,nboot=1000,alpha=.05,MC=FALSE,SEED=TRUE,g=NULL,dp=NULL){
#
# Variation of Cliff method based on median of X-Y
# i.e., use p=P(X<Y) as effect size.
# test p=.5
# All pairwise comparisons performed.
# FWE controlled via Hochberg method.
# x can be a matrix (columns are groups) or have list mode
#
#   g=NULL, x is assumed to be a matrix or have list mode
#   if g is specifed, it is assumed that column g of x is
#   a factor variable and that the dependent variable of interest is in column
#   dp of x, which can be a matrix or data frame.
#
if(!is.null(g)){
if(is.null(dp))stop("Specify a value for dp, the column containing the data")
x=fac2list(x[,dp],x[,g])
}
if(SEED)set.seed(2)
if(MC)library(parallel)
if(is.data.frame(x))x=as.matrix(x)
if(is.matrix(x))x=listm(x)
chk=tlist(x)
if(chk!=0)print("Warning: tied values detected. Suggest using cidmulv2")
J=length(x)
L=(J^2-J)/2
CC=L
pvec=NA
boot=list()
MAT=matrix(NA,nrow=nboot,ncol=L)
for(i in 1:nboot){
jcom=0
for (j in 1:J){
boot[[j]]=sample(x[[j]],size=length(x[[j]]),replace=TRUE)
}
MAT[i,]=wmwloc2(boot)
}
#
pvec=NA
test<-matrix(NA,CC,8)
dimnames(test)<-list(NULL,c("Group","Group","p-value","p.crit",
"P(X<Y)","P(X=Y)","P(X>Y)","p.hat"))
dvec<-alpha/c(1:CC)
for(j in 1:J){
for(k in 1:J){
if(j<k){
jcom=jcom+1
p.value=mean(MAT[,jcom]>0)+.5*mean(MAT[,jcom]==0)
pvec[jcom]=2*min(c(p.value,1-p.value))
if(is.na(pvec[jcom]))pvec=1
test[jcom,1]<-j
test[jcom,2]<-k
test[jcom,3]<-pvec[jcom]
test[jcom,5:7]<-cid(x[[j]],x[[k]])$summary.dvals
test[jcom,8]<-test[jcom,5]+.5*test[jcom,6]
}}}
temp2<-order(0-test[,3])
test[temp2,4]=dvec
list(test=test)
}