wilcox_R: What the package does (short line)

pbmcp <-
function(x,alpha=.05,nboot=NA,grp=NA,est=onestep,con=0,bhop=FALSE,
SEED=TRUE,...){
#
#   Multiple comparisons for  J independent groups.
#
#   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 an 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.
#
okay=FALSE
if(identical(est,onestep))okay=TRUE
if(identical(est,mom))okay=TRUE
if(!okay)stop('For estimators other than onestep and mom, use linconpb')
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[i]]]
x<-xx
}
J<-length(x)
tempn<-0
mvec<-NA
for(j in 1:J){
temp<-x[[j]]
temp<-temp[!is.na(temp)] # Remove missing values.
tempn[j]<-length(temp)
x[[j]]<-temp
mvec[j]<-est(temp,...)
}
nmax=max(tempn)
Jm<-J-1
#
# Determine contrast matrix
#
if(sum(con^2)==0){
ncon<-(J^2-J)/2
con<-matrix(0,J,ncon)
id<-0
for (j in 1:Jm){
jp<-j+1
for (k in jp:J){
id<-id+1
con[j,id]<-1
con[k,id]<-0-1
}}}
ncon<-ncol(con)
if(nrow(con)!=J){
stop("Something is wrong with con; the number of rows does not match the number of groups.")
}
#  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(!bhop){
if(!identical(est,onestep))print('When est is not equal to  onestep, suggest using bhop=TRUE')
if(alpha==.05){
dvec<-c(.025,.025,.0169,.0127,.0102,.00851,.0073,.00639,.00568,.00511)
if(ncon > 10){
avec<-.05/c(11:ncon)
dvec<-c(dvec,avec)
}}
if(alpha==.01){
dvec<-c(.005,.005,.00334,.00251,.00201,.00167,.00143,.00126,.00112,.00101)
if(nmax>=100)dvec[1]=.01
if(ncon > 10){
avec<-.01/c(11:ncon)
dvec<-c(dvec,avec)
}}
if(alpha != .05 && alpha != .01){
dvec<-alpha/c(1:ncon)
dvec[1]<-alpha/2
}
dvec<-2*dvec
}
if(nmax>80){
if(alpha==.05){
dvec<-c(.05,.025,.0169,.0127,.0102,.00851,.0073,.00639,.00568,.00511)
if(ncon > 10){
avec<-.05/c(11:ncon)
dvec<-c(dvec,avec)
}}
if(alpha==.01){
dvec<-c(.01,.005,.00334,.00251,.00201,.00167,.00143,.00126,.00112,.00101)
if(ncon > 10){
avec<-.01/c(11:ncon)
dvec<-c(dvec,avec)
}}
if(alpha != .05 && alpha != .01){
dvec<-alpha/c(1:ncon)
}
}
if(bhop)dvec<-(ncon-c(1:ncon)+1)*alpha/ncon
bvec<-matrix(NA,nrow=J,ncol=nboot)
if(SEED)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 values for jth group
}
chkna=sum(is.na(bvec))
if(chkna>0){
print("Bootstrap estimates of location could not be computed")
print("This can occur when using an M-estimator")
print("Might try est=tmean")
}
test<-NA
bcon<-t(con)%*%bvec #ncon by nboot matrix
tvec<-t(con)%*%mvec
for (d in 1:ncon){
test[d]<-(sum(bcon[d,]>0)+.5*sum(bcon[d,]==0))/nboot
if(test[d]> .5)test[d]<-1-test[d]
}
test<-2*test
output<-matrix(0,ncon,6)
dimnames(output)<-list(NULL,c("con.num","psihat","p.value","p.crit",
"ci.lower","ci.upper"))
temp2<-order(0-test)
zvec<-dvec[1:ncon]
sigvec<-(test[temp2]>=zvec)
output[temp2,4]<-zvec
icl<-round(dvec[ncon]*nboot/2)+1
icu<-nboot-icl-1
for (ic in 1:ncol(con)){
output[ic,2]<-tvec[ic,]
output[ic,1]<-ic
output[ic,3]<-test[ic]
temp<-sort(bcon[ic,])
output[ic,5]<-temp[icl]
output[ic,6]<-temp[icu]
}
num.sig<-sum(output[,3]<=output[,4])
list(output=output,con=con,num.sig=num.sig)
}