wilcox_R: What the package does (short line)

rmmcppbd <-
function(x,y=NULL,alpha=.05,con=0,est=onestep,plotit=TRUE,grp=NA,nboot=NA,
hoch=TRUE,SEED=TRUE,...){
#
#   Use a percentile bootstrap method to  compare dependent groups
#   based on difference scores.
#   By default,
#   compute a .95 confidence interval for all linear contrasts
#   specified by con, a J by C matrix, where  C is the number of
#   contrasts to be tested, and the columns of con are the
#   contrast coefficients.
#   If con is not specified, all pairwise comparisons are done.
#
#   By default, one-step M-estimator is used
#    and a sequentially rejective method
#   is used to control the probability of at least one Type I error.
#
#   nboot is the bootstrap sample size. If not specified, a value will
#   be chosen depending on the number of contrasts there are.
#
#   x can be an n by J matrix or it can have list mode
#   for two groups, data for second group can be put in y
#   otherwise, assume x is a matrix (n by J) or has list mode.
#
#   A sequentially rejective method is used to control alpha.
#   If n>=80, hochberg's method is used.
#
if(!is.null(y[1]))x<-cbind(x,y)
if(!is.list(x) && !is.matrix(x))stop("Data must be stored in a matrix or in list mode.")
if(is.list(x)){
if(is.matrix(con)){
if(length(x)!=nrow(con))stop("The number of rows in con is not equal to the number of groups.")
}}
if(is.list(x)){
# put the data in an n by J matrix
mat<-matl(x)
}
if(is.matrix(x) && is.matrix(con)){
if(ncol(x)!=nrow(con))stop("The number of rows in con is not equal to the number of groups.")
mat<-x
}
if(is.matrix(x))mat<-x
if(!is.na(sum(grp)))mat<-mat[,grp]
x<-mat
mat<-elimna(mat) # Remove rows with missing values.
x<-mat
J<-ncol(mat)
n=nrow(mat)
if(n>=80)hoch=T
Jm<-J-1
if(sum(con^2)==0){
d<-(J^2-J)/2
con<-matrix(0,J,d)
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
}}}
d<-ncol(con)
if(is.na(nboot)){
nboot<-5000
if(d<=10)nboot<-3000
if(d<=6)nboot<-2000
if(d<=4)nboot<-1000
}
n<-nrow(mat)
crit.vec<-alpha/c(1:d)
connum<-ncol(con)
# Create set of differences based on contrast coefficients
xx<-x%*%con
xx<-as.matrix(xx)
if(SEED)set.seed(2) # set seed of random number generator so that
#             results can be duplicated.
psihat<-matrix(0,connum,nboot)
bvec<-matrix(NA,ncol=connum,nrow=nboot)
data<-matrix(sample(n,size=n*nboot,replace=TRUE),nrow=nboot)
# data is an nboot by n matrix
if(ncol(xx)==1){
for(ib in 1:nboot)psihat[1,ib]<-est(xx[data[ib,]],...)
}
if(ncol(xx)>1){
for(ib in 1:nboot)psihat[,ib]<-apply(xx[data[ib,],],2,est,...)
}
#
# Now have an nboot by connum matrix of bootstrap values.
#
test<-1
for (ic in 1:connum){
test[ic]<-(sum(psihat[ic,]>0)+.5*sum(psihat[ic,]==0))/nboot
test[ic]<-min(test[ic],1-test[ic])
}
test<-2*test
ncon<-ncol(con)
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(ncon > 10){
avec<-.01/c(11:ncon)
dvec<-c(dvec,avec)
}}
if(alpha != .05 && alpha != .01){
dvec<-alpha/c(1:ncon)
dvec[2]<-alpha/2
}
if(hoch)dvec<-alpha/(2*c(1:ncon))
dvec<-2*dvec
if(plotit && connum==1){
plot(c(psihat[1,],0),xlab="",ylab="Est. Difference")
points(psihat[1,])
abline(0,0)
}
temp2<-order(0-test)
ncon<-ncol(con)
zvec<-dvec[1:ncon]
sigvec<-(test[temp2]>=zvec)
output<-matrix(0,connum,6)
dimnames(output)<-list(NULL,c("con.num","psihat","p.value","p.crit","ci.lower","ci.upper"))
tmeans<-apply(xx,2,est,...)
psi<-1
icl<-round(dvec[ncon]*nboot/2)+1
icu<-nboot-icl-1
for (ic in 1:ncol(con)){
output[ic,2]<-tmeans[ic]
output[ic,1]<-ic
output[ic,3]<-test[ic]
output[temp2,4]<-zvec
temp<-sort(psihat[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)
}