wilcox_R: What the package does (short line)

rmmcp <-
function(x, con = 0, tr = 0.2, alpha = 0.05,dif=TRUE,hoch=TRUE){
#
# MCP on trimmed means with FWE controlled with Hochberg's method
#  hoch=FALSE, will use Rom's method if alpha=.05 or .01 and number of tests is <=10
#
flagcon=F
if(!is.matrix(x))x<-matl(x)
if(!is.matrix(x))stop("Data must be stored in a matrix or in list mode.")
con<-as.matrix(con)
J<-ncol(x)
xbar<-vector("numeric",J)
x<-elimna(x)  # Remove missing values
nval<-nrow(x)
h1<-nrow(x)-2*floor(tr*nrow(x))
df<-h1-1
for(j in 1: J)xbar[j]<-mean(x[,j],tr)
if(sum(con^2!=0))CC<-ncol(con)
if(sum(con^2)==0)CC<-(J^2-J)/2
ncon<-CC
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(hoch)dvec<-alpha/c(1:ncon)
if(alpha != .05 && alpha != .01)dvec<-alpha/c(1:ncon)
if(sum(con^2)==0){
flagcon<-T
psihat<-matrix(0,CC,5)
dimnames(psihat)<-list(NULL,c("Group","Group","psihat","ci.lower","ci.upper"))
test<-matrix(NA,CC,6)
dimnames(test)<-list(NULL,c("Group","Group","test","p.value","p.crit","se"))
temp1<-0
jcom<-0
for (j in 1:J){
for (k in 1:J){
if (j < k){
jcom<-jcom+1
q1<-(nrow(x)-1)*winvar(x[,j],tr)
q2<-(nrow(x)-1)*winvar(x[,k],tr)
q3<-(nrow(x)-1)*wincor(x[,j],x[,k],tr)$cov
sejk<-sqrt((q1+q2-2*q3)/(h1*(h1-1)))
if(!dif){
test[jcom,6]<-sejk
test[jcom,3]<-(xbar[j]-xbar[k])/sejk
temp1[jcom]<-2 * (1 - pt(abs(test[jcom,3]), df))
test[jcom,4]<-temp1[jcom]
psihat[jcom,1]<-j
psihat[jcom,2]<-k
test[jcom,1]<-j
test[jcom,2]<-k
psihat[jcom,3]<-(xbar[j]-xbar[k])
}
if(dif){
dv<-x[,j]-x[,k]
test[jcom,6]<-trimse(dv,tr)
temp<-trimci(dv,alpha=alpha/CC,pr=FALSE,tr=tr)
test[jcom,3]<-temp$test.stat
temp1[jcom]<-temp$p.value
test[jcom,4]<-temp1[jcom]
psihat[jcom,1]<-j
psihat[jcom,2]<-k
test[jcom,1]<-j
test[jcom,2]<-k
psihat[jcom,3]<-mean(dv,tr=tr)
psihat[jcom,4]<-temp$ci[1]
psihat[jcom,5]<-temp$ci[2]
}
}}}
temp2<-order(0-temp1)
zvec<-dvec[1:ncon]
sigvec<-(test[temp2]>=zvec)
if(sum(sigvec)<ncon){
dd<-ncon-sum(sigvec) #number that are sig.
ddd<-sum(sigvec)+1
zvec[ddd:ncon]<-dvec[ddd]
}
test[temp2,5]<-zvec
if(!dif){
psihat[,4]<-psihat[,3]-qt(1-alpha/(2*CC),df)*test[,6]
psihat[,5]<-psihat[,3]+qt(1-alpha/(2*CC),df)*test[,6]
}}
if(sum(con^2)>0){
if(nrow(con)!=ncol(x))warning("The number of groups does not match the number
 of contrast coefficients.")
ncon<-ncol(con)
psihat<-matrix(0,ncol(con),4)
dimnames(psihat)<-list(NULL,c("con.num","psihat","ci.lower","ci.upper"))
test<-matrix(0,ncol(con),5)
dimnames(test)<-list(NULL,c("con.num","test","p.value","p.crit","se"))
temp1<-NA
for (d in 1:ncol(con)){
psihat[d,1]<-d
if(!dif){
psihat[d,2]<-sum(con[,d]*xbar)
sejk<-0
for(j in 1:J){
for(k in 1:J){
djk<-(nval-1)*wincor(x[,j],x[,k], tr)$cov/(h1*(h1-1))
sejk<-sejk+con[j,d]*con[k,d]*djk
}}
sejk<-sqrt(sejk)
test[d,1]<-d
test[d,2]<-sum(con[,d]*xbar)/sejk
test[d,5]<-sejk
temp1[d]<-2 * (1 - pt(abs(test[d,2]), df))
}
if(dif){
for(j in 1:J){
if(j==1)dval<-con[j,d]*x[,j]
if(j>1)dval<-dval+con[j,d]*x[,j]
}
temp1[d]<-trimci(dval,tr=tr,pr=FALSE)$p.value
test[d,1]<-d
test[d,2]<-trimci(dval,tr=tr,pr=FALSE)$test.stat
test[d,5]<-trimse(dval,tr=tr)
psihat[d,2]<-mean(dval,tr=tr)
}}
test[,3]<-temp1
temp2<-order(0-temp1)
zvec<-dvec[1:ncon]
sigvec<-(test[temp2,3]>=zvec)
if(sum(sigvec)<ncon){
dd<-ncon-sum(sigvec) #number that are sig.
ddd<-sum(sigvec)+1
#zvec[ddd:ncon]<-dvec[ddd]
}
test[temp2,4]<-zvec
psihat[,3]<-psihat[,2]-qt(1-test[,4]/2,df)*test[,5]
psihat[,4]<-psihat[,2]+qt(1-test[,4]/2,df)*test[,5]
}
if(flagcon)num.sig<-sum(test[,4]<=test[,5])
if(!flagcon)num.sig<-sum(test[,3]<=test[,4])
list(n=nval,test=test,psihat=psihat,con=con,num.sig=num.sig)
}