##### https://github.com/cran/gclus
Tip revision: c27795b
hclust.R
``````
# This function accepts a "dist" or matrix of scores and
# returns an ordering, based on hierarchical clustering.
# If reorder is FALSE, the order returned by hclust is used,
# otherwise clusters are ordered by placing the nearest end points
# adjacent to each other at a merge.

order.hclust <-
function(merit,reorder=TRUE,...) {
dis <- - merit
if (is.matrix(dis))
disd <- as.dist(dis)
else {
disd <- dis
dis <- as.matrix(dis)}
n <- nrow(dis)
if (n <= 2)
ord <- 1:n
else {
hc <- hclust(disd,...)
if (reorder)
hc <- reorder.hclust(hc,dis)
ord <- hc\$order}
ord }

# This function accepts hc, the results of a hierarchical clustering
# and a "dist" or distance matrix. It returns a hierarchical clustering obtained by placing
# the nearest end points adjacent to each other at each
#  merge of the hierarchical clustering

reorder.hclust <-
function(x,dis,...) {
if (! is.matrix(dis)) dis <- as.matrix(dis)
merges <- x\$merge
n <- nrow(merges)
endpoints <- matrix(0,n,2)
dir <- matrix(1L,n,2)
for (i in 1L:n) {
j <- merges[i,1]
k <- merges[i,2]
if ((j < 0) && (k < 0)) {
endpoints[i,1] <- -j
endpoints[i,2] <- -k}
else if (j < 0) {
j <- -j
endpoints[i,1] <- j
e1 <- endpoints[k,1]; e2 <- endpoints[k,2]
if (dis[j,e1] < dis[j,e2])
endpoints[i,2] <- e2
else {
endpoints[i,2] <- e1
dir[i,2] <- -1}}
else if (k < 0) {
k <- -k
endpoints[i,2] <- k
e1 <- endpoints[j,1]; e2 <- endpoints[j,2]
if (dis[k,e1] < dis[k,e2]){
endpoints[i,1] <- e2
dir[i,1] <- -1 }
else {
endpoints[i,1] <-e1
}}
else {
ek1 <- endpoints[k,1]; ek2 <- endpoints[k,2]
ej1 <- endpoints[j,1]; ej2 <- endpoints[j,2]

d11 <- dis[ej1,ek1]
d12 <- dis[ej1,ek2]
d21 <- dis[ej2,ek1]
d22 <- dis[ej2,ek2]
dmin <- min(d11,d12,d21,d22)
if (dmin == d21) {
endpoints[i,1] <- ej1
endpoints[i,2] <- ek2
}

else if (dmin == d11) {
endpoints[i,1] <- ej2
endpoints[i,2] <- ek2
dir[i,1] <- -1
}
else if (dmin == d12) {
endpoints[i,1] <- ej2
endpoints[i,2] <- ek1
dir[i,1] <- -1
dir[i,2] <- -1
}
else  {
endpoints[i,1] <- ej1
endpoints[i,2] <- ek1
dir[i,2] <- -1}}
}
for (i in n:2L) {
if (dir[i,1] == -1) {
m <- merges[i,1]
if (m > 0) {
m1 <- merges[m,1]
merges[m,1] <- merges[m,2]
merges[m,2] <- m1
if (dir[m,1] == dir[m,2])
dir[m,] <- -dir[m,]
}}
if (dir[i,2] == -1) {
m <- merges[i,2]
if (m > 0) {
m1 <- merges[m,1]
merges[m,1] <- merges[m,2]
merges[m,2] <- m1
if (dir[m,1] == dir[m,2])
dir[m,] <- -dir[m,]
}}

}
clusters <- as.list(1:n)
for (i in 1:n) {
j <- merges[[i,1]]
k <- merges[[i,2]]
if ((j < 0) && (k < 0))
clusters[[i]] <- c(-j,-k)
else if (j < 0)
clusters[[i]] <- c(-j,clusters[[k]])
else if (k < 0)
clusters[[i]] <- c(clusters[[j]],-k)
else clusters[[i]] <- c(clusters[[j]], clusters[[k]])}

x1 <- x
x1\$merge <- merges
x1\$order <- clusters[[n]]
x1

}

# reorder.hclust<-
# function(x,dis,...) {
# if (! is.matrix(dis)) dis <- as.matrix(dis)
# merges <- x\$merge
# n <- nrow(merges)
# endpoints <- matrix(0,n,2)
# dir <- matrix(1L,n,2)
# for (i in 1L:n) {
# j <- merges[i,1]
# k <- merges[i,2]
# if ((j < 0) && (k < 0)) {
# endpoints[i,1] <- -j
# endpoints[i,2] <- -k}
# else if (j < 0) {
# j <- -j
# endpoints[i,1] <- j
# if (dis[j,endpoints[k,1]] < dis[j,endpoints[k,2]])
# endpoints[i,2] <- endpoints[k,2]
# else {
# endpoints[i,2] <- endpoints[k,1]
# dir[i,2] <- -1}}
# else if (k < 0) {
# k <- -k
# endpoints[i,2] <- k
# if (dis[k,endpoints[j,1]] < dis[k,endpoints[j,2]]){
# endpoints[i,1] <- endpoints[j,2]
# dir[i,1] <- -1 }
# else {
# endpoints[i,1] <- endpoints[j,1]
# }}
# else {
# d11 <- dis[endpoints[j,1],endpoints[k,1]]
# d12 <- dis[endpoints[j,1],endpoints[k,2]]
# d21 <- dis[endpoints[j,2],endpoints[k,1]]
# d22 <- dis[endpoints[j,2],endpoints[k,2]]
# dmin <- min(d11,d12,d21,d22)
# if (dmin == d21) {
# endpoints[i,1] <- endpoints[j,1]
# endpoints[i,2] <- endpoints[k,2]
# }

# else if (dmin == d11) {
# endpoints[i,1] <- endpoints[j,2]
# endpoints[i,2] <- endpoints[k,2]
# dir[i,1] <- -1
# }
# else if (dmin == d12) {
# endpoints[i,1] <- endpoints[j,2]
# endpoints[i,2] <- endpoints[k,1]
# dir[i,1] <- -1
# dir[i,2] <- -1
# }
# else  {
# endpoints[i,1] <- endpoints[j,1]
# endpoints[i,2] <- endpoints[k,1]
# dir[i,2] <- -1}}
# }
# for (i in n:2) {
# if (dir[i,1] == -1) {
# m <- merges[i,1]
# if (m > 0) {
# m1 <- merges[m,1]
# merges[m,1] <- merges[m,2]
# merges[m,2] <- m1
# if (dir[m,1] == dir[m,2])
# dir[m,] <- -dir[m,]
# }}
# if (dir[i,2] == -1) {
# m <- merges[i,2]
# if (m > 0) {
# m1 <- merges[m,1]
# merges[m,1] <- merges[m,2]
# merges[m,2] <- m1
# if (dir[m,1] == dir[m,2])
# dir[m,] <- -dir[m,]
# }}

# }

# clusters <- as.list(1:n)
# for (i in 1:n) {
# j <- merges[i,1]
# k <- merges[i,2]
# if ((j < 0) && (k < 0))
# clusters[[i]] <- c(-j,-k)
# else if (j < 0)
# clusters[[i]] <- c(-j,clusters[[k]])
# else if (k < 0)
# clusters[[i]] <- c(clusters[[j]],-k)
# else clusters[[i]] <- c(clusters[[j]], clusters[[k]])}

# x1 <- x
# x1\$merge <- merges
# x1\$order <- clusters[[n]]
# x1

# }

reorder.hclust <- compiler::cmpfun(reorder.hclust)``````