Highlight distint edges in a tree (compared to another one) by changing
the branches' color, line width, or line type.

This function enables this feature in dend_diff and tanglegram

```
highlight_distinct_edges(dend, ...)
# S3 method for dendrogram
highlight_distinct_edges(
dend,
dend2,
value = 2,
edgePar = c("col", "lty", "lwd"),
...
)
# S3 method for dendlist
highlight_distinct_edges(dend, ..., which = c(1L, 2L))
```

## Arguments

- dend
a dendrogram or dendlist to find unique edges in (to highlight)

- ...
Ignored.

- dend2
a dendrogram to compare with

- value
a new value scalar for the edgePar attribute.

- edgePar
a character indicating the value inside edgePar to adjust.
Can be either "col", "lty", or "lwd".

- which
an integer vector indicating, in the case "dend" is a dendlist,
on which of the trees should the modification be performed.
If missing - the change will be performed on all of objects in the dendlist.

## Value

A dendrogram with modified edges - the distinct ones are changed (color, line width, or line type)

## Examples

```
x <- 1:5 %>%
dist() %>%
hclust() %>%
as.dendrogram()
y <- set(x, "labels", 5:1)
distinct_edges(x, y)
#> [1] 5 7
distinct_edges(y, x)
#> [1] 2 5
par(mfrow = c(1, 2))
plot(highlight_distinct_edges(x, y))
plot(y)
# tanglegram(highlight_distinct_edges(x, y),y)
# dend_diff(x, y)
if (FALSE) {
# using highlight_distinct_edges combined with dendlist and set
# to clearly highlight "stable" branches.
data(iris)
ss <- c(1:5, 51:55, 101:105)
iris1 <- iris[ss, -5] %>%
dist() %>%
hclust(method = "single") %>%
as.dendrogram()
iris2 <- iris[ss, -5] %>%
dist() %>%
hclust(method = "complete") %>%
as.dendrogram()
iris12 <- dendlist(iris1, iris2) %>%
set("branches_k_color", k = 3) %>%
set("branches_lwd", 3) %>%
highlight_distinct_edges(value = 1, edgePar = "lwd")
iris12 %>%
untangle(method = "step2side") %>%
tanglegram(
sub = "Iris dataset", main_left = "'single' clustering",
main_right = "'complete' clustering"
)
}
```