Given a distance matrix for sorted objects, compute a hierarchical clustering preserving this
order. That is, this is similar to `hclust`

with the constraint that the result's order is
always `1:N`

.

oclust(distances, method = "ward.D2", order = NULL, members = NULL)

## Arguments

distances |
A distances object (as created by `stats::dist` ). |

method |
The clustering method to use (only `ward.D` and `ward.D2` are supported). |

order |
If specified, assume the data will be re-ordered by this order. |

members |
Optionally, the number of members for each row/column of the distances (by default, one each). |

## Value

A clustering object (as created by `hclust`

).

## Details

If an `order`

is specified, assumes that the data will be re-ordered by this order. That is,
the indices in the returned `hclust`

object will refer to the post-reorder data locations,
**not** to the current data locations.

This can be applied to the results of `slanted_reorder`

, to give a "plausible"
clustering for the data.

## Examples

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