Description Usage Arguments Value Examples

For a matrix expressing the cross-similarity between two (possibly different) sets of entities,
this produces better results than clustering (e.g. as done by `pheatmap`

). This is because
clustering does not care about the order of each two sub-partitions. That is, clustering is as
happy with `((2, 1), (4, 3))`

as it is with the more sensible `((1, 2), (3, 4))`

. As a
result, visualizations of similarities using naive clustering can be misleading.

1 2 3 4 5 6 7 8 9 |

`data` |
A rectangular matrix containing non-negative values. |

`order_rows` |
Whether to reorder the rows. |

`order_cols` |
Whether to reorder the columns. |

`squared_order` |
Whether to reorder to minimize the l2 norm (otherwise minimizes the l1 norm). |

`same_order` |
Whether to apply the same order to both rows and columns. |

`discount_outliers` |
Whether to do a final order phase discounting outlier values far from the diagonal. |

`max_spin_count` |
How many times to retry improving the solution before giving up. |

A list with two keys, `rows`

and `cols`

, which contain the order.

1 | ```
slanter::slanted_orders(cor(t(mtcars)))
``` |

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