Calculate the entropy, joint entropy, entropy distance and information
content of two splits, treating each split as a division of *n* leaves into
two groups.
Further details are available in a
vignette,
MacKay (2003) and Meilă (2007).

`SplitEntropy(split1, split2 = split1)`

- split1, split2
Logical vectors listing leaves in a consistent order, identifying each leaf as a member of the ingroup (

`TRUE`

) or outgroup (`FALSE`

) of the split in question.

A numeric vector listing, in bits:

`H1`

The entropy of split 1;`H2`

The entropy of split 2;`H12`

The joint entropy of both splits;`I`

The mutual information of the splits;`Hd`

The entropy distance (variation of information) of the splits.

MacKay DJC (2003).
*Information Theory, Inference, and Learning Algorithms*.
Cambridge University Press, Cambridge.
https://www.inference.org.uk/itprnn/book.pdf.

Meila M (2007).
“Comparing clusterings---an information based distance.”
*Journal of Multivariate Analysis*, **98**(5), 873--895.
doi: 10.1016/j.jmva.2006.11.013
.

Other information functions:
`SplitSharedInformation()`

,
`TreeInfo`