Entropy

Calculate the entropy of a vector of probabilities, in bits.
Probabilities should sum to one.
Probabilities equalling zero will be ignored.

Implements measures of tree similarity, including
information-based generalized Robinson-Foulds distances
(Phylogenetic Information Distance, Clustering Information Distance,
Matching Split Information Distance; Smith 2020)
<doi:10.1093/bioinformatics/btaa614>;
Jaccard-Robinson-Foulds distances (Bocker et al. 2013)
<doi:10.1007/978-3-642-40453-5_13>,
including the Nye et al. (2006) metric <doi:10.1093/bioinformatics/bti720>;
the Matching Split Distance (Bogdanowicz & Giaro 2012)
<doi:10.1109/TCBB.2011.48>;
Maximum Agreement Subtree distances;
the Kendall-Colijn (2016) distance <doi:10.1093/molbev/msw124>, and the
Nearest Neighbour Interchange (NNI) distance, approximated per Li et al.
(1996) <doi:10.1007/3-540-61332-3_168>.
Includes tools for visualizing mappings of tree space (Smith 2022)
<doi:10.1093/sysbio/syab100>,
for identifying islands of trees (Silva and Wilkinson 2021)
<doi:10.1093/sysbio/syab015>,
for calculating the median of sets of trees,
and for computing the information content of trees and splits.

Martin R. Smith

TreeDist

Calculate and Map Distances Between Phylogenetic Trees

Roy Jonker

Yong Yang

Yi Cao

Entropy function

<dl><dt>...</dt>
<dd>Numerics or numeric vector specifying probabilities of outcomes.</dd></dl>

Arguments

<a href="https://orcid.org/0000-0001-5660-1727">Martin R. Smith</a>
(<a href="/link/martin.smith%40durham.ac.uk?package=TreeDist&version=2.9.2" data-mini-rdoc="TreeDist::martin.smith@durham.ac.uk">martin.smith@durham.ac.uk</a>)

Author

Entropy in bits — Entropy

<dl>

<dt>...</dt>
<dd>Numerics or numeric vector specifying probabilities of outcomes.</dd>

</dl>

<a href='https://orcid.org/0000-0001-5660-1727'>Martin R. Smith</a>
(<a href='mailto:martin.smith@durham.ac.uk'>martin.smith@durham.ac.uk</a>)

Entropy: Entropy in bits

Description

Usage

Value

Arguments

Author

Examples