jensen_shannon

The lowlevel function for computing the jensen_shannon distance.

Computes 46 optimized distance and similarity measures for comparing probability functions (Drost (2018) <doi:10.21105/joss.00765>). These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions.

Hajk-Georg Drost

philentropy

Similarity and Distance Quantification Between Probability
Functions

Jakub Nowosad

jensen_shannon function

<dl><dt>P</dt>
<dd>a numeric vector storing the first distribution.</dd>
<dt>Q</dt>
<dd>a numeric vector storing the second distribution.</dd>
<dt>testNA</dt>
<dd>a logical value indicating whether or not distributions shall be checked for <code>NA</code> values.</dd>
<dt>unit</dt>
<dd>type of <code>log</code> function. Option are<ul>
<li><code>unit = "log"</code></li>
<li><code>unit = "log2"</code></li>
<li><code>unit = "log10"</code></li>
</ul></dd></dl>

Arguments

Author

Jensen-Shannon distance (lowlevel function) — jensen_shannon

<dl>

<dt>P</dt>
<dd>a numeric vector storing the first distribution.</dd>


<dt>Q</dt>
<dd>a numeric vector storing the second distribution.</dd>


<dt>testNA</dt>
<dd>a logical value indicating whether or not distributions shall be checked for <code>NA</code> values.</dd>


<dt>unit</dt>
<dd>type of <code>log</code> function. Option are<ul>
<li><code>unit = "log"</code></li>
<li><code>unit = "log2"</code></li>
<li><code>unit = "log10"</code></li>
</ul></dd>

</dl>

jensen_shannon: Jensen-Shannon distance (lowlevel function)

Description

Usage

Arguments

Author

Examples