entropy

kl_div

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cross_entropy

Compute information-based estimates and distances.

A comprehensive framework for bioinformatics exploratory analysis of bulk and single-cell
T-cell receptor and antibody repertoires. It provides seamless data loading, analysis and
visualisation for AIRR (Adaptive Immune Receptor Repertoire) data, both bulk immunosequencing (RepSeq)
and single-cell sequencing (scRNAseq). Immunarch implements most of the widely used AIRR analysis methods,
such as: clonality analysis, estimation of repertoire similarities in distribution of clonotypes
and gene segments, repertoire diversity analysis, annotation of clonotypes using external immune receptor
databases and clonotype tracking in vaccination and cancer studies. A successor to our
previously published 'tcR' immunoinformatics package (Nazarov 2015) <doi:10.1186/s12859-015-0613-1>.

Vadim I. Nazarov

immunarch

Bioinformatics Analysis of T-Cell and B-Cell Immune Repertoires

Vasily O. Tsvetkov

Siarhei Fiadziushchanka

Eugene Rumynskiy

Aleksandr A. Popov

Ivan Balashov

Maria Samokhina

Anna Lorenc

Daniel J. Moore

Victor Greiff

ImmunoMind 

entropy function

<dl><dt>.data</dt>
<dd>Numeric vector. Any distribution.</dd>
<dt>.base</dt>
<dd>Numeric. A base of logarithm.</dd>
<dt>.norm</dt>
<dd>Logical. If TRUE then normalises the entropy by the maximal value of the entropy.</dd>
<dt>.do.norm</dt>
<dd>If TRUE then normalises the input distributions to make them sum up to 1.</dd>
<dt>.laplace</dt>
<dd>Numeric. A value for the laplace correction.</dd>
<dt>.alpha</dt>
<dd>Numeric vector. A distribution of some random value.</dd>
<dt>.beta</dt>
<dd>Numeric vector. A distribution of some random value.</dd>
<dt>.norm.entropy</dt>
<dd>Logical. If TRUE then normalises the resulting value by the average entropy of input distributions.</dd></dl>

Arguments

Information measures — entropy

<dl>

<dt>.data</dt>
<dd>Numeric vector. Any distribution.</dd>


<dt>.base</dt>
<dd>Numeric. A base of logarithm.</dd>


<dt>.norm</dt>
<dd>Logical. If TRUE then normalises the entropy by the maximal value of the entropy.</dd>


<dt>.do.norm</dt>
<dd>If TRUE then normalises the input distributions to make them sum up to 1.</dd>


<dt>.laplace</dt>
<dd>Numeric. A value for the laplace correction.</dd>


<dt>.alpha</dt>
<dd>Numeric vector. A distribution of some random value.</dd>


<dt>.beta</dt>
<dd>Numeric vector. A distribution of some random value.</dd>


<dt>.norm.entropy</dt>
<dd>Logical. If TRUE then normalises the resulting value by the average entropy of input distributions.</dd>

</dl>

Information measures

entropy: Information measures

Description

Usage

Value

Arguments

Examples