InfoMating: Non-random mating and Information Theory

Citation

If you use Infomating please cite:

Carvajal-Rodríguez, A. 2020. Multi-model inference of non-random mating from an information theoretic approach.

Theoretical Population Biology 131: 38-53 .

10.1016/j.tpb.2019.11.002.

The preprint has been**recommended** by PCI EvolBiol.

Carvajal-Rodríguez, A. 2020. Multi-model inference of non-random mating from an information theoretic approach.

Theoretical Population Biology 131: 38-53 .

10.1016/j.tpb.2019.11.002.

The preprint has been

References

- Carvajal-Rodríguez, A., 2018. Non-random mating and information theory. Theoretical Population Biology 120, 103-113.

Contact

For any questions about the options of the program you can contact me

Disclaimer

This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as
published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program
is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
Public License for more details. You should have received a copy of the GNU General Public License along with this
program; if not, write to the Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
See the file "gpl.html" under the "license" directory.

Return to AC-R home

Introduction

The study of The causes of both type of effects can be a different preference between different mating types, i.e.

We can consider jointly both concepts by means of the so called mutual mating propensity parameters m

Relying on the informational partition of the non-random mating effects, and by modeling mate choice and competition by the mutual propensity parameters, it is possible to identify the necessary and sufficient conditions of random mating and from here, develop and connect different kinds of models producing different effects. These models can be used to generate inferences on the parameters of interest. The software InfoMating implements the methodology to do so.

A. Carvajal-Rodriguez - Departamento de Bioquímica Genética e Inmunología - Universidad de Vigo.
( * Last update: 02022020)*