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Related Experiment Video

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Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

MATICCE: mapping transitions in continuous character evolution.

Andrew L Hipp¹, Marcial Escudero

¹Herbarium, The Morton Arboretum, 4100 Illinois Route 53, Lisle, IL 60532-1293, USA. ahipp@mortonarb.org

Bioinformatics (Oxford, England)

|November 3, 2009

Summary

This summary is machine-generated.

This study introduces MATICCE, a new R software package for mapping phylogenetic trait evolution with continuous distributions. It accounts for uncertainty and visualizes evolutionary scenarios.

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Area of Science:

Evolutionary Biology
Phylogenetics
Computational Biology

Background:

Understanding the evolutionary history of continuous traits is crucial in biology.
Phylogenetic comparative methods are essential for studying trait evolution.
Existing methods may not adequately address phylogenetic and model uncertainty.

Purpose of the Study:

To introduce MATICCE, a novel R package for analyzing phylogenetic transitions of continuous traits.
To provide a tool that integrates phylogenetic and model uncertainty.
To enable visualization of evolutionary scenarios.

Main Methods:

Developed in the R language.
Freely available via the Comprehensive R Archive Network (CRAN).
Implements functions for mapping trait transitions and integrating uncertainty.

Main Results:

MATICCE enables mapping of phylogenetic transitions for continuous traits.
The package accounts for phylogenetic and model uncertainty.
Simulation functions allow visualization of evolutionary scenarios.

Conclusions:

MATICCE is a valuable new software package for evolutionary biologists.
It facilitates the study of continuous trait evolution across phylogenies.
The integration of uncertainty and simulation capabilities enhances evolutionary analysis.