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mvMORPH

mvMORPH: an R package for fitting multivariate evolutionary models to morphometric data

This package allows the fitting of multivariate evolutionary models (Ornstein-Uhlenbeck, Brownian motion, Early burst, Shift models) on species trees and time series. It also provides functions to compute log-likelihood of users specified models with fast methods (e.g., for Bayesian approaches or customized comparative methods), simulates correlated traits under various models, constrain various parts of multivariate models...

The package implement now efficient methods for high-dimensional multivariate comparative methods (mvgls) based on Penalized likelihood

The package is designed to handle ultrametric and non-ultrametric trees (i.e. with fossil species) and missing data in multivariate datasets (NA values), SIMMAP mapping of discrete traits, measurement error, etc...

See the packages vignettes for details and examples: browseVignettes("mvMORPH").

mvMORPH 1.1.0

  1. This is the version 1.1.0:
  • Penalized Likelihood methods for High-Dimensional datasets (mvgls)
  • Multivariate GLS (mvgls)
  • PCA on GLS covariance estimate (mvgls)
  • Some minor bugs corrections (see NEWS)
  1. TODO:
  • Incorporation of a tests-suite
  • Implement the sampler (upcomming mvMORPH)
  • Code improvements
  • Extend the shift model to TS
  • Improved mvOU model
  • Threshold model for categorical data

The current stable version of the mvMORPH package (1.0.9) is on the CRAN repository. https://cran.r-project.org/package=mvMORPH

Package Installation

You can install the package directly from gitHub through devtools:

library(devtools)

install_github("JClavel/mvMORPH", build_vignettes = TRUE)

(The installation may crash if your dependencies are not up to date. Note that you may also need to install Rtools to compile the C codes included in the package. For [Windows] (https://cran.r-project.org/bin/windows/Rtools/) and for [Mac] (http://r.research.att.com) (and [Tools] (https://r.research.att.com/tools/) )

Report an issue

Any bugs encountered when using the package can be reported here

Package citation

Clavel, J., Escarguel, G., Merceron, G. 2015. mvMORPH: an R package for fitting multivariate evolutionary models to morphometric data. Methods in Ecology and Evolution, 6(11):1311-1319. DOI: 10.1111/2041-210X.12420

Download version with appended supplementary material.

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Version

Install

install.packages('mvMORPH')

Monthly Downloads

781

Version

1.1.0

License

GPL (>= 2.0)

Issues

Pull Requests

Stars

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Maintainer

Julien Clavel

Last Published

August 4th, 2018

Functions in mvMORPH (1.1.0)

mvBM

Multivariate Brownian Motion models of continuous traits evolution
mvLL

Multivariate (and univariate) algorithms for log-likelihood estimation of arbitrary covariance matrix/trees
mvEB

Multivariate Early Burst model of continuous traits evolution
mvgls.pca

Principal Component Analysis (PCA) based on GLS estimate of the traits variance-covariance matrix (possibly regularized).
pruning

Pruning algorithm to compute the square root of the phylogenetic covariance matrix and it's determinant.
LRT

Likelihood Ratio Test
mvOU

Multivariate Ornstein-Uhlenbeck model of continuous traits evolution
vcov.mvgls

Calculate variance-covariance matrix for a fitted object of class 'mvgls'
mvMORPH-package

Multivariate Comparative Methods for Fitting Evolutionary Models to Morphometric Data
mvOUTS

Multivariate continuous trait evolution for a stationary time series (Ornstein-Uhlenbeck model)
halflife

The phylogenetic half-life for an Ornstein-Uhlenbeck process
mv.Precalc

Model parameterization for the various mvMORPH functions
mvRWTS

Multivariate Brownian motion / Random Walk model of continuous traits evolution on time series
residuals.mvgls

Extract gls model residuals
stationary

The stationary variance of an Ornstein-Uhlenbeck process
mvSHIFT

Multivariate change in mode of continuous trait evolution
aicw

Akaike weights
coef.mvgls

Extract multivariate gls model coefficients
mvSIM

Simulation of (multivariate) continuous traits on a phylogeny
mvgls

Fit linear model using Generalized Least Squares to multivariate (high-dimensional) data sets.
GIC

Generalized Information Criterion (GIC) to compare models fit with mvgls by Maximum Likelihood (ML) or Penalized Likelihood (PL).
estim

Ancestral states reconstructions and missing value imputation with phylogenetic/time-series models
fitted.mvgls

Extract multivariate gls model fitted values