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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 and Empirical Bayes principle, as well as associated tests for multivariate linear models (Wilks, Pillai...)

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 1.2.3

  1. This is the version 1.2.3:
  • Check the NEWS file for detailled updates
  1. TODO:
  • Incorporation of a tests-suite
  • Implement the sampler
  • Code improvements
  • Extend the shift model to TS
  • Improved mvOU model
  • Threshold model for categorical data

The current stable version of the mvMORPH package (1.2.3) 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] (https://mac.r-project.org/) (and [Tools] (https://mac.r-project.org/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.

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Version

Install

install.packages('mvMORPH')

Monthly Downloads

1,016

Version

1.2.3

License

GPL (>= 2.0)

Issues

7

Pull Requests

0

Stars

20

Forks

9

Maintainer

Julien Clavel

Last Published

October 1st, 2026

Functions in mvMORPH (1.2.3)

coef

Extract multivariate gls (or ols) model coefficients
mvOU

Multivariate Ornstein-Uhlenbeck model of continuous traits evolution
mvOUTS

Multivariate continuous trait evolution for a stationary time series (Ornstein-Uhlenbeck model)
manova.gls

Multivariate Analysis of Variance
mvEB

Multivariate Early Burst model of continuous traits evolution
halflife

The phylogenetic half-life for an Ornstein-Uhlenbeck process
mvMORPH-package

Multivariate Comparative Methods for Fitting Evolutionary Models to Morphometric Data
mvLL

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

Multivariate Brownian Motion models of continuous traits evolution
mapping.asr

Mapping ancestral character estimates onto a phylogenetic tree
mv.Precalc

Model parameterization for the various mvMORPH functions
mvSIM

Simulation of (multivariate) continuous traits on a phylogeny
mvgls.pca

Principal Component Analysis (PCA) based on GLS (or OLS) estimate of the traits variance-covariance matrix (possibly regularized)
mvSHIFT

Multivariate change in mode of continuous trait evolution
mvRWTS

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

Discriminant Function Analysis (DFA) - also called Linear Discriminant Analysis (LDA) or Canonical Variate Analysis (CVA) - based on multivariate GLS (or OLS) model fit
pairwise.contrasts

Pairwise contrasts
p3ca

Modelling multivariate trait evolution using a Probabilistic and Phylogenetic Principal Component Analysis
predict

Predictions from (multivariate) gls or ols model fit
mvqqplot

Quantile-Quantile plots for multivariate models fit with mvgls or mvols
pcaLoadings

Plot the loadings from Principal Component Analysis (PCA) or Probabilistic Phylogenetic Principal Component Analysis (P3CA)
mvgls

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

Fit linear model using Ordinary Least Squares to multivariate (high-dimensional) data sets
vcov

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

Extract gls (or ols) model residuals
phyllostomid

Phylogeny and trait data for a sample of Phyllostomid bats
stationary

The stationary variance of an Ornstein-Uhlenbeck process
pcaShape

Projection of 2D and 3D shapes (from geometric morphometric datasets) on Principal Component Axes (PCA)
pairwise.glh

Pairwise multivariate tests between levels of a factor
pruning

Pruning algorithm to compute the square root of the phylogenetic covariance matrix and its determinant.
predict.mvgls.dfa

Predictions from Discriminant analysis conducted with a mvgls model fit
ancestral

Estimation of traits ancestral states.
fitted

Extract multivariate gls (or ols) model fitted values
effectsize

Multivariate measure of association/effect size for objects of class "manova.gls"
EIC

Extended Information Criterion (EIC) to compare models fit with mvgls (or mvols) by Maximum Likelihood (ML) or Penalized Likelihood (PL)
dfaShape

Projection of 2D and 3D shapes (from geometric morphometric datasets) on Discriminant axes
GIC

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

Ancestral states reconstructions and missing value imputation with phylogenetic/time-series models
aicw

Akaike weights
LRT

Likelihood Ratio Test