phylocurve (version 2.1.1)

Phylogenetic Comparative Methods for High-Dimensional Traits

Description

Tools for studying the evolution of high-dimensional traits (morphometric, function-valued, etc.) including ancestral state reconstruction, estimating phylogenetic signal, and assessing correlated trait evolution. Visit for more information.

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Version

Install

install.packages('phylocurve')

Monthly Downloads

110

Version

2.1.1

License

GPL (>= 2)

Maintainer

Eric Goolsby

Last Published

May 14th, 2020

Functions in phylocurve (2.1.1)

evo.model

Fit an evolutionary model

multipic

Efficient PIC algorithm for multiple traits

get.tip.coefficients

Estimate regression coefficients for tip species curves

print.evo.model

Print evo.model

sim.curves

Simulate function-valued curve evolution

paint.edges

Paint tree edges based on species regimes

nonlinear.fit

Fit nonlinear curves to species data

polynomial.fit

Fit polynomial curves to species data using stepwise regression

phylocurve.generalized

Align curves of any shape

sim.traits

Simulate multivariate trait evolution

ultraFastAnc

Ultra-fast maximum likelihood ancestral state reconstruction

phylocurve

Ancestral curve reconstruction for logistic regression (glm with logit link)

phylocurve-package

Phylogenetic Comparative Methods for Function-Valued and Other High-Dimensional Traits

fast.geomorph.procD.pgls

Fast covariance-based implementations of distance-based methods

K.mult

Test phylogenetic signal (Kmult) using phylogenetic simulation

fast.geomorph.phylo.integration

Fast covariance-based implementations of distance-based methods

fast.geomorph.compare.evol.rates

Fast covariance-based implementations of distance-based methods

compare.models

Compare evolutionary hypotheses

fast.geomorph.compare.multi.evol.rates

Fast covariance-based implementations of distance-based methods

get.aligned.function.data

Estimate aligned data for function-valued traits

GP.fit

Fit Gaussian process curves to species data

fast.geomorph.physignal

Fast covariance-based implementations of distance-based methods