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evoTS - Analyses of evolutionary time-series

https://cran.r-project.org/package=evoTS

The evoTS package facilitates univariate and multivariate analyses of phenotypic change within lineages.

The evoTS package extends the modeling framework available in the paleoTS package. All model-fitting procedures in evoTS have been implemented to mirror the user experience from paleoTS. For example, all univariate models implemented in evoTS can be fitted to a paleoTS object, i.e. the data format used in paleoTS. The fit of all univariate models available in paleoTS and evoTS are directly comparable using the reported AICc.

evoTS contains functions that allow for fitting different models to separate parts of an evolutionary sequence (mode-shift models). Functions for investigating likelihood surfaces of fitted models are also included.

Multivariate models implemented in evoTS include different versions of multivariate unbiased random walks and Ornstein-Uhlenbeck processes. These multivariate models allow the user to test a variety of hypotheses of adaptation and evolution using phenotypic time-series.

The development version

The GitHub repository contains a copy of the current development version of the R package evoTS. This version is as recent as or more recent than the official release of evoTS on the Comprehensive R Archive Network (CRAN), which is available at https://cran.r-project.org/package=evoTS.

Where is the official (stable) release?

For the most recent official and stable release of evoTS, see https://cran.r-project.org/package=evoTS

Installation

Installing the official release

## Installing from CRAN
> install.packages("evoTS")

> library(evoTS)

Installing the the development version from GitHub

## Installing from GitHub
> install.packages("devtools")

> devtools::install_github("klvoje/evoTS")

> library(evoTS)

Documentation

The package website contains a vignette (detailed walk-through) on how to use the various features of the evoTS package.

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Version

Install

install.packages('evoTS')

Monthly Downloads

170

Version

1.0.3

License

GPL (>= 2)

Maintainer

Kjetil Lysne Voje

Last Published

June 20th, 2024

Functions in evoTS (1.0.3)

logL.joint.Stasis.OU

Log-likelihoods for evolutionary models
fit.mode.shift

Fit two models to two separate segments to an evolutionary sequence (time-series).
fit.multivariate.URW.shift

Fit separate multivariate Unbiased Random Walk models to two different segments of a multivariate evolutionary sequence (time-series).
as.evoTS.multi.URW.fit

Class for fit to evolutionary sequence (time-series) models
as.evoTS.multi.BW.acceldecel.fit

Class for fit to evolutionary sequence (time-series) models
logL.joint.accel.decel.single.R

Log-likelihoods for evolutionary models
fit.multivariate.OU

Fit predefined multivariate Ornstein-Uhlenbeck models to multivariate evolutionary sequence (time-series) data.
as.evoTS.multi.BW.fit

Class for fit to evolutionary sequence (time-series) models
logL.joint.accel_decel

Log-likelihoods for evolutionary models
logL.joint.accel.decel.single.R.zero.corr

Log-likelihoods for evolutionary models
diameter_S.yellowstonensis

Evolutionary sequence (time-series) of phenotypic change in diameter in the lineage Stephanodiscus yellowstonensis
logL.joint.URW.URW

Log-likelihoods for evolutionary models
fit.multivariate.OU.user.defined

Fit user-defined multivariate Ornstein-Uhlenbeck models to multivariate evolutionary sequence (time-series) data.
loglik.surface.accel

Calculate the log-likelihood surface for a part of parameter space
logL.joint.multi.OUOU.user

Log-likelihoods for evolutionary models
logL.joint.OU.BM

Log-likelihoods for evolutionary models
logL.joint.multi.OUOU

Log-likelihoods for evolutionary models
fit.all.univariate

Fit all univariate models to an evolutionary sequence (time-series).
fit.multivariate.URW

Fit multivariate Unbiased Random Walk models to multivariate evolutionary sequence (time-series) data.
opt.joint.OUBM

Fit an Ornstein-Uhlenbeck model with an optimum that evolves according to a Unbiased Random Walk.
logL.joint.single.R

Log-likelihoods for evolutionary models
loglik.surface.OUBM

Calculate the log-likelihood surface for a part of parameter space
loglik.surface.GRW

Calculate the log-likelihood surface for a part of parameter space
logL.joint.single.R.zero.corr

Log-likelihoods for evolutionary models
loglik.surface.decel

Calculate the log-likelihood surface for a part of parameter space
opt.joint.URW.Stasis

Optimization and log-likelihoods for pairs of models.
opt.single.R

Fit multivariate Unbiased Random Walk model.
loglik.surface.URW

Calculate the log-likelihood surface for a part of parameter space
sim.accel.decel

Simulate an Unbiased Random Walk with an accelerating or decelerating rate of change through time.
opt.single.R.zero.corr

Fit multivariate Unbiased Random Walk model with uncorrelated trait changes.
sim.multi.OU

Simulate multivariate Ornstein-Uhlenbeck evolutionary sequence data sets
opt.joint.accel

Fit an Unbiased Random Walk with an accelerating rate of change through time.
opt.accel.single.R

Fit multivariate Unbiased Random Walk with increasing (exponential accelerating) rate of change through time.
opt.joint.decel

Fit an Unbiased Random Walk with an decelerating rate of change through time.
logL.joint.multi.R

Log-likelihoods for evolutionary models
opt.accel.single.R.zero.corr

Fit multivariate Unbiased Random Walk model with uncorrelated trait changes and with increasing (exponential accelerating) rate of change through time.
loglik.surface.OU

Calculate the log-likelihood surface for a part of parameter space
plotevoTS

Plot a paleoTS object
plotevoTS.multivariate

Plots multivariate evolutionary sequence (time-series) data set
opt.decel.single.R.zero.corr

Fit multivariate Unbiased Random Walk model with uncorrelated trait changes and with decreasing (exponential decaying) rate of change through time.
opt.decel.single.R

Fit multivariate Unbiased Random Walk with decreasing (exponential decaying) rate of change through time.
sim.multi.URW

Simulate multivariate evolutionary sequence data that evolve according to an Unbiased Random Walk
loglik.surface.stasis

Calculate the log-likelihood surface for a part of parameter space
make.multivar.evoTS

Makes a multivariate data set of
ribs_S.yellowstonensis

Evolutionary sequence (time-series) of phenotypic change in the number of ribs in the lineage Stephanodiscus yellowstonensis
sim.OUBM

Simulate an Ornstein-Uhlenbeck process with optimum changing according to an unbiased random walk
as.evoTS.multi.URW.shift.fit

Class for fit to evolutionary sequence (time-series) models
as.evoTS.multi.OU.fit

Class for fit to evolutionary sequence (time-series) models
as.evoTSfit.OUBM

Class for fit to evolutionary sequence (time-series) models