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paleoTS (version 0.4-4)

Analyze paleontological time-series

Description

This package facilitates analysis of paleontological sequences of trait values from an evolving lineage. Functions are provided to fit, using maximum likelihood, evolutionary models including unbiased random walks, directional evolution, stasis, Ornstein-Uhlenbeck, punctuated change, and evolutionary models in which traits track some measured covariate.

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Version

Install

install.packages('paleoTS')

Monthly Downloads

460

Version

0.4-4

License

GPL (>= 2)

Maintainer

Gene Hunt

Last Published

June 12th, 2012

Functions in paleoTS (0.4-4)

cat.paleoTS

Miscellaneous functions used internally for punctuations
LRI

Log-Rate, Log-Interval (LRI) method of Gingerich
fitGpunc

Analyze evolutionary models with unsampled punctuations
opt.covTrack

Covariate-tracking model
logL.GRW

Compute log-likelihoods for random walk and stasis models
fit.sgs

Analyze evolutionary models with well-sampled punctuations
std.paleoTS

Standardize paleontological time series data
as.paleoTSfit

Class for fit to paleontological time-series models
sim.covTrack

Simulate time-series that tracks a covariate
plot.paleoTS

Plots paleoTS objects
compareModels

Compare output from any set of model fits
sub.paleoTS

Subset an evolutionary time series
opt.GRW.shift

Functions for random walks with shifting parameters
paleoTS-package

Analysis of evoltuionary time-series
opt.joint.GRW

Optimize evolutionary models (joint parameterization)
opt.RW.Mult

Functions to analyze multiple time-series jointly
ln.paleoTS

Log transform paleontological time series data
mle.GRW

Maximum likelihood parameter estimators
as.paleoTS

Paleontological time-series class
IC

Compute information criterion scores and Akaike weights for evoltuionary models
opt.GRW

Numerically find maximum likelihood solutions to evolutionary models
logL.joint.GRW

Log-likelihoods for evolutionary models (joint parameterization)
fit3models

Do model fits for three standard evolutionary models
sim.GRW

Simulate evolutionary time-series
lynchD

Compute rate metric from Lynch (1990)
Stickleback data

Stickleback data from Bell et al. (2006)
sim.punc

Simulate evolutionary time-series with changing dynamics
paleoTS-internal

Internal paleoTS objects
sim.OU

Simulate evolutionary time-series
modelCurves

Function computes model expectations and 95
test.var.het

Variance heterogeneity test