Modeling evolution in paleontological time-series


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Documentation for package ‘paleoTS’ version 0.3-1

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paleoTS-package Analysis of evoltuionary time-series
add.OU.curves Adds curves to an existing plot indicating best-fit OU model
akaike.wts Compute information criterion scores and Akaike weights for evoltuionary models
as.paleoTS Paleontological time-series class
cat.paleoTS Miscellaneous functions used internally for punctuations
dorsal.spines Stickleback data from Bell et al. (2006)
fit.sgs Analyze evolutionary models with well-sampled punctuations
fit3models Do model fits for three standard evolutionary models
fit3models.joint Do model fits for three standard evolutionary models
fitGpunc Analyze evolutionary models with unsampled punctuations
IC Compute information criterion scores and Akaike weights for evoltuionary models
ln.paleoTS Log transform paleontological time series data
logL.GRW Compute log-likelihoods for random walk and stasis models
logL.joint.GRW Log-likelihoods for evolutionary models (joint parameterization)
logL.joint.OU Log-likelihoods for evolutionary models (joint parameterization)
logL.joint.Stasis Log-likelihoods for evolutionary models (joint parameterization)
logL.joint.URW Log-likelihoods for evolutionary models (joint parameterization)
logL.Mult Functions to analyze multiple time-series jointly
logL.punc Analyze evolutionary models with unsampled punctuations
logL.punc.omega Analyze evolutionary models with unsampled punctuations
logL.SameMs Functions to analyze multiple time-series jointly
logL.SameVs Functions to analyze multiple time-series jointly
logL.sgs Analyze evolutionary models with well-sampled punctuations
logL.sgs.omega Analyze evolutionary models with well-sampled punctuations
logL.Stasis Compute log-likelihoods for random walk and stasis models
logL.URW Compute log-likelihoods for random walk and stasis models
lynchD Compute rate metric from Lynch (1990)
mle.GRW Maximum likelihood parameter estimators
mle.Stasis Maximum likelihood parameter estimators
mle.URW Maximum likelihood parameter estimators
opt.GRW Numerically find maximum likelihood solutions to evolutionary models
opt.GRW.shift Functions for random walks with shifting parameters
opt.joint.GRW Optimize evolutionary models (joint parameterization)
opt.joint.OU Optimize evolutionary models (joint parameterization)
opt.joint.Stasis Optimize evolutionary models (joint parameterization)
opt.joint.URW Optimize evolutionary models (joint parameterization)
opt.punc Analyze evolutionary models with unsampled punctuations
opt.RW.Mult Functions to analyze multiple time-series jointly
opt.RW.SameMs Functions to analyze multiple time-series jointly
opt.RW.SameVs Functions to analyze multiple time-series jointly
opt.sgs Analyze evolutionary models with well-sampled punctuations
opt.Stasis Numerically find maximum likelihood solutions to evolutionary models
opt.URW Numerically find maximum likelihood solutions to evolutionary models
ou.M Simulate evolutionary time-series
ou.V Simulate evolutionary time-series
paleoTS Analysis of evoltuionary time-series
pelvic.score Stickleback data from Bell et al. (2006)
plot.paleoTS Plots paleoTS objects
pool.var Variance heterogeneity test
pterygiophores Stickleback data from Bell et al. (2006)
read.paleoTS Paleontological time-series class
shift2gg Miscellaneous functions used internally for punctuations
shifts Miscellaneous functions used internally for punctuations
sim.GRW Simulate evolutionary time-series
sim.GRW.shift Simulate evolutionary time-series with changing dynamics
sim.OU Simulate evolutionary time-series
sim.punc Simulate evolutionary time-series with changing dynamics
sim.sgs Simulate evolutionary time-series with changing dynamics
sim.Stasis Simulate evolutionary time-series
split4punc Miscellaneous functions used internally for punctuations
std.paleoTS Standardize paleontological time series data
sub.paleoTS Subset an evolutionary time series
test.var.het Variance heterogeneity test