paleoTS-package |
Analysis of evoltuionary time-series |
akaike.wts |
Compute information criterion scores and Akaike weights for evoltuionary models |
as.paleoTS |
Paleontological time-series class |
fit3models |
Do model fits for three evolutionary models |
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.Mult |
Functions to analyze multiple time-series jointly |
logL.SameMs |
Functions to analyze multiple time-series jointly |
logL.SameVs |
Functions to analyze multiple time-series jointly |
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.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.Stasis |
Numerically find maximum likelihood solutions to evolutionary models |
opt.URW |
Numerically find maximum likelihood solutions to evolutionary models |
paleoTS |
Analysis of evoltuionary time-series |
plot.paleoTS |
Plots paleoTS objects |
pool.var |
Variance heterogeneity test |
read.paleoTS |
Paleontological time-series class |
sim.GRW |
Simulate evolutionary time-series |
sim.Stasis |
Simulate evolutionary time-series |
std.paleoTS |
Standardize paleontological time series data |
sub.paleoTS |
Subset an evolutionary time series |
test.var.het |
Variance heterogeneity test |