Analyze Paleontological Time-Series
Compute Akaike weights from AIC scores
Make a Paleontological Time-series object
Create a paleoTSfit object
Bootstrap test to see if a complex model is significantly better than ...
Compute and (optionally) plot residuals from SSM model fit
Compare model fits for a paleontological time-series
Compute Expected Squared Divergence (ESD) for Evolutionary Models
Fit a model of trait evolution with a protracted punctuation.
Fit a set of standard evolutionary models
Fit large set of models to a time-series
Fit trait evolution model with punctuations estimated from the data
Fit model in which the mode of trait evolution shifts once
Fit the same simple model across multiple time-series
Fit simple models of trait evolution
Compute Information Criteria
Time-varying Kalman filter calculations
Approximate log-transformation of time-series data
Log-rate, Log-interval (LRI) method of Gingerich
Compute Lynch's Delta rate metric
Analytical ML estimator for random walk and stasis models
Fit a model in which a trait tracks a covariate
Fit evolutionary model using "AD" parameterization
Fit random walk model with shift(s) in generating parameters
Fit evolutionary models using the "Joint" parameterization
Fit Ornstein-Uhlenbeck model using the "Joint" parameterization
Fit a model of trait evolution with specified punctuation(s)
Fit evolutionary models using state-space models (SSM)
paleoTS: Analyze Paleontological Time-Series
Plot a paleoTS object
Compute a pooled variance
Print a paleoTSfit object
Read a text-file with data from a paleontological time-series
Simulate trait evolution that tracks a covariate
Simulate random walk or directional time-series for trait evolution
Simulate (general) random walk with shift(s) in generating parameters
Simulate an Ornstein-Uhlenbeck time-series
Simulate a punctuated time-series
Simulate protracted punctuation
Simulate Stasis time-series for trait evolution
Simulate trait evolution with a mode shift
Convert time-series to standard deviation units
Subsample a paleontological time-series
Test for heterogeneity of variances among samples in a time-series
Facilitates analysis of paleontological sequences of trait values. Functions are provided to fit, using maximum likelihood, simple evolutionary models (including unbiased random walks, directional evolution,stasis, Ornstein-Uhlenbeck, covariate-tracking) and complex models (punctuation, mode shifts).