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| 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 |