Analysis of evolutionary time-series

Share:

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.

Details

Package: paleoTS
Type: Package
Version: 0.4-3
Date: 2011-12-22
License: GPL 2

Author(s)

Gene Hunt

References

Hunt, G. 2006. Fitting and comparing models of phyletic evolution: random walks and beyond. Paleobiology 32:578–601.
Hunt, G., M. Bell & M. Travis. 2008. Evolution towards a new adaptive optimum: phenotypic evolution in a fossil stickleback lineage. Evolution 62:700-710.
Hunt, G. 2008. Gradual or pulsed evolution: when should punctuational explanations be preferred? Paleobiology 34:360–377.
Hunt, G. 2008. Evolutionary patterns within fossil lineages: model-based assessment of modes, rates, punctuations and process.. In R.K. Bambach and P.H. Kelley, eds. From Evolution to Geobiology: Research Questions Driving Paleontology at the Start of a New Century:578–601.
Hunt, G., S. Wicaksono, J. E. Brown, and G. K. Macleod. 2010. Climate-driven body size trends in the ostracod fauna of the deep Indian Ocean. Palaeontology 53(6):1255-1268.
Hunt, G., M. J. Hopkins, and S. L. Lidgard 2015. Simple versus complex models of trait evolution and stasis as a response to environmental change. PNAS 112:4885–4890.

Examples

1
2
 x <- sim.GRW(ns=30, ms=0, vs=0.3)	# simulate unbiased random walk
 fit3models(x)		# compare fits of directional, random walk, and stasis models

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.