Description Details Author(s) References

This method models the covariance in allele frequencies between populations on a landscape as a decreasing function of their pairwise geographic and ecological distance. Allele frequencies are modeled as a spatial Gaussian process with a parametric covariance function. The parameters of this covariance function, as well as the spatially smoothed allele frequencies, are estimated in a custom Markov chain Monte Carlo.

Package: | BEDASSLE |

Type: | Package |

Version: | 1.5 |

Date: | 2013-09-12 |

License: | GPL (>=2) |

The two inference functions are `MCMC`

and `MCMC_BB`

, which call the
Markov chain Monte Carlo algorithms on the standard and overdispersion (Beta-Binomial)
models, respectively. To evaluate MCMC performance, there are a number of MCMC diagnosis
and visualization functions, which variously show the trace, plots, marginal and joint
marginal densities, and parameter acceptance rates. To evaluate model adequacy, there is
a posterior predictive sample function (`posterior.predictive.sample`

), and an
accompanying function to plot its output and visually assess the model's ability to
describe the user's data.

Gideon Bradburd

Maintainer: Gideon Bradburd <gbradburd@ucdavis.edu>

Bradburd, G.S., Ralph, P.L., and Coop, G.M. Disentangling the effects of geographic and
ecological isolation on genetic differentiation. *Evolution* 2013.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.