Description: The Pritchard-Stephens-Donnelly (PSD) admixture model has k intermediate subpopulations from which n individuals draw their alleles dictated by their individual-specific admixture proportions.  The BN-PSD model additionally imposes the Balding-Nichols (BN) allele frequency model to the intermediate populations, which therefore evolved independently from a common ancestral population T with subpopulation-specific FST (Wright's fixation index) parameters.  The BN-PSD model can be used to yield complex population structures.  This simulation approach is now extended to subpopulations related by a tree.  Method described in Ochoa and Storey (2021) <doi:10.1371/journal.pgen.1009241>.
Suggests: popkin (>= 1.3.9), testthat, knitr, rmarkdown, RColorBrewer

Title: Spatial Implementation of Bayesian Networks and Mapping
Authors@R: c(person(given = "Dario", family = "Masante", role = c("aut", "cre"), email = "dmasan@ceh.ac.uk"))
Imports: raster, gRbase, gRain, doParallel, foreach, parallel
Description: Package for the spatial implementation of Bayesian Networks and mapping in geographical space.
It makes maps of expected value (or most likely state) given known and unknown conditions, maps of uncertainty
measured as both coefficient of variation or Shannon index (entropy), maps of probability associated to any states
of any node of the network. Some additional features are provided as well, such as parallel processing options,
data discretization routines and function wrappers designed for users with minimal knowledge of the R programming language.

Authors@R: c(person(given = "Dario", family = "Masante", role = c("aut", "cre"), email = "dario.masante@gmail.com"))
Copyright: Natural Environment Research Council (NERC) and Centre for
Imports: raster, gRbase, gRain, doParallel, foreach
Description: Package for the spatial implementation of Bayesian Networks and mapping in geographical space. It makes maps of expected value (or most likely state) given known and unknown conditions, maps of uncertainty measured as coefficient of variation or Shannon index (entropy), maps of probability associated to any states of any node of the network. Some additional features are provided as well: parallel processing options, data discretization routines and function wrappers designed for users with minimal knowledge of the R language. Outputs can be exported to any common GIS format. Development was funded by the European Union FP7 (2007-2013), under project ROBIN (agreement 283093).