\HeaderA{Geneland-package}{Simulation and inference of a 
spatial statistical model for landscape genetics.}{Geneland.Rdash.package}
\aliasA{Geneland}{Geneland-package}{Geneland}
\begin{Description}\relax
The main function \code{\LinkA{mcmcFmodel}{mcmcFmodel}} 
takes geo-referenced individual multilocus genetic data and tries to detect
population structure, i.e sub-populations making use of both spatial and genetic information.
\end{Description}
\begin{Details}\relax
\Tabular{ll}{
Package: & Geneland\\
Type: & Package\\
Version: & 1.0.5\\
Date: & 2006-01-10\\
License: & GPL\\
}

The overall population is viewed as a co-existence of sub-populations at 
Hardy-Weiberg equilibrium. The sub-populations are supposed to be spatially 
organised through the so-called 
colored Poisson-Voronoi tessellation. 
Allele frequencies are assumed to be drawn from the F-model 
as described in (Falush 2003) although 
the particular case of independent Dirichlet allele frequencies, 
as described by Pritchard (2000),  is also handled. 
Individuals within populations are assumed to be randomly 
located and  Hardy-Weinberg equilibrium 
and linkage equilibrium are assumed.

The main purpose of the program is to perform Bayesian inference of all the parameters involved 
through Markov Chain Monte-Carlo simulation.
This is achievied by the function   \code{\LinkA{mcmcFmodel}{mcmcFmodel}}.
Function \code{\LinkA{PostProcessChain}{PostProcessChain}} read some output files of
\code{\LinkA{mcmcFmodel}{mcmcFmodel}}
and computes some statistics suitable to print maps of inferred
populations.

See \code{Storage format} section in \code{\LinkA{mcmcFmodel}{mcmcFmodel} help page.}

The following functions are provided by the package:


\code{\LinkA{simFmodel}{simFmodel}}: simulation from the prior of the spatial
F-model

\code{\LinkA{mcmcFmodel}{mcmcFmodel}}: Full Bayesian Markov Chain Monte Carlo
inference of parameters in the spatial F-model

\code{\LinkA{PostProcessChain}{PostProcessChain}}: Post-procesing of MCMC output
for maps of posterior probability of populations subdomains

\code{\LinkA{PlotTessellation}{PlotTessellation}}: Graphical display of inferred
sub-domains

The following  functions are very basic and are only intended to be an
aid for those not familiar with R. Most probably you may want to use
directly the output files of \code{\LinkA{mcmcFmodel}{mcmcFmodel}} and
\code{\LinkA{PostProcessChain}{PostProcessChain}} to print your own figures.

\code{\LinkA{PlotDrift}{PlotDrift}}: Graphical display of drift factors along MCMC
run

\code{\LinkA{PlotFreqA}{PlotFreqA}}: Graphical display of allele frequencies in the
ancestral population along MCMC run

\code{\LinkA{PlotFreq}{PlotFreq}}: Graphical display of allele frequencies in the
present time population along MCMC run

\code{\LinkA{Plotnpop}{Plotnpop}}: Graphical display of number of populations
along MCMC run

\code{\LinkA{Plotntile}{Plotntile}}: Graphical display of number of tiles along
MCMC run

\code{\LinkA{PosteriorMode}{PosteriorMode}}: Computation and/or graphical display of
mode in the posterior distribution of class membership at each pixel

\code{\LinkA{Fstat}{Fstat}}: Computations of pairwise F statistics between
inferred subpopulations 

\code{\LinkA{FormatGenotypes}{FormatGenotypes}}: Internal function. 
Transform a file of genotypes into
a format suitable for function \code{\LinkA{mcmcFmodel}{mcmcFmodel}}

\code{\LinkA{setplot}{setplot}}: Internal function
\end{Details}
\begin{Author}\relax
Gilles Guillot

\url{www.inapg.inra.fr/ens_rech/mathinfo/personnel/guillot/welcome.html}

See also

\url{www.inapg.inra.fr/ens_rech/mathinfo/personnel/guillot/Geneland.html}
\end{Author}
\begin{References}\relax
On the implementation of mixture models in population genetics:

- J.K. Pritchard, M. Stephens and P. Donnelly,
Inference of population structure using multilocus genotype data,
Genetics, pp 945-959 vol. 155, 2000

- Falush D., M. Stephens and  J.K. Pritchard, 
Inference of population structure using multilocus genotype data:
Linked loci and correlated allele frequencies, Genetics, pp 1567-1587,
vol 164, 2003

On the implementation of variable dimension MCMC algorihtm in
population genetics:

- Corander, J.C., Waldmann, P. and Sillanpaa, M.J.,
Bayesian analysis of genetic differentiation between populations,
Genetics, 2003, 163, 367-374

- Corander, J.C.,  P. Waldmann, P. Martinen and  M.J. Sillanpaa, ,
BAPS2: Enhanced possibilities for the analysis of genetic population structure,
Bioinformatics, vol. 20,number 15, 2004

On the use of Voronoi tessellations in population genetics :

- Dupanloup, I., Schneider, S. and Excoffier, L.,
A simulated annealing approach to define genetic structure of populations,
Molecular Ecology, 2002, 11, 2571-2581

On the model (and sub-models) implemented in \code{Geneland}

- G. Guillot, Estoup, A., Mortier, F. Cosson, J.F. A spatial statistical
model for landscape genetics. Genetics, 170, 1261-1280, 2005.


- G. Guillot, Mortier, F., Estoup, A. Geneland : A program for landscape
genetics. Molecular Ecology Notes, 5, 712-715, 2005.
\end{References}
\begin{SeeAlso}\relax
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}\end{ExampleCode}
\end{Examples}

