GeneTS provides methods for analyzing gene expression
(time series) data. Examples are the analysis of cell cycle data
and methods for inferring large sparse graphical Gaussian models
to identify gene association networks. In addition, GeneTS offers
data structures and functions for longitudinal data with multiple
variables, repeated measurements, and irregularly spaced time points.
Other functions in GeneTS include fast SVD, density estimation
via poisson regression, large-scale covariance resp. correlation
matrix estimation via shrinkage etc.   For plotting the gene networks
GeneTS uses the "graph" and "Rgraphviz" packages, available from
http://www.bioconductor.org.

Miika Ahdesmaki <miika.ahdesmaki@tut.fi>,
Konstantinos Fokianos <fokianos@ucy.ac.cy>,
Juliane Schaefer <schaefer@stat.uni-muenchen.de>, and
Korbinian Strimmer <korbinian.strimmer@lmu.de>.
GeneTS is a package for analyzing gene expression
(time series) data. Currently, two kinds of analyzes are supported:
the idenfication of periodic genes, and the inference of
large-scale gene association networks.  In order to recover
networks GeneTS implements a strategy for fast model selection
of graphical Gaussian models (GGMs) via local fdr multiple testing.
In addition, GeneTS offers general data structures and functions for
longitudinal data with multiple variables, repeated measurements,
and irregularly spaced time points. For plotting the gene networks
GeneTS uses the "graph" and "Rgraphviz" packages, available from
http://www.bioconductor.org.

Simulation and analysis of spatial structure of population
genetics data
Sophie Ancelet <Sophie.Ancelet@orange.fr> , Gilles Guillot
<gigu@imm.dtu.dk>

MCMC inference from individual genetic data based on a spatial statistical model
Implementation of models described in Guillot et al.,  Genetics,  2004

Implementation of models described in Guillot et al.,  Genetics,  2005

Detection of spatial structure from genetic data. Model and MCMC inference as described in [Guillot, Estoup, Mortier, Cosson, Genetics, 2005]