Genetic Time Series and Graphical Models
GeneTS is a package for analysing multiple gene expression
time series data. Currently, GeneTS implements methods for cell cycle
analysis and for inferring large sparse graphical Gaussian models.
For plotting the inferred genetic networks GeneTS requires the
"graph" and "Rgraphviz" packages (available from www.bioconductor.org).

Microarray Time Series and Network Analysis
Miika Ahdesmaki, Konstantinos Fokianos,
Rainer Opgen-Rhein, Juliane Schaefer, and
Korbinian Strimmer.
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.
For plotting the gene networks
GeneTS uses the "graph" and "Rgraphviz" packages, available from
http://www.bioconductor.org.

With the present version, GeneTS has become a meta-package that
exists mainly for reasons of backward compatibility. Its only purpose
is to load the GeneCycle and GeneNet packages.You may load
either of these packages directly.

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 analysing multiple gene expression
time series data. Currently, GeneTS implements methods for cell cycle
analysis and for inferring large sparse graphical Gaussian models.
For plotting the inferred gene association networks GeneTS requires the
"graph" and "Rgraphviz" packages (available from www.bioconductor.org).

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. GeneTS offers data structures
and functions for longitudinal data with multiple variables,
repeated measurements. The multiple time series data may also be
irregularily spaced. Other functions in GeneTS include fast svd,
density estimation, large-scale covariance resp. correlation
estimation etc.  For plotting the gene networks the
optional "graph" and "Rgraphviz" packages are required (available
from www.bioconductor.org).