Title: Wrapper for the Duke Deploy REST API
Author: person("Niall", "McGearailt", role = c("aut", "cre"),email = "niall@dukeanalytics.com")
Description: A wrapper for the Duke Analytics model deployment API. See <http://www.dukeanalytics.com/products/dukedeployR.php> for more details.

Title: Dynamical Deterministic Effects Propagation Networks: Infer
signalling networks for timecourse RPPA data.
Imports: graph, Rgraphviz, KEGGgraph, RBGL
Description: Given a matrix of reverse-phase-protein-array timecourse
data, containing one or more experimental measurements for the
signalling patterns among proteins in cells, the package
attempts to model the time-dependent propagation of active and
passive states depending on a network. The most likely network
structure given the data is reconstructed.

Title: Dynamic Deterministic Effects Propagation Networks: Infer
KEGGgraph (>= 1.1.0), RBGL, genefilter, gam
Description: DDEPN (Dynamic Deterministic Effects Propagation
Networks): Infer signalling networks for timecourse data. Given
a matrix of high-throughput genomic or proteomic timecourse
data, generated after external perturbation of the biological
system, DDEPN models the time-dependent propagation of active
and passive states depending on a network structure. Optimal
network structures given the experimental data are
reconstructed. Two network inference algorithms can be used:
inhibMCMC, a Markov Chain Monte Carlo sampling approach and GA,
a Genetic Algorithm network optimisation. Inclusion of prior
biological knowledge can be done using different network prior

KEGGgraph (>= 1.1.0), RBGL, genefilter, gam, lattice, coda

KEGGgraph (>= 1.1.0), RBGL, genefilter, gam, lattice, coda,

Depends: R (>= 2.10.0), genefilter, gam, lattice, coda, gplots, graph,

Depends: R (>= 2.14), genefilter, gam, lattice, coda, gplots, graph,

Package: ddepn
Type: Package
Title: Dynamic Deterministic Effects Propagation Networks: Infer
signalling networks for timecourse RPPA data.
Version: 2.1
Date: 2011-06-27
Author: Christian Bender
Maintainer: Christian Bender <christian.bender@tron-mainz.de>
Depends: R (>= 2.10.0), genefilter, gam, lattice, coda, gplots, graph,
igraph, RBGL
Suggests: multicore, Rgraphviz
Description: DDEPN (Dynamic Deterministic Effects Propagation
Networks): Infer signalling networks for timecourse data. Given
a matrix of high-throughput genomic or proteomic timecourse
data, generated after external perturbation of the biological
system, DDEPN models the time-dependent propagation of active
and passive states depending on a network structure. Optimal
network structures given the experimental data are
reconstructed. Two network inference algorithms can be used:
inhibMCMC, a Markov Chain Monte Carlo sampling approach and GA,
a Genetic Algorithm network optimisation. Inclusion of prior
biological knowledge can be done using different network prior
models.
License: GPL (>= 2)
LazyLoad: TRUE
Packaged: 2012-04-16 06:41:35 UTC; benderc
Repository: CRAN
Date/Publication: 2012-04-16 07:19:56