EDISON-package {EDISON}

Allows for network reconstruction and changepoint detection.

Description

This package runs an MCMC simulation to reconstruct networks from time series data, using a non-homogeneous, time-varying dynamic Bayesian network. Networks segments and changepoints are inferred concurrently, and information sharing priors provide a reduction of the inference uncertainty.

Details

Package:	EDISON
Type:	Package
Version:	1.1.1
Date:	2016-03-30
License:	GPL-2
LazyLoad:	yes

Author(s)

Frank Dondelinger, Sophie Lebre

Maintainer: Frank Dondelinger <[e-mail]>

References

Dondelinger et al. (2012), "Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure", Machine Learning.

Husmeier et al. (2010), "Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks", NIPS.

See Also

corpcor

Examples

# Generate random gene network and simulate data from it
dataset = simulateNetwork(l=25)

# Run MCMC simulation to infer networks and changepoint locations
result = EDISON.run(dataset$sim_data, num.iter=500)

# Calculate posterior probabilities of changepoints
cps = calculateCPProbabilities(result)

# Calculate marginal posterior probabilities of edges in the network
network = calculateEdgeProbabilities(result)