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ebdbNet: Empirical Bayes Estimation of Dynamic Bayesian Networks

Author: Andrea Rau

This package is used to infer the adjacency matrix of a network from time course data using an empirical Bayes estimation procedure based on Dynamic Bayesian Networks.

Posterior distributions (mean and variance) of network parameters are estimated using time-course data based on a linear feedback state space model that allows for a set of hidden states to be in- corporated. The algorithm is composed of three principal parts: choice of hidden state dimension (see hankel), estimation of hidden states via the Kalman filter and smoother, and calculation of posterior distributions based on the empirical Bayes estimation of hyperparameters in a hierarchical Bayesian framework (see ebdbn).

Plot functionalities are provided via the igraph package.

Reference

A. Rau, F. Jaffrezic, J.-L. Foulley, R. W. Doerge (2010). An empirical Bayesian method for estimating biological networks from temporal microarray data. Statistical Applications in Genetics and Molecular Biology, vol. 9, iss. 1, article 9.

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install.packages('ebdbNet')

Monthly Downloads

708

Version

1.2.8

License

GPL (>= 3)

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Last Published

September 4th, 2023

Functions in ebdbNet (1.2.8)

ebdbn-internal

Internal functions for Empirical Bayes Dynamic Bayesian Network (EBDBN) Estimation
calcSensSpec

Calculate Sensitivity and Specificity of a Network
hankel

Perform Singular Value Decomposition of Block-Hankel Matrix
ebdbNet-package

Empirical Bayes Dynamic Bayesian Network (EBDBN) Inference
dataFormat

Change the Format of Longitudinal Data to be Compatible with EBDBN
calcAUC

Calculate the Approximate Area Under the Curve (AUC)
plot.ebdbNet

Visualize EBDBN network
simulateVAR

Simulate Simple Autoregressive Process
ebdbn

Empirical Bayes Dynamic Bayesian Network (EBDBN) Estimation