Package: ebdbNet 1.2.8
Andrea Rau
ebdbNet: Empirical Bayes Estimation of Dynamic Bayesian Networks
Infer the adjacency matrix of a network from time course data using an empirical Bayes estimation procedure based on Dynamic Bayesian Networks.
Authors:
ebdbNet_1.2.8.tar.gz
ebdbNet_1.2.8.tar.gz(r-4.5-noble)ebdbNet_1.2.8.tar.gz(r-4.4-noble)
ebdbNet_1.2.8.tgz(r-4.4-emscripten)ebdbNet_1.2.8.tgz(r-4.3-emscripten)
ebdbNet.pdf |ebdbNet.html✨
ebdbNet/json (API)
NEWS
| # Install 'ebdbNet' in R: |
| install.packages('ebdbNet', repos = 'https://cloud.r-project.org') |
Bug tracker:https://github.com/andreamrau/ebdbnet/issues0 issues
Last updated 2 years agofrom:e8a9534550. Checks:3 OK. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 09 2025 |
| R-4.5-linux-x86_64 | OK | Mar 09 2025 |
| R-4.4-linux-x86_64 | OK | Mar 09 2025 |
Exports:calcAUCcalcSensSpecdataFormatebdbnhankelplot.ebdbNetsimulateVAR
Dependencies:clicpp11glueigraphlatticelifecyclemagrittrMatrixpkgconfigrlangvctrs
Citation
To cite the R package 'ebdbNet' in publications use:
Rau A, Jaffrezic F, Foulley J, Doerge R (2010). “An empirical Bayesian method for estimating biological networks from temporal microarray data.” Statistical Applications in Genetics and Molecular Biology, 9(1).
Corresponding BibTeX entry:
@Article{,
title = {An empirical Bayesian method for estimating biological
networks from temporal microarray data},
journal = {Statistical Applications in Genetics and Molecular
Biology},
volume = {9},
number = {1},
year = {2010},
author = {Andrea Rau and Florence Jaffrezic and Jean-Louis Foulley
and Rebecca Doerge},
}
Readme and manuals
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.
Help Manual
| Help page | Topics |
|---|---|
| Empirical Bayes Dynamic Bayesian Network (EBDBN) Inference | ebdbNet-package ebdbNet |
| Calculate the Approximate Area Under the Curve (AUC) | calcAUC |
| Calculate Sensitivity and Specificity of a Network | calcSensSpec |
| Change the Format of Longitudinal Data to be Compatible with EBDBN | dataFormat |
| Empirical Bayes Dynamic Bayesian Network (EBDBN) Estimation | ebdbn |
| Internal functions for Empirical Bayes Dynamic Bayesian Network (EBDBN) Estimation | ebdbn-internal fdbkFunc sumFunc |
| Perform Singular Value Decomposition of Block-Hankel Matrix | hankel |
| Visualize EBDBN network | plot.ebdbNet |
| Simulate Simple Autoregressive Process | simulateVAR |
