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BANFF (version 1.1)

Bayesian Network Feature Finder

Description

Provides a full package of posterior inference, model comparison, and graphical illustration of model fitting. A parallel computing algorithm for the Markov chain Monte Carlo (MCMC) based posterior inference and an Expectation-Maximization (EM) based algorithm for posterior approximation are are developed, both of which greatly reduce the computational time for model inference.

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Version

Install

install.packages('BANFF')

Monthly Downloads

4

Version

1.1

License

GPL-2

Maintainer

Zhou Lan

Last Published

July 12th, 2016

Functions in BANFF (1.1)

Grid.Adjmatrix.Transfer

Connector for Transferring Grid Coordinates into Adjacency Matrix
DPM.HODC

Hierarchical Ordered Density Clustering (HODC) for Dirichlet Process Mixture Model Fitting
Networks.STD

Bayesian Network Discovery using a Standard MCMC Algorithm
plot.Networks.STD

Plotting Bayesian Network Discovery using a Standard MCMC Algorithm
Plot.Subnetwork

Plotting the Network Feature Selected
HyperPara.Select

Selecting Hyper Parameters by Bayesian Model Averaging
LikelihoodHistory

Calculating and Plot the history of log-Likelihood value
Networks.Fast

Bayesian Network Discovery using a Hybrid Fast Algorithm
plot.Networks.Fast

Plotting Bayesian Network Discovery using a Hybrid Fast Algorithm
EM.HODC

Hierarchical Ordered Density Clustering (HODC) for Finite Mixture Model Fitting
summary.Networks.STD

Summarizing Bayesian Network Discovery using a Standard MCMC Algorithm
SummaryAccuracy

Summary of the Accuracy of Node and Network selection
Subnetwork.Select

Summarize the information of the sub networks selected by Network.Fast() and Network.STD()
summary.Networks.Fast

Summarizing Bayesian Network Discovery using a Hybrid Fast Algorithm