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BDgraph (version 2.43)

Bayesian Structure Learning in Graphical Models using Birth-Death MCMC

Description

Provides statistical tools for Bayesian structure learning in undirected graphical models for continuous, discrete, and mixed data. The package is implemented the recent improvements in the Bayesian graphical models literature, including Mohammadi and Wit (2015) and Mohammadi et al. (2017) . To speed up the computations, the BDMCMC sampling algorithms are implemented in parallel using OpenMP in C++.

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Version

Install

install.packages('BDgraph')

Monthly Downloads

2,168

Version

2.43

License

GPL (>= 2)

Maintainer

Abdolreza Mohammadi

Last Published

November 18th, 2017

Functions in BDgraph (2.43)

gnorm

Normalizing constant for G-Wishart
BDgraph-internal

Internal BDgraph functions and datasets
geneExpression

Human gene expression dataset
pgraph

Posterior probabilities of the graphs
bdgraph

Search algorithm in graphical models
bdgraph.sim

Graph data simulation
bdgraph.mpl

Search algorithm in graphical models using marginal pseudo-likehlihood
BDgraph-package

Bayesian Structure Learning in Graphical Models
compare

Graph structure comparison
bdgraph.npn

Nonparametric transfer
plinks

Estimated posterior link probabilities
print.bdgraph

Print function for S3 class "bdgraph"
plot.bdgraph

Plot function for S3 class "bdgraph"
rwish

Sampling from Wishart distribution
reinis

Risk factors of coronary heart disease
print.sim

Print function for S3 class "sim"
surveyData

Labor force survey data
plot.sim

Plot function for S3 class "sim"
traceplot

Trace plot of graph size
transfer

transfer for discrete data
plotcoda

Convergence plot
rgwish

Sampling from G-Wishart distribution
plotroc

ROC plot
select

Graph selection
summary.bdgraph

Summary function for S3 class "bdgraph"