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BDgraph

Overview

The R package BDgraph provides statistical tools for Bayesian structure learning for undirected graphical models with continuous, count, binary, and mixed data. The package is implemented the recent improvements in the Bayesian graphical models' literature, including Mohammadi and Wit (2015), Mohammadi et al. (2021), Mohammadi et al. (2017), and Dobra and Mohammadi (2018). Besides, the package contains several functions for simulation and visualization, as well as several multivariate datasets taken from the literature. To speed up the computations, the computationally intensive tasks of the package are implemented in C++ in parallel using OpenMP.

Installation

You can install the latest version from CRAN using:

install.packages( "BDgraph" )

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library( BDgraph )

Simple Examples for BDgraph package

To see how to use the functionality of the package:

See also Mohammadi and Wit (2019).

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Version

Install

install.packages('BDgraph')

Monthly Downloads

1,822

Version

2.72

License

GPL (>= 2)

Maintainer

Abdolreza Mohammadi

Last Published

December 25th, 2022

Functions in BDgraph (2.72)

bdgraph

Search algorithm in graphical models
BDgraph-package

Bayesian Structure Learning in Graphical Models
bf

Bayes factor for two graphs
bdgraph.sim

Graph data simulation
bdgraph.dw

Search algorithm for Gaussian copula graphical models for count data
adj2link

Extract links from an adjacency matrix
bdgraph.npn

Nonparametric transfer
bdw.reg

Bayesian estimation of (zero-inflated) Discrete Weibull regression
conf.mat.plot

Plot Confusion Matrix
plotcoda

Convergence plot
compare

Graph structure comparison
auc

Compute the area under the ROC curve
pgraph

Posterior probabilities of the graphs
link2adj

Extract links from an adjacency matrix
bdgraph.mpl

Search algorithm in graphical models using marginal pseudo-likehlihood
plotroc

ROC plot
conf.mat

Confusion Matrix
gnorm

Normalizing constant for G-Wishart
graph.sim

Graph simulation
plinks

Estimated posterior link probabilities
Discrete Weibull

The Discrete Weibull Distribution (Type 1)
summary.bdgraph

Summary function for S3 class "bdgraph"
plot.bdgraph

Plot function for S3 class "bdgraph"
geneExpression

Human gene expression dataset
select

Graph selection
sparsity

Compute the sparsity of a graph
roc

Build a ROC curve
plot.graph

Plot function for S3 class "graph"
surveyData

Labor force survey data
rwish

Sampling from Wishart distribution
covariance

Estimated covariance matrix
predict.bdgraph

Predict function for S3 class "bdgraph"
reinis

Risk factors of coronary heart disease
plot.sim

Plot function for S3 class "sim"
print.sim

Print function for S3 class "sim"
traceplot

Trace plot of graph size
print.bdgraph

Print function for S3 class "bdgraph"
posterior.predict

Posterior Predictive Samples
precision

Estimated precision matrix
transfer

transfer for count data
rgwish

Sampling from G-Wishart distribution
rmvnorm

Generate data from the multivariate Normal distribution