Package: BDgraph 2.73
Reza Mohammadi
BDgraph: Bayesian Structure Learning in Graphical Models using Birth-Death MCMC
Advanced statistical tools for Bayesian structure learning in undirected graphical models, accommodating continuous, ordinal, discrete, count, and mixed data. It integrates recent advancements in Bayesian graphical models as presented in the literature, including the works of Mohammadi and Wit (2015) <doi:10.1214/14-BA889>, Mohammadi et al. (2021) <doi:10.1080/01621459.2021.1996377>, Dobra and Mohammadi (2018) <doi:10.1214/18-AOAS1164>, and Mohammadi et al. (2023) <doi:10.48550/arXiv.2307.00127>.
Authors:
BDgraph_2.73.tar.gz
BDgraph_2.73.tar.gz(r-4.5-noble)BDgraph_2.73.tar.gz(r-4.4-noble)
BDgraph_2.73.tgz(r-4.4-emscripten)BDgraph_2.73.tgz(r-4.3-emscripten)
BDgraph.pdf |BDgraph.html✨
BDgraph/json (API)
NEWS
| # Install 'BDgraph' in R: |
| install.packages('BDgraph', repos = 'https://cloud.r-project.org') |
- geneExpression - Human gene expression dataset
- reinis - Risk factors of coronary heart disease
- surveyData - Labor force survey data
Conda:r-bdgraph-2.73(2025-03-25)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 7 months agofrom:66de8c40e7. Checks:3 OK. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 22 2025 |
| R-4.5-linux-x86_64 | OK | Mar 22 2025 |
| R-4.4-linux-x86_64 | OK | Mar 22 2025 |
Exports:adj2linkaucbdgraphbdgraph.dwbdgraph.mplbdgraph.npnbdgraph.simbdw.regbfcompareconf.matconf.mat.plotcovarianceddweibullddweibull_regdetect_coresget_bounds_dwget_coresget_Ds_tgm_Rget_g_priorget_g_startget_graphget_K_startget_S_n_pget_Ts_Rgnormgraph.simlink2adjlog_post_cond_dwmsenear_positive_definitepdweibullpgraphplinksplot.bdgraphplot.graphplot.simplotcodaplotrocposterior.predictprecisionpredict.bdgraphprint.bdgraphprint.simqdweibullrdweibullrgwishrmvnormrocrwishselectsparsitysummary.bdgraphtraceplottransferupdate_mu_Rupdate_tu_R
Dependencies:clicolorspacecpp11fansifarverggplot2gluegtableigraphisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrpROCR6RColorBrewerRcpprlangscalestibbleutf8vctrsviridisLitewithr
Citation
The methods within the package can be cited as:
Reza Mohammadi, Ernst Wit (2019), BDgraph: An R Package for Bayesian Structure Learning in Graphical Models, Journal of Statistical Software, 89(3), 1-30,<doi:10.18637/jss.v089.i03>
This free open-source software implements academic research by the authors and co-workers. If you use it, please support the project by citing the appropriate journal articles.
Corresponding BibTeX entry:
@Article{Ernst Wit,
author = {Reza Mohammadi},
title = {{BDgraph}: An {R} Package for {B}ayesian Structure
Learning in Graphical Models},
journal = {Journal of Statistical Software},
year = {2019},
volume = {89},
number = {3},
pages = {1--30},
doi = {10.18637/jss.v089.i03},
url = {https://www.jstatsoft.org/article/view/v089i03},
}
Readme and manuals
BDgraph
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" )
Loading
library( BDgraph )
Simple Examples for BDgraph package
To see how to use the functionality of the package:
See also Mohammadi and Wit (2019).
