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easybgm: Extracting and Visualizing Bayesian Graphical Models

Fit and visualize the results of a Bayesian analysis of networks commonly found in psychology. The package supports fitting cross-sectional network models fitted using the packages 'BDgraph', 'bgms' and 'BGGM'. The package provides the parameter estimates, posterior inclusion probabilities, inclusion Bayes factor, and the posterior density of the parameters. In addition, for 'BDgraph' and 'bgms' it allows to assess the posterior structure space. Furthermore, the package comes with an extensive suite for visualizing results.

Version: 0.1.2
Imports: BDgraph, BGGM, bgms (≥ 0.1.3), dplyr, ggplot2, HDInterval, igraph, qgraph
Suggests: testthat (≥ 3.0.0), vdiffr
Published: 2024-03-13
Author: Karoline Huth ORCID iD [aut, cre], Sara Keetelaar [ctb]
Maintainer: Karoline Huth <k.huth at uva.nl>
BugReports: https://github.com/KarolineHuth/easybgm/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/KarolineHuth/easybgm
NeedsCompilation: no
Materials: README
CRAN checks: easybgm results

Documentation:

Reference manual: easybgm.pdf

Downloads:

Package source: easybgm_0.1.2.tar.gz
Windows binaries: r-devel: easybgm_0.1.2.zip, r-release: easybgm_0.1.2.zip, r-oldrel: easybgm_0.1.2.zip
macOS binaries: r-release (arm64): easybgm_0.1.2.tgz, r-oldrel (arm64): easybgm_0.1.2.tgz, r-release (x86_64): easybgm_0.1.2.tgz, r-oldrel (x86_64): easybgm_0.1.2.tgz
Old sources: easybgm archive

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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