Fit Bayesian Gaussian graphical models. The methods are separated into two Bayesian approaches for inference: hypothesis testing and estimation. There are extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, and node wise predictability. These methods were recently introduced in the Gaussian graphical model literature, including Williams (2019) <doi:10.31234/osf.io/x8dpr>, Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>, Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.
Version: 2.1.5 Depends: R (≥ 4.0.0) Imports: BFpack (≥ 1.2.3), GGally (≥ 1.4.0), ggplot2 (≥ 3.2.1), ggridges (≥ 0.5.1), grDevices, MASS (≥ 7.3-51.5), methods, mvnfast (≥ 0.2.5), network (≥ 1.15), reshape (≥ 0.8.8), Rcpp (≥ 1.0.4.6), Rdpack (≥ 0.11-1), sna (≥ 2.5), stats, utils LinkingTo: Rcpp, RcppArmadillo, RcppDist, RcppProgress Suggests: abind (≥ 1.4-5), assortnet (≥ 0.12), networktools (≥ 1.3.0), mice (≥ 3.8.0), psych, knitr, rmarkdown, testthat (≥ 3.0.0) Published: 2024-12-22 DOI: 10.32614/CRAN.package.BGGM Author: Donald Williams [aut], Joris Mulder [aut], Philippe Rast [aut, cre] Maintainer: Philippe Rast <rast.ph at gmail.com> BugReports: https://github.com/donaldRwilliams/BGGM/issues License: GPL-2 URL: https://donaldrwilliams.github.io/BGGM/ NeedsCompilation: yes Citation: BGGM citation info Materials: NEWS CRAN checks: BGGM resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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