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: | 1.0.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | mvnfast (≥ 0.2.5), foreach (≥ 1.4.7), doParallel (≥ 1.0.15), ggplot2 (≥ 3.2.1), mvtnorm (≥ 1.0.11), stringr (≥ 1.4.0), ggridges (≥ 0.5.1), GGally (≥ 1.4.0), pracma (≥ 2.2.5), network (≥ 1.15), bayesplot (≥ 1.7.1), sna (≥ 2.5), shiny (≥ 1.4.0), reshape2 (≥ 1.4.3), cowplot (≥ 1.0.0), stats, parallel, Matrix, reshape, MASS |
| Suggests: | knitr, rmarkdown, dplyr |
| Published: | 2020-02-06 |
| Author: | Donald Williams [aut, cre], Joris Mulder [aut] |
| Maintainer: | Donald Williams <drwwilliams at ucdavis.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | README |
| In views: | Psychometrics |
| CRAN checks: | BGGM results |
| Reference manual: | BGGM.pdf |
| Vignettes: |
Credible Intervals Plotting the Network Structure Comparing GGMs with the Posterior Predicive Distributions Predictability: Part One |
| Package source: | BGGM_1.0.0.tar.gz |
| Windows binaries: | r-devel: BGGM_1.0.0.zip, r-devel-gcc8: BGGM_1.0.0.zip, r-release: BGGM_1.0.0.zip, r-oldrel: BGGM_1.0.0.zip |
| OS X binaries: | r-release: BGGM_1.0.0.tgz, r-oldrel: BGGM_1.0.0.tgz |
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