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BGGM
View on CRAN: Click
here
Download and install BGGM package within the R console
Install from CRAN:
install.packages("BGGM")
Install from Github:
library("remotes")
install_github("cran/BGGM") Install by package version:
library("remotes")
install_version("BGGM", "2.2.0") Attach the package and use:
library("BGGM")
Maintained by
Philippe Rast
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-02-06
Latest Update: 2024-12-22
Description:
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) ,
Williams and Mulder (2019) ,
Williams, Rast, Pericchi, and Mulder (2019) .
How to cite:
Philippe Rast (2020). BGGM: Bayesian Gaussian Graphical Models. R package version 2.2.0, https://cran.r-project.org/web/packages/BGGM. Accessed 07 Oct. 2026.
Previous versions and publish date:
1.0.0 (2020-02-06 17:20), 2.0.0 (2020-05-31 23:10), 2.0.1 (2020-07-23 22:32), 2.0.2 (2020-07-24 21:12), 2.0.3 (2020-12-03 09:20), 2.0.4 (2021-08-20 17:50), 2.1.1 (2024-02-23 09:00), 2.1.2 (2024-06-22 08:20), 2.1.3 (2024-07-05 22:30), 2.1.4 (2024-12-13 20:20), 2.1.5 (2024-12-22 22:40), 2.1.6 (2025-12-02 08:40), (2026-09-24 09:20)
Other packages that cited BGGM R package
View BGGM citation profile
Other R packages that BGGM depends,
imports, suggests or enhances
Complete documentation for BGGM
Functions, R codes and Examples using
the BGGM R package
Some associated functions: BGGM-package . Sachs . asd_ocd . bfi . bggm_missing . coef.estimate . coef.explore . confirm . constrained_posterior . convergence . csws . depression_anxiety_t1 . depression_anxiety_t2 . estimate . explore . fisher_r_to_z . fisher_z_to_r . gen_ordinal . ggm_compare_confirm . ggm_compare_estimate . ggm_compare_explore . ggm_compare_ppc . gss . ifit . impute_data . iri . map . pcor_mat . pcor_sum . pcor_to_cor . plot.confirm . plot.ggm_compare_ppc . plot.pcor_sum . plot.predictability . plot.roll_your_own . plot.select . plot.summary.estimate . plot.summary.explore . plot.summary.ggm_compare_estimate . plot.summary.ggm_compare_explore . plot.summary.select.explore . plot.summary.var_estimate . plot_prior . posterior_predict . posterior_samples . precision . predict.estimate . predict.explore . predict.var_estimate . predictability . predicted_probability . print.BGGM . ptsd . ptsd_cor1 . ptsd_cor2 . ptsd_cor3 . ptsd_cor4 . regression_summary . roll_your_own . rsa . select.estimate . select.explore . select.ggm_compare_estimate . select.ggm_compare_explore . select . select.var_estimate . summary.coef . summary.estimate . summary.explore . summary.ggm_compare_estimate . summary.ggm_compare_explore . summary.predictability . summary.select.explore . summary.var_estimate . tas . var_estimate . weighted_adj_mat . women_math . zero_order_cors .
Some associated R codes: BGGM-package.R . RcppExports.R . bggm_missing.R . bma_posterior.R . coef.estimate.R . confirm.R . constrained_post.R . convergence.R . datasets.R . estimate.R . explore.default.R . fisher_r2z.R . fisher_z2r.R . gen_ordinal.R . ggm_compare_bf.default.R . ggm_compare_confirm.R . ggm_compare_estimate.default.R . ggm_compare_ppc.default.R . ggm_search.R . helpers.R . map.R . mvn_imputation.R . pcor_2_cor.BGGM.R . pcor_mat.R . plot.select.R . plot_prior.R . posterior_predict.R . posterior_samples.R . posterior_sum.R . precision.R . pred_prob.R . predict.estimate.R . predictability.R . print_BGGM.R . regression_summary.R . roll_your_own.R . select.VAR_estimate.R . select.estimate.R . select.explore.R . select.ggm_compare_bf.R . select.ggm_compare_estimate.R . var_estimate.R . weighted_adj_mat.R . zero_order.R . Full BGGM package functions and examples
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