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BGGM (version 2.1.6)

Bayesian Gaussian Graphical Models

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) .

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Version

Install

install.packages('BGGM')

Monthly Downloads

930

Version

2.1.6

License

GPL-2

Maintainer

Philippe Rast

Last Published

December 2nd, 2025

Functions in BGGM (2.1.6)

coef.explore

Compute Regression Parameters for explore Objects
asd_ocd

Data: Autism and Obssesive Compulsive Disorder
confirm

GGM: Confirmatory Hypothesis Testing
bma_posterior

Compute Posterior Distributions from Graph Search Results
Sachs

Data: Sachs Network
gen_net

Simulate a Partial Correlation Matrix
csws

Data: Contingencies of Self-Worth Scale (CSWS)
fisher_r_to_z

Fisher Z Transformation
gen_ordinal

Generate Ordinal and Binary data
convergence

MCMC Convergence
fisher_z_to_r

Fisher Z Back Transformation
explore

GGM: Exploratory Hypothesis Testing
estimate

GGM: Estimation
depression_anxiety_t1

Data: Depression and Anxiety (Time 1)
depression_anxiety_t2

Data: Depression and Anxiety (Time 2)
map

Maximum A Posteriori Precision Matrix
ggm_search

Perform Bayesian Graph Search and Optional Model Averaging
ggm_compare_explore

GGM Compare: Exploratory Hypothesis Testing
ifit

Data: ifit Intensive Longitudinal Data
iri

Data: Interpersonal Reactivity Index (IRI)
gss

Data: 1994 General Social Survey
impute_data

Obtain Imputed Datasets
plot.predictability

Plot predictability Objects
plot.select

Network Plot for select Objects
plot.summary.estimate

Plot summary.estimate Objects
plot.roll_your_own

Plot roll_your_own Objects
ggm_compare_confirm

GGM Compare: Confirmatory Hypothesis Testing
pcor_mat

Extract the Partial Correlation Matrix
pcor_sum

Partial Correlation Sum
ggm_compare_estimate

GGM Compare: Estimate
plot.summary.ggm_compare_estimate

Plot summary.ggm_compare_estimate Objects
plot.summary.explore

Plot summary.explore Objects
precision

Precision Matrix Posterior Distribution
predict.estimate

Model Predictions for estimate Objects
plot.pcor_sum

Plot pcor_sum Object
plot.ggm_compare_ppc

Plot ggm_compare_ppc Objects
ggm_compare_ppc

GGM Compare: Posterior Predictive Check
predictability

Predictability: Bayesian Variance Explained (R2)
ptsd_cor1

Data: Post-Traumatic Stress Disorder (Sample # 1)
plot.confirm

Plot confirm objects
plot.summary.var_estimate

Plot summary.var_estimate Objects
pcor_to_cor

Compute Correlations from the Partial Correlations
plot.summary.ggm_compare_explore

Plot summary.ggm_compare_explore Objects
plot_prior

Plot: Prior Distribution
ptsd_cor2

Data: Post-Traumatic Stress Disorder (Sample # 2)
predicted_probability

Predicted Probabilities
plot.summary.select.explore

Plot summary.select.explore Objects
print.BGGM

Print method for BGGM objects
prior_belief_ggm

Prior Belief Gaussian Graphical Model
posterior_predict

Posterior Predictive Distribution
posterior_samples

Extract Posterior Samples
prior_belief_var

Prior Belief Graphical VAR
ptsd_cor3

Data: Post-Traumatic Stress Disorder (Sample # 3)
ptsd

Data: Post-Traumatic Stress Disorder
select.ggm_compare_explore

Graph selection for ggm_compare_explore Objects
select.ggm_compare_estimate

Graph Selection for ggm_compare_estimate Objects
predict.var_estimate

Model Predictions for var_estimate Objects
predict.explore

Model Predictions for explore Objects
summary.ggm_compare_estimate

Summary method for ggm_compare_estimate objects
summary.ggm_compare_explore

Summary Method for ggm_compare_explore Objects
select.explore

Graph selection for explore Objects
select.estimate

Graph Selection for estimate Objects
select.var_estimate

Graph Selection for var.estimate Object
summary.predictability

Summary Method for predictability Objects
regression_summary

Summarary Method for Multivariate or Univarate Regression
roll_your_own

Compute Custom Network Statistics
zero_order_cors

Zero-Order Correlations
women_math

Data: Women and Mathematics
summary.coef

Summarize coef Objects
tas

Data: Toronto Alexithymia Scale (TAS)
summary.var_estimate

Summary Method for var_estimate Objects
summary.select.explore

Summary Method for select.explore Objects
ptsd_cor4

Data: Post-Traumatic Stress Disorder (Sample # 4)
weighted_adj_mat

Extract the Weighted Adjacency Matrix
var_estimate

VAR: Estimation
select

S3 select method
rsa

Data: Resilience Scale of Adults (RSA)
summary.explore

Summary Method for explore.default Objects
summary.estimate

Summary method for estimate.default objects
constrained_posterior

Constrained Posterior Distribution
bggm_missing

GGM: Missing Data
coef.estimate

Compute Regression Parameters for estimate Objects
bfi

Data: 25 Personality items representing 5 factors
BGGM-package

BGGM: Bayesian Gaussian Graphical Models