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BGGM (version 2.2.0)

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

745

Version

2.2.0

License

GPL-2

Maintainer

Philippe Rast

Last Published

September 24th, 2026

Functions in BGGM (2.2.0)

depression_anxiety_t2

Data: Depression and Anxiety (Time 2)

Data: Interpersonal Reactivity Index (IRI)

Data: 1994 General Social Survey

Perform Bayesian Graph Search and Optional Model Averaging

ggm_compare_ppc

GGM Compare: Posterior Predictive Check

ggm_compare_explore

GGM Compare: Exploratory Hypothesis Testing

Plot pcor_sum Object

plot.ggm_compare_ppc

Plot ggm_compare_ppc Objects

Plot confirm objects

ggm_compare_confirm

GGM Compare: Confirmatory Hypothesis Testing

Data: ifit Intensive Longitudinal Data

Extract the Partial Correlation Matrix

Obtain Imputed Datasets

Partial Correlation Sum

plot.summary.explore

Plot summary.explore Objects

ggm_compare_estimate

GGM Compare: Estimate

Compute Correlations from the Partial Correlations

plot.roll_your_own

Plot roll_your_own Objects

predict.estimate

Model Predictions for estimate Objects

plot.predictability

Plot predictability Objects

Precision Matrix Posterior Distribution

plot.summary.ggm_compare_estimate

Plot summary.ggm_compare_estimate Objects

posterior_samples

Extract Posterior Samples

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

posterior_predict

Posterior Predictive Distribution

predicted_probability

Predicted Probabilities

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

Predictability: Bayesian Variance Explained (R2)

predict.explore

Model Predictions for explore Objects

regression_summary

Summarary Method for Multivariate or Univarate Regression

Compute Custom Network Statistics

plot.summary.ggm_compare_explore

Plot summary.ggm_compare_explore Objects

predict.var_estimate

Model Predictions for var_estimate Objects

plot.summary.select.explore

Plot summary.select.explore Objects

summary.predictability

Summary Method for predictability Objects

summary.select.explore

Summary Method for select.explore Objects

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

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

Maximum A Posteriori Precision Matrix

plot.summary.estimate

Plot summary.estimate Objects

prior_belief_var

Prior Belief Graphical VAR

plot.summary.var_estimate

Plot summary.var_estimate Objects

Network Plot for select Objects

Plot: Prior Distribution

summary.var_estimate

Summary Method for var_estimate Objects

S3 select method

Data: Toronto Alexithymia Scale (TAS)

Data: Resilience Scale of Adults (RSA)

select.estimate

Graph Selection for estimate Objects

Data: Post-Traumatic Stress Disorder

Print method for BGGM objects

summary.explore

Summary Method for explore.default Objects

summary.estimate

Summary method for estimate.default objects

prior_belief_ggm

Prior Belief Gaussian Graphical Model

Graph selection for explore Objects

select.var_estimate

Graph Selection for var.estimate Object

Summarize coef Objects

select.ggm_compare_estimate

Graph Selection for ggm_compare_estimate Objects

summary.ggm_compare_estimate

Summary method for ggm_compare_estimate objects

select.ggm_compare_explore

Graph selection for ggm_compare_explore Objects

VAR: Estimation

weighted_adj_mat

Extract the Weighted Adjacency Matrix

summary.ggm_compare_explore

Summary Method for ggm_compare_explore Objects

Data: Women and Mathematics

zero_order_cors

Zero-Order Correlations

constrained_posterior

Constrained Posterior Distribution

Data: Sachs Network

Compute Regression Parameters for explore Objects

GGM: Missing Data

GGM: Confirmatory Hypothesis Testing

Compute Regression Parameters for estimate Objects

Compute Posterior Distributions from Graph Search Results

Fisher Z Back Transformation

GGM: Exploratory Hypothesis Testing

Fisher Z Transformation

MCMC Convergence

Simulate a Partial Correlation Matrix

GGM: Estimation

Generate Ordinal and Binary data

BGGM: Bayesian Gaussian Graphical Models

Data: Autism and Obssesive Compulsive Disorder

Data: 25 Personality items representing 5 factors

depression_anxiety_t1

Data: Depression and Anxiety (Time 1)

Data: Contingencies of Self-Worth Scale (CSWS)