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BGGM (version 2.0.0)

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

12,621

Version

2.0.0

License

GPL-2

Maintainer

Donald Williams

Last Published

May 31st, 2020

Functions in BGGM (2.0.0)

Compute Regression Parameters for estimate Objects

Data: Autism and Obssesive Compulsive Disorder

Compute Regression Parameters for explore Objects

BGGM: Bayesian Gaussian Graphical Models

MCMC Convergence

GGM: Missing Data

GGM: Confirmatory Hypothesis Testing

Data: 25 Personality items representing 5 factors

Generate Ordinal and Binary data

depression_anxiety_t1

Data: Depression and Anxiety (Time 1)

Data: Sachs Network

Extract the Partial Correlation Matrix

Partial Correlation Sum

Data: Contingencies of Self-Worth Scale (CSWS)

GGM: Estimation

Compute Correlations from the Partial Correlations

GGM: Exploratory Hypothesis Testing

Plot confirm objects

plot.summary.ggm_compare_explore

Plot summary.ggm_compare_explore Objects

depression_anxiety_t2

Data: Depression and Anxiety (Time 2)

ggm_compare_ppc

GGM Compare: Posterior Predictive Check

ggm_compare_confirm

GGM Compare: Confirmatory Hypothesis Testing

Fisher Z Transformation

plot.summary.select.explore

Plot summary.select.explore Objects

Network Plot for select Objects

ggm_compare_estimate

GGM Compare: Estimate

Data: Post-Traumatic Stress Disorder

plot.summary.estimate

Plot summary.estimate Objects

select.ggm_compare_explore

Graph selection for ggm_compare_explore Objects

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

Data: Women and Mathematics

Maximum A Posteriori Precision Matrix

Fisher Z Back Transformation

Data: Interpersonal Reactivity Index (IRI)

plot.predictability

Plot predictability Objects

ggm_compare_explore

GGM Compare: Exploratory Hypothesis Testing

predict.explore

Model Predictions for explore Objects

predict.estimate

Model Predictions for estimate Objects

Compute Custom Network Statistics

summary.ggm_compare_estimate

Summary method for ggm_compare_estimate objects

Data: Resilience Scale of Adults (RSA)

plot.ggm_compare_ppc

Plot ggm_compare_ppc Objects

Plot pcor_sum Object

plot.roll_your_own

Plot roll_your_own Objects

Print method for BGGM objects

plot.summary.ggm_compare_estimate

Plot summary.ggm_compare_estimate Objects

summary.estimate

Summary method for estimate.default objects

plot.summary.explore

Plot summary.explore Objects

summary.explore

Summary Method for explore.default Objects

Predictability: Bayesian Variance Explained (R2)

zero_order_cors

Zero-Order Correlations

Data: 1994 General Social Survey

Summarize coef Objects

summary.predictability

Summary Method for predictability Objects

summary.ggm_compare_explore

Summary Method for ggm_compare_explore Objects

summary.select.explore

Summary Method for select.explore Objects

Plot: Prior Distribution

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

Graph selection for explore Objects

select.ggm_compare_estimate

Graph Selection for ggm_compare_estimate Objects

posterior_samples

Extract Posterior Samples

Data: Toronto Alexithymia Scale (TAS)

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

weighted_adj_mat

Extract the Weighted Adjacency Matrix

regression_summary

Summarary Method for Multivariate or Univarate Regression

S3 select method

select.estimate

Graph Selection for estimate Objects

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