Bayesian Gaussian Graphical Models
Obtain Imputed Datasets
Maximum A Posteriori Precision Matrix
Plot pcor_sum Object
Summary Method for explore.default Objects
Extract the Weighted Adjacency Matrix
Zero-Order Correlations
GGM: Missing Data
BGGM: Bayesian Gaussian Graphical Models
Compute Regression Parameters for estimate Objects
Compute Regression Parameters for explore Objects
GGM: Confirmatory Hypothesis Testing
Constrained Posterior Distribution
MCMC Convergence
GGM: Estimation
GGM: Exploratory Hypothesis Testing
Fisher Z Transformation
Fisher Z Back Transformation
Simulate a Partial Correlation Matrix
Generate Ordinal and Binary data
GGM Compare: Confirmatory Hypothesis Testing
GGM Compare: Estimate
GGM Compare: Exploratory Hypothesis Testing
GGM Compare: Posterior Predictive Check
Extract the Partial Correlation Matrix
Partial Correlation Sum
Compute Correlations from the Partial Correlations
Plot: Prior Distribution
Plot confirm objects
Plot ggm_compare_ppc Objects
Plot predictability Objects
Plot roll_your_own Objects
Network Plot for select Objects
Plot summary.estimate Objects
Plot summary.explore Objects
Plot summary.ggm_compare_estimate Objects
Plot summary.ggm_compare_explore Objects
Plot summary.select.explore Objects
Plot summary.var_estimate Objects
Posterior Predictive Distribution
Extract Posterior Samples
Precision Matrix Posterior Distribution
Model Predictions for estimate Objects
Model Predictions for explore Objects
Model Predictions for var_estimate Objects
Predictability: Bayesian Variance Explained (R2)
Predicted Probabilities
Print method for BGGM objects
Prior Belief Gaussian Graphical Model
Prior Belief Graphical VAR
Summarary Method for Multivariate or Univarate Regression
Compute Custom Network Statistics
Graph Selection for estimate Objects
Graph selection for explore Objects
Graph Selection for ggm_compare_estimate Objects
Graph selection for ggm_compare_explore Objects
S3 select method
Graph Selection for var.estimate Object
Summarize coef Objects
Summary method for estimate.default objects
Summary method for ggm_compare_estimate objects
Summary Method for ggm_compare_explore Objects
Summary Method for predictability Objects
Summary Method for select.explore Objects
Summary Method for var_estimate Objects
VAR: Estimation
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>.