Reference

Missing Data Handle Missing Values.
bggm_missing()	GGM: Missing Data
mvn_imputation()	Multivariate Normal Imputation
Estimation Based Methods ‘Estimation’ indicates that the methods to not employ Bayes factor testing. Rather, the graph is determined with the posterior distribution. The prior distribtuion has a minimal influence.
estimate()	GGM: Estimation
coef(<estimate>)	Compute Regression Parameters for estimate Objects
predict(<estimate>)	Model Predictions for estimate Objects
plot(<summary.estimate>)	Plot summary.estimate Objects
select(<estimate>)	Graph Selection for estimate Objects
summary(<estimate>)	Summary method for estimate.default objects
Exploratory Hypothesis Testing Bayes factor testing to determine the graph. ‘Exploratory’ reflects that there is not a specific hypothesis being test.
explore()	GGM: Exploratory Hypothesis Testing
coef(<explore>)	Compute Regression Parameters for explore Objects
predict(<explore>)	Model Predictions for explore Objects
plot(<summary.explore>)	Plot summary.explore Objects
plot(<summary.select.explore>)	Plot summary.select.explore Objects
select(<explore>)	Graph selection for explore Objects
summary(<explore>)	Summary Method for explore.default Objects
summary(<select.explore>)	Summary Method for select.explore Objects
Confirmatory Hypothesis Testing Test (in)equality constrained hypotheses with the Bayes factor.
confirm()	GGM: Confirmatory Hypothesis Testing
plot(<confirm>)	Plot confirm objects
Compare Gaussian Graphical Models A variety of methods for comparing GGMs.
Posterior Predictive Check Compare groups with a posterior predictive check, where the null model is that the groups are equal. This works with any number of groups. There is also an option to compare the groups with a user defined test-statistic.
ggm_compare_ppc()	GGM Compare: Posterior Predictive Check
plot(<ggm_compare_ppc>)	Plot ggm_compare_ppc Objects
Partial Correlation Differences Pairwise comparisons for each partial correlation in the respective models. This can be used for any number of groups. There is also an analytical solution.
ggm_compare_estimate()	GGM Compare: Estimate
plot(<summary.ggm_compare_estimate>)	Plot summary.ggm_compare_estimate Objects
select(<ggm_compare_estimate>)	Graph Selection for ggm_compare_estimate Objects
summary(<ggm_compare_estimate>)	Summary method for ggm_compare_estimate objects
Exploratory Hypothesis Testing Pairwise comparisions with exploratory hypothesis testing. This method can be used to compare several groups simultaneously.
ggm_compare_explore()	GGM Compare: Exploratory Hypothesis Testing
plot(<summary.ggm_compare_explore>)	Plot summary.ggm_compare_explore Objects
select(<ggm_compare_explore>)	Graph selection for ggm_compare_explore Objects
summary(<ggm_compare_explore>)	Summary Method for ggm_compare_explore Objects
Confirmatory Hypothesis Testing Test (in)equality constrained hypotheses with the Bayes factor.
ggm_compare_confirm()	GGM Compare: Confirmatory Hypothesis Testing
plot(<confirm>)	Plot confirm objects
Predictability Bayesian variance explained for each node in the model.
predictability()	Predictability: Bayesian Variance Explained (R2)
plot(<predictability>)	Plot predictability Objects
summary(<predictability>)	Summary Method for predictability Objects
Network Statistics Compute network statistics from a partial correlation matrix or a weighted adjacency matrix.
roll_your_own()	Compute Custom Network Statistics
plot(<roll_your_own>)	Plot roll_your_own Objects
Partial Correlation Sums Compute the sum of partial correlations within (one group) or between (two groups) GGMs. This can be used to compare sums.
pcor_sum()	Partial Correlation Sum
plot(<pcor_sum>)	Plot pcor_sum Object
Network Plot Network plot for the selected graphs. This works with all method for which there is a selected graph.
plot(<select>)	Network Plot for select Objects
Graphical VAR (vector autoregression) A variety of methods for time series data. These particular models are VAR(1) models which are also known as time series chain graphical models.
Estimation ‘Estimation’ indicates that the methods to not employ Bayes factor testing. Rather, the graph is determined with the posterior distribution. The prior distribtuion has a minimal influence.
var_estimate()	VAR: Estimation
select(<var_estimate>)	Graph Selection for var.estimate Object
summary(<var_estimate>)	Summary Method for var_estimate Objects
plot(<summary.var_estimate>)	Plot summary.var_estimate Objects
predict(<var_estimate>)	Model Predictions for var_estimate Objects
Miscellaneous
convergence()	MCMC Convergence
fisher_z_to_r()	Fisher Z Back Transformation
fisher_r_to_z()	Fisher Z Transformation
gen_ordinal()	Generate Ordinal and Binary data
pcor_to_cor()	Compute Correlations from the Partial Correlations
pcor_mat()	Extract the Partial Correlation Matrix
plot_prior()	Plot: Prior Distribution
posterior_samples()	Extract Posterior Samples
map()	Maximum A Posteriori Precision Matrix
regression_summary()	Summarary Method for Multivariate or Univarate Regression
summary(<coef>)	Summarize coef Objects
weighted_adj_mat()	Extract the Weighted Adjacency Matrix
zero_order_cors()	Zero-Order Correlations
Data Example datasets and correlation matrices.
asd_ocd	Data: Autism and Obssesive Compulsive Disorder
bfi	Data: 25 Personality items representing 5 factors
csws	Data: Contingencies of Self-Worth Scale (CSWS)
depression_anxiety_t1	Data: Depression and Anxiety (Time 1)
depression_anxiety_t2	Data: Depression and Anxiety (Time 2)
gss	Data: 1994 General Social Survey
ifit	Data: ifit Intensive Longitudinal Data
iri	Data: Interpersonal Reactivity Index (IRI)
ptsd	Data: Post-Traumatic Stress Disorder
ptsd_cor1	Data: Post-Traumatic Stress Disorder (Sample # 1)
ptsd_cor2	Data: Post-Traumatic Stress Disorder (Sample # 2)
ptsd_cor3	Data: Post-Traumatic Stress Disorder (Sample # 3)
ptsd_cor4	Data: Post-Traumatic Stress Disorder (Sample # 4)
rsa	Data: Resilience Scale of Adults (RSA)
Sachs	Data: Sachs Network
tas	Data: Toronto Alexithymia Scale (TAS)
women_math	Data: Women and Mathematics