# Gaussian Graphical Models with Nonconvex Regularization

## Documentation for package ‘GGMncv’ version 2.1.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

GGMncv-package | GGMncv: Gaussian Graphical Models with Nonconvex Regularization
bfi | Data: 25 Personality items representing 5 factors
boot_eip | Bootstrapped Edge Inclusion 'Probabilities'
coef.ggmncv | Regression Coefficients from 'ggmncv' Objects
compare_edges | Compare Edges Between Gaussian Graphical Models
confirm_edges | Confirm Edges
constrained | Precision Matrix with Known Graph
desparsify | De-Sparsified Graphical Lasso Estimator
gen_net | Simulate a Partial Correlation Matrix
get_graph | Extract Graph from 'ggmncv' Objects
ggmncv | GGMncv
head.eip | Print the Head of 'eip' Objects
inference | Statistical Inference for Regularized Gaussian Graphical Models
kl_mvn | Kullback-Leibler Divergence
ledoit_wolf | Ledoit and Wolf Shrinkage Estimator
mle_known_graph | Precision Matrix with Known Graph
nct | Network Comparison Test
penalty_derivative | Penalty Derivative
penalty_function | Penalty Function
plot.eip | Plot Edge Inclusion 'Probabilities'
plot.ggmncv | Plot 'ggmncv' Objects
plot.graph | Network Plot for 'select' Objects
plot.penalty_derivative | Plot 'penalty_derivative' Objects
plot.penalty_function | Plot 'penalty_function' Objects
predict.ggmncv | Predict method for 'ggmncv' Objects
print.eip | Print 'eip' Objects
print.ggmncv | Print 'ggmncv' Objects
print.nct | Print 'nct' Objects
ptsd | Data: Post-Traumatic Stress Disorder
Sachs | Data: Sachs Network
score_binary | Binary Classification
significance_test | Statistical Inference for Regularized Gaussian Graphical Models