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 |