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() mle_known_graph()
|
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-package
|
GGMncv: Gaussian Graphical Models with Nonconvex Regularization |
ggmncv()
|
GGMncv |
head(<eip>)
|
Print the Head of eip Objects |
inference() significance_test()
|
Statistical Inference for Regularized Gaussian Graphical Models |
kl_mvn()
|
Kullback-Leibler Divergence |
ledoit_wolf()
|
Ledoit and Wolf Shrinkage Estimator |
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 |