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