Gaussian Graphical Models with Nonconvex Regularization
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Documentation for package ‘GGMncv’ version 2.1.1
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 |