Smooth L0 Penalty Approximations for Gaussian Graphical Models
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Documentation for package ‘L0ggm’ version 0.0.1
Help Pages
| L0ggm-package | L0ggm-package |
| auto_correlate | Automatic correlations |
| basic_smallworld | Toy Small-world Network Data Example |
| categorize | Categorize Continuous Data |
| edge_confusion | Confusion Matrix Metrics for Edge Comparison and Recovery |
| L0ggm | L0ggm-package |
| network_estimation | L0 Norm Regularized Network Estimation |
| network_fit | Traditional Fit Metrics for Networks |
| polychoric_matrix | Computes Polychoric Correlations |
| ring2lattice | Construct a Near-Degree-Preserving Ring Lattice |
| simulate_sbm | Simulates Stochastic Block Model Data |
| simulate_smallworld | Simulates Small-World GGM Data |
| skew_tables | Skew Tables |
| weibull_parameters | Predict Weibull Parameters for Edge Weight Distributions |
| weibull_weights | SUR Model Coefficients and Residuals for Weibull Parameter Prediction |