R: Smooth L0 Penalty Approximations for Gaussian Graphical Models

# Smooth L0 Penalty Approximations for Gaussian Graphical Models

## Documentation for package ‘L0ggm’ version 0.0.1

- DESCRIPTION file
- Package NEWS

## 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