R: Gaussian Graphical Models Using Ridge Penalty Followed by Thresholding and Reestimation

# Gaussian Graphical Models Using Ridge Penalty Followed by Thresholding and Reestimation

## Documentation for package ‘GGMridge’ version 1.5

- DESCRIPTION file
- Package NEWS

## Help Pages

EM.mixture | Estimation of the mixture distribution using EM algorithm
getEfronp | Estimation of empirical null distribution.
ksStat | The Kolmogorov-Smirnov Statistic for p-Values
lambda.cv | Choose the Tuning Parameter of the Ridge Inverse
lambda.pcut.cv | Choose the Tuning Parameter of the Ridge Inverse and Thresholding Level of the Empirical p-Values
lambda.pcut.cv1 | Choose the Tuning Parameter of the Ridge Inverse and Thresholding Level of the Empirical p-Values. Calculate total prediction error for test data after fitting partial correlations from train data for all values of lambda and pcut.
lambda.TargetD | Shrinkage Estimation of a Covariance Matrix Toward an Identity Matrix
ne.lambda.cv | Choose the Tuning Parameter of a Ridge Regression Using Cross-Validation
R.separate.ridge | Estimation of Partial Correlation Matrix Using p Separate Ridge Regressions.
scaledMat | Scale a square matrix
simulateData | Generate Simulation Data from a Random Network.
structuredEstimate | Estimation of Partial Correlation Matrix Given Zero Structure.
transFisher | Fisher's Z-Transformation