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GGMridge (version 1.3)

Gaussian Graphical Models Using Ridge Penalty Followed by Thresholding and Reestimation

Description

Estimation of partial correlation matrix using ridge penalty followed by thresholding and reestimation. Under multivariate Gaussian assumption, the matrix constitutes an Gaussian graphical model (GGM).

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Version

Install

install.packages('GGMridge')

Monthly Downloads

545

Version

1.3

License

GPL-2

Maintainer

Shannon Holloway

Last Published

October 1st, 2023

Functions in GGMridge (1.3)

EM.mixture

Estimation of the mixture distribution using EM algorithm

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.

lambda.TargetD

Shrinkage Estimation of a Covariance Matrix Toward an Identity Matrix

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.

simulateData

Generate Simulation Data from a Random Network.

structuredEstimate

Estimation of Partial Correlation Matrix Given Zero Structure.

transFisher

Fisher's Z-Transformation

lambda.cv

Choose the Tuning Parameter of the Ridge Inverse

getEfronp

Estimation of empirical null distribution.

scaledMat

Scale a square matrix

lambda.pcut.cv

Choose the Tuning Parameter of the Ridge Inverse and Thresholding Level of the Empirical p-Values

ksStat

The Kolmogorov-Smirnov Statistic for p-Values