Ridge Estimation of Precision Matrices from High-Dimensional Data

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Documentation for package ‘rags2ridges’ version 1.4

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rags2ridges-package Ridge estimation for high-dimensional precision matrices
adjacentMat Transform real matrix into an adjacency matrix
conditionNumberPlot Visualize the spectral condition number against the regularization parameter
covML Maximum likelihood estimation of the covariance matrix
default.target Generate a (data-driven) default target for usage in ridge-type shrinkage estimation
edgeHeat Visualize (precision) matrix as a heatmap
evaluateS Evaluate numerical properties square matrix
evaluateSfit Visual inspection of the fit of a regularized precision matrix
fullMontyS Wrapper function
GGMblockNullPenalty Generate the distribution of the penalty parameter under the null hypothesis of block-independence
GGMblockTest Test for block-indepedence
GGMnetworkStats Gaussian graphical model network statistics
GGMpathStats Gaussian graphical model node pair path statistics
KLdiv Kullback-Leibler divergence between two multivariate normal distributions
loss Evaluate regularized precision under various loss functions
optPenalty.aLOOCV Select optimal penalty parameter by approximate leave-one-out cross-validation
optPenalty.LOOCV Select optimal penalty parameter by leave-one-out cross-validation
optPenalty.LOOCVauto Automatic search for optimal penalty parameter
pcor Compute partial correlation matrix or standardized precision matrix
rags2ridges Ridge estimation for high-dimensional precision matrices
ridgePathS Visualize the regularization path
ridgeS Ridge estimation for high-dimensional precision matrices
sparsify Determine the support of a partial correlation/precision matrix
symm Symmetrize matrix
Ugraph Visualize undirected graph