Ridge Estimation of Precision Matrices from High-Dimensional Data

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Documentation for package ‘rags2ridges’ version 2.2.6

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

rags2ridges-package	Ridge estimation for high-dimensional precision matrices
ADdata	R-objects related to metabolomics data on patients with Alzheimer's Disease
adjacentMat	Transform real matrix into an adjacency matrix
ADmetabolites	R-objects related to metabolomics data on patients with Alzheimer's Disease
CNplot	Visualize the spectral condition number against the regularization parameter
Communities	Search and visualize community-structures
conditionNumberPlot	Visualize the spectral condition number against the regularization parameter
covML	Maximum likelihood estimation of the covariance matrix
covMLknown	Maximum likelihood estimation of the covariance matrix with assumptions on its structure
createS	Simulate sample covariances or datasets
default.penalty	Construct commonly used penalty matrices
default.target	Generate a (data-driven) default target for usage in ridge-type shrinkage estimation
default.target.fused	Generate data-driven targets for fused ridge estimation
DiffGraph	Visualize the differential graph
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
fused.test	Test the necessity of fusion
GGMblockNullPenalty	Generate the distribution of the penalty parameter under the null hypothesis of block-independence
GGMblockTest	Test for block-indepedence
GGMmutualInfo	Mutual information between two sets of variates within a multivariate normal distribution
GGMnetworkStats	Gaussian graphical model network statistics
GGMnetworkStats.fused	Gaussian graphical model network statistics
GGMpathStats	Gaussian graphical model node pair path statistics
GGMpathStats.fused	Fused gaussian graphical model node pair path statistics
hist.ptest	Plot the results of a fusion test
is.Xlist	Test if fused list-formats are correctly used
isSymmetricPD	Test for symmetric positive (semi-)definiteness
isSymmetricPSD	Test for symmetric positive (semi-)definiteness
kegg.target	Construct target matrix from KEGG
KLdiv	Kullback-Leibler divergence between two multivariate normal distributions
KLdiv.fused	Fused Kullback-Leibler divergence for sets of distributions
loss	Evaluate regularized precision under various loss functions
momentS	Moments of the sample covariance matrix.
NLL	Evaluate the (penalized) (fused) likelihood
NLL.fused	Evaluate the (penalized) (fused) likelihood
optPenalty.aLOOCV	Select optimal penalty parameter by approximate leave-one-out cross-validation
optPenalty.fused	Identify optimal ridge and fused ridge penalties
optPenalty.fused.auto	Identify optimal ridge and fused ridge penalties
optPenalty.fused.grid	Identify optimal ridge and fused ridge penalties
optPenalty.kCV	Select optimal penalty parameter by K-fold cross-validation
optPenalty.kCVauto	Automatic search for optimal penalty parameter
optPenalty.LOOCV	Select optimal penalty parameter by leave-one-out cross-validation
optPenalty.LOOCVauto	Automatic search for optimal penalty parameter
optPenaltyPchordal	Automatic search for penalty parameter of ridge precision estimator with known chordal support
pcor	Compute partial correlation matrix or standardized precision matrix
plot.optPenaltyFusedGrid	Print and plot functions for fused grid-based cross-validation
plot.ptest	Plot the results of a fusion test
PNLL	Evaluate the (penalized) (fused) likelihood
PNLL.fused	Evaluate the (penalized) (fused) likelihood
pooledP	Compute the pooled covariance or precision matrix estimate
pooledS	Compute the pooled covariance or precision matrix estimate
print.optPenaltyFusedGrid	Print and plot functions for fused grid-based cross-validation
print.ptest	Print and summarize fusion test
pruneMatrix	Prune square matrix to those variables having nonzero entries
rags2ridges	Ridge estimation for high-dimensional precision matrices
ridgeP	Ridge estimation for high-dimensional precision matrices
ridgeP.fused	Fused ridge estimation
ridgePathS	Visualize the regularization path
ridgePchordal	Ridge estimation for high-dimensional precision matrices with known chordal support
ridgePsign	Ridge estimation for high-dimensional precision matrices with known sign of off-diagonal precision elements.
ridgeS	Ridge estimation for high-dimensional precision matrices
rmvnormal	Multivariate Gaussian simulation
sampleInfo	R-objects related to metabolomics data on patients with Alzheimer's Disease
sparsify	Determine the support of a partial correlation/precision matrix
sparsify.fused	Determine support of multiple partial correlation/precision matrices
summary.ptest	Print and summarize fusion test
support4ridgeP	Support of the adjacency matrix to cliques and separators.
symm	Symmetrize matrix
Ugraph	Visualize undirected graph
Union	Subset 2 square matrices to union of variables having nonzero entries
variableInfo	R-objects related to metabolomics data on patients with Alzheimer's Disease