Package index • rags2ridges

Core ridge functions The core functions for graphical ridge regression are:
`ridgeP()`	Ridge estimation for high-dimensional precision matrices
`default.target()`	Generate a (data-driven) default target for usage in ridge-type shrinkage estimation
`createS()`	Simulate sample covariances or datasets
Fused ridge functions The core fused graphical ridge regression are:
`GGMnetworkStats.fused()`	Gaussian graphical model network statistics
`GGMpathStats.fused()`	Fused gaussian graphical model node pair path statistics
`KLdiv.fused()`	Fused Kullback-Leibler divergence for sets of distributions
`NLL()` `PNLL()` `NLL.fused()` `PNLL.fused()`	Evaluate the (penalized) (fused) likelihood
`default.target.fused()`	Generate data-driven targets for fused ridge estimation
`optPenalty.fused.grid()` `optPenalty.fused.auto()` `optPenalty.fused()`	Identify optimal ridge and fused ridge penalties
`ridgeP.fused()`	Fused ridge estimation
`sparsify.fused()`	Determine support of multiple partial correlation/precision matrices
`fused.test()`	Test the necessity of fusion
`createS()`	Simulate sample covariances or datasets
Postprocessing Post-processing of estimated inverse covariance matrices:
`sparsify()`	Determine the support of a partial correlation/precision matrix
Data Datasets shipped with rags2ridges:
`ADdata` `ADmetabolites` `sampleInfo` `variableInfo`	R-objects related to metabolomics data on patients with Alzheimer's Disease
Helper & auxiliary functions Various helpful functions:
`is.Xlist()`	Test if fused list-formats are correctly used
`isSymmetricPD()` `isSymmetricPSD()`	Test for symmetric positive (semi-)definiteness
`NLL()` `PNLL()` `NLL.fused()` `PNLL.fused()`	Evaluate the (penalized) (fused) likelihood
All All exported functions and datasets:
`ADdata` `ADmetabolites` `sampleInfo` `variableInfo`	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
`DiffGraph()`	Visualize the differential graph
`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
`KLdiv()`	Kullback-Leibler divergence between two multivariate normal distributions
`KLdiv.fused()`	Fused Kullback-Leibler divergence for sets of distributions
`NLL()` `PNLL()` `NLL.fused()` `PNLL.fused()`	Evaluate the (penalized) (fused) likelihood
`Ugraph()`	Visualize undirected graph
`Union()`	Subset 2 square matrices to union of variables having nonzero entries
`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
`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
`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
`is.Xlist()`	Test if fused list-formats are correctly used
`isSymmetricPD()` `isSymmetricPSD()`	Test for symmetric positive (semi-)definiteness
`kegg.target()`	Construct target matrix from KEGG
`loss()`	Evaluate regularized precision under various loss functions
`momentS()`	Moments of the sample covariance matrix.
`optPenalty.LOOCV()`	Select optimal penalty parameter by leave-one-out cross-validation
`optPenalty.LOOCVauto()`	Automatic search for optimal penalty parameter
`optPenalty.aLOOCV()`	Select optimal penalty parameter by approximate leave-one-out cross-validation
`optPenalty.fused.grid()` `optPenalty.fused.auto()` `optPenalty.fused()`	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
`pcor()`	Compute partial correlation matrix or standardized precision matrix
`hist(<ptest>)` `plot(<ptest>)`	Plot the results of a fusion test
`pooledS()` `pooledP()`	Compute the pooled covariance or precision matrix estimate
`print(<optPenaltyFusedGrid>)` `plot(<optPenaltyFusedGrid>)`	Print and plot functions for fused grid-based cross-validation
`print(<ptest>)` `summary(<ptest>)`	Print and summarize fusion test
`pruneMatrix()`	Prune square matrix to those variables having nonzero entries
`rags2ridges-package` `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
`ridgeS()`	Ridge estimation for high-dimensional precision matrices
`rmvnormal()`	Multivariate Gaussian simulation
`sparsify()`	Determine the support of a partial correlation/precision matrix
`sparsify.fused()`	Determine support of multiple partial correlation/precision matrices
`symm()`	Symmetrize matrix

Reference

Core ridge functions

Fused ridge functions

Postprocessing

Data

Helper & auxiliary functions

All