ADdata                  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                     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
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
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   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
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-package     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
