# Ridge Estimation of Precision Matrices from High-Dimensional Data

## Documentation for package ‘rags2ridges’ version 2.1.1

- DESCRIPTION file.
- Package NEWS.

## Help Pages

rags2ridges-package | Ridge estimation for high-dimensional precision matrices
adjacentMat | Transform real matrix into an adjacency matrix
CNplot | Visualize the spectral condition number against the regularization parameter
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
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
NLL | Evaulate the (penalized) (fused) likelihood
NLL.fused | Evaulate 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.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
plot.optPenaltyFusedGrid | Print and plot functions for fused grid-based cross-validation
plot.ptest | Plot the results of a fusion test
PNLL | Evaulate the (penalized) (fused) likelihood
PNLL.fused | Evaulate 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
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
summary.ptest | Print and summarize fusion test
symm | Symmetrize matrix
Ugraph | Visualize undirected graph