ADdata | R-objects related to metabolomics data on patients with... |
adjacentMat | Transform real matrix into an adjacency matrix |
CNplot | Visualize the spectral condition number against the... |
Communities | Search and visualize community-structures |
conditionNumberPlot | Visualize the spectral condition number against the... |
covML | Maximum likelihood estimation of the covariance matrix |
covMLknown | Maximum likelihood estimation of the covariance matrix with... |
createS | Simulate sample covariances or datasets |
default.penalty | Construct commonly used penalty matrices |
default.target | Generate a (data-driven) default target for usage in... |
default.target.fused | Generate data-driven targets for fused ridge estimation |
DiffGraph | Visualize the differential graph |
dot-armaRidgeP | Core ridge precision estimators |
edgeHeat | Visualize (precision) matrix as a heatmap |
evaluateS | Evaluate numerical properties square matrix |
evaluateSfit | Visual inspection of the fit of a regularized precision... |
fullMontyS | Wrapper function |
fused.test | Test the necessity of fusion |
getKEGGPathway | Download KEGG pathway |
GGMblockNullPenalty | Generate the distribution of the penalty parameter under the... |
GGMblockTest | Test for block-indepedence |
GGMmutualInfo | Mutual information between two sets of variates within a... |
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 |
isSymmetricPD | Test for symmetric positive (semi-)definiteness |
is.Xlist | Test if fused list-formats are correctly used |
kegg.target | Construct target matrix from KEGG |
KLdiv | Kullback-Leibler divergence between two multivariate normal... |
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 |
optPenalty.aLOOCV | Select optimal penalty parameter by approximate leave-one-out... |
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 |
optPenalty.LOOCV | Select optimal penalty parameter by leave-one-out... |
optPenalty.LOOCVauto | Automatic search for optimal penalty parameter |
pcor | Compute partial correlation matrix or standardized precision... |
plot.ptest | Plot the results of a fusion test |
pooledS | Compute the pooled covariance or precision matrix estimate |
print.optPenaltyFusedGrid | Print and plot functions for fused grid-based... |
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 |
ridgePathS | Visualize the regularization path |
ridgeP.fused | Fused ridge estimation |
ridgeS | Ridge estimation for high-dimensional precision matrices |
rmvnormal | Multivariate Gaussian simulation |
sparsify | Determine the support of a partial correlation/precision... |
sparsify.fused | Determine support of multiple partial correlation/precision... |
symm | Symmetrize matrix |
Ugraph | Visualize undirected graph |
Union | Subset 2 square matrices to union of variables having nonzero... |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.