Ridge Estimation of Precision Matrices from High-Dimensional Data

ADdata | R-objects related to metabolomics data on patients with... |

adjacentMat | Transform real matrix into an adjacency matrix |

armaRidgeP | Core ridge precision estimators |

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 |

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 | Evaulate 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 |

optPenaltyPchordal | Automatic search for penalty parameter of ridge precision... |

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 |

ridgePchordal | Ridge estimation for high-dimensional precision matrices with... |

ridgeP.fused | Fused ridge estimation |

ridgePsign | Ridge estimation for high-dimensional precision matrices with... |

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... |

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... |

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