rags2ridges

R-objects related to metabolomics data on patients with Alzheimer's Disease

Visualize the spectral condition number against the regularization parameter

Search and visualize community-structures

Visualize the differential graph

Generate the distribution of the penalty parameter under the null hypothesis of block-independence

Test for block-indepedence

Mutual information between two sets of variates within a multivariate normal distribution

Gaussian graphical model network statistics

Gaussian graphical model network statistics

Gaussian graphical model node pair path statistics

Fused gaussian graphical model node pair path statistics

Kullback-Leibler divergence between two multivariate normal distributions

Fused Kullback-Leibler divergence for sets of distributions

Evaluate the (penalized) (fused) likelihood

Visualize undirected graph

Subset 2 square matrices to union of variables having nonzero entries

Transform real matrix into an adjacency matrix

Visualize the spectral condition number against the regularization parameter

Maximum likelihood estimation of the covariance matrix

Maximum likelihood estimation of the covariance matrix with assumptions on its structure

Simulate sample covariances or datasets

Construct commonly used penalty matrices

Generate a (data-driven) default target for usage in ridge-type shrinkage estimation

Generate data-driven targets for fused ridge estimation

Core ridge precision estimators

Visualize (precision) matrix as a heatmap

Evaluate numerical properties square matrix

Visual inspection of the fit of a regularized precision matrix

Wrapper function

Test the necessity of fusion

Download KEGG pathway

Test if fused list-formats are correctly used

Test for symmetric positive (semi-)definiteness

Construct target matrix from KEGG

Evaluate regularized precision under various loss functions

Moments of the sample covariance matrix.

Select optimal penalty parameter by leave-one-out cross-validation

Automatic search for optimal penalty parameter

Select optimal penalty parameter by approximate leave-one-out cross-validation

Identify optimal ridge and fused ridge penalties

Select optimal penalty parameter by K-fold cross-validation

Automatic search for optimal penalty parameter

Automatic search for penalty parameter of ridge precision estimator with known chordal support

Compute partial correlation matrix or standardized precision matrix

Plot the results of a fusion test

Compute the pooled covariance or precision matrix estimate

Print and plot functions for fused grid-based cross-validation

Print and summarize fusion test

Prune square matrix to those variables having nonzero entries

Ridge estimation for high-dimensional precision matrices

Ridge estimation for high-dimensional precision matrices

Fused ridge estimation

Visualize the regularization path

Ridge estimation for high-dimensional precision matrices with known chordal support

Ridge estimation for high-dimensional precision matrices with known sign of off-diagonal precision elements.

Ridge estimation for high-dimensional precision matrices

Multivariate Gaussian simulation

Determine the support of a partial correlation/precision matrix

Determine support of multiple partial correlation/precision matrices

Support of the adjacency matrix to cliques and separators.

Symmetrize matrix