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rags2ridges (version 1.2)

Ridge Estimation of Precision Matrices from High-Dimensional Data

Description

Package contains proper L2-penalized ML estimators for the precision matrix as well as supporting functions to employ these estimators in a graphical modeling setting.

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Version

Install

install.packages('rags2ridges')

Monthly Downloads

1,013

Version

1.2

License

GPL (>= 2)

Maintainer

Carel FW Peeters

Last Published

May 6th, 2014

Functions in rags2ridges (1.2)

optPenaltyCV

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

adjacentMat

Transform real matrix into an adjacency matrix

sparsify

Determine the support of a partial correlation/precision matrix

covML

Maximum likelihood estimation of the covariance matrix

Ugraph

Visualize undirected graph

symm

Symmetrize matrix

conditionNumber

Visualize the spectral condition number against the regularization parameter

ridgeS

Ridge estimation for high-dimensional precision matrices

KLdiv

Kullback-Leibler divergence between two multivariate normal distributions

evaluateS

Evaluate numerical properties square matrix

rags2ridges-package

Ridge estimation for high-dimensional precision matrices

edgeHeat

Visualize (precision) matrix as a heatmap

loss

Evaluate regularized precision under various loss functions

pcor

Compute partial correlation matrix or standardized precision matrix

optPenalty.aLOOCV

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