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rags2ridges

The R-package rags2ridges performs L2-penalized estimation of precison (and covariance) matrices. The package contains proper L2-penalized ML estimators for the precision matrix as well as supporting functions to employ these estimators in a (integrative or meta-analytic) graphical modeling setting. The package has a modular setup and features fast and efficient algorithms.

Installation

The released and tested version of rags2ridges is available at CRAN (Comprehensive R Archive Network). It can be easily be installed from within R by running

install.packages("rags2ridges")

If you wish to install the latest version of rags2ridges directly from the master branch here at GitHub, run

#install.packages("devtools")  # Uncomment if devtools is not installed
devtools::install_github("CFWP/rags2ridges")

Note, that this version is in development and is different from the version at CRAN. As such, it may be unstable. Be sure that you have the package development prerequisites if you wish to install the package from the source.

When installed, run news(package = "rags2ridges") to view the latest notable changes to rags2ridges.

For previous versions of rags2ridges, visit the archive at CRAN.

References

Relevant publications to rags2ridges include (ordered according to year):

  1. Peeters, C.F.W., Bilgrau, A.E., & van Wieringen, W.N. (2020). "rags2ridges: Ridge Estimation of Precision Matrices from High-Dimensional Data". R package, version 2.2.3
  2. Peeters, C.F.W., van de Wiel, M.A., & van Wieringen, W.N. (2020) "The Spectral Condition Number Plot for Regularization Parameter Evaluation", Computational Statistics, vol. 35:629-646 (doi:10.1007/s00180-019-00912-z).
  3. Bilgrau*, A.E., Peeters*, C.F.W., Eriksen, P.S., Boegsted, M., & van Wieringen, W.N. (2020). "Targeted Fused Ridge Estimation of Inverse Covariance Matrices from Multiple High-Dimensional Data Classes", Journal of Machine Learning Research, vol. 21(26):1-52 (PDF).
  4. van Wieringen, W.N. & Peeters, C.F.W. (2016). "Ridge Estimation of Inverse Covariance Matrices from High-Dimensional Data", Computational Statistics & Data Analysis, vol. 103:284-303 (doi:10.1016/j.csda.2016.05.012).
  5. van Wieringen, W.N. & Peeters, C.F.W. (2015). "Application of a New Ridge Estimator of the Inverse Covariance Matrix to the Reconstruction of Gene-Gene Interaction Networks". In: di Serio, C., Lio, P., Nonis, A., and Tagliaferri, R. (Eds.) `Computational Intelligence Methods for Bioinformatics and Biostatistics'. Lecture Notes in Computer Science, vol. 8623. Springer, pp. 170-179 (doi:10.1007/978-3-319-24462-4_15).

Please cite the relevant publications if you use rags2ridges.

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Version

Install

install.packages('rags2ridges')

Monthly Downloads

996

Version

2.2.3

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Maintainer

Carel FW Peeters

Last Published

August 28th, 2020

Functions in rags2ridges (2.2.3)

GGMnetworkStats

Gaussian graphical model network statistics
GGMblockTest

Test for block-indepedence
GGMpathStats

Gaussian graphical model node pair path statistics
GGMmutualInfo

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

Gaussian graphical model network statistics
Communities

Search and visualize community-structures
GGMblockNullPenalty

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

Visualize the differential graph
ADdata

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

Visualize the spectral condition number against the regularization parameter
conditionNumberPlot

Visualize the spectral condition number against the regularization parameter
Union

Subset 2 square matrices to union of variables having nonzero entries
Ugraph

Visualize undirected graph
KLdiv.fused

Fused Kullback-Leibler divergence for sets of distributions
NLL

Evaulate the (penalized) (fused) likelihood
adjacentMat

Transform real matrix into an adjacency matrix
.armaRidgeP

Core ridge precision estimators
covML

Maximum likelihood estimation of the covariance matrix
KLdiv

Kullback-Leibler divergence between two multivariate normal distributions
GGMpathStats.fused

Fused gaussian graphical model node pair path statistics
fullMontyS

Wrapper function
fused.test

Test the necessity of fusion
evaluateSfit

Visual inspection of the fit of a regularized precision matrix
evaluateS

Evaluate numerical properties square matrix
default.target.fused

Generate data-driven targets for fused ridge estimation
edgeHeat

Visualize (precision) matrix as a heatmap
default.target

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

Construct target matrix from KEGG
isSymmetricPD

Test for symmetric positive (semi-)definiteness
default.penalty

Construct commonly used penalty matrices
createS

Simulate sample covariances or datasets
covMLknown

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

Test if fused list-formats are correctly used
getKEGGPathway

Download KEGG pathway
optPenalty.fused

Identify optimal ridge and fused ridge penalties
optPenalty.aLOOCV

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

Evaluate regularized precision under various loss functions
optPenalty.LOOCV

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

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

Compute partial correlation matrix or standardized precision matrix
print.ptest

Print and summarize fusion test
print.optPenaltyFusedGrid

Print and plot functions for fused grid-based cross-validation
optPenalty.LOOCVauto

Automatic search for optimal penalty parameter
ridgeP

Ridge estimation for high-dimensional precision matrices
ridgeP.fused

Fused ridge estimation
support4ridgeP

Support of the adjacency matrix to cliques and separators.
momentS

Moments of the sample covariance matrix.
sparsify.fused

Determine support of multiple partial correlation/precision matrices
optPenalty.kCV

Select optimal penalty parameter by \(K\)-fold cross-validation
optPenalty.kCVauto

Automatic search for optimal penalty parameter
pruneMatrix

Prune square matrix to those variables having nonzero entries
ridgePchordal

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

Visualize the regularization path
rags2ridges-package

Ridge estimation for high-dimensional precision matrices
plot.ptest

Plot the results of a fusion test
pooledS

Compute the pooled covariance or precision matrix estimate
ridgeS

Ridge estimation for high-dimensional precision matrices
ridgePsign

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

Symmetrize matrix
sparsify

Determine the support of a partial correlation/precision matrix
rmvnormal

Multivariate Gaussian simulation