Package: rags2ridges 2.2.7

Carel F.W. Peeters

rags2ridges: Ridge Estimation of Precision Matrices from High-Dimensional Data

Proper L2-penalized maximum likelihood estimators for precision matrices and supporting functions to employ these estimators in a graphical modeling setting. For details, see Peeters, Bilgrau, & van Wieringen (2022) <doi:10.18637/jss.v102.i04> and associated publications.

Authors:Carel F.W. Peeters [aut, cre, cph], Anders Ellern Bilgrau [aut, cph], Wessel N. van Wieringen [aut]

rags2ridges_2.2.7.tar.gz
rags2ridges_2.2.7.tar.gz(r-4.5-noble)rags2ridges_2.2.7.tar.gz(r-4.4-noble)
rags2ridges_2.2.7.tgz(r-4.4-emscripten)rags2ridges_2.2.7.tgz(r-4.3-emscripten)
rags2ridges.pdf |rags2ridges.html
rags2ridges/json (API)
NEWS

# Install 'rags2ridges' in R:
install.packages('rags2ridges', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/cfwp/rags2ridges/issues

Pkgdown:https://cfwp.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • ADmetabolites - R-objects related to metabolomics data on patients with Alzheimer's Disease
  • sampleInfo - R-objects related to metabolomics data on patients with Alzheimer's Disease
  • variableInfo - R-objects related to metabolomics data on patients with Alzheimer's Disease

openblascpp

3.66 score 2 stars 46 scripts 838 downloads 1 mentions 58 exports 84 dependencies

Last updated 1 years agofrom:713579370f. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 07 2024
R-4.5-linux-x86_64NOTEDec 07 2024

Exports:adjacentMatCNplotCommunitiesconditionNumberPlotcovMLcovMLknowncreateSdefault.penaltydefault.targetdefault.target.fusedDiffGraphedgeHeatevaluateSevaluateSfitfullMontySfused.testgetKEGGPathwayGGMblockNullPenaltyGGMblockTestGGMmutualInfoGGMnetworkStatsGGMnetworkStats.fusedGGMpathStatsGGMpathStats.fusedis.XlistisSymmetricPDisSymmetricPSDkegg.targetKLdivKLdiv.fusedlossmomentSNLLNLL.fusedoptPenalty.aLOOCVoptPenalty.fusedoptPenalty.fused.autooptPenalty.fused.gridoptPenalty.kCVoptPenalty.kCVautooptPenalty.LOOCVoptPenalty.LOOCVautopcorPNLLPNLL.fusedpooledPpooledSpruneMatrixridgePridgeP.fusedridgePathSridgeSrmvnormalsparsifysparsify.fusedsymmUgraphUnion

Dependencies:backportsbase64encBHBiocGenericsbslibcachemcheckmatecliclustercolorspacecpp11data.tabledigestevaluateexpmfansifarverfastmapfdrtoolfontawesomeforeignFormulafsgenericsggplot2gluegraphgRbasegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsigraphisobandjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmennetpillarpkgconfigplyrR6rappdirsRBGLRColorBrewerRcppRcppArmadilloRcppEigenreshaperlangrmarkdownrpartRSpectrarstudioapisassscalessfsmiscsnowsnowfallstringistringrtibbletinytexutf8vctrsviridisviridisLitewithrxfunyaml

Introduction to rags2ridges

Rendered fromrags2ridges.Rmdusingknitr::rmarkdownon Dec 07 2024.

Last update: 2021-06-07
Started: 2021-06-07

Readme and manuals

Help Manual

Help pageTopics
Ridge estimation for high-dimensional precision matricesrags2ridges-package rags2ridges
R-objects related to metabolomics data on patients with Alzheimer's DiseaseADdata ADmetabolites sampleInfo variableInfo
Transform real matrix into an adjacency matrixadjacentMat
Visualize the spectral condition number against the regularization parameterCNplot
Search and visualize community-structuresCommunities
Visualize the spectral condition number against the regularization parameterconditionNumberPlot
Maximum likelihood estimation of the covariance matrixcovML
Maximum likelihood estimation of the covariance matrix with assumptions on its structurecovMLknown
Simulate sample covariances or datasetscreateS
Construct commonly used penalty matricesdefault.penalty
Generate a (data-driven) default target for usage in ridge-type shrinkage estimationdefault.target
Generate data-driven targets for fused ridge estimationdefault.target.fused
Visualize the differential graphDiffGraph
Visualize (precision) matrix as a heatmapedgeHeat
Evaluate numerical properties square matrixevaluateS
Visual inspection of the fit of a regularized precision matrixevaluateSfit
Wrapper functionfullMontyS
Test the necessity of fusionfused.test
Generate the distribution of the penalty parameter under the null hypothesis of block-independenceGGMblockNullPenalty
Test for block-indepedenceGGMblockTest
Mutual information between two sets of variates within a multivariate normal distributionGGMmutualInfo
Gaussian graphical model network statisticsGGMnetworkStats
Gaussian graphical model network statisticsGGMnetworkStats.fused
Gaussian graphical model node pair path statisticsGGMpathStats
Fused gaussian graphical model node pair path statisticsGGMpathStats.fused
Plot the results of a fusion testhist.ptest plot.ptest
Test if fused list-formats are correctly usedis.Xlist
Test for symmetric positive (semi-)definitenessisSymmetricPD isSymmetricPSD
Construct target matrix from KEGGkegg.target
Kullback-Leibler divergence between two multivariate normal distributionsKLdiv
Fused Kullback-Leibler divergence for sets of distributionsKLdiv.fused
Evaluate regularized precision under various loss functionsloss
Moments of the sample covariance matrix.momentS
Evaluate the (penalized) (fused) likelihoodNLL NLL.fused PNLL PNLL.fused
Select optimal penalty parameter by approximate leave-one-out cross-validationoptPenalty.aLOOCV
Identify optimal ridge and fused ridge penaltiesoptPenalty.fused optPenalty.fused.auto optPenalty.fused.grid
Select optimal penalty parameter by K-fold cross-validationoptPenalty.kCV
Automatic search for optimal penalty parameteroptPenalty.kCVauto
Select optimal penalty parameter by leave-one-out cross-validationoptPenalty.LOOCV
Automatic search for optimal penalty parameteroptPenalty.LOOCVauto
Compute partial correlation matrix or standardized precision matrixpcor
Compute the pooled covariance or precision matrix estimatepooledP pooledS
Print and plot functions for fused grid-based cross-validationplot.optPenaltyFusedGrid print.optPenaltyFusedGrid
Print and summarize fusion testprint.ptest summary.ptest
Prune square matrix to those variables having nonzero entriespruneMatrix
Ridge estimation for high-dimensional precision matricesridgeP
Fused ridge estimationridgeP.fused
Visualize the regularization pathridgePathS
Ridge estimation for high-dimensional precision matricesridgeS
Multivariate Gaussian simulationrmvnormal
Determine the support of a partial correlation/precision matrixsparsify
Determine support of multiple partial correlation/precision matricessparsify.fused
Symmetrize matrixsymm
Visualize undirected graphUgraph
Subset 2 square matrices to union of variables having nonzero entriesUnion