Package: rags2ridges 2.2.7

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]

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rags2ridges/json (API)
NEWS

# Install 'rags2ridges' in R:

install.packages('rags2ridges', repos = c('https://cfwp.r-universe.dev', 'https://cloud.r-project.org'))

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

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

On CRAN:

c-plus-plus graphical-models machine-learning networkscience statistics

5.57 score 8 stars 46 scripts 911 downloads 1 mentions 58 exports 84 dependencies

Last updated 1 years agofrom:226f7535c4. Checks:OK: 7 NOTE: 2. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 06 2024
R-4.5-win-x86_64	NOTE	Nov 06 2024
R-4.5-linux-x86_64	NOTE	Nov 06 2024
R-4.4-win-x86_64	OK	Nov 06 2024
R-4.4-mac-x86_64	OK	Nov 06 2024
R-4.4-mac-aarch64	OK	Nov 06 2024
R-4.3-win-x86_64	OK	Nov 06 2024
R-4.3-mac-x86_64	OK	Nov 06 2024
R-4.3-mac-aarch64	OK	Nov 06 2024

Exports:adjacentMat CNplot Communities conditionNumberPlot covML covMLknown createS default.penalty default.target default.target.fused DiffGraph edgeHeat evaluateS evaluateSfit fullMontyS fused.test getKEGGPathway GGMblockNullPenalty GGMblockTest GGMmutualInfo GGMnetworkStats GGMnetworkStats.fused GGMpathStats GGMpathStats.fused is.Xlist isSymmetricPD isSymmetricPSD kegg.target KLdiv KLdiv.fused loss momentS NLL NLL.fused optPenalty.aLOOCV optPenalty.fused optPenalty.fused.auto optPenalty.fused.grid optPenalty.kCV optPenalty.kCVauto optPenalty.LOOCV optPenalty.LOOCVauto pcor PNLL PNLL.fused pooledP pooledS pruneMatrix ridgeP ridgeP.fused ridgePathS ridgeS rmvnormal sparsify sparsify.fused symm Ugraph Union

Dependencies:backports base64enc BH BiocGenerics bslib cachem checkmate cli cluster colorspace cpp11 data.table digest evaluate expm fansi farver fastmap fdrtool fontawesome foreign Formula fs generics ggplot2 glue graph gRbase gridExtra gtable highr Hmisc htmlTable htmltools htmlwidgets igraph isoband jquerylib jsonlite knitr labeling lattice lifecycle magrittr MASS Matrix memoise mgcv mime munsell nlme nnet pillar pkgconfig plyr R6 rappdirs RBGL RColorBrewer Rcpp RcppArmadillo RcppEigen reshape rlang rmarkdown rpart RSpectra rstudioapi sass scales sfsmisc snow snowfall stringi stringr tibble tinytex utf8 vctrs viridis viridisLite withr xfun yaml

Introduction to rags2ridges

Rendered fromrags2ridges.Rmdusingknitr::rmarkdownon Nov 06 2024.

Last update: 2021-06-07
Started: 2020-01-19

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Ridge estimation for high-dimensional precision matrices	rags2ridges-package rags2ridges
R-objects related to metabolomics data on patients with Alzheimer's Disease	ADdata ADmetabolites sampleInfo variableInfo
Transform real matrix into an adjacency matrix	adjacentMat
Visualize the spectral condition number against the regularization parameter	CNplot
Search and visualize community-structures	Communities
Visualize the spectral condition number against the regularization parameter	conditionNumberPlot
Maximum likelihood estimation of the covariance matrix	covML
Maximum likelihood estimation of the covariance matrix with assumptions on its structure	covMLknown
Simulate sample covariances or datasets	createS
Construct commonly used penalty matrices	default.penalty
Generate a (data-driven) default target for usage in ridge-type shrinkage estimation	default.target
Generate data-driven targets for fused ridge estimation	default.target.fused
Visualize the differential graph	DiffGraph
Visualize (precision) matrix as a heatmap	edgeHeat
Evaluate numerical properties square matrix	evaluateS
Visual inspection of the fit of a regularized precision matrix	evaluateSfit
Wrapper function	fullMontyS
Test the necessity of fusion	fused.test
Generate the distribution of the penalty parameter under the null hypothesis of block-independence	GGMblockNullPenalty
Test for block-indepedence	GGMblockTest
Mutual information between two sets of variates within a multivariate normal distribution	GGMmutualInfo
Gaussian graphical model network statistics	GGMnetworkStats
Gaussian graphical model network statistics	GGMnetworkStats.fused
Gaussian graphical model node pair path statistics	GGMpathStats
Fused gaussian graphical model node pair path statistics	GGMpathStats.fused
Plot the results of a fusion test	hist.ptest plot.ptest
Test if fused list-formats are correctly used	is.Xlist
Test for symmetric positive (semi-)definiteness	isSymmetricPD isSymmetricPSD
Construct target matrix from KEGG	kegg.target
Kullback-Leibler divergence between two multivariate normal distributions	KLdiv
Fused Kullback-Leibler divergence for sets of distributions	KLdiv.fused
Evaluate regularized precision under various loss functions	loss
Moments of the sample covariance matrix.	momentS
Evaluate the (penalized) (fused) likelihood	NLL NLL.fused PNLL PNLL.fused
Select optimal penalty parameter by approximate leave-one-out cross-validation	optPenalty.aLOOCV
Identify optimal ridge and fused ridge penalties	optPenalty.fused optPenalty.fused.auto optPenalty.fused.grid
Select optimal penalty parameter by K-fold cross-validation	optPenalty.kCV
Automatic search for optimal penalty parameter	optPenalty.kCVauto
Select optimal penalty parameter by leave-one-out cross-validation	optPenalty.LOOCV
Automatic search for optimal penalty parameter	optPenalty.LOOCVauto
Compute partial correlation matrix or standardized precision matrix	pcor
Compute the pooled covariance or precision matrix estimate	pooledP pooledS
Print and plot functions for fused grid-based cross-validation	plot.optPenaltyFusedGrid print.optPenaltyFusedGrid
Print and summarize fusion test	print.ptest summary.ptest
Prune square matrix to those variables having nonzero entries	pruneMatrix
Ridge estimation for high-dimensional precision matrices	ridgeP
Fused ridge estimation	ridgeP.fused
Visualize the regularization path	ridgePathS
Ridge estimation for high-dimensional precision matrices	ridgeS
Multivariate Gaussian simulation	rmvnormal
Determine the support of a partial correlation/precision matrix	sparsify
Determine support of multiple partial correlation/precision matrices	sparsify.fused
Symmetrize matrix	symm
Visualize undirected graph	Ugraph
Subset 2 square matrices to union of variables having nonzero entries	Union