Package: iCARH 2.0.2.1

iCARH: Integrative Conditional Autoregressive Horseshoe Model

Implements the integrative conditional autoregressive horseshoe model discussed in Jendoubi, T., Ebbels, T.M. Integrative analysis of time course metabolic data and biomarker discovery. BMC Bioinformatics 21, 11 (2020) <doi:10.1186/s12859-019-3333-0>. The model consists in three levels: Metabolic pathways level modeling interdependencies between variables via a conditional auto-regressive (CAR) component, integrative analysis level to identify potential associations between heterogeneous omic variables via a Horseshoe prior and experimental design level to capture experimental design conditions through a mixed-effects model. The package also provides functions to simulate data from the model, construct pathway matrices, post process and plot model parameters.

Authors:Takoua Jendoubi [aut, cre], Timothy M.D. Ebbels [aut]

iCARH_2.0.2.1.tar.gz
iCARH_2.0.2.1.zip(r-4.5)iCARH_2.0.2.1.zip(r-4.4)iCARH_2.0.2.1.zip(r-4.3)
iCARH_2.0.2.1.tgz(r-4.5-any)iCARH_2.0.2.1.tgz(r-4.4-any)iCARH_2.0.2.1.tgz(r-4.3-any)
iCARH_2.0.2.1.tar.gz(r-4.5-noble)iCARH_2.0.2.1.tar.gz(r-4.4-noble)
iCARH_2.0.2.1.tgz(r-4.4-emscripten)iCARH_2.0.2.1.tgz(r-4.3-emscripten)
iCARH.pdf |iCARH.html✨
iCARH/json (API)

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

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 6 scripts 192 downloads 1 mentions 18 exports 100 dependencies

Last updated 5 years agofrom:75dbc3f6d4. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKApr 03 2025
R-4.5-winOKApr 03 2025
R-4.5-macOKApr 03 2025
R-4.5-linuxOKApr 03 2025
R-4.4-winOKApr 03 2025
R-4.4-macOKApr 03 2025
R-4.4-linuxOKApr 03 2025
R-4.3-winOKApr 03 2025
R-4.3-macOKApr 03 2025

Exports:iCARH.checkNormalityiCARH.checkRhatsiCARH.getARCoeffiCARH.getBetaiCARH.getDataImputationiCARH.getPathwaysCoeffiCARH.getPathwaysMatiCARH.getTreatmentEffectiCARH.madiCARH.modeliCARH.paramsiCARH.plotARCoeffiCARH.plotBetaiCARH.plotDataImputationiCARH.plotPathwayPerturbationiCARH.plotTreatmentEffectiCARH.simulateiCARH.waic

Dependencies:abindbackportsBHBiocGenericsbitopsbootbroomcallrcarcarDatacheckmateclicolorspacecorrplotcowplotcpp11DerivdescdistributionaldoBydplyrfansifarverFormulagenericsggplot2ggpubrggrepelggsciggsignifgluegraphgridExtragtableigraphinlineisobandKEGGgraphlabelinglatticelifecyclelme4loomagrittrMASSMatrixMatrixModelsmatrixStatsmc2dmgcvmicrobenchmarkminqamodelrmunsellmvtnormnlmenloptrnnetnumDerivpbkrtestpillarpkgbuildpkgconfigplyrpolynomposteriorprocessxpspurrrquantregQuickJSRR6rbibutilsRColorBrewerRcppRcppEigenRcppParallelRCurlRdpackreformulasreshape2RgraphvizrlangrstanrstatixscalesSparseMStanHeadersstringistringrsurvivaltensorAtibbletidyrtidyselectutf8vctrsviridisLitewithrXML

Example for simulating and running the iCARH model

Rendered fromexample.Rmdusingknitr::rmarkdownon Apr 03 2025.

Last update: 2020-08-27
Started: 2019-06-04

Readme and manuals

Help Manual

Help pageTopics
Return model parametersiCARH.getARCoeff iCARH.getBeta iCARH.getDataImputation iCARH.getPathwaysCoeff iCARH.getTreatmentEffect
Builds pathways adjacency matricesiCARH.getPathwaysMat
Runs the integrative CAR Horseshoe modeliCARH.model
Summarize and return model parametersiCARH.params
Postprocess and plot model parametersiCARH.checkNormality iCARH.checkRhats iCARH.mad iCARH.plotARCoeff iCARH.plotBeta iCARH.plotDataImputation iCARH.plotPathwayPerturbation iCARH.plotTreatmentEffect iCARH.waic
Simulates longitudinal data based on the iCARH model.iCARH.simulate