Package: Coxmos 1.2.0

Pedro Salguero

Coxmos: Cox MultiBlock Survival

This software package provides Cox survival analysis for high-dimensional and multiblock datasets. It encompasses a suite of functions dedicated from the classical Cox regression to newest analysis, including Cox proportional hazards model, Stepwise Cox regression, and Elastic-Net Cox regression, Sparse Partial Least Squares Cox regression (sPLS-COX) incorporating three distinct strategies, and two Multiblock-PLS Cox regression (MB-sPLS-COX) methods. This tool is designed to adeptly handle high-dimensional data, and provides tools for cross-validation, plot generation, and additional resources for interpreting results. While references are available within the corresponding functions, key literature is mentioned below. Terry M Therneau (2024) <https://CRAN.R-project.org/package=survival>, Noah Simon et al. (2011) <doi:10.18637/jss.v039.i05>, Philippe Bastien et al. (2005) <doi:10.1016/j.csda.2004.02.005>, Philippe Bastien (2008) <doi:10.1016/j.chemolab.2007.09.009>, Philippe Bastien et al. (2014) <doi:10.1093/bioinformatics/btu660>, Kassu Mehari Beyene and Anouar El Ghouch (2020) <doi:10.1002/sim.8671>, Florian Rohart et al. (2017) <doi:10.1371/journal.pcbi.1005752>.

Authors:Pedro Salguero [aut, cre, rev], Sonia Tarazona Campos [ths], Kassu Mehari Beyene [ctb], Luis Meira Machado [ctb], Marta Sestelo [ctb], Artur Araújo [ctb]

Coxmos_1.2.0.tar.gz
Coxmos_1.2.0.zip(r-4.7)Coxmos_1.2.0.zip(r-4.6)Coxmos_1.2.0.zip(r-4.5)
Coxmos_1.2.0.tgz(r-4.6-x86_64)Coxmos_1.2.0.tgz(r-4.6-arm64)Coxmos_1.2.0.tgz(r-4.5-x86_64)Coxmos_1.2.0.tgz(r-4.5-arm64)
Coxmos_1.2.0.tar.gz(r-4.7-arm64)Coxmos_1.2.0.tar.gz(r-4.7-x86_64)Coxmos_1.2.0.tar.gz(r-4.6-arm64)Coxmos_1.2.0.tar.gz(r-4.6-x86_64)
Coxmos_1.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
Coxmos/json (API)

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

Bug tracker:https://github.com/biostatomics/coxmos/issues

Datasets:

On CRAN:

Conda:

5.48 score 2 stars 4 scripts 257 downloads 80 exports 177 dependencies

Last updated from:0c3bcb3778. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK346
linux-devel-x86_64OK362
source / vignettesOK463
linux-release-arm64OK342
linux-release-x86_64OK366
macos-release-arm64OK210
macos-release-x86_64OK389
macos-oldrel-arm64OK288
macos-oldrel-x86_64OK466
windows-develOK339
windows-releaseOK296
windows-oldrelOK254
wasm-releaseOK263

Exports:coxcox.predictioncoxENcoxmoscoxSWcv.coxENcv.coxmoscv.isb.splsdacoxcv.isb.splsdrcoxcv.isb.splsdrcox_penaltycv.isb.splsicoxcv.mb.coxmoscv.mb.splsdacoxcv.mb.splsdrcoxcv.sb.splsdacoxcv.sb.splsdrcoxcv.sb.splsdrcox_penaltycv.sb.splsicoxcv.splsdacoxcv.splsdrcoxcv.splsdrcox_penaltycv.splsicoxdeleteNearZeroCoefficientOfVariationdeleteNearZeroCoefficientOfVariation.mbdeleteZeroOrNearZeroVariabilitydeleteZeroOrNearZeroVariability.mbeval_Coxmos_model_per_variableeval_Coxmos_model_per_variable.listeval_Coxmos_modelsfactorToBinarygetAutoKMgetAutoKM.listgetCutoffAutoKMgetCutoffAutoKM.listgetDesign.MBgetEPVgetEPV.mbgetTestKMgetTestKM.listgetTrainTestisb.splsdacoxisb.splsdrcoxisb.splsdrcox_penaltyisb.splsicoxloadingplot.Coxmosmb.coxmosmb.splsdacoxmb.splsdrcoxplot_cox.eventplot_cox.event.listplot_divergent.biplotplot_evaluationplot_evaluation.listplot_eventsplot_forestplot_forest.listplot_multipleObservations.LPplot_multipleObservations.LP.listplot_observation.eventDensityplot_observation.eventHistogramplot_observation.pseudobetaplot_observation.pseudobeta.listplot_proportionalHazardplot_proportionalHazard.listplot_pseudobetaplot_pseudobeta.listplot_sPLS_Coxmosplot_time.listsave_ggplotsave_ggplot_lstsb.splsdacoxsb.splsdrcoxsb.splsdrcox_penaltysb.splsicoxsplsdacoxsplsdrcoxsplsdrcox_penaltysplsicoxtransformIllegalCharsw.starplot.Coxmos

Dependencies:abindbackportsbase64encBHBiocParallelbootbootstrapbroombslibcachemcarcarDatacaretclasscliclockcodetoolscolorspacecommonmarkcorpcorcorrplotcowplotcpp11crayoncurldata.tableDerivdiagramdigestdoBydplyre1071ellipseevaluateexactRankTestsfarverfastmapfontawesomeforeachforecastformatRFormulafracdifffsfurrrfutile.loggerfutile.optionsfuturefuture.applygenericsggplot2ggpubrggrepelggsciggsignifggtextglmnetglobalsgluegowergridExtragridtextgtablehardhathighrhmshtmltoolshtmlwidgetsigraphipredisobanditeratorsjpegjquerylibjsonliteKernSmoothknitrlabelinglambda.rlatticelavalifecyclelistenvlitedownlme4lmtestlubridatemagrittrmarkdownMASSMatrixMatrixModelsmatrixStatsmaxstatmemoisemgcvmicrobenchmarkmimeminqamixOmicsModelMetricsmodelrmvtnormnlmenloptrnnetnumDerivparallellypatchworkpbkrtestpillarpkgconfigplyrpngpolynomprettyunitspROCprodlimprogressprogressrproxypurrrquantregR6rappdirsrARPACKrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackrecipesreformulasreshape2rglrlangrmarkdownrmetarpartRSpectrarstatixS7sassscalesscattermoreshapesnowSparseMsparsevctrsSQUAREMstringistringrSuppDistssurvcompsurvivalsurvivalROCsurvminersvglitesystemfontstextshapingtibbletidyrtidyselecttimechangetimeDatetinytextzdburcautf8vctrsviridisLitewithrxfunxml2yamlzoo

Step-by-step guide to the High-Dimensional Coxmos pipeline

Rendered fromCoxmos-pipeline.Rmdusingknitr::rmarkdownon May 25 2026.

Last update: 2025-06-02
Started: 2023-09-18

Step-by-step guide to the MultiBlock Coxmos pipeline

Rendered fromCoxmos-MO-pipeline.Rmdusingknitr::rmarkdownon May 25 2026.

Last update: 2025-06-02
Started: 2023-09-18

Readme and manuals

Help Manual

Help pageTopics
Estimation of the conditional distribution function of the response, given the covariate under random censoring.Beran
Estimation of the time-dependent ROC curve for right censored survival datacenROC
coxcox
cox.predictioncox.prediction
coxENcoxEN
Coxmos Modeling Functioncoxmos
coxSWcoxSW
The cross-validation bandwidth selection for weighted dataCV
coxEN Cross-Validationcv.coxEN
Cross-Validation for COX Modelscv.coxmos
Iterative SB.sPLS-DACOX-Dynamic Cross-Validationcv.isb.splsdacox
Iterative SB.sPLS-DRCOX-Dynamic Cross-Validationcv.isb.splsdrcox
Iterative SB.sPLS-DRCOX-Dynamic Cross-Validationcv.isb.splsdrcox_penalty
Iterative SB.sPLS-ICOX-Dynamic Cross-Validationcv.isb.splsicox
Multiblock COX Cross-Validation Functioncv.mb.coxmos
MB.sPLS-DACOX Cross-Validationcv.mb.splsdacox
MB.sPLS-DRCOX Cross-Validationcv.mb.splsdrcox
SB.sPLS-DACOX-Dynamic Cross-Validationcv.sb.splsdacox
SB.sPLS-DRCOX-Dynamic Cross-Validationcv.sb.splsdrcox
SB.sPLS-DRCOX Cross-Validationcv.sb.splsdrcox_penalty
Cross validation cv.sb.splsicoxcv.sb.splsicox
Cross validation splsdacox_dynamiccv.splsdacox
Cross validation sPLS-DRCOXcv.splsdrcox
sPLS-DRCOX Cross-Validationcv.splsdrcox_penalty
sPLS-ICOX Cross-Validationcv.splsicox
deleteNearZeroCoefficientOfVariationdeleteNearZeroCoefficientOfVariation
deleteNearZeroCoefficientOfVariation.mbdeleteNearZeroCoefficientOfVariation.mb
deleteZeroOrNearZeroVariabilitydeleteZeroOrNearZeroVariability
deleteZeroOrNearZeroVariability.mbdeleteZeroOrNearZeroVariability.mb
eval_Coxmos_model_per_variableeval_Coxmos_model_per_variable
eval_Coxmos_model_per_variable.listeval_Coxmos_model_per_variable.list
eval_Coxmos_modelseval_Coxmos_models
factorToBinaryfactorToBinary
getAutoKMgetAutoKM
getAutoKM.listgetAutoKM.list
getCutoffAutoKMgetCutoffAutoKM
getCutoffAutoKM.listgetCutoffAutoKM.list
getDesign.MBgetDesign.MB
getEPVgetEPV
getEPV.mbgetEPV.mb
getTestKMgetTestKM
getTestKM.listgetTestKM.list
getTrainTestgetTrainTest
Iterative single-block sPLS-DACOX Dynamicisb.splsdacox
Iterative single-block sPLS-DRCOX Dynamicisb.splsdrcox
Iterative single-block sPLS-DRCOXisb.splsdrcox_penalty
Iterative single-block sPLS-ICOXisb.splsicox
loadingplot.Coxmosloadingplot.Coxmos
loadingplot.fromVector.Coxmosloadingplot.fromVector.Coxmos
Multiblock COX Modeling Functionmb.coxmos
MB.sPLS-DACOXmb.splsdacox
MB.sPLS-DRCOXmb.splsdrcox
norm01norm01
The normal reference bandwidth selection for weighted dataNR
The plug-in bandwidth selection for weighted dataPI
plot_cox.eventplot_cox.event
plot_cox.event.listplot_cox.event.list
plot_Coxmos.MB.PLS.modelplot_Coxmos.MB.PLS.model
plot_Coxmos.PLS.modelplot_Coxmos.PLS.model
plot_divergent.biplotplot_divergent.biplot
plot_evaluationplot_evaluation
plot_evaluation.listplot_evaluation.list
plot_eventsplot_events
plot_forestplot_forest
plot_forest.listplot_forest.list
plot_multipleObservations.LPplot_multipleObservations.LP
plot_multipleObservations.LP.listplot_multipleObservations.LP.list
plot_observation.eventDensityplot_observation.eventDensity
plot_observation.eventHistogramplot_observation.eventHistogram
plot_pseudobeta.newObservationplot_observation.pseudobeta
plot_observation.pseudobeta.listplot_observation.pseudobeta.list
plot_proportionalHazardplot_proportionalHazard
plot_proportionalHazard.listplot_proportionalHazard.list
plot_pseudobetaplot_pseudobeta
plot_pseudobeta.listplot_pseudobeta.list
plot_sPLS_Coxmosplot_sPLS_Coxmos
Time consuming plot.plot_time.list
predict.Coxmospredict.Coxmos
print.Coxmosprint.Coxmos
save_ggplotsave_ggplot
save_ggplot_lstsave_ggplot_lst
SB.sPLS-DACOX-Dynamicsb.splsdacox
SB.sPLS-DRCOX-Dynamicsb.splsdrcox
SB.sPLS-DRCOXsb.splsdrcox_penalty
SB.sPLS-ICOXsb.splsicox
sPLS-DACOX Dynamicsplsdacox
sPLS-DRCOX Dynamicsplsdrcox
sPLS-DRCOXsplsdrcox_penalty
sPLS-ICOXsplsicox
transformIllegalCharstransformIllegalChars
w.starplot.Coxmosw.starplot.Coxmos
X_multiomic DataX_multiomic
X_proteomic DataX_proteomic
Y_multiomic DataY_multiomic
Y_proteomic DataY_proteomic