Package: Coxmos 1.1.0

Pedro Salguero García

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 García [aut, cre, rev], Sonia Tarazona Campos [ths], Ana Conesa Cegarra [ths], Kassu Mehari Beyene [ctb], Luis Meira Machado [ctb], Marta Sestelo [ctb], Artur Araújo [ctb]

Coxmos_1.1.0.tar.gz
Coxmos_1.1.0.zip(r-4.5)Coxmos_1.1.0.zip(r-4.4)Coxmos_1.1.0.zip(r-4.3)
Coxmos_1.1.0.tgz(r-4.4-x86_64)Coxmos_1.1.0.tgz(r-4.4-arm64)Coxmos_1.1.0.tgz(r-4.3-x86_64)Coxmos_1.1.0.tgz(r-4.3-arm64)
Coxmos_1.1.0.tar.gz(r-4.5-noble)Coxmos_1.1.0.tar.gz(r-4.4-noble)
Coxmos_1.1.0.tgz(r-4.4-emscripten)Coxmos_1.1.0.tgz(r-4.3-emscripten)
Coxmos.pdf |Coxmos.html
Coxmos/json (API)

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

Peer review:

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

Datasets:

On CRAN:

5.11 score 4 scripts 170 downloads 73 exports 174 dependencies

Last updated 23 hours agofrom:27fab41f9b. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 22 2024
R-4.5-win-x86_64OKNov 22 2024
R-4.5-linux-x86_64OKNov 22 2024
R-4.4-win-x86_64OKNov 22 2024
R-4.4-mac-x86_64OKNov 22 2024
R-4.4-mac-aarch64OKNov 22 2024
R-4.3-win-x86_64OKNov 22 2024
R-4.3-mac-x86_64OKNov 22 2024
R-4.3-mac-aarch64OKNov 22 2024

Exports:coxcox.predictioncoxENcoxSWcv.coxENcv.isb.splsdacoxcv.isb.splsdrcoxcv.isb.splsdrcox_penaltycv.isb.splsicoxcv.mb.splsdacoxcv.mb.splsdrcoxcv.sb.splsdacoxcv.sb.splsdrcoxcv.sb.splsdrcox_penaltycv.sb.splsicoxcv.splsdacoxcv.splsdrcoxcv.splsdrcox_penaltycv.splsicoxdeleteNearZeroCoefficientOfVariationdeleteNearZeroCoefficientOfVariation.mbdeleteZeroOrNearZeroVariancedeleteZeroOrNearZeroVariance.mbeval_Coxmos_model_per_variableeval_Coxmos_modelsfactorToBinarygetAutoKMgetAutoKM.listgetCutoffAutoKMgetCutoffAutoKM.listgetDesign.MBgetEPVgetEPV.mbgetTestKMgetTestKM.listisb.splsdacoxisb.splsdrcoxisb.splsdrcox_penaltyisb.splsicoxloadingplot.Coxmosmb.splsdacoxmb.splsdrcoxplot_cox.eventplot_cox.event.listplot_divergent.biplotplot_evaluationplot_evaluation.listplot_eventsplot_forestplot_forest.listplot_LP.multipleObservationsplot_LP.multipleObservations.listplot_observation.eventDensityplot_observation.eventHistogramplot_PLS_Coxmosplot_proportionalHazardplot_proportionalHazard.listplot_pseudobetaplot_pseudobeta_newObservationplot_pseudobeta_newObservation.listplot_pseudobeta.listplot_time.listsave_ggplotsave_ggplot_lstsb.splsdacoxsb.splsdrcoxsb.splsdrcox_penaltysb.splsicoxsplsdacoxsplsdrcoxsplsdrcox_penaltysplsicoxw.starplot.Coxmos

Dependencies:abindbackportsbase64encBHBiocParallelbootbootstrapbroombslibcachemcarcarDatacaretclasscliclockcodetoolscolorspacecommonmarkcorpcorcorrplotcowplotcpp11crayoncurldata.tableDerivdiagramdigestdoBydplyre1071ellipseevaluateexactRankTestsfansifarverfastmapfontawesomeforeachformatRFormulafsfurrrfutile.loggerfutile.optionsfuturefuture.applygenericsggplot2ggpubrggrepelggsciggsignifggtextglmnetglobalsgluegowergridExtragridtextgsignalgtablehardhathighrhmshtmltoolshtmlwidgetsigraphipredisobanditeratorsjpegjquerylibjsonliteKernSmoothkm.ciKMsurvknitrlabelinglambda.rlatticelavalifecyclelistenvlme4lubridatemagrittrmarkdownMASSMatrixMatrixModelsmatrixStatsmaxstatmemoisemgcvmicrobenchmarkmimeminqamixOmicsModelMetricsmodelrmunsellmvtnormnlmenloptrnnetnumDerivparallellypbkrtestpillarpkgconfigplyrpngpolynompracmaprettyunitspROCprodlimprogressprogressrproxypurrrquantregR6rappdirsrARPACKrbibutilsRColorBrewerRcppRcppEigenRdpackrecipesreshape2rglrlangrmarkdownrmetarpartRSpectrarstatixsassscalesscattermoreshapesnowSparseMSQUAREMstringistringrSuppDistssurvcompsurvivalsurvivalROCsurvminersurvMiscsvglitesystemfontstibbletidyrtidyselecttimechangetimeDatetinytextzdbutf8vctrsviridisLitewithrxfunxml2xtableyamlzoo

Step-by-step guide to the Coxmos pipeline

Rendered fromCoxmos-pipeline.Rmdusingknitr::rmarkdownon Nov 22 2024.

Last update: 2024-11-22
Started: 2023-09-18

Step-by-step guide to the MO-Coxmos pipeline

Rendered fromCoxmos-MO-pipeline.Rmdusingknitr::rmarkdownon Nov 22 2024.

Last update: 2024-11-22
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
coxSWcoxSW
The cross-validation bandwidth selection for weighted dataCV
coxEN Cross-Validationcv.coxEN
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
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
deleteZeroOrNearZeroVariancedeleteZeroOrNearZeroVariance
deleteZeroOrNearZeroVariance.mbdeleteZeroOrNearZeroVariance.mb
eval_Coxmos_model_per_variableeval_Coxmos_model_per_variable
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
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
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_LP.multipleObservationsplot_LP.multipleObservations
plot_LP.multipleObservations.listplot_LP.multipleObservations.list
plot_observation.eventDensityplot_observation.eventDensity
plot_observation.eventHistogramplot_observation.eventHistogram
plot_PLS_Coxmosplot_PLS_Coxmos
plot_proportionalHazardplot_proportionalHazard
plot_proportionalHazard.listplot_proportionalHazard.list
plot_pseudobetaplot_pseudobeta
plot_pseudobeta.newObservationplot_pseudobeta_newObservation
plot_pseudobeta_newObservation.listplot_pseudobeta_newObservation.list
plot_pseudobeta.listplot_pseudobeta.list
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
w.starplot.Coxmosw.starplot.Coxmos
X_multiomic DataX_multiomic
X_proteomic DataX_proteomic
Y_multiomic DataY_multiomic
Y_proteomic DataY_proteomic