Cox MultiBlock Survival
Estimation of the conditional distribution function of the response, g...
Estimation of the time-dependent ROC curve for right censored survival...
cox.prediction
cox
coxEN
coxSW
Survival probability conditional to the observed data estimation for r...
coxEN Cross-Validation
Iterative SB.sPLS-DACOX-Dynamic Cross-Validation
Iterative SB.sPLS-DRCOX-Dynamic Cross-Validation
Iterative SB.sPLS-DRCOX-Dynamic Cross-Validation
Iterative SB.sPLS-ICOX-Dynamic Cross-Validation
MB.sPLS-DACOX Cross-Validation
MB.sPLS-DRCOX Cross-Validation
The cross-validation bandwidth selection for weighted data
SB.sPLS-DACOX-Dynamic Cross-Validation
SB.sPLS-DRCOX Cross-Validation
SB.sPLS-DRCOX-Dynamic Cross-Validation
Cross validation cv.sb.splsicox
Cross validation splsdacox_dynamic
sPLS-DRCOX Cross-Validation
Cross validation sPLS-DRCOX
sPLS-ICOX Cross-Validation
deleteNearZeroCoefficientOfVariation.mb
deleteNearZeroCoefficientOfVariation
deleteZeroOrNearZeroVariance.mb
deleteZeroOrNearZeroVariance
Derivative of normal distribution
eval_Coxmos_model_per_variable.list
eval_Coxmos_model_per_variable
eval_Coxmos_models
factorToBinary
getAutoKM.list
getAutoKM
getCutoffAutoKM.list
getCutoffAutoKM
getDesign.MB
getEPV.mb
getEPV
getTestKM.list
getTestKM
Numerical Integral function using Simpson's rule
Iterative single-block sPLS-DACOX Dynamic
Iterative single-block sPLS-DRCOX
Iterative single-block sPLS-DRCOX Dynamic
Iterative single-block sPLS-ICOX
Distribution function without the ith observation
Function to evaluate the matrix of data vector minus the grid points d...
Kernel distribution function
loadingplot.Coxmos
loadingplot.fromVector.Coxmos
MB.sPLS-DACOX
MB.sPLS-DRCOX
The value of squared integral x^2 k(x) dx and integral x k(x) K(x) dx
norm01
The normal reference bandwidth selection for weighted data
The plug-in bandwidth selection for weighted data
plot_cox.event.list
plot_cox.event
plot_Coxmos.MB.PLS.model
plot_Coxmos.PLS.model
plot_divergent.biplot
plot_evaluation.list
plot_evaluation
plot_events
plot_forest.list
plot_forest
plot_multipleObservations.LP.list
plot_multipleObservations.LP
plot_observation.eventDensity
plot_observation.eventHistogram
plot_observation.pseudobeta.list
plot_pseudobeta.newObservation
plot_PLS_Coxmos
plot_proportionalHazard.list
plot_proportionalHazard
plot_pseudobeta.list
plot_pseudobeta
Time consuming plot.
predict.Coxmos
print.Coxmos
ROC estimation function
save_ggplot_lst
save_ggplot
SB.sPLS-DACOX-Dynamic
SB.sPLS-DRCOX
SB.sPLS-DRCOX-Dynamic
SB.sPLS-ICOX
sPLS-DACOX Dynamic
sPLS-DRCOX
sPLS-DRCOX Dynamic
sPLS-ICOX
w.starplot.Coxmos
Function to select the bandwidth parameter needed for smoothing the ti...
Weighted inter-quartile range estimation
Weighted quartile estimation
Weighted variance estimation
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>.