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
Cross validation cv.isb.splsdrcox
Cross validation cv.isb.splsicox
MB.sPLS-DACOX Cross-Validation
MB.sPLS-DRCOX Cross-Validation
The cross-validation bandwidth selection for weighted data
SB.sPLS-DRCOX 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
eval_Coxmos_models
factorToBinary
getAutoKM.list
getAutoKM
getCutoffAutoKM.list
getCutoffAutoKM
getEPV.mb
getEPV
getTestKM.list
getTestKM
Numerical Integral function using Simpson's rule
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_LP.multipleObservations.list
plot_LP.multipleObservations
plot_observation.eventDensity
plot_observation.eventHistogram
plot_PLS_Coxmos
plot_proportionalHazard.list
plot_proportionalHazard
plot_pseudobeta.list
plot_pseudobeta
plot_pseudobeta_newObservation.list
plot_pseudobeta.newObservation
Time consuming plot.
predict.Coxmos
print.Coxmos
ROC estimation function
save_ggplot
save_ggplot.svg
save_ggplot_lst
save_ggplot_lst.svg
SB.sPLS-DRCOX
SB.sPLS-ICOX
sPLSDA-COX 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>.
