Package: depCensoring 0.1.7

Negera Wakgari Deresa

depCensoring: Statistical Methods for Survival Data with Dependent Censoring

Several statistical methods for analyzing survival data under various forms of dependent censoring are implemented in the package. In addition to accounting for dependent censoring, it offers tools to adjust for unmeasured confounding factors. The implemented approaches allow users to estimate the dependency between survival time and dependent censoring time, based solely on observed survival data. For more details on the methods, refer to Deresa and Van Keilegom (2021) <doi:10.1093/biomet/asaa095>, Czado and Van Keilegom (2023) <doi:10.1093/biomet/asac067>, Crommen et al. (2024) <doi:10.1007/s11749-023-00903-9>, Deresa and Van Keilegom (2024) <doi:10.1080/01621459.2022.2161387>, Rutten et al. (2024+) <doi:10.48550/arXiv.2403.11860> and Ding and Van Keilegom (2024).

Authors:Ilias Willems [aut], Gilles Crommen [aut], Negera Wakgari Deresa [aut, cre], Jie Ding [aut], Claudia Czado [aut], Ingrid Van Keilegom [aut]

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depCensoring.pdf |depCensoring.html✨
depCensoring/json (API)
NEWS

# Install 'depCensoring' in R:
install.packages('depCensoring', repos = '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.

1.70 score 261 downloads 16 exports 71 dependencies

Last updated 22 days agofrom:26430120d2. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 12 2025
R-4.5-linuxOKMar 12 2025
R-4.4-linuxOKMar 12 2025

Exports:Chronometercophfuncestimate.cmprskfitDepCensfitIndepCensktau.to.copparLikelihood.Profile.SolveNonParTransParamCoppi.survSolveHSolveLSolveLISurvDCSurvDC.GoFTCsim

Dependencies:ADGofTestassertthatBHclicodetoolscolorspacecopulacpp11digestdoParallelEnvStatsfansifarverforeachgenericsggplot2gluegslgtableisobanditeratorskde1dlabelinglatticelifecyclelubridatemagrittrMASSMatrixmatrixcalcmgcvmunsellmvtnormnleqslvnlmenloptrnortestnumDerivOpenMxpbivnormpcaPPpillarpkgconfigpsplineR6rafalibrandtoolboxRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRcppThreadrlangrngWELLrpfrvinecopulibscalessplines2stabledistStanHeadersstringistringrsurvivaltibbletimechangeutf8vctrsviridisLitewdmwithr

Readme and manuals

Help Manual

Help pageTopics
A-step in the EAM algorithm described in KMS19A_step
Nonparametric bootstrap approach for the dependent censoring modelboot.fun
Nonparametric bootstrap approach for the independent censoring modelboot.funI
Nonparametric bootstrap approach for a Semiparametric transformation model under dependent censpringboot.nonparTrans
Evaluate the specified B-spline, defined on the unit intervalBspline.unit.interval
Compute bivariate survival probabilityBvprob
Combine bounds based on majority vote.cbMV
Check argument consistency.check.args.pisurv
Transform Cholesky decomposition to covariance matrixchol2par
Transform Cholesky decomposition to covariance matrix parameter element.chol2par.elem
Chronometer objectChronometer
Clear plotting windowclear.plt.wdw
Compute phi functionCompC
Prepare initial values within the control argumentscontrol.arguments
The distribution function of the Archimedean copulacopdist.Archimedean
The h-function of the copulacophfunc
Convert the copula parameter the Kendall's taucoppar.to.ktau
Competing risk likelihood function.cr.lik
Obtain the diagonal matrix of sample variances of moment functionsD.hat
Data generation function for competing risks datadat.sim.reg.comp.risks
Derivative of transform Cholesky decomposition to covariance matrix.dchol2par
Derivative of transform Cholesky decomposition to covariance matrix element.dchol2par.elem
Obtain the matrix of partial derivatives of the sample variances.dD.hat
Distance between vectorsDistance
Derivative of link function (AFT model)dLambda_AFT_ll
Derivative of link function (Cox model)dLambda_Cox_wb
Vector of sample average of each moment function (\bar{m}_n(theta)).dm.bar
Optimize the expected improvementdo.optimization.Mstep
Draw initial set of starting values for optimizing the expected improvement.draw.sv.init
Derivative of the Yeo-Johnson transformation functionDYJtrans
E-step in the EAM algorithm as described in KMS19.E_step
Main function to run the EAM algorithmEAM
Check convergence of the EAM algorithm.EAM.converged
Expected improvementEI
Estimate the control functionestimate.cf
Estimate the competing risks model of Rutten, Willems et al. (20XX).estimate.cmprsk
Method for finding initial points of the EAM algorithmfeasible_point_search
Fit Dependent Censoring ModelsfitDepCens
Fit Independent Censoring ModelsfitIndepCens
Family of box functionsG.box
Family of continuous/discrete instrumental functionG.cd
Family of discrete/continuous instrumental functions, in the case of many covariates.G.cd.mc
Compute the Gn matrix in step 3b of Bei (2024).G.hat
Family of spline instrumental functionsG.spline
The generator function of the Archimedean copulagenerator.Archimedean
Get anchor points on which to base the instrumental functionsget.anchor.points
Compute the conditional moment evaluationsget.cond.moment.evals
Compute the critical value of the test statistic.get.cvLLn
Matrix of derivatives of conditional moment functionsget.deriv.mom.func
Faster implementation to obtain the tensor of the evaluations of the derivatives of the moment functions at each observation.get.dmi.tens
Get extra evaluation points for E-stepget.extra.Estep.points
Evaluate each instrumental function at each of the observations.get.instrumental.function.evals
Faster implementation of vector of moment functions.get.mi.mat
Obtain next point for feasible point search.get.next.point
Main function for obtaining the starting values of the expected improvement maximization step.get.starting.values
Obtain the test statistic by minimizing the S-function over the feasible region beta(r).get.test.statistic
Grid search algorithm for finding the identified setgridSearch
Rudimentary, bidirectional 1D grid search algorithm.gs.algo.bidir
Return the next point to evaluate when doing binary searchgs.binary
Return the next point to evaluate when doing interpolation searchgs.interpolation
Return the next point to evaluate when doing regular grid searchgs.regular
Insert row into a matrix at a given row indexinsert.row
Inverse Yeo-Johnson transformation functionIYJtrans
Calculate the kernel functionKernel
Convert the Kendall's tau into the copula parameterktau.to.coppar
Link function (AFT model)Lambda_AFT_ll
Link function (Cox model)Lambda_Cox_wb
Inverse of link function (AFT model)Lambda_inverse_AFT_ll
Inverse of link function (Cox model)Lambda_inverse_Cox_wb
Loss function to compute Delta(beta).lf.delta.beta1
'Loss function' of the test statistic.lf.ts
Loglikehood function under independent censoringLikCopInd
Calculate the likelihood function for the fully parametric joint distributionLikelihood.Parametric
Calculate the profiled likelihood function with kernel smoothingLikelihood.Profile.Kernel
Solve the profiled likelihood functionLikelihood.Profile.Solve
Calculate the semiparametric version of profiled likelihood functionLikelihood.Semiparametric
Second step log-likelihood function.LikF.cmprsk
Wrapper implementing likelihood function using Cholesky factorization.likF.cmprsk.Cholesky
First step log-likelihood function for Z continuousLikGamma1
First step log-likelihood function for Z binary.LikGamma2
Second likelihood function needed to fit the independence model in the second step of the estimation procedure.LikI.bis
Second step log-likelihood function under independence assumption.LikI.cmprsk
Wrapper implementing likelihood function assuming independence between competing risks and censoring using Cholesky factorization.LikI.cmprsk.Cholesky
Full likelihood (including estimation of control function).likIFG.cmprsk.Cholesky
Logarithmic transformation function.log_transform
Log-likelihood function for the Clayton copula.loglike.clayton.unconstrained
Log-likelihood function for the Frank copula.loglike.frank.unconstrained
Log-likelihood function for the Gaussian copula.loglike.gaussian.unconstrained
Log-likelihood function for the Gumbel copula.loglike.gumbel.unconstrained
Log-likelihood function for the independence copula.loglike.indep.unconstrained
Long formatLongfun
Change H to long formatLongNPT
M-step in the EAM algorithm described in KMS19.M_step
Vector of sample average of each moment function (\bar{m}_n(theta)).m.bar
Analogue to KMS_AUX4_MSpoints(...) in MATLAB code of Bei (2024).MSpoint
Fit a semiparametric transformation model for dependent censoringNonParTrans
Normalize the covariates of a data set to lie in the unit interval by scaling based on the ranges of the covariates.normalize.covariates
Normalize the covariates of a data set to lie in the unit interval by transforming based on PCA.normalize.covariates2
Obtain the correlation matrix of the moment functionsOmega.hat
Fit the dependent censoring models.optimlikelihood
Obtain the value of the density functionparafam.d
Obtain the value of the distribution functionparafam.p
Obtain the adjustment value of truncationparafam.trunc
Estimation of a parametric dependent censoring model without covariates.ParamCop
Generate constraints of parametersParameters.Constraints
Estimate the model of Willems et al. (2024+).pi.surv
Draw points to be evaluatedplot_addpte
Draw evaluated points.plot_addpte.eval
Draw base plotplot_base
Power transformation function.power_transform
Likelihood function under dependent censoringPseudoL
S-functionS.func
Score equations of finite parametersScoreEqn
Search functionSearchIndicate
Set default hyperparameters for EAM algorithmset.EAM.hyperparameters
Set default hyperparameters for grid search algorithmset.GS.hyperparameters
Define the hyperparameters used for finding the identified intervalset.hyperparameters
Compute the variance-covariance matrix of the moment functions.Sigma.hat
Estimate a nonparametric transformation functionSolveH
Estimating equation for Ht1SolveHt1
Cumulative hazard function of survival time under dependent censoringSolveL
Cumulative hazard function of survival time under independent censoringSolveLI
Estimate finite parameters based on score equationsSolveScore
Summary of 'depCensoringFit' objectsummary.depFit
Summary of 'indepCensoringFit' objectsummary.indepFit
Semiparametric Estimation of the Survival Function under Dependent CensoringSurvDC
Calculate the goodness-of-fit test statisticSurvDC.GoF
Estimated survival function based on copula-graphic estimator (Archimedean copula only)SurvFunc.CG
Estimated survival function based on Kaplan-Meier estimatorSurvFunc.KM
Maximum likelihood estimator for a given parametric distributionSurvMLE
Likelihood for a given parametric distributionSurvMLE.Likelihood
Function to simulate (Y,Delta) from the copula based model for (T,C).TCsim
Perform the test of Bei (2024) for a given pointtest.point_Bei
Perform the test of Bei (2024) simultaneously for multiple time points.test.point_Bei_MT
Standardize data formatuniformize.data
Compute the variance of the estimates.variance.cmprsk
Yeo-Johnson transformation functionYJtrans