Package: depCensoring 0.1.5

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> and Willems 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]

depCensoring_0.1.5.tar.gz
depCensoring_0.1.5.tar.gz(r-4.5-noble)depCensoring_0.1.5.tar.gz(r-4.4-noble)
depCensoring_0.1.5.tgz(r-4.4-emscripten)depCensoring_0.1.5.tgz(r-4.3-emscripten)
depCensoring.pdf |depCensoring.html✨
depCensoring/json (API)
NEWS

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

Peer review:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.60 score 5 scripts 259 downloads 14 exports 65 dependencies

Last updated 14 days agofrom:a6c8d7cbdf. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 12 2024
R-4.5-linuxOKDec 12 2024

Exports:cophfuncestimate.cmprskfitDepCensfitIndepCensktau.to.copparLikelihood.Profile.SolveNonParTransParamCopSolveHSolveLSolveLISurvDCSurvDC.GoFTCsim

Dependencies:ADGofTestassertthatBHclicodetoolscolorspacecopuladigestdoParallelEnvStatsfansifarverforeachggplot2gluegslgtableisobanditeratorskde1dlabelinglatticelifecyclemagrittrMASSMatrixmatrixcalcmgcvmunsellmvtnormnleqslvnlmenloptrnortestnumDerivOpenMxpbivnormpcaPPpillarpkgconfigpsplineR6rafalibrandtoolboxRColorBrewerRcppRcppEigenRcppParallelRcppThreadrlangrngWELLrpfrvinecopulibscalesstabledistStanHeadersstringistringrsurvivaltibbleutf8vctrsviridisLitewdmwithr

Readme and manuals

Help Manual

Help pageTopics
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
Compute bivariate survival probabilityBvprob
Transform Cholesky decomposition to covariance matrixchol2par
Transform Cholesky decomposition to covariance matrix parameter element.chol2par.elem
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
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
Distance between vectorsDistance
Derivative of the Yeo-Johnson transformation functionDYJtrans
Estimate the control functionestimate.cf
Estimate the competing risks model of Rutten, Willems et al. (20XX).estimate.cmprsk
Fit Dependent Censoring ModelsfitDepCens
Fit Independent Censoring ModelsfitIndepCens
The generator function of the Archimedean copulagenerator.Archimedean
Inverse Yeo-Johnson transformation functionIYJtrans
Calculate the kernel functionKernel
Convert the Kendall's tau into the copula parameterktau.to.coppar
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
Fit a semiparametric transformation model for dependent censoringNonParTrans
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
Power transformation function.power_transform
Likelihood function under dependent censoringPseudoL
Score equations of finite parametersScoreEqn
Search functionSearchIndicate
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
Standardize data formatuniformize.data
Compute the variance of the estimates.variance.cmprsk
Yeo-Johnson transformation functionYJtrans