Statistical Methods for Survival Data with Dependent Censoring
![[R logo]](../../../doc/html/Rlogo.svg)
![[Up]](../../../doc/html/left.jpg)
![[Top]](../../../doc/html/up.jpg)
Documentation for package ‘depCensoring’ version 0.1.5
Help Pages
| boot.fun | Nonparametric bootstrap approach for the dependent censoring model |
| boot.funI | Nonparametric bootstrap approach for the independent censoring model |
| boot.nonparTrans | Nonparametric bootstrap approach for a Semiparametric transformation model under dependent censpring |
| Bvprob | Compute bivariate survival probability |
| chol2par | Transform Cholesky decomposition to covariance matrix |
| chol2par.elem | Transform Cholesky decomposition to covariance matrix parameter element. |
| CompC | Compute phi function |
| control.arguments | Prepare initial values within the control arguments |
| copdist.Archimedean | The distribution function of the Archimedean copula |
| cophfunc | The h-function of the copula |
| coppar.to.ktau | Convert the copula parameter the Kendall's tau |
| cr.lik | Competing risk likelihood function. |
| dat.sim.reg.comp.risks | Data generation function for competing risks data |
| dchol2par | Derivative of transform Cholesky decomposition to covariance matrix. |
| dchol2par.elem | Derivative of transform Cholesky decomposition to covariance matrix element. |
| Distance | Distance between vectors |
| DYJtrans | Derivative of the Yeo-Johnson transformation function |
| estimate.cf | Estimate the control function |
| estimate.cmprsk | Estimate the competing risks model of Rutten, Willems et al. (20XX). |
| fitDepCens | Fit Dependent Censoring Models |
| fitIndepCens | Fit Independent Censoring Models |
| generator.Archimedean | The generator function of the Archimedean copula |
| IYJtrans | Inverse Yeo-Johnson transformation function |
| Kernel | Calculate the kernel function |
| ktau.to.coppar | Convert the Kendall's tau into the copula parameter |
| LikCopInd | Loglikehood function under independent censoring |
| Likelihood.Parametric | Calculate the likelihood function for the fully parametric joint distribution |
| Likelihood.Profile.Kernel | Calculate the profiled likelihood function with kernel smoothing |
| Likelihood.Profile.Solve | Solve the profiled likelihood function |
| Likelihood.Semiparametric | Calculate the semiparametric version of profiled likelihood function |
| LikF.cmprsk | Second step log-likelihood function. |
| likF.cmprsk.Cholesky | Wrapper implementing likelihood function using Cholesky factorization. |
| LikGamma1 | First step log-likelihood function for Z continuous |
| LikGamma2 | First step log-likelihood function for Z binary. |
| LikI.bis | Second likelihood function needed to fit the independence model in the second step of the estimation procedure. |
| LikI.cmprsk | Second step log-likelihood function under independence assumption. |
| LikI.cmprsk.Cholesky | Wrapper implementing likelihood function assuming independence between competing risks and censoring using Cholesky factorization. |
| likIFG.cmprsk.Cholesky | Full likelihood (including estimation of control function). |
| loglike.clayton.unconstrained | Log-likelihood function for the Clayton copula. |
| loglike.frank.unconstrained | Log-likelihood function for the Frank copula. |
| loglike.gaussian.unconstrained | Log-likelihood function for the Gaussian copula. |
| loglike.gumbel.unconstrained | Log-likelihood function for the Gumbel copula. |
| loglike.indep.unconstrained | Log-likelihood function for the independence copula. |
| log_transform | Logarithmic transformation function. |
| Longfun | Long format |
| LongNPT | Change H to long format |
| NonParTrans | Fit a semiparametric transformation model for dependent censoring |
| optimlikelihood | Fit the dependent censoring models. |
| parafam.d | Obtain the value of the density function |
| parafam.p | Obtain the value of the distribution function |
| parafam.trunc | Obtain the adjustment value of truncation |
| ParamCop | Estimation of a parametric dependent censoring model without covariates. |
| Parameters.Constraints | Generate constraints of parameters |
| power_transform | Power transformation function. |
| PseudoL | Likelihood function under dependent censoring |
| ScoreEqn | Score equations of finite parameters |
| SearchIndicate | Search function |
| SolveH | Estimate a nonparametric transformation function |
| SolveHt1 | Estimating equation for Ht1 |
| SolveL | Cumulative hazard function of survival time under dependent censoring |
| SolveLI | Cumulative hazard function of survival time under independent censoring |
| SolveScore | Estimate finite parameters based on score equations |
| summary.depFit | Summary of 'depCensoringFit' object |
| summary.indepFit | Summary of 'indepCensoringFit' object |
| SurvDC | Semiparametric Estimation of the Survival Function under Dependent Censoring |
| SurvDC.GoF | Calculate the goodness-of-fit test statistic |
| SurvFunc.CG | Estimated survival function based on copula-graphic estimator (Archimedean copula only) |
| SurvFunc.KM | Estimated survival function based on Kaplan-Meier estimator |
| SurvMLE | Maximum likelihood estimator for a given parametric distribution |
| SurvMLE.Likelihood | Likelihood for a given parametric distribution |
| TCsim | Function to simulate (Y,Delta) from the copula based model for (T,C). |
| uniformize.data | Standardize data format |
| variance.cmprsk | Compute the variance of the estimates. |
| YJtrans | Yeo-Johnson transformation function |