Statistical Methods for Survival Data with Dependent Censoring
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Documentation for package ‘depCensoring’ version 0.1.7
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A B C D E F G I K L M N O P S T U V Y
-- A --
| A_step | A-step in the EAM algorithm described in KMS19 |
-- B --
| 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 |
| Bspline.unit.interval | Evaluate the specified B-spline, defined on the unit interval |
| Bvprob | Compute bivariate survival probability |
-- C --
| cbMV | Combine bounds based on majority vote. |
| check.args.pisurv | Check argument consistency. |
| chol2par | Transform Cholesky decomposition to covariance matrix |
| chol2par.elem | Transform Cholesky decomposition to covariance matrix parameter element. |
| Chronometer | Chronometer object |
| clear.plt.wdw | Clear plotting window |
| 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. |
-- D --
| D.hat | Obtain the diagonal matrix of sample variances of moment functions |
| 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. |
| dD.hat | Obtain the matrix of partial derivatives of the sample variances. |
| Distance | Distance between vectors |
| dLambda_AFT_ll | Derivative of link function (AFT model) |
| dLambda_Cox_wb | Derivative of link function (Cox model) |
| dm.bar | Vector of sample average of each moment function (\bar{m}_n(theta)). |
| do.optimization.Mstep | Optimize the expected improvement |
| draw.sv.init | Draw initial set of starting values for optimizing the expected improvement. |
| DYJtrans | Derivative of the Yeo-Johnson transformation function |
-- E --
| EAM | Main function to run the EAM algorithm |
| EAM.converged | Check convergence of the EAM algorithm. |
| EI | Expected improvement |
| estimate.cf | Estimate the control function |
| estimate.cmprsk | Estimate the competing risks model of Rutten, Willems et al. (20XX). |
| E_step | E-step in the EAM algorithm as described in KMS19. |
-- F --
| feasible_point_search | Method for finding initial points of the EAM algorithm |
| fitDepCens | Fit Dependent Censoring Models |
| fitIndepCens | Fit Independent Censoring Models |
-- G --
| G.box | Family of box functions |
| G.cd | Family of continuous/discrete instrumental function |
| G.cd.mc | Family of discrete/continuous instrumental functions, in the case of many covariates. |
| G.hat | Compute the Gn matrix in step 3b of Bei (2024). |
| G.spline | Family of spline instrumental functions |
| generator.Archimedean | The generator function of the Archimedean copula |
| get.anchor.points | Get anchor points on which to base the instrumental functions |
| get.cond.moment.evals | Compute the conditional moment evaluations |
| get.cvLLn | Compute the critical value of the test statistic. |
| get.deriv.mom.func | Matrix of derivatives of conditional moment functions |
| get.dmi.tens | Faster implementation to obtain the tensor of the evaluations of the derivatives of the moment functions at each observation. |
| get.extra.Estep.points | Get extra evaluation points for E-step |
| get.instrumental.function.evals | Evaluate each instrumental function at each of the observations. |
| get.mi.mat | Faster implementation of vector of moment functions. |
| get.next.point | Obtain next point for feasible point search. |
| get.starting.values | Main function for obtaining the starting values of the expected improvement maximization step. |
| get.test.statistic | Obtain the test statistic by minimizing the S-function over the feasible region beta(r). |
| gridSearch | Grid search algorithm for finding the identified set |
| gs.algo.bidir | Rudimentary, bidirectional 1D grid search algorithm. |
| gs.binary | Return the next point to evaluate when doing binary search |
| gs.interpolation | Return the next point to evaluate when doing interpolation search |
| gs.regular | Return the next point to evaluate when doing regular grid search |
-- I --
| insert.row | Insert row into a matrix at a given row index |
| IYJtrans | Inverse Yeo-Johnson transformation function |
-- K --
| Kernel | Calculate the kernel function |
| ktau.to.coppar | Convert the Kendall's tau into the copula parameter |
-- L --
| Lambda_AFT_ll | Link function (AFT model) |
| Lambda_Cox_wb | Link function (Cox model) |
| Lambda_inverse_AFT_ll | Inverse of link function (AFT model) |
| Lambda_inverse_Cox_wb | Inverse of link function (Cox model) |
| lf.delta.beta1 | Loss function to compute Delta(beta). |
| lf.ts | 'Loss function' of the test statistic. |
| 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 |
-- M --
| m.bar | Vector of sample average of each moment function (\bar{m}_n(theta)). |
| MSpoint | Analogue to KMS_AUX4_MSpoints(...) in MATLAB code of Bei (2024). |
| M_step | M-step in the EAM algorithm described in KMS19. |
-- N --
| NonParTrans | Fit a semiparametric transformation model for dependent censoring |
| normalize.covariates | Normalize the covariates of a data set to lie in the unit interval by scaling based on the ranges of the covariates. |
| normalize.covariates2 | Normalize the covariates of a data set to lie in the unit interval by transforming based on PCA. |
-- O --
| Omega.hat | Obtain the correlation matrix of the moment functions |
| optimlikelihood | Fit the dependent censoring models. |
-- P --
| 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 |
| pi.surv | Estimate the model of Willems et al. (2024+). |
| plot_addpte | Draw points to be evaluated |
| plot_addpte.eval | Draw evaluated points. |
| plot_base | Draw base plot |
| power_transform | Power transformation function. |
| PseudoL | Likelihood function under dependent censoring |
-- S --
| S.func | S-function |
| ScoreEqn | Score equations of finite parameters |
| SearchIndicate | Search function |
| set.EAM.hyperparameters | Set default hyperparameters for EAM algorithm |
| set.GS.hyperparameters | Set default hyperparameters for grid search algorithm |
| set.hyperparameters | Define the hyperparameters used for finding the identified interval |
| Sigma.hat | Compute the variance-covariance matrix of the moment functions. |
| 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 |
-- T --
| TCsim | Function to simulate (Y,Delta) from the copula based model for (T,C). |
| test.point_Bei | Perform the test of Bei (2024) for a given point |
| test.point_Bei_MT | Perform the test of Bei (2024) simultaneously for multiple time points. |
-- U --
| uniformize.data | Standardize data format |
-- V --
| variance.cmprsk | Compute the variance of the estimates. |
-- Y --
| YJtrans | Yeo-Johnson transformation function |