| A-step in the EAM algorithm described in KMS19 | A_step |
| Nonparametric bootstrap approach for the dependent censoring model | boot.fun |
| Nonparametric bootstrap approach for the independent censoring model | boot.funI |
| Nonparametric bootstrap approach for a Semiparametric transformation model under dependent censpring | boot.nonparTrans |
| Evaluate the specified B-spline, defined on the unit interval | Bspline.unit.interval |
| Compute bivariate survival probability | Bvprob |
| Combine bounds based on majority vote. | cbMV |
| Check argument consistency. | check.args.pisurv |
| Transform Cholesky decomposition to covariance matrix | chol2par |
| Transform Cholesky decomposition to covariance matrix parameter element. | chol2par.elem |
| Chronometer object | Chronometer |
| Clear plotting window | clear.plt.wdw |
| Compute phi function | CompC |
| Prepare initial values within the control arguments | control.arguments |
| The distribution function of the Archimedean copula | copdist.Archimedean |
| The h-function of the copula | cophfunc |
| Convert the copula parameter the Kendall's tau | coppar.to.ktau |
| Competing risk likelihood function. | cr.lik |
| Obtain the diagonal matrix of sample variances of moment functions | D.hat |
| Data generation function for competing risks data | dat.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 vectors | Distance |
| 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 improvement | do.optimization.Mstep |
| Draw initial set of starting values for optimizing the expected improvement. | draw.sv.init |
| Derivative of the Yeo-Johnson transformation function | DYJtrans |
| E-step in the EAM algorithm as described in KMS19. | E_step |
| Main function to run the EAM algorithm | EAM |
| Check convergence of the EAM algorithm. | EAM.converged |
| Expected improvement | EI |
| Estimate the control function | estimate.cf |
| Estimate the competing risks model of Rutten, Willems et al. (20XX). | estimate.cmprsk |
| Method for finding initial points of the EAM algorithm | feasible_point_search |
| Fit Dependent Censoring Models | fitDepCens |
| Fit Independent Censoring Models | fitIndepCens |
| Family of box functions | G.box |
| Family of continuous/discrete instrumental function | G.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 functions | G.spline |
| The generator function of the Archimedean copula | generator.Archimedean |
| Get anchor points on which to base the instrumental functions | get.anchor.points |
| Compute the conditional moment evaluations | get.cond.moment.evals |
| Compute the critical value of the test statistic. | get.cvLLn |
| Matrix of derivatives of conditional moment functions | get.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-step | get.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 set | gridSearch |
| Rudimentary, bidirectional 1D grid search algorithm. | gs.algo.bidir |
| Return the next point to evaluate when doing binary search | gs.binary |
| Return the next point to evaluate when doing interpolation search | gs.interpolation |
| Return the next point to evaluate when doing regular grid search | gs.regular |
| Insert row into a matrix at a given row index | insert.row |
| Inverse Yeo-Johnson transformation function | IYJtrans |
| Calculate the kernel function | Kernel |
| Convert the Kendall's tau into the copula parameter | ktau.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 censoring | LikCopInd |
| Calculate the likelihood function for the fully parametric joint distribution | Likelihood.Parametric |
| Calculate the profiled likelihood function with kernel smoothing | Likelihood.Profile.Kernel |
| Solve the profiled likelihood function | Likelihood.Profile.Solve |
| Calculate the semiparametric version of profiled likelihood function | Likelihood.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 continuous | LikGamma1 |
| 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 format | Longfun |
| Change H to long format | LongNPT |
| 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 censoring | NonParTrans |
| 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 functions | Omega.hat |
| Fit the dependent censoring models. | optimlikelihood |
| Obtain the value of the density function | parafam.d |
| Obtain the value of the distribution function | parafam.p |
| Obtain the adjustment value of truncation | parafam.trunc |
| Estimation of a parametric dependent censoring model without covariates. | ParamCop |
| Generate constraints of parameters | Parameters.Constraints |
| Estimate the model of Willems et al. (2024+). | pi.surv |
| Draw points to be evaluated | plot_addpte |
| Draw evaluated points. | plot_addpte.eval |
| Draw base plot | plot_base |
| Power transformation function. | power_transform |
| Likelihood function under dependent censoring | PseudoL |
| S-function | S.func |
| Score equations of finite parameters | ScoreEqn |
| Search function | SearchIndicate |
| Set default hyperparameters for EAM algorithm | set.EAM.hyperparameters |
| Set default hyperparameters for grid search algorithm | set.GS.hyperparameters |
| Define the hyperparameters used for finding the identified interval | set.hyperparameters |
| Compute the variance-covariance matrix of the moment functions. | Sigma.hat |
| Estimate a nonparametric transformation function | SolveH |
| Estimating equation for Ht1 | SolveHt1 |
| Cumulative hazard function of survival time under dependent censoring | SolveL |
| Cumulative hazard function of survival time under independent censoring | SolveLI |
| Estimate finite parameters based on score equations | SolveScore |
| Summary of 'depCensoringFit' object | summary.depFit |
| Summary of 'indepCensoringFit' object | summary.indepFit |
| Semiparametric Estimation of the Survival Function under Dependent Censoring | SurvDC |
| Calculate the goodness-of-fit test statistic | SurvDC.GoF |
| Estimated survival function based on copula-graphic estimator (Archimedean copula only) | SurvFunc.CG |
| Estimated survival function based on Kaplan-Meier estimator | SurvFunc.KM |
| Maximum likelihood estimator for a given parametric distribution | SurvMLE |
| Likelihood for a given parametric distribution | SurvMLE.Likelihood |
| Function to simulate (Y,Delta) from the copula based model for (T,C). | TCsim |
| Perform the test of Bei (2024) for a given point | test.point_Bei |
| Perform the test of Bei (2024) simultaneously for multiple time points. | test.point_Bei_MT |
| Standardize data format | uniformize.data |
| Compute the variance of the estimates. | variance.cmprsk |
| Yeo-Johnson transformation function | YJtrans |
