A_step                  A-step in the EAM algorithm described in KMS19
Bspline.unit.interval   Evaluate the specified B-spline, defined on the
                        unit interval
Bvprob                  Compute bivariate survival probability
Chronometer             Chronometer object
CompC                   Compute phi function
D.hat                   Obtain the diagonal matrix of sample variances
                        of moment functions
DYJtrans                Derivative of the Yeo-Johnson transformation
                        function
Distance                Distance between vectors
EAM                     Main function to run the EAM algorithm
EAM.converged           Check convergence of the EAM algorithm.
EI                      Expected improvement
E_step                  E-step in the EAM algorithm as described in
                        KMS19.
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
IYJtrans                Inverse Yeo-Johnson transformation function
Kernel                  Calculate the kernel function
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)
LikCopInd               Loglikehood function under independent
                        censoring
LikF.cmprsk             Second step log-likelihood function.
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.
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
LongNPT                 Change H to long format
Longfun                 Long format
MSpoint                 Analogue to KMS_AUX4_MSpoints(...) in MATLAB
                        code of Bei (2024).
M_step                  M-step in the EAM algorithm described in KMS19.
NonParTrans             Fit a semiparametric transformation model for
                        dependent censoring
Omega.hat               Obtain the correlation matrix of the moment
                        functions
ParamCop                Estimation of a parametric dependent censoring
                        model without covariates.
Parameters.Constraints
                        Generate constraints of parameters
PseudoL                 Likelihood function under dependent censoring
S.func                  S-function
ScoreEqn                Score equations of finite parameters
SearchIndicate          Search function
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
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).
YJtrans                 Yeo-Johnson transformation function
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
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.
clear.plt.wdw           Clear plotting window
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.
dD.hat                  Obtain the matrix of partial derivatives of the
                        sample variances.
dLambda_AFT_ll          Derivative of link function (AFT model)
dLambda_Cox_wb          Derivative of link function (Cox model)
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.
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.
estimate.cf             Estimate the control function
estimate.cmprsk         Estimate the competing risks model of Rutten,
                        Willems et al. (20XX).
feasible_point_search   Method for finding initial points of the EAM
                        algorithm
fitDepCens              Fit Dependent Censoring Models
fitIndepCens            Fit Independent Censoring Models
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
insert.row              Insert row into a matrix at a given row index
ktau.to.coppar          Convert the Kendall's tau into the copula
                        parameter
lf.delta.beta1          Loss function to compute Delta(beta).
lf.ts                   'Loss function' of the test statistic.
likF.cmprsk.Cholesky    Wrapper implementing likelihood function using
                        Cholesky factorization.
likIFG.cmprsk.Cholesky
                        Full likelihood (including estimation of
                        control function).
log_transform           Logarithmic transformation 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.
m.bar                   Vector of sample average of each moment
                        function (\bar{m}_n(theta)).
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.
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
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.
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
summary.depFit          Summary of 'depCensoringFit' object
summary.indepFit        Summary of 'indepCensoringFit' object
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.
uniformize.data         Standardize data format
variance.cmprsk         Compute the variance of the estimates.
