AA.MultS                Compute the multiple-surrogate adjusted
                        association
ARMD                    Data of the Age-Related Macular Degeneration
                        Study
ARMD.MultS              Data of the Age-Related Macular Degeneration
                        Study with multiple candidate surrogates
ARMD_S                  Data of the Age-Related Macular Degeneration
                        Study (Strobl workshop)
BifixedContCont         Fits a bivariate fixed-effects model to assess
                        surrogacy in the meta-analytic multiple-trial
                        setting (Continuous-continuous case)
BimixedCbCContCont      Fits a bivariate mixed-effects model using the
                        cluster-by-cluster (CbC) estimator to assess
                        surrogacy in the meta-analytic multiple-trial
                        setting (Continuous-continuous case)
BimixedContCont         Fits a bivariate mixed-effects model to assess
                        surrogacy in the meta-analytic multiple-trial
                        setting (Continuous-continuous case)
Bootstrap.MEP.BinBin    Bootstrap 95% CI around the maximum-entropy ICA
                        and SPF (surrogate predictive function)
CausalDiagramBinBin     Draws a causal diagram depicting the median
                        informational coefficients of correlation (or
                        odds ratios) between the counterfactuals for a
                        specified range of values of the ICA in the
                        binary-binary setting.
CausalDiagramContCont   Draws a causal diagram depicting the median
                        correlations between the counterfactuals for a
                        specified range of values of ICA or MICA in the
                        continuous-continuous setting
Dvine_ICA_confint       Confidence interval for the ICA given the
                        unidentifiable parameters
ECT                     Apply the Entropy Concentration Theorem
Fano.BinBin             Evaluate the possibility of finding a good
                        surrogate in the setting where both S and T are
                        binary endpoints
FederatedApproachStage1
                        Fits the first stage model in the two-stage
                        federated data analysis approach.
FederatedApproachStage2
                        Fits the second stage model in the two-stage
                        federated data analysis approach.
FixedBinBinIT           Fits (univariate) fixed-effect models to assess
                        surrogacy in the binary-binary case based on
                        the Information-Theoretic framework
FixedBinContIT          Fits (univariate) fixed-effect models to assess
                        surrogacy in the case where the true endpoint
                        is binary and the surrogate endpoint is
                        continuous (based on the Information-Theoretic
                        framework)
FixedContBinIT          Fits (univariate) fixed-effect models to assess
                        surrogacy in the case where the true endpoint
                        is continuous and the surrogate endpoint is
                        binary (based on the Information-Theoretic
                        framework)
FixedContContIT         Fits (univariate) fixed-effect models to assess
                        surrogacy in the continuous-continuous case
                        based on the Information-Theoretic framework
FixedDiscrDiscrIT       Investigates surrogacy for binary or ordinal
                        outcomes using the Information Theoretic
                        framework
ICA.BinBin              Assess surrogacy in the causal-inference
                        single-trial setting in the binary-binary case
ICA.BinBin.CounterAssum
                        ICA (binary-binary setting) that is obtaied
                        when the counterfactual correlations are
                        assumed to fall within some prespecified
                        ranges.
ICA.BinBin.Grid.Full    Assess surrogacy in the causal-inference
                        single-trial setting in the binary-binary case
                        when monotonicity for S and T is assumed using
                        the full grid-based approach
ICA.BinBin.Grid.Sample
                        Assess surrogacy in the causal-inference
                        single-trial setting in the binary-binary case
                        when monotonicity for S and T is assumed using
                        the grid-based sample approach
ICA.BinBin.Grid.Sample.Uncert
                        Assess surrogacy in the causal-inference
                        single-trial setting in the binary-binary case
                        when monotonicity for S and T is assumed using
                        the grid-based sample approach, accounting for
                        sampling variability in the marginal pi.
ICA.BinCont             Assess surrogacy in the causal-inference
                        single-trial setting in the binary-continuous
                        case
ICA.BinCont.BS          Assess surrogacy in the causal-inference
                        single-trial setting in the binary-continuous
                        case with an additional bootstrap procedure
                        before the assessment
ICA.ContCont            Assess surrogacy in the causal-inference
                        single-trial setting (Individual Causal
                        Association, ICA) in the Continuous-continuous
                        case
ICA.ContCont.MultS      Assess surrogacy in the causal-inference
                        single-trial setting (Individual Causal
                        Association, ICA) using a continuous univariate
                        T and multiple continuous S
ICA.ContCont.MultS.MPC
                        Assess surrogacy in the causal-inference
                        single-trial setting (Individual Causal
                        Association, ICA) using a continuous univariate
                        T and multiple continuous S, by simulating
                        correlation matrices using a modified algorithm
                        based on partial correlations
ICA.ContCont.MultS.PC   Assess surrogacy in the causal-inference
                        single-trial setting (Individual Causal
                        Association, ICA) using a continuous univariate
                        T and multiple continuous S, by simulating
                        correlation matrices using an algorithm based
                        on partial correlations
ICA.ContCont.MultS_alt
                        Assess surrogacy in the causal-inference
                        single-trial setting (Individual Causal
                        Association, ICA) using a continuous univariate
                        T and multiple continuous S, alternative
                        approach
ICA.Sample.ContCont     Assess surrogacy in the causal-inference
                        single-trial setting (Individual Causal
                        Association, ICA) in the Continuous-continuous
                        case using the grid-based sample approach
ICA.Sample.ControlTreat
                        Assess surrogacy in the causal-inference
                        single-trial setting (Individual Causal
                        Association, ICA) in the Continuous-continuous
                        case using the grid-based sample approach when
                        data is only avalable for the control treatment
ICA_alpha_ContCont      Assess surrogacy using a Rényi divergence based
                        family of metrics in the causal-inference
                        single-trial setting in normal case
ICA_contcont_long_cre   Assess surrogacy in the information-theoretic
                        causal-inference framework (Individual Causal
                        Association, ICA) for a continuous surrogate
                        and true endpoint measured repeatedly over time
                        in a single-trial setting
ICA_contcont_long_galecki
                        Assess surrogacy in the information-theoretic
                        causal-inference framework (Individual Causal
                        Association, ICA) for a continuous surrogate
                        and true endpoint measured repeatedly over time
                        in a single-trial setting
ICA_contcont_long_ri    Assess surrogacy in the information-theoretic
                        causal-inference framework (Individual Causal
                        Association, ICA) for a continuous surrogate
                        and true endpoint measured repeatedly over time
                        in a single-trial setting
ICA_given_model_constructor
                        Constructor for the function that returns that
                        ICA as a function of the identifiable
                        parameters
ICA_given_model_constructor_SurvSurv
                        Constructor for the function that returns that
                        ICA as a function of the identifiable
                        parameters for survival-survival
ICA_t                   The function 'ICA_t()' is to evaluate surrogacy
                        in the single-trial causal-inference framework.
ISTE.ContCont           Individual-level surrogate threshold effect for
                        continuous normally distributed surrogate and
                        true endpoints.
LongToWide              Reshapes a dataset from the 'long' format
                        (i.e., multiple lines per patient) into the
                        'wide' format (i.e., one line per patient)
MICA.ContCont           Assess surrogacy in the causal-inference
                        multiple-trial setting (Meta-analytic
                        Individual Causal Association; MICA) in the
                        continuous-continuous case
MICA.Sample.ContCont    Assess surrogacy in the causal-inference
                        multiple-trial setting (Meta-analytic
                        Individual Causal Association; MICA) in the
                        continuous-continuous case using the grid-based
                        sample approach
MarginalProbs           Computes marginal probabilities for a dataset
                        where the surrogate and true endpoints are
                        binary
MaxEntContCont          Use the maximum-entropy approach to compute ICA
                        in the continuous-continuous sinlge-trial
                        setting
MaxEntICABinBin         Use the maximum-entropy approach to compute ICA
                        in the binary-binary setting
MaxEntSPFBinBin         Use the maximum-entropy approach to compute SPF
                        (surrogate predictive function) in the
                        binary-binary setting
MetaAnalyticSurvBin     Compute surrogacy measures for a binary
                        surrogate and a time-to-event true endpoint in
                        the meta-analytic multiple-trial setting.
MetaAnalyticSurvCat     Compute surrogacy measures for a categorical
                        (ordinal) surrogate and a time-to-event true
                        endpoint in the meta-analytic multiple-trial
                        setting.
MetaAnalyticSurvCont    Compute surrogacy measures for a continuous
                        (normally-distributed) surrogate and a
                        time-to-event true endpoint in the
                        meta-analytic multiple-trial setting.
MetaAnalyticSurvSurv    Compute surrogacy measures for a time-to-event
                        surrogate and a time-to-event true endpoint in
                        the meta-analytic multiple-trial setting.
MinSurrContCont         Examine the plausibility of finding a good
                        surrogate endpoint in the Continuous-continuous
                        case
MixedContContIT         Fits (univariate) mixed-effect models to assess
                        surrogacy in the continuous-continuous case
                        based on the Information-Theoretic framework
MufixedContCont.MultS   Fits a multivariate fixed-effects model to
                        assess surrogacy in the meta-analytic
                        multiple-trial setting (Continuous-continuous
                        case with multiple surrogates)
MumixedContCont.MultS   Fits a multivariate mixed-effects model to
                        assess surrogacy in the meta-analytic
                        multiple-trial setting (Continuous-continuous
                        case with multiple surrogates)
Ovarian                 The Ovarian dataset
PANSS                   PANSS subscales and total score based on the
                        data of five clinical trials in schizophrenia
PPE.BinBin              Evaluate a surrogate predictive value based on
                        the minimum probability of a prediction error
                        in the setting where both S and T are binary
                        endpoints
PROC.BinBin             Evaluate the individual causal association
                        (ICA) and reduction in probability of a
                        prediction error (RPE) in the setting where
                        both S and T are binary endpoints
Pos.Def.Matrices        Generate 4 by 4 correlation matrices and flag
                        the positive definite ones
Pred.TrialT.ContCont    Compute the expected treatment effect on the
                        true endpoint in a new trial (when both S and T
                        are normally distributed continuous endpoints)
Prentice                Evaluates surrogacy based on the Prentice
                        criteria for continuous endpoints (single-trial
                        setting)
RandVec                 Generate random vectors with a fixed sum
Restrictions.BinBin     Examine restrictions in pi_{f} under different
                        montonicity assumptions for binary S and T
SPF.BinBin              Evaluate the surrogate predictive function
                        (SPF) in the binary-binary setting
                        (sensitivity-analysis based approach)
SPF.BinCont             Evaluate the surrogate predictive function
                        (SPF) in the causal-inference single-trial
                        setting in the binary-continuous case
Schizo                  Data of five clinical trials in schizophrenia
Schizo_Bin              Data of a clinical trial in Schizophrenia (with
                        binary outcomes).
Schizo_BinCont          Data of a clinical trial in schizophrenia, with
                        binary and continuous endpoints
Schizo_PANSS            Longitudinal PANSS data of five clinical trials
                        in schizophrenia
Sim.Data.Counterfactuals
                        Simulate a dataset that contains
                        counterfactuals
Sim.Data.CounterfactualsBinBin
                        Simulate a dataset that contains
                        counterfactuals for binary endpoints
Sim.Data.MTS            Simulates a dataset that can be used to assess
                        surrogacy in the multiple-trial setting
Sim.Data.STS            Simulates a dataset that can be used to assess
                        surrogacy in the single-trial setting
Sim.Data.STSBinBin      Simulates a dataset that can be used to assess
                        surrogacy in the single trial setting when S
                        and T are binary endpoints
Single.Trial.ContCont   Conducts a surrogacy analysis in the
                        single-trial setting for Continuous S and T
Single.Trial.RE.AA      Conducts a surrogacy analysis based on the
                        single-trial meta-analytic framework
SurvSurv                Assess surrogacy for two survival endpoints
                        based on information theory and a two-stage
                        approach
Test.Mono               Test whether the data are compatible with
                        monotonicity for S and/or T (binary endpoints)
TrialLevelIT            Estimates trial-level surrogacy in the
                        information-theoretic framework
TrialLevelMA            Estimates trial-level surrogacy in the
                        meta-analytic framework
TwoStageSurvSurv        Assess trial-level surrogacy for two survival
                        endpoints using a two-stage approach
UnifixedContCont        Fits univariate fixed-effect models to assess
                        surrogacy in the meta-analytic multiple-trial
                        setting (continuous-continuous case)
UnimixedContCont        Fits univariate mixed-effect models to assess
                        surrogacy in the meta-analytic multiple-trial
                        setting (continuous-continuous case)
association_gof_copula
                        Produce Associational GoF plot
binary_continuous_loglik
                        Loglikelihood function for binary-continuous
                        copula model
cdf_fun                 Function factory for distribution functions
clayton_loglik_copula_scale
                        Loglikelihood on the Copula Scale for the
                        Clayton Copula
colorectal              The Colorectal dataset with a binary surrogate.
colorectal4             The Colorectal dataset with an ordinal
                        surrogate.
comb27.BinBin           Assesses the surrogate predictive value of each
                        of the 27 prediction functions in the setting
                        where both S and T are binary endpoints
compute_ICA             Compute Individual Causal Association for a
                        given D-vine copula model in the setting of
                        choice.
compute_ICA_BinCont     Compute Individual Causal Association for a
                        given D-vine copula model in the
                        Binary-Continuous Setting
compute_ICA_ContCont    Compute Individual Causal Association for a
                        given D-vine copula model in the
                        Continuous-Continuous Setting
compute_ICA_OrdCont     Compute Individual Causal Association for a
                        given D-vine copula model in the
                        Ordinal-Continuous Setting
compute_ICA_OrdOrd      Compute Individual Causal Association for a
                        given D-vine copula model in the
                        Ordinal-Ordinal Setting
compute_ICA_SurvSurv    Compute Individual Causal Association for a
                        given D-vine copula model in the
                        Survival-Survival Setting
constructor_ICA_estimator
                        Function constructor to estimate the ICA given
                        a set of sampled patient-level treatment
                        effects
continuous_continuous_loglik
                        Loglikelihood function for
                        continuous-continuous copula model
delta_method_log_mutinfo
                        Variance of log-mutual information based on the
                        delta method
estimate_ICA_BinCont    Estimate ICA in Binary-Continuous Setting
estimate_ICA_ContCont   Estimate ICA in Ordinal-Ordinal Setting
estimate_ICA_OrdCont    Estimate ICA in Ordinal-Continuous Setting
estimate_ICA_OrdOrd     Estimate ICA in Ordinal-Ordinal Setting
estimate_marginal       Estimate marginal distribution using ML
estimate_mutual_information_SurvSurv
                        Estimate the Mutual Information in the
                        Survival-Survival Setting
fit_copula_ContCont     Fit continuous-continuous vine copula model
fit_copula_OrdCont      Fit ordinal-continuous vine copula model
fit_copula_OrdOrd       Fit ordinal-ordinal vine copula model
fit_copula_model_BinCont
                        Fit copula model for binary true endpoint and
                        continuous surrogate endpoint
fit_copula_submodel_BinCont
                        Fit binary-continuous copula submodel
fit_copula_submodel_ContCont
                        Fit ordinal-continuous copula submodel
fit_copula_submodel_OrdCont
                        Fit ordinal-continuous copula submodel
fit_copula_submodel_OrdOrd
                        Fit ordinal-continuous copula submodel
fit_model_SurvSurv      Fit Survival-Survival model
frank_loglik_copula_scale
                        Loglikelihood on the Copula Scale for the Frank
                        Copula
gaussian_loglik_copula_scale
                        Loglikelihood on the Copula Scale for the
                        Gaussian Copula
gumbel_loglik_copula_scale
                        Loglikelihood on the Copula Scale for the
                        Gumbel Copula
log_likelihood_copula_model
                        Computes loglikelihood for a given copula model
loglik_copula_scale     Loglikelihood on the Copula Scale
marginal_distribution   Fit marginal distribution
marginal_gof_copula     Produce marginal GoF plot
marginal_gof_plots_scr
                        Marginal survival function goodness of fit
marginal_gof_scr_S_plot
                        Goodness-of-fit plot for the marginal survival
                        functions
mean_S_before_T_plot_scr
                        Goodness of fit plot for the fitted copula
model_fit_measures      Goodness of fit information for
                        survival-survival model
new_vine_copula_fit     Constructor for vine copula model
new_vine_copula_ss_fit
                        Constructor for vine copula model
ordinal_continuous_loglik
                        Loglikelihood function for ordinal-continuous
                        copula model
ordinal_ordinal_loglik
                        Loglikelihood function for ordinal-ordinal
                        copula model
ordinal_to_cutpoints    Convert Ordinal Observations to Latent
                        Cutpoints
pdf_fun                 Function factory for density functions
plot Causal-Inference BinBin
                        Plots the (Meta-Analytic) Individual Causal
                        Association and related metrics when S and T
                        are binary outcomes
plot Causal-Inference ContCont
                        Plots the (Meta-Analytic) Individual Causal
                        Association when S and T are continuous
                        outcomes
plot FixedDiscrDiscrIT
                        Provides plots of trial-level surrogacy in the
                        Information-Theoretic framework
plot ISTE.ContCont      Plots the individual-level surrogate threshold
                        effect (STE) values and related metrics
plot Information-Theoretic
                        Provides plots of trial- and individual-level
                        surrogacy in the Information-Theoretic
                        framework
plot Information-Theoretic BinCombn
                        Provides plots of trial- and individual-level
                        surrogacy in the Information-Theoretic
                        framework when both S and T are binary, or when
                        S is binary and T is continuous (or vice versa)
plot MaxEnt ContCont    Plots the sensitivity-based and maximum entropy
                        based Individual Causal Association when S and
                        T are continuous outcomes in the single-trial
                        setting
plot MaxEntICA BinBin   Plots the sensitivity-based and maximum entropy
                        based Individual Causal Association when S and
                        T are binary outcomes
plot MaxEntSPF BinBin   Plots the sensitivity-based and maximum entropy
                        based surrogate predictive function (SPF) when
                        S and T are binary outcomes.
plot Meta-Analytic      Provides plots of trial- and individual-level
                        surrogacy in the meta-analytic framework
plot MinSurrContCont    Graphically illustrates the theoretical
                        plausibility of finding a good surrogate
                        endpoint in the continuous-continuous case
plot PredTrialTContCont
                        Plots the expected treatment effect on the true
                        endpoint in a new trial (when both S and T are
                        normally distributed continuous endpoints)
plot SPF BinBin         Plots the surrogate predictive function (SPF)
                        in the binary-binary settinf.
plot.Fano.BinBin        Plots the distribution of R^2_{HL} either as a
                        density or as function of pi_{10} in the
                        setting where both S and T are binary endpoints
plot.ICA.BinCont        Plot the individual causal association (ICA) in
                        the causal-inference single-trial setting in
                        the binary-continuous case.
plot.ICA.ContCont.MultS
                        Plots the Individual Causal Association in the
                        setting where there are multiple continuous S
                        and a continuous T
plot.MetaAnalyticSurvBin
                        Generates a plot of the estimated treatment
                        effects for the surrogate endpoint versus the
                        estimated treatment effects for the true
                        endpoint for an object fitted with the
                        'MetaAnalyticSurvBin()' function.
plot.MetaAnalyticSurvCat
                        Generates a plot of the estimated treatment
                        effects for the surrogate endpoint versus the
                        estimated treatment effects for the true
                        endpoint for an object fitted with the
                        'MetaAnalyticSurvCat()' function.
plot.MetaAnalyticSurvCont
                        Generates a plot of the estimated treatment
                        effects for the surrogate endpoint versus the
                        estimated treatment effects for the true
                        endpoint for an object fitted with the
                        'MetaAnalyticSurvCont()' function.
plot.MetaAnalyticSurvSurv
                        Generates a plot of the estimated treatment
                        effects for the surrogate endpoint versus the
                        estimated treatment effects for the true
                        endpoint for an object fitted with the
                        'MetaAnalyticSurvSurv()' function.
plot.PPE.BinBin         Plots the distribution of either PPE, RPE or
                        R^2_{H} either as a density or as a histogram
                        in the setting where both S and T are binary
                        endpoints
plot.SPF.BinCont        Plot the surrogate predictive function (SPF) in
                        the causal-inference single-trial setting in
                        the binary-continuous case.
plot.SurvSurv           Provides plots of trial- and individual-level
                        surrogacy in the Information-Theoretic
                        framework when both S and T are time-to-event
                        endpoints
plot.TrialLevelIT       Provides a plots of trial-level surrogacy in
                        the information-theoretic framework based on
                        the output of the 'TrialLevelIT()' function
plot.TrialLevelMA       Provides a plots of trial-level surrogacy in
                        the meta-analytic framework based on the output
                        of the 'TrialLevelMA()' function
plot.TwoStageSurvSurv   Plots trial-level surrogacy in the
                        meta-analytic framework when two survival
                        endpoints are considered.
plot.comb27.BinBin      Plots the distribution of prediction error
                        functions in decreasing order of appearance.
plot.vine_copula_fit    Goodness-of-fit plots for the fitted copula
                        models
print.MetaAnalyticSurvBin
                        Prints all the elements of an object fitted
                        with the 'MetaAnalyticSurvBin()' function.
print.MetaAnalyticSurvCat
                        Prints all the elements of an object fitted
                        with the 'MetaAnalyticSurvCat()' function.
print.MetaAnalyticSurvCont
                        Prints all the elements of an object fitted
                        with the 'MetaAnalyticSurvCont()' function.
print.MetaAnalyticSurvSurv
                        Prints all the elements of an object fitted
                        with the 'MetaAnalyticSurvSurv()' function.
print.vine_copula_fit   Print summary of fitted copula model
prostate                The prostate dataset with a continuous
                        surrogate.
sample_copula_parameters
                        Sample Unidentifiable Copula Parameters
sample_deltas_BinCont   Sample individual casual treatment effects from
                        given D-vine copula model in binary continuous
                        setting
sample_dvine            Sample copula data from a given
                        four-dimensional D-vine copula
sensitivity_analysis_BinCont_copula
                        Perform Sensitivity Analysis for the Individual
                        Causal Association with a Continuous Surrogate
                        and Binary True Endpoint
sensitivity_analysis_SurvSurv_copula
                        Sensitivity analysis for individual causal
                        association
sensitivity_analysis_copula
                        Perform Sensitivity Analysis for the Individual
                        Causal Association based on a D-vine copula
                        model
sensitivity_intervals_Dvine
                        Compute Sensitivity Intervals
summary.FederatedApproachStage2
                        Provides a summary of the surrogacy measures
                        for an object fitted with the
                        'FederatedApproachStage2()' function.
summary.MetaAnalyticSurvBin
                        Provides a summary of the surrogacy measures
                        for an object fitted with the
                        'MetaAnalyticSurvBin()' function.
summary.MetaAnalyticSurvCat
                        Provides a summary of the surrogacy measures
                        for an object fitted with the
                        'MetaAnalyticSurvCat()' function.
summary.MetaAnalyticSurvCont
                        Provides a summary of the surrogacy measures
                        for an object fitted with the
                        'MetaAnalyticSurvCont()' function.
summary.MetaAnalyticSurvSurv
                        Provides a summary of the surrogacy measures
                        for an object fitted with the
                        'MetaAnalyticSurvSurv()' function.
summary_level_bootstrap_ICA
                        Bootstrap based on the multivariate normal
                        sampling distribution
twostep_BinCont         Fit binary-continuous copula submodel with
                        two-step estimator
twostep_SurvSurv        Fit survival-survival copula submodel with
                        two-step estimator
