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