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| ARMD | Data of the Age-Related Macular Degeneration Study |
| BifixedContCont | Fits a bivariate fixed-effects model 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) |
| 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 |
| CIGTS | Data of the Collaborative Initial Glaucoma Treatment Study |
| 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.ContCont | Assess surrogacy in the causal-inference single-trial setting (Individual Causal Association, ICA) in the Continuous-continuous case |
| 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 |
| LongToWide | Reshapes a dataset from the 'long' format (i.e., multiple lines per patient) into the 'wide' format (i.e., one line per patient) |
| MarginalProbs | Computes marginal probabilities for a dataset where the surrogate and true endpoints are binary |
| 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 |
| 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 |
| 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 |
| Ovarian | The Ovarian dataset |
| 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 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 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). |
| plot.BifixedContCont | Provides plots of trial- and individual-level surrogacy in the meta-analytic framework |
| plot.BimixedContCont | Provides plots of trial- and individual-level surrogacy in the meta-analytic framework |
| plot.FixedBinBinIT | 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.FixedBinContIT | 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.FixedContBinIT | 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.FixedContContIT | Provides plots of trial- and individual-level surrogacy in the Information-Theoretic framework |
| plot.FixedDiscrDiscrIT | Provides plots of trial-level surrogacy in the Information-Theoretic framework |
| plot.ICA.BinBin | Plots the (Meta-Analytic) Individual Causal Association and related metrics when S and T are binary outcomes |
| plot.ICA.ContCont | Plots the (Meta-Analytic) Individual Causal Association when S and T are continuous outcomes |
| 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.MICA.ContCont | Plots the (Meta-Analytic) Individual Causal Association when S and T are continuous outcomes |
| plot.MinSurrContCont | Graphically illustrates the theoretical plausibility of finding a good surrogate endpoint in the continuous-continuous case |
| plot.MixedContContIT | Provides plots of trial- and individual-level surrogacy in the Information-Theoretic framework |
| 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.Single.Trial.RE.AA | Conducts a surrogacy analysis based on the single-trial meta-analytic framework |
| plot.SPF.BinBin | Plots the surrogate predictive function (SPF). |
| 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.UnifixedContCont | Provides plots of trial- and individual-level surrogacy in the meta-analytic framework |
| plot.UnimixedContCont | Provides plots of trial- and individual-level surrogacy in the meta-analytic framework |
| 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 |
| Schizo | Data of five clinical trials in schizophrenia |
| Schizo_Bin | Data of a clinical trial in Schizophrenia (with binary outcomes). |
| 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.RE.AA | Conducts a surrogacy analysis based on the single-trial meta-analytic framework |
| SPF.BinBin | Evaluate the surrogate predictive function (SPF) in the binary-binary setting (sensitivity-analysis based approach) |
| 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) |