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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 |
| FixedContContIT | Fits (univariate) fixed-effect models to assess surrogacy in the continuous-continuous case based on 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 |
| 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 |
| 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 Information-Theoretic | Provides plots of trial- and individual-level surrogacy in the Information-Theoretic framework |
| 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 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.FixedContContIT | Provides plots of trial- and individual-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.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.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.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 |
| 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 |
| Test.Mono | Test whether the data are compatible with monotonicity for S and/or T (binary endpoints) |
| 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) |