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
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
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
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.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
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
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
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 binary-continuous setting
                        (sensitivity-analysis based approach)
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.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)
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
plot Causal-Inference BinBin
                        Plots the (Meta-Analytic) Individual Causal
                        Association and related metrics when S and T
                        are binary outcomes
plot Causal-Inference BinCont
                        Plots the (Meta-Analytic) Individual Causal
                        Association and related metrics when S is
                        continuous and T is binary
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 SPF BinCont        Plots the surrogate predictive function (SPF)
                        in the binary-continuous setting.
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.ContCont.MultS
                        Plots the Individual Causal Association in the
                        setting where there are multiple continuous S
                        and a continuous T
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.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.
