FRESA.CAD-package       FeatuRE Selection Algorithms for Computer-Aided
                        Diagnosis (FRESA.CAD)
FRESA.Model             Automated model selection
ForwardSelection.Model.Bin
                        IDI/NRI-based feature selection procedure for
                        linear, logistic, and Cox proportional hazards
                        regresion models
ForwardSelection.Model.Res
                        NeRI-based feature selection procedure for
                        linear, logistic, or Cox proportional hazards
                        regression models
backVarElimination_Bin
                        IDI/NRI-based backwards variable elimination
backVarElimination_Res
                        NeRI-based backwards variable elimination
baggedModel             Get the bagged model from a list of forward
                        models
bootstrapValidation_Bin
                        Bootstrap validation of binary classification
                        models
bootstrapValidation_Res
                        Bootstrap validation of regression models
bootstrapVarElimination_Bin
                        IDI/NRI-based backwards variable elimination
                        with bootstrapping
bootstrapVarElimination_Res
                        NeRI-based backwards variable elimination with
                        bootstrapping
cancerVarNames          Data frame used in several examples of this
                        package
crossValidationFeatureSelection_Bin
                        IDI/NRI-based selection of a linear, logistic,
                        or Cox proportional hazards regression model
                        from a set of candidate variables
crossValidationFeatureSelection_Res
                        NeRI-based selection of a linear, logistic, or
                        Cox proportional hazards regression model from
                        a set of candidate variables
featureAdjustment       Adjust each listed variable to the provided set
                        of covariates
getKNNpredictionFromFormula
                        Predict classification using KNN
getVar.Bin              Analysis of the effect of each term of a binary
                        classification model by analyzing its
                        reclassification performance
getVar.Res              Analysis of the effect of each term of a linear
                        regression model by analyzing its residuals
heatMaps                Plot a heat map of selected variables
improvedResiduals       Estimate the significance of the reduction of
                        predicted residuals
listTopCorrelatedVariables
                        List the variables that are highly correlated
                        with each other
medianPredict           The median prediction from a list of models
modelFitting            Fit a model to the data
plot.bootstrapValidation_Bin
                        Plot ROC curves of bootstrap results
plot.bootstrapValidation_Res
                        Plot ROC curves of bootstrap results
plotModels.ROC          Plot test ROC curves of each cross-validation
                        model
predictForFresa         Linear or probabilistic prediction
rankInverseNormalDataFrame
                        Perform a z-transformation of the data using
                        the rank-based inverse normal transformation
reportEquivalentVariables
                        Report the set of variables that will perform
                        an equivalent IDI discriminant function
residualForFRESA        Return residuals from prediction
summary.bootstrapValidation_Bin
                        Generate a report of the results obtained using
                        the bootstrapValidation_Bin function
summaryReport           Report the univariate analysis, the
                        cross-validation analysis and the correlation
                        analysis
timeSerieAnalysis       Fit the listed time series variables to a given
                        model
uniRankVar              Univariate analysis of features (additional
                        values returned)
univariateRankVariables
                        Univariate analysis of features
update.uniRankVar       Update the univariate analysis using new data
updateModel.Bin         Update the IDI/NRI-based model using new data
                        or new threshold values
updateModel.Res         Update the NeRI-based model using new data or
                        new threshold values
