BSWiMS.model            BSWiMS model selection
BinaryBenchmark         Compare performance of different model
                        fitting/filtering algorithms
CVsignature             Cross-validated Signature
EmpiricalSurvDiff       Estimate the LR value and its associated
                        p-values
FRESA.CAD-package       FeatuRE Selection Algorithms for Computer-Aided
                        Diagnosis (FRESA.CAD)
FRESA.Model             Automated model selection
FRESAScale              Data frame normalization
FilterUnivariate        Univariate Filters
ForwardSelection.Model.Bin
                        IDI/NRI-based feature selection procedure for
                        linear, logistic, and Cox proportional hazards
                        regression models
ForwardSelection.Model.Res
                        NeRI-based feature selection procedure for
                        linear, logistic, or Cox proportional hazards
                        regression models
KNN_method              KNN Setup for KNN prediction
LASSO                   CV LASSO fit with s="lambda.min" or
                        s="lambda.1se"
LM_RIDGE_MIN            Ridge Linear Models
NAIVE_BAYES             Naive Bayes Modeling
backVarElimination_Bin
                        IDI/NRI-based backwards variable elimination
backVarElimination_Res
                        NeRI-based backwards variable elimination
baggedModel             Get the bagged model from a list of models
barPlotCiError          Bar plot with error bars
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
ensemblePredict         The median prediction from a list of models
featureAdjustment       Adjust each listed variable to the provided set
                        of covariates
getKNNpredictionFromFormula
                        Predict classification using KNN
getSignature            Returns a CV signature template
getVar.Bin              Analysis of the effect of each term of a binary
                        classification model by analysing its
                        reclassification performance
getVar.Res              Analysis of the effect of each term of a linear
                        regression model by analysing 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
mRMR.classic_FRESA      FRESA.CAD wrapper of mRMRe::mRMR.classic
modelFitting            Fit a model to the data
nearestNeighborImpute   nearest neighbor NA imputation
plot.FRESA_benchmark    Plot the results of the model selection
                        benchmark
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
predict.FRESAKNN        Predicts 'class::knn' models
predict.FRESA_LASSO     Predicts LASSO fitted objects
predict.FRESA_NAIVEBAYES
                        Predicts 'NAIVE_BAYES' models
predict.FRESA_RIDGE     Predicts 'LM_RIDGE_MIN' models
predict.FRESAsignature
                        Predicts 'CVsignature' models
predict.fitFRESA        Linear or probabilistic prediction
predictionStats_binary
                        Prediction Evaluation
randomCV                Cross Validation of Prediction Models
rankInverseNormalDataFrame
                        rank-based inverse normal transformation of the
                        data
reportEquivalentVariables
                        Report the set of variables that will perform
                        an equivalent IDI discriminant function
residualForFRESA        Return residuals from prediction
signatureDistance       Distance to the signature template
summary.bootstrapValidation_Bin
                        Generate a report of the results obtained using
                        the bootstrapValidation_Bin function
summary.fitFRESA        Returns the summary of the fit
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
