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| FRESA.CAD-package | FeatuRE Selection Algorithms for Computer-Aided Diagnosis (FRESA.CAD) |
| 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 |
| 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 |
| FRESA.CAD | FeatuRE Selection Algorithms for Computer-Aided Diagnosis (FRESA.CAD) |
| FRESA.Model | Automated model selection |
| 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 | Plot ROC curves of bootstrap results |
| 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 | Generate a report of the results obtained using the bootstrapValidation_Bin function |
| 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 | Update the univariate analysis using new data |
| 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 |