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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 models |
| barPlotCiError | Bar plot with error bars |
| BinaryBenchmark | Compare performance of different model fitting/filtering algorithms |
| 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 |
| BSWiMS.model | BSWiMS model selection |
| cancerVarNames | Data frame used in several examples of this package |
| correlated_Remove | Univariate Filters |
| 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 |
| CVsignature | Cross-validated Signature |
| EmpiricalSurvDiff | Estimate the LR value and its associated p-values |
| ensemblePredict | The median prediction from a list of models |
| featureAdjustment | Adjust each listed variable to the provided set of covariates |
| 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 |
| FRESA.CAD | FeatuRE Selection Algorithms for Computer-Aided Diagnosis (FRESA.CAD) |
| FRESA.Model | Automated model selection |
| FRESAScale | Data frame normalization |
| 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 |
| KNN_method | KNN Setup for KNN prediction |
| LASSO | CV LASSO fit with s="lambda.min" or s="lambda.1se" |
| LASSO_1SE | CV LASSO fit with s="lambda.min" or s="lambda.1se" |
| LASSO_MIN | CV LASSO fit with s="lambda.min" or s="lambda.1se" |
| listTopCorrelatedVariables | List the variables that are highly correlated with each other |
| LM_RIDGE_MIN | Ridge Linear Models |
| modelFitting | Fit a model to the data |
| mRMR.classic_FRESA | FRESA.CAD wrapper of mRMRe::mRMR.classic |
| NAIVE_BAYES | Naive Bayes Modeling |
| nearestNeighborImpute | nearest neighbor NA imputation |
| OrdinalBenchmark | Compare performance of different model fitting/filtering algorithms |
| 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 |
| plot.FRESA_benchmark | Plot the results of the model selection benchmark |
| plotModels.ROC | Plot test ROC curves of each cross-validation model |
| predict | Linear or probabilistic prediction |
| predict.fitFRESA | Linear or probabilistic prediction |
| predict.FRESAKNN | Predicts 'class::knn' models |
| predict.FRESAsignature | Predicts 'CVsignature' models |
| predict.FRESA_LASSO | Predicts LASSO fitted objects |
| predict.FRESA_NAIVEBAYES | Predicts 'NAIVE_BAYES' models |
| predict.FRESA_RIDGE | Predicts 'LM_RIDGE_MIN' models |
| predictionStats_binary | Prediction Evaluation |
| predictionStats_ordinal | Prediction Evaluation |
| predictionStats_regression | Prediction Evaluation |
| randomCV | Cross Validation of Prediction Models |
| rankInverseNormalDataFrame | rank-based inverse normal transformation of the data |
| RegresionBenchmark | Compare performance of different model fitting/filtering algorithms |
| 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 | Returns the summary of the fit |
| 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 |
| univariate_correlation | Univariate Filters |
| univariate_Logit | Univariate Filters |
| univariate_residual | Univariate Filters |
| univariate_tstudent | Univariate Filters |
| univariate_Wilcoxon | Univariate Filters |
| 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 |