Feature Selection Algorithms for Computer Aided Diagnosis

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 |

benchmarking | Compare performance of different model fitting/filtering... |

bootstrapValidation.Bin | Bootstrap validation of binary classification models |

bootstrapValidation.Res | Bootstrap validation of regression models |

bootstrapVarElimination.Bin | IDI/NRI-based backwards variable elimination with... |

bootstrapVarElimination.Res | NeRI-based backwards variable elimination with bootstrapping |

BSWiMS | BSWiMS model selection |

CancerVarNames | Data frame used in several examples of this package |

crossValidationFeatureSelection.Bin | IDI/NRI-based selection of a linear, logistic, or Cox... |

crossValidationFeatureSelection.Res | NeRI-based selection of a linear, logistic, or Cox... |

CV_signature | 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 |

Filter_mRMR | FRESA.CAD wrapper of mRMRe::mRMR.classic |

FilterUnivariate | Univariate Filters |

ForwardModel.Bin | IDI/NRI-based feature selection procedure for linear,... |

ForwardModel.Res | NeRI-based feature selection procedure for linear, logistic,... |

FRESA.CAD-package | FeatuRE Selection Algorithms for Computer-Aided Diagnosis... |

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... |

getVar.Res | Analysis of the effect of each term of a linear regression... |

heatMaps | Plot a heat map of selected variables |

improvedResiduals | Estimate the significance of the reduction of predicted... |

KNN_method | KNN Setup for KNN prediction |

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 |

NAIVE_BAYES | Naive Bayes Modeling |

nearestneighborimpute | nearest neighbor NA imputation |

plot.bootstrapValidation.Bin | Plot ROC curves of bootstrap results |

plot.bootstrapValidation.Res | Plot ROC curves of bootstrap results |

plot.FRESABenchmark | Plot the results of the model selection benchmark |

plotModels.ROC | Plot test ROC curves of each cross-validation model |

predictForFresa | Linear or probabilistic prediction |

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 |

predictionStats | 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... |

residualForFRESA | Return residuals from prediction |

signatureDistance | Distance to the signature template |

summary.bootstrapValidation | Generate a report of the results obtained using the... |

summary.fitFRESA | Returns the summary of the fit |

summaryReport | Report the univariate analysis, the cross-validation analysis... |

timeSeriesAnalysis | Fit the listed time series variables to a given model |

uniRankVar | Univariate analysis of features (additional values returned) |

univariateRankVariables | Univariate analysis of features |

updateModel.Bin | Update the IDI/NRI-based model using new data or new... |

updateModel.Res | Update the NeRI-based model using new data or new threshold... |

update.uniRankVar | Update the univariate analysis using new data |

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