All functions

applyBenjaminiHochberg()

applyBenjaminiHochberg

buildLRModel()

buildLRModel()

buildRecipe()

buildRecipe()

buildTuningGrid()

buildTuningGrid()

buildWflow()

buildWflow()

calculateEvalMets()

calculateEvalMets()

calculateMinSamples()

calculateMinSamples()

computeFeatureFreq()

computeFeatureFreq

createMLResultDir()

Create machine learning result directories

createMLinputList()

Create machine learning input list

.calculateAUPRC()

.calculateAUPRC()

.calculateBalAcc()

.calculateBalAcc()

.calculateF1()

.calculateF1()

.calculateLog2APOP()

.calculateLog2APOP()

.calculateSensitivity()

.calculateSensitivity()

.calculateSpecificity()

.calculateSpecificity()

.calculatenMCC()

.calculatenMCC()

.getFitHps()

.getFitHps()

.getTargetVarName()

.getTargetVarName()

.parquet2LOOMatrix()

Build leave-one-out (LOO) merged parquet matrices from stratified parquet files.

encodePhenotype()

encodePhenotype

extractTopFeats()

extractTopFeats()

fitBestModel()

fitBestModel()

generateMLInputs()

Generate all ML input matrices

getConfusionMatrix()

getConfusionMatrix()

getNumFeat()

getNumFeat()

loadMLInputTibble()

loadMLInputTibble()

plotBaselineComparison()

plotBaselineComparison()

plotDefaultEval()

plotDefaultEval()

plotFishers()

Plot top features' Fisher's significance

plotPRC()

plotPRC()

plotTopFeatsVI()

plotTopFeatsVI()

predictML()

predictML()

removeTopFeats()

removeTopFeats

runFisherTests()

runFisherTests

runFishers()

runFishers

runIFE()

runIFE Removes top features identified by ML models and retrains iteratively; returns nMCC at each iteration.

runMDRmodels()

Run MDR (multi-drug resistance) machine learning models

runMLPipeline()

runMLPipeline()

runMLmodels()

Run machine learning models with multiple configurations

runModelingPipeline()

Run the entire AMR ML pipeline from a parquet-backed DuckDB

selectBestModel()

selectBestModel()

shuffleLabels()

shuffleLabels()

splitMLInputTibble()

splitMLInputTibble()

tuneGrid()

tuneGrid()