Package index • amRml

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
`demo_fit`	Demo LR fit
`demo_ml_tibble`	Demo ML input tibble
`.calculateAUPRC()`	.calculateAUPRC()
`.calculateBalAcc()`	.calculateBalAcc()
`.calculateF1()`	.calculateF1()
`.calculateLog2APOP()`	.calculateLog2APOP()
`.calculateMCC()`	.calculateMCC()
`.calculateSensitivity()`	.calculateSensitivity()
`.calculateSpecificity()`	.calculateSpecificity()
`.calculatenMCC()`	.calculatenMCC()
`.getFitHps()`	.getFitHps()
`.getTargetVarName()`	.getTargetVarName()
`.parquet2LOODrugMatrix()`	Build leave-one-out (LOO) merged parquet matrices from drug parquet files.
`.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()`	Compare Baseline Performance With and Without Shuffled Labels
`plotCM()`	Plot a Confusion Matrix Heatmap
`plotCrossDrug()`	Plot cross-drug prediction as a heatmap
`plotDrugDist()`	Plot drug phenotype distribution
`plotDrugPerf()`	Plot drug-level model performance
`plotFishers()`	Plot top features' Fisher's significance
`plotMDR()`	Plot multi-drug resistance (MDR) model performance
`plotPRC()`	Plot a Precision-Recall Curve
`plotROC()`	Plot a Receiver Operating Characteristic (ROC) Curve
`plotShuffleVsReal()`	Compare shuffled vs real model performance
`plotStratifiedPerf()`	Plot stratified model performance
`plotTopClusters()`	Plot top contributing feature clusters
`plotTopFeatsVI()`	Plot Top Feature Importances
`predictML()`	predictML()
`removeTopFeats()`	removeTopFeats
`runFisherTests()`	runFisherTests
`runFishers()`	runFishers
`runIFE()`	runIFE Removes top features identified by ML models and retrains iteratively; returns MCC 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()

Reference

All functions