Merge branch 'main' into Merging_ML_results

Author: epbrenner

.Rbuildignore

@@ -5,3 +5,5 @@
 ^cran-comments\.md$
 ^\.github$
 ^CODE_OF_CONDUCT\.md$
+^doc$
+^Meta$

DESCRIPTION

@@ -15,8 +15,8 @@ Description: Comprehensive machine learning (ML) pipeline for predicting antimic
     (MDR) and cross-resistance predictions with multiple holdout strategies
     (geographic, temporal, species). Includes model evaluation, feature importance
     analysis, cross-scale performance comparison, and functional characterization
-    of top features. Designed to work with data from amR_data package and generate
-    results for amR_shiny visualization. Can work independently with example
+    of top features. Designed to work with data from amRdata package and generate
+    results for amRshiny visualization. Can work independently with example
     ESKAPE pathogen datasets, too.
 License: BSD_3_clause + file LICENSE
 Encoding: UTF-8
@@ -37,7 +37,11 @@ Imports:
     DBI,
     dplyr,
     duckdb,
+    future,
+    future.apply,
     ggplot2,
+    ggrepel,
+    glmnet,
     glue,
     hardhat,
     parsnip,
@@ -61,5 +65,5 @@ biocViews:
     MicrobialGenomics,
     Pathogen,
     Visualization
-URL: https://github.com/JRaviLab/amR_ml
-BugReports: https://github.com/JRaviLab/amR_ml/issues
+URL: https://github.com/JRaviLab/amRml
+BugReports: https://github.com/JRaviLab/amRml/issues

Meta/vignette.rds

@@ -1,46 +1,45 @@
 # Generated by roxygen2: do not edit by hand
 
+export(.getTargetVarName)
 export(applyBenjaminiHochberg)
 export(buildLRModel)
 export(buildRecipe)
 export(buildTuningGrid)
 export(buildWflow)
-export(calculateAUPRC)
-export(calculateBalAcc)
 export(calculateEvalMets)
-export(calculateF1)
-export(calculateLog2APOP)
 export(calculateMinSamples)
-export(calculateSensitivity)
-export(calculateSpecificity)
-export(calculatenMCC)
 export(computeFeatureFreq)
+export(createMLResultDir)
+export(createMLinputList)
 export(encodePhenotype)
 export(extractTopFeats)
 export(fitBestModel)
 export(generateMLInputs)
-export(getBaselineComparisonBarplot)
 export(getConfusionMatrix)
-export(getFitHps)
 export(getNumFeat)
-export(getTargetVarName)
 export(loadMLInputTibble)
+export(plotBaselineComparison)
 export(plotDefaultEval)
+export(plotFishers)
 export(plotPRC)
 export(plotTopFeatsVI)
-export(predict)
+export(predictML)
 export(removeTopFeats)
 export(runFisherTests)
 export(runFishers)
 export(runIFE)
+export(runMDRmodels)
 export(runMLPipeline)
+export(runMLmodels)
+export(runModelingPipeline)
 export(selectBestModel)
 export(shuffleLabels)
-export(skipImbalancedMatrix)
 export(splitMLInputTibble)
 export(tuneGrid)
 import(arrow)
 import(dplyr)
+import(ggplot2)
+import(ggrepel)
 import(purrr)
 import(stringr)
 import(tibble)

NAMESPACE

@@ -429,7 +429,7 @@ NULL
 #' @noRd
 #' @keywords internal
 #' @param select_best_metric [chr] Metric to select best model: "f_meas",
-#' "pr_auc", "mcc, or "bal_accuracy"
+#' "pr_auc", "mcc", or "bal_accuracy"
 #'
 .checkArgSelectBestMetric <- function(select_best_metric) {
   if (!(select_best_metric %in% c("f_meas", "pr_auc", "mcc", "bal_accuracy"))) {
@@ -454,7 +454,7 @@ NULL
   if (!(".pred_class" %in% colnames(test_data_plus_predictions))) {
     stop(paste(
       "`test_data_plus_predictions` is missing a `.pred_class`",
-      "column. Try using the output of `predict()`."
+      "column. Try using the output of `predictML()`."
     ))
   }
 }