@@ -3,8 +3,8 @@ Type: Package

 Title: A framework for cross-validated classification problems, with

        applications to differential variability and differential

        distribution testing

-Version: 3.3.14

-Date: 2023-02-20

+Version: 3.3.15

+Date: 2023-02-21

 Authors@R:

     c(

     person(given = "Dario", family = "Strbenac", email = "dario.strbenac@sydney.edu.au", role = c("aut", "cre")),

@@ -235,15 +235,15 @@ setMethod("prepareData", "list",

   if("clinical" %in% names(measurements))

     measurements[["clinical"]] <- measurements[["clinical"]][, clinicalPredictors]

-  allMetadata <- mapply(function(measurementsOne, assayID) {

+  allMetadata <- do.call(rbind, mapply(function(measurementsOne, assayID) {

                         data.frame(assay = assayID, feature = colnames(measurementsOne))

-                        }, measurements, names(measurements))

+                        }, measurements, names(measurements), SIMPLIFY = FALSE))

   allMeasurements <- do.call("cbind", measurements)

   # Different assays e.g. mRNA, protein could have same feature name e.g. BRAF.

   colnames(allMeasurements) <- paste(allMetadata[, "assay"], allMetadata[, "feature"], sep = '_')

-  allDataFrame <- DataFrame(allMeasurements)

+  allMeasurements <- DataFrame(allMeasurements)

   S4Vectors::mcols(allMeasurements) <- allMetadata

   # Do other filtering and preparation in DataFrame function.

-  prepareData(dataTable, outcome, clinicalPredictors = NULL, ...)

+  prepareData(allMeasurements, outcome, clinicalPredictors = NULL, ...)

 })

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