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

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

        applications to differential variability and differential

        distribution testing

-Version: 3.1.9

-Date: 2022-07-25

+Version: 3.1.10

+Date: 2022-08-02

 Author: Dario Strbenac, Ellis Patrick, John Ormerod, Graham Mann, Jean Yang

 Maintainer: Dario Strbenac <dario.strbenac@sydney.edu.au>

 VignetteBuilder: knitr

@@ -345,7 +345,7 @@ setMethod("crossValidate", "data.frame", # data.frame of numeric measurements.

                    nCores = 1,

                    characteristicsLabel = NULL)

           {

-              measurements <- DataFrame(measurements)

+              measurements <- S4Vectors::DataFrame(measurements)

               crossValidate(measurements = measurements,

                             outcome = outcome,

                             nFeatures = nFeatures,

@@ -170,7 +170,7 @@ standardGeneric("elasticNetGLMpredictInterface"))

 setMethod("elasticNetGLMpredictInterface", c("multnet", "matrix"),

           function(model, measurementsTest, ...)

 {

-  elasticNetGLMpredictInterface(model, DataFrame(measurementsTest, check.names = FALSE), ...)

+  elasticNetGLMpredictInterface(model, S4Vectors::DataFrame(measurementsTest, check.names = FALSE), ...)

 })

 # Sample information data, for example.

@@ -64,9 +64,9 @@ setGeneric("fisherDiscriminant", function(measurementsTrain, ...) standardGeneri

 #' @export

 setMethod("fisherDiscriminant", "matrix", function(measurementsTrain, classesTrain, measurementsTest, ...) # Matrix of numeric measurements.

 {

-  fisherDiscriminant(DataFrame(measurementsTrain[, , drop = FALSE], check.names = FALSE),

+  fisherDiscriminant(S4Vectors::DataFrame(measurementsTrain[, , drop = FALSE], check.names = FALSE),

                      classesTrain,

-                     DataFrame(measurementsTest[, , drop = FALSE], check.names = FALSE), ...)

+                     S4Vectors::DataFrame(measurementsTest[, , drop = FALSE], check.names = FALSE), ...)

 })

 #' @rdname fisherDiscriminant

@@ -144,7 +144,7 @@ standardGeneric("GLMpredictInterface"))

 setMethod("GLMpredictInterface", c("glm", "matrix"),

           function(model, measurementsTest, ...)

 {

-  GLMpredictInterface(model, DataFrame(measurementsTest, check.names = FALSE), ...)

+  GLMpredictInterface(model, S4Vectors::DataFrame(measurementsTest, check.names = FALSE), ...)

 })

 #' @rdname GLM

@@ -92,7 +92,7 @@ setMethod("runTest", "DataFrame", # Sample information data or one of the other

 function(measurementsTrain, outcomeTrain, measurementsTest, outcomeTest,

          crossValParams = CrossValParams(), # crossValParams might be used for tuning optimisation.

          modellingParams = ModellingParams(), characteristics = S4Vectors::DataFrame(), verbose = 1, .iteration = NULL)

-{if(!is.null(.iteration) && .iteration != "internal")

+{

   if(is.null(.iteration)) # Not being called by runTests but by user. So, check the user input.

   {

     if(is.null(rownames(measurementsTrain)))

@@ -258,7 +258,7 @@ input data. Autmomatically reducing to smaller number.")

     performanceChanges <- round(performancesWithoutEach - calcExternalPerformance(outcomeTest, predictedOutcome, performanceType), 2)

     if(is.null(S4Vectors::mcols(measurementsTrain))) selectedFeatures <- featuresInfo[selectedFeaturesIndices, "Original Feature"] else selectedFeatures <- featuresInfo[selectedFeaturesIndices, c("Original Assay", "Original Feature")]

-    importanceTable <- DataFrame(selectedFeatures, performanceChanges)

+    importanceTable <- S4Vectors::DataFrame(selectedFeatures, performanceChanges)

     if(ncol(importanceTable) == 2) colnames(importanceTable)[1] <- "feature"

     colnames(importanceTable)[ncol(importanceTable)] <- paste("Change in", performanceType)

   }

@@ -310,7 +310,7 @@ input data. Autmomatically reducing to smaller number.")

     }

     ClassifyResult(characteristics, allSamples, featuresInfo, list(rankedFeatures), list(selectedFeatures),

-                   list(models), tuneDetails, DataFrame(sample = rownames(measurementsTest), predictedOutcome, check.names = FALSE), allOutcome, importanceTable)

+                   list(models), tuneDetails, S4Vectors::DataFrame(sample = rownames(measurementsTest), predictedOutcome, check.names = FALSE), allOutcome, importanceTable)

   }  

 })

@@ -159,10 +159,10 @@ input data. Autmomatically reducing to smaller number.")

         predictsColumnName <- "risk"

     else # Classification task. A factor.

         predictsColumnName <- "class"

-    predictionsTable <- DataFrame(sample = unlist(lapply(results, "[[", "testSet")), splitsTestInfo, unlist(lapply(results, "[[", "predictions")), check.names = FALSE)

+    predictionsTable <- S4Vectors::DataFrame(sample = unlist(lapply(results, "[[", "testSet")), splitsTestInfo, unlist(lapply(results, "[[", "predictions")), check.names = FALSE)

     colnames(predictionsTable)[ncol(predictionsTable)] <- predictsColumnName

   } else { # data frame

-    predictionsTable <- DataFrame(sample = unlist(lapply(results, "[[", "testSet")), splitsTestInfo, do.call(rbind, lapply(results, "[[", "predictions")), check.names = FALSE)

+    predictionsTable <- S4Vectors::DataFrame(sample = unlist(lapply(results, "[[", "testSet")), splitsTestInfo, do.call(rbind, lapply(results, "[[", "predictions")), check.names = FALSE)

   }

   rownames(predictionsTable) <- NULL

   tuneList <- lapply(results, "[[", "tune")

@@ -578,12 +578,12 @@

       renamedInfo[rowsAssay, "feature"] <- paste("Feature", seq_along(rowsAssay), sep = '')

       renamedInfo[rowsAssay, "assay"] <- renamedAssays[match(assay, assays)]

     }

-    featuresInfo <- DataFrame(originalInfo, renamedInfo)

+    featuresInfo <- S4Vectors::DataFrame(originalInfo, renamedInfo)

     colnames(featuresInfo) <- c("Original Assay", "Original Feature", "Renamed Assay", "Renamed Feature")

   } else {

     originalFeatures <- colnames(measurements)

     renamedInfo <- paste("Feature", seq_along(measurements), sep = '')

-    featuresInfo <- DataFrame(originalFeatures, renamedInfo)

+    featuresInfo <- S4Vectors::DataFrame(originalFeatures, renamedInfo)

     colnames(featuresInfo) <- c("Original Feature", "Renamed Feature")

   }

   featuresInfo