@@ -3,7 +3,7 @@ Type: Package

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

        applications to differential variability and differential

        distribution testing

-Version: 3.1.21

+Version: 3.1.22

 Date: 2022-10-14

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

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

@@ -945,7 +945,7 @@ train.DataFrame <- function(x, outcomeTrain, classifier = "randomForest", perfor

               if(assayIDs == "all") assayIDs <- unique(mcols(measurements)[, "assay"])

               if(is.null(assayIDs)) assayIDs <- 1

               names(assayIDs) <- assayIDs

-              names(classifier) <- classifier

+              names(classifier) <- assayIDs

               if(multiViewMethod == "none"){

                   resClassifier <-

@@ -1050,7 +1050,7 @@ train.list <- function(x, outcomeTrain, ...)

                   stop("All datasets must have the same samples")

                 # Check the number of outcome is the same

-                if (!all(sapply(x, nrow) == length(x)) && !is.character(x))

+                if (!all(sapply(x, nrow) == length(outcomeTrain)) && !is.character(outcomeTrain))

                   stop("outcome must have same number of samples as measurements")

               df_list <- sapply(x, S4Vectors::DataFrame)

@@ -1065,6 +1065,7 @@ train.list <- function(x, outcomeTrain, ...)

               # Each list of tabular data has been collapsed into a DataFrame.

               # Will be subset to relevant assayIDs inside the DataFrame method.

+              

               train(combined_df, outcomeTrain, ...)

 }

@@ -1115,7 +1116,7 @@ predict.trainedByClassifyR <- function(object, newData, ...)

               newData <- prepareData(newData, useFeatures = allFeatureNames(object))

               # Some classifiers dangerously use positional matching rather than column name matching.

               # newData columns are sorted so that the right column ordering is guaranteed.

-            } else {stop("'newData' is not one of the valid data types. It is of type ", class(newData), '.')}

+            }

     if (is(object, "listOfModels")) 

          mapply(function(model, assay) predict(model, assay), object, newData, SIMPLIFY = FALSE)

@@ -140,9 +140,9 @@

     tuneParamsTrain <- list(topN = topNfeatures)

     tuneParamsTrain <- append(tuneParamsTrain, modellingParams@trainParams@tuneParams)

-    tuneParamsTrain <- tuneParamsTrain[-match("performanceType", names(tuneParamsTrain))]

     tuneCombosTrain <- expand.grid(tuneParamsTrain, stringsAsFactors = FALSE)  

     modellingParams@trainParams@tuneParams <- NULL

+    

     allPerformanceTables <- lapply(rankings, function(rankingsVariety)

     {

       # Creates a matrix. Columns are top n features, rows are varieties (one row if None).

@@ -280,7 +280,6 @@ The naive Bayes kernel classifier by default uses the vertical distance between

 Now, the classification error for each sample is also calculated for both the differential means and differential distribution classifiers and both *ClassifyResult* objects generated so far are plotted with *samplesMetricMap*.

 ```{r, fig.width = 10, fig.height = 7}

-library(grid)

 DMresults <- calcCVperformance(DMresults, "Sample Error")

 DDresults <- calcCVperformance(DDresults, "Sample Error")

 resultsList <- list(Abundance = DMresults, Distribution = DDresults)