Fixed various tidyverse warnings.

Fixed an error when calculating the mds dimensions for multiple class comparisons with differing numbers of observations.

Added the transformPercent() method for the AnalysisData S4 class to scale as a percentage of feature maximum intensity.

Feature intensities are now displayed as relative percent intensities in heat maps generated by plotExplanatoryHeatmap().

Reduced the gap between the dendrogram and heat map in outputs of plotExplanatoryHeatmap().

Fixed the margin value displayed in plots from plotSupervisedRF()

The plotExplanatoryHeatmap() method for the Analysis S4 class now returns a warning and skips plotting if an error is encountered whilst trying to plot a heat map.

Added the argument refactor to the method transformTICnorm() to enable the feature intensities of total ion count (TIC) normalised data to be refactored back to a range consistent with the original data by multiplying the normalised values by the median TIC.

Removed the permutation cap when the perm argument of randomForest() is less than the total number of permutations possible.

The class occupancy methods now throw a helpful error message if no features are available on which to compute class occupancy.

Fixed a bug where it was not possible to customize the order of class labels in the legend of plotLDA().

It is now possible to specify multiple cls arguments to the aggregation methods.

Correlation thresholds are now available for both coefficient and total number using the minCoef and maxCor arguments in the correlations() method.

Added the predictions() accessor method for the RandomForest S4 class to enable the retrieval of the out-of-bag model response predictions.

The occupancy filtering methods now error if the value supplied to argument occupancy is non-numeric.

Memory usage and performance improvements for the randomForest() method.

Added type() and response() methods for the Univariate S4 class.

plotLDA() now returns a warning and skips plotting if an error is encountered during PC-LDA.

The value pre-treated is now the default for argument type in the Analysis S4 class accessor methods.

Added the value argument to the explanatoryFeatures() method to allow the specification of on which importance value to apply the specified threshold.

The specified cls argument is now correctly displayed on the x-axis title of the resulting plots from the plotFeature() method.

Added the method predict() for the RandomForest S4 class to predict model response values.

Added the method mtry() for the AnalysisData S4 class to return the default mtry random forest parameter for a given response variable.

Added the method tune() for the AnalysisData S4 class to tune the random forest parameters mtry and ntree for a given response variable.

Suppressed name repair console message encountered during random forest permutation testing.

Added the proximity() method for extracting sample proximities from the RandomForest S4 class.

Added the mds() method to perform multidimensional scaling on sample proximities from the RandomForest S4 class.

Added the roc() method to calculate receiver-operator characteristic curves from the RandomForest S4 class.

An error is now thrown during random forest classification when less than two classes are specified.

plotSupervisedRF() now skips plotting if errors are encountered during random forest training.

plotExplanatoryHeatmap method for the Analysis class now returns the plot only if the number of plots is equal to 1.

Removed reference to the nCores parameter from the documentation example of metabolyse().