Changes in version 2.2.0:

    o   getClasses is no longer a slot of PredictParams. Every predictor
	 function needs to return either a factor vector of classes, a
	 numeric vector of class scores for the second class, or a data
	 frame with a column for the predicted classes and another for the
	 second-class scores.

    o   Cross-validations which use folds ensure that samples belonging to
	 each class are in approximately the same proportions as they are
	 for the entire data set.

    o   Classification can reuse fitted model from previous classification
	 by using previousTrained function.

    o   Feature selection using gene sets and networks. Classification can
	 use meta-features derived from the individual features used for
	 feature selection.

    o   tTestSelection function for feature selection based on ordinary
	 t-test statistic ranking. Now the default feature selection
	 function, if none is specified.

    o   Tuning parameter optimisation metric is specified by providing a
	 tuneOptimise parameter to TrainParams rather than depending on
	 ResubstituteParams being used during feature selection.

Changes in version 2.0.0:

    o   Broad support for DataFrame and MultiAssayExperiment data sets by
	 feature selection and classification functions.

    o   The majority of processing is now done in the DataFrame method for
	 functions that implement methods for multiple kinds of inputs.

    o   Elastic net GLM classifier and multinomial logistic regression
	 classifier wrapper functions.

    o   Plotting functions have a new default style using a white background
	 with black axes.

    o   Vignette simplified and uses a new mass cytometry data set with
	 clearer differences between classes to demonstrate classification
	 and its performance evaluation.

Changes in version 1.12.0:

    o   Alterations to make plots compatible with ggplot versions 2.2 and
	 greater.

    o   calcPerformance can calculate some performance metrics for
	 classification tasks based on data sets with more than two classes.

    o   Sample-wise metrics, like sample-specific error rate and
	 sample-specific accuracy are calculated by calcPerformance and
	 added to the ClassifyResult object, rather than by samplesMetricMap
	 and being inaccessible to the end-user.

Changes in version 1.10.0:

    o   errorMap replaced by samplesMetricMap. The plot can now show either
	 error rate or accuracy.

Changes in version 1.8.0:

    o   Ordinary k-fold cross-validation option added.

    o   Absolute difference of group medians feature selection function
	 added.

Changes in version 1.4.0:

    o   Weighted voting mode that uses the distance from an observation to
	 the nearest crossover point of the class densities added.

    o   Bartlett Test selection function included.

    o   New class SelectResult. rankPlot and selectionPlot can additionally
	 work with lists of SelectResult objects. All feature selection
	 functions now return a SelectResult object or a list of them.

    o   priorSelection is a new selection function for using features
	 selected in a prior cross validation for a new data set
	 classification.

    o   New weighted voting mode, where the weight is the distance of the x
	 value from the nearest crossover point of the two densities. Useful
	 for predictions with skewed features.

Changes in version 1.2.0:

    o   More classification flexibility, now with parameter tuning
	 integrated into the process.

    o   New performance evaluation functions, such as a ROC curve and a
	 performance plot.

    o   Some existing predictor functions are able to return class scores,
	 not just class labels.

Changes in version 1.0.0:

    o   First release of the package, which allows parallelised and
	 customised classification, with many convenient performance
	 evaluation functions.