Changes in version 2.6.0 o Two-stage easy-hard classifier added. 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.