Metric calculation done at fold level rather than permutation level.
Extraction of seed for random number generator fixed.
Balancing of non-censored event times across folds.
Multiview methods with combinations of views have improved plots that switch to UpSet axis.
Multiview methods have feature extractors to retain chosen features for analysis.
Companion website with more in-depth explanation and examples.
Default random forest classifier based on ranger now does two-step classification; one for variable importance and one for model fitting, as recommended by ranger's developer.
More functions use automatic parameter value selection as their defaults.
randomSelection function to choose random sets of features in cross-validation.
crossValidate function now permits custom parameter tuning via extraParams.
Elastic net GLM and ordinary GLM now calculate class weights be default, so as to perform well in class-imbalanced scenarios.
precisionPathwaysTrain and precisionPathwaysPredict functions for building tree-like models of multiple assays and their accessory functions calcCostsAndPerformance, bubblePlot, flowchart, strataPlot for model performance evaluation.
crissCrossValidate function that takes a list of data sets with the same set of features and the same set of outcomes and does all possible pairs of training and prediction to evaluate generalisability. crissCrossPlot for visual evaluation.
Fast Cox survival analysis.
Simple parameter sets, as used by crossVaildate, now come with tuning parameter grid as standard.
Wrappers are greatly simplified. Now, there is only one method for a data frame and they are not exported because they are not used directly by the end-user anyway.
prepareData function to filter and subset input data using common ways, such as missingness and variability.
Invalid column names of data (e.g. spaces, hyphens) are converted into safe names before modelling but converted back into original names for tracking ranked and selected features.
available function shows the keywords corresponding to transformation, selection, classifier functions.
More functions have automatically-selected parameters based on input data, reducing required user-specified parameters.
New classifiers added for random survival forests and extreme gradient boosting.
Adaptive sampling for modelling with uncertainty of class labels can be enabled with adaptiveResamplingDelta.
Parameter tuning fixed to only use samples from the training set.
Now supports survival models and their evaluation, in addition to existing classification functionality.
Cross-validation no longer requires specific annotations like data set name and classifier name. Now, the user can specify any characteristics they want and use these as variables to group by or change line appearances. Also, characteristics like feature selection name and classifier name are automatically filled in from an internal table.
Ease of use greatly inproved by crossValidate function which allows specification of classifiers by a single keyword. Previously, parameter objects such as SelectParams and TrainParams had to be explicitly specified, making it challenging for users not familar with S4 object-oriented programming.
Basic multi-omics data integration functionality available via crossValidate which allows combination of different tables. Pre-validation and PCA dimensionality techniques provide a fair way to compare high-dimensional omics data with low-dimensional clinical data. Also, it is possible to simply concatenate all data tables.
Model-agnostic variable importance calculated by training when leaving out one selected variable at a time. Turned off by default as it substantially increases run time. See doImportance parameter of ModellingParams for more details.
Parameters specifying the cross-validation procedure and data modelling formalised as CrossValParams and ModellingParams classes.
Feature selection can now be done either based a on resubstitution metric (i.e. train and test on the training data) or a cross-validation metric (i.e. split the training data into training and testing partitions to tune the selected features). all feature selection functions have been converted into feature ranking functions, because the selection procedure is a feature of cross-validation.
All function and class documentation coverted from manually written Rd files to Roxygen format.
Human Reference Interactome (binary experimental PPI) included in bundled data for pairs-based classification. See ?HuRI for more details.
Performance plots can now do either box plots or violin plots. Box plot remains the default style.
Upsampling and downsampling to equalise class sizes added.
Two-stage easy-hard classifier added.
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.
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.
Classification can reuse fitted model from previous classification by using previousTrained function.
Feature selection using gene sets and networks. Classification can use meta-features derived from the individual features used for feature selection.
tTestSelection function for feature selection based on ordinary t-test statistic ranking. Now the default feature selection function, if none is specified.
Tuning parameter optimisation metric is specified by providing a tuneOptimise parameter to TrainParams rather than depending on ResubstituteParams being used during feature selection.
Broad support for DataFrame and MultiAssayExperiment data sets by feature selection and classification functions.
The majority of processing is now done in the DataFrame method for functions that implement methods for multiple kinds of inputs.
Elastic net GLM classifier and multinomial logistic regression classifier wrapper functions.
Plotting functions have a new default style using a white background with black axes.
Vignette simplified and uses a new mass cytometry data set with clearer differences between classes to demonstrate classification and its performance evaluation.
Alterations to make plots compatible with ggplot versions 2.2 and greater.
calcPerformance can calculate some performance metrics for classification tasks based on data sets with more than two classes.
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.
errorMap replaced by samplesMetricMap. The plot can now show either error rate or accuracy.
Ordinary k-fold cross-validation option added.
Absolute difference of group medians feature selection function added.
Weighted voting mode that uses the distance from an observation to the nearest crossover point of the class densities added.
Bartlett Test selection function included.
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.
priorSelection is a new selection function for using features selected in a prior cross validation for a new data set classification.
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.
More classification flexibility, now with parameter tuning integrated into the process.
New performance evaluation functions, such as a ROC curve and a performance plot.
Some existing predictor functions are able to return class scores, not just class labels.
First release of the package, which allows parallelised and customised classification, with many convenient performance evaluation functions.