decisiontree            Decision tree Trains a decision on the given
                        training dataset and uses it to predict
                        classification for test dataset. The resulting
                        accuracy, sensitivity and specificity are
                        returned, as well as a tree summary.
dtreevoting             Decision tree voting scheme. Implements a
                        feature selection approach based on Decision
                        Trees, using a voting scheme across the top
                        levels on trees trained on multiple subsamples.
eGA                     Embryonic Genetic Algorithm. Feature selection
                        based on Embryonic Genetic Algorithms. It
                        performs feature selection by maintaining an
                        ongoing set of 'good' set of features which are
                        improved run by run. It outputs training and
                        test accuracy, sensitivity and specificity and
                        a list of <=k features.
feamiR                  feamiR: Classification and feature selection
                        for microRNA/mRNA interactions
forwardfeatureselection
                        Forward Feature Selection. Performs forward
                        feature selection on the given list of
                        features, placing them in order of
                        discriminative power using a given model on the
                        given dataset up to the accuracy plateau.
geneticalgorithm        Standard Genetic Algorithm. Implements a
                        standard genetic algorithm using GA package
                        (ga) with a fitness function specialised for
                        feature selection.
preparedataset          Dataset preparation This step performs all
                        preparation necessary to perform feamiR
                        analysis, taking a set of mRNAs, a set of
                        miRNAs and an interaction dataset and creating
                        corresponding positive and negative datasets
                        for ML modelling.
randomforest            Random Forest. Trains a random forest on the
                        training dataset and uses it to predict the
                        classification of the test dataset. The
                        resulting accuracy, sensitivity and specificity
                        are returned, as well as a summary of the
                        importance of features in the dataset.
rfgini                  Random Forest cumulative MeanDecreaseGini
                        feature selection. Implements a feature
                        selection approach based on cumulative
                        MeanDecreaseGini using Random Forests trained
                        on multiple subsamples.
runallmodels            Run all models. Trains and tests Decision Tree,
                        Random Forest and SVM models on 100 subsamples
                        and provides a summary of the results, to
                        select the best model. The number of trees and
                        kernel chosen by selectsvmkernel and
                        selectrfnumtrees should be used for SVM and
                        Random Forest respectively. We can use this
                        function to inform feature selection, using a
                        Decision Tree voting scheme and a Random Forest
                        measure based on the Gini index.
selectrfnumtrees        Tuning number of trees hyperparameter. Trains
                        random forests with a range of number of trees
                        so the optimal number can be identified (using
                        the resulting plot) with cross validation
selectsvmkernel         Tuning SVM kernel. Trains SVMs with a range of
                        kernels (linear, polynomial degree 2, 3 and 4,
                        radial and sigmoid) using cross validation so
                        the optimal kernel can be chosen (using the
                        resulting plots). If specified (by
                        showplots=FALSE) the plots are saved as jpegs.
svm                     SVM
svmlinear               Linear SVM Implements a linear SVM using the
                        general svm function (for ease of use in
                        feature selection)
svmpolynomial2          Polynomial degree 2 SVM Implements a polynomial
                        degree 2 SVM using the general svm function
                        (for ease of use in feature selection)
svmpolynomial3          Polynomial degree 3 SVM Implements a polynomial
                        degree 3 SVM using the general svm function
                        (for ease of use in feature selection)
svmpolynomial4          Polynomial degree 4 SVM Implements a polynomial
                        degree 4 SVM using the general svm function
                        (for ease of use in feature selection)
svmradial               Radial SVM Implements a radial SVM using the
                        general svm function (for ease of use in
                        feature selection)
svmsigmoid              Sigmoid SVM Implements a sigmoid SVM using
                        general svm function (for ease of use in
                        feature selection)
