| compareOrig | function to compare the original matrix of correct classes to each component of the output object for a certain classifier | 
| confusionMatrix.nlcv | compute a confusion matrix for the optimal number of features for a given technique used in the nested loop cross validation | 
| inTrainingSample | Function to define a learning sample based on balanced sampling | 
| limmaTwoGroups | Wrapper around limma for the comparison of two groups | 
| mcrPlot | Misclassification Rate Plot | 
| nlcv | Nested Loop Cross-Validation | 
| nlcvRF_R | nlcv results on random data with random forest feature selection | 
| nlcvRF_SHS | nlcv results on strong hetero signal data with random forest feature selection | 
| nlcvRF_SS | nlcv results on strong signal data a with random forest feature selection | 
| nlcvRF_WHS | nlcv results on weak signal data with random forest feature selection | 
| nlcvRF_WS | nlcv results on weak hetero signal data with random forest feature selection | 
| nlcvTT_R | nlcv results on random data with t-test feature selection | 
| nlcvTT_SHS | nlcv results on strong hetero signal data with t-test feature selection | 
| nlcvTT_SS | nlcv results on strong signal data a with t-test feature selection | 
| nlcvTT_WHS | nlcv results on weak signal data with t-test feature selection | 
| nlcvTT_WS | nlcv results on weak hetero signal data with t-test feature selection | 
| nldaI | new MLInterfaces schema for lda from MASS | 
| pamrI | Instance of a learnerSchema for pamr models | 
| pamrIconverter | convert from 'pamrML' to 'classifierOutput' | 
| pamrML | Wrapper function around the pamr.* functions | 
| pamrTrain | Function providing a formula interface to pamr.train | 
| predict.pamrML | predict 'pamrML' object | 
| print.nlcvConfusionMatrix | print object 'nlcvConfusionMatrix' | 
| print.pamrML | print 'pamrML' object | 
| print.summary.mcrPlot | 'print' function for 'summary.mcrPlot' object | 
| rankDistributionPlot | Plot the Distribution of Ranks of Features Across nlcv Runs | 
| rocPlot | Produce a ROC plot for a classification model belonging to a given technique and with a given number of features. | 
| scoresPlot | Function to Plot a Scores Plot | 
| summary.mcrPlot | 'summary' function for 'mcrPlot' object | 
| topTable | Methods for topTable | 
| topTable-method | Methods for topTable | 
| topTable-methods | Methods for topTable | 
| xtable.confusionMatrix | xtable method for confusionMatrix objects | 
| xtable.summary.mcrPlot | xtable method for summary.mcrPlot objects |