![[R logo]](../../../doc/html/logo.jpg)
A B C D E F G H K L M N O P R S T V X
| absorp | Fat, Water and Protein Content of Maat Samples |
| applyProcessing | Data Processing on Predictor Variables (Deprecated) |
| aucRoc | Compute the area under an ROC curve |
| bagEarth | Bagged Earth |
| bagFDA | Bagged FDA |
| bbbDescr | Blood Brain Barrier Data |
| best | Selecting tuning Parameters |
| BloodBrain | Blood Brain Barrier Data |
| confusionMatrix | Create a confusion matrix |
| cox2 | COX-2 Activity Data |
| cox2Class | COX-2 Activity Data |
| cox2Descr | COX-2 Activity Data |
| cox2IC50 | COX-2 Activity Data |
| createDataPartition | Data Splitting functions |
| createFolds | Data Splitting functions |
| createGrid | Tuning Parameter Grid |
| createResample | Data Splitting functions |
| defaultSummary | Calculates performance across resamples |
| densityplot.train | Lattice functions for plotting resampling results |
| dotPlot | Create a dotplot of variable importance values |
| endpoints | Fat, Water and Protein Content of Maat Samples |
| extractPrediction | Extract predictions and class probabilities from train objects |
| extractProb | Extract predictions and class probabilities from train objects |
| fattyAcids | Fatty acid composition of commercial oils |
| featurePlot | Wrapper for Lattice Plotting of Predictor Variables |
| filterVarImp | Calculation of filter-based variable importance |
| findCorrelation | Determine highly correlated variables |
| findLinearCombos | Determine linear combinations in a matrix |
| format.bagEarth | Format 'bagEarth' objects |
| generateExprVal.method.trimMean | Generate Expression Values from Probes |
| histogram.train | Lattice functions for plotting resampling results |
| knn3 | k-Nearest Neighbour Classification |
| knn3Train | k-Nearest Neighbour Classification |
| logBBB | Blood Brain Barrier Data |
| maxDissim | Maximum Dissimilarity Sampling |
| mdrr | Multidrug Resistance Reversal (MDRR) Agent Data |
| mdrrClass | Multidrug Resistance Reversal (MDRR) Agent Data |
| mdrrDescr | Multidrug Resistance Reversal (MDRR) Agent Data |
| minDiss | Maximum Dissimilarity Sampling |
| nearZeroVar | Identification of near zero variance predictors |
| negPredValue | Calculate Sensitivity, Specificity and predictive values |
| normalize.AffyBatch.normalize2Reference | Quantile Normalization to a Reference Distribution |
| normalize2Reference | Quantile Normalize Columns of a Matrix Based on a Reference Distribution |
| oil | Fatty acid composition of commercial oils |
| oilType | Fatty acid composition of commercial oils |
| oneSE | Selecting tuning Parameters |
| panel.needle | Needle Plot Lattice Panel |
| pcaNNet | Neural Networks with a Principal Component Step |
| plot.train | Plot Method for the train Class |
| plot.varImp.train | Plotting variable importance measures |
| plotClassProbs | Plot Predicted Probabilities in Classification Models |
| plotObsVsPred | Plot Observed versus Predicted Results in Regression and Classification Models |
| plsda | Partial Least Squares Discriminant Analysis |
| posPredValue | Calculate Sensitivity, Specificity and predictive values |
| postResample | Calculates performance across resamples |
| pottery | Pottery from Pre-Classical Sites in Italy |
| potteryClass | Pottery from Pre-Classical Sites in Italy |
| predict.bagEarth | Predicted values based on bagged Earth and FDA models |
| predict.bagFDA | Predicted values based on bagged Earth and FDA models |
| predict.knn3 | Predictions from k-Nearest Neighbors |
| predict.list | Extract predictions and class probabilities from train objects |
| predict.pcaNNet | Neural Networks with a Principal Component Step |
| predict.plsda | Partial Least Squares Discriminant Analysis |
| predict.preProcess | Pre-Processing of Predictors |
| predict.train | Extract predictions and class probabilities from train objects |
| predictors | List predictors used in the model |
| preProcess | Pre-Processing of Predictors |
| print.bagEarth | Bagged Earth |
| print.bagFDA | Bagged FDA |
| print.confusionMatrix | Print method for confusionMatrix |
| print.train | Print Method for the train Class |
| processData | Data Processing on Predictor Variables (Deprecated) |
| resampleHist | Plot the resampling distribution of the model statistics |
| resampleSummary | Summary of resampled performance estimates |
| roc | Compute the points for an ROC curve |
| sensitivity | Calculate Sensitivity, Specificity and predictive values |
| spatialSign | Compute the multivariate spatial sign |
| specificity | Calculate Sensitivity, Specificity and predictive values |
| stripplot.train | Lattice functions for plotting resampling results |
| sumDiss | Maximum Dissimilarity Sampling |
| summary.bagEarth | Summarize a bagged earth or FDA fit |
| summary.bagFDA | Summarize a bagged earth or FDA fit |
| tecator | Fat, Water and Protein Content of Maat Samples |
| tolerance | Selecting tuning Parameters |
| train | Fit Predictive Models over Different Tuning Parameters |
| trainControl | Control parameters for train |
| varImp | Calculation of variable importance for regression and classification models |
| xyplot.train | Lattice functions for plotting resampling results |