![[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 |
| anovaFilter | Selection By Filtering (SBF) Helper Functions |
| applyProcessing | Data Processing on Predictor Variables (Deprecated) |
| as.matrix.confusionMatrix | Save Confusion Table Results |
| as.table.confusionMatrix | Save Confusion Table Results |
| aucRoc | Compute the area under an ROC curve |
| bagEarth | Bagged Earth |
| bagEarth.default | Bagged Earth |
| bagEarth.formula | Bagged Earth |
| bagFDA | Bagged FDA |
| bagFDA.default | Bagged FDA |
| bagFDA.formula | Bagged FDA |
| bbbDescr | Blood Brain Barrier Data |
| best | Selecting tuning Parameters |
| BloodBrain | Blood Brain Barrier Data |
| caretFuncs | Backwards Feature Selection Helper Functions |
| caretSBF | Selection By Filtering (SBF) Helper Functions |
| cars | Kelly Blue Book resale data for 2005 model year GM cars |
| classDist | Compute and predict the distances to class centroids |
| classDist.default | Compute and predict the distances to class centroids |
| confusionMatrix | Create a confusion matrix |
| confusionMatrix.default | Create a confusion matrix |
| confusionMatrix.table | 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.rfe | Lattice functions for plotting resampling results of recursive feature selection |
| densityplot.train | Lattice functions for plotting resampling results |
| dhfr | Dihydrofolate Reductase Inhibitors Data |
| 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 |
| gamFilter | Selection By Filtering (SBF) Helper Functions |
| generateExprVal.method.trimMean | Generate Expression Values from Probes |
| histogram.rfe | Lattice functions for plotting resampling results of recursive feature selection |
| histogram.train | Lattice functions for plotting resampling results |
| knn3 | k-Nearest Neighbour Classification |
| knn3.formula | k-Nearest Neighbour Classification |
| knn3.matrix | k-Nearest Neighbour Classification |
| knn3Train | k-Nearest Neighbour Classification |
| knnreg | k-Nearest Neighbour Regression |
| knnreg.data.frame | k-Nearest Neighbour Regression |
| knnreg.default | k-Nearest Neighbour Regression |
| knnreg.formula | k-Nearest Neighbour Regression |
| knnreg.matrix | k-Nearest Neighbour Regression |
| knnregTrain | k-Nearest Neighbour Regression |
| ldaFuncs | Backwards Feature Selection Helper Functions |
| ldaSBF | Selection By Filtering (SBF) Helper Functions |
| lmFuncs | Backwards Feature Selection Helper Functions |
| lmSBF | Selection By Filtering (SBF) Helper Functions |
| 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 |
| nbFuncs | Backwards Feature Selection Helper Functions |
| nbSBF | Selection By Filtering (SBF) Helper Functions |
| nearZeroVar | Identification of near zero variance predictors |
| negPredValue | Calculate sensitivity, specificity and predictive values |
| negPredValue.default | Calculate sensitivity, specificity and predictive values |
| negPredValue.matrix | Calculate sensitivity, specificity and predictive values |
| negPredValue.table | 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 |
| nullModel | Fit a simple, non-informative model |
| nullModel.default | Fit a simple, non-informative model |
| 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 |
| pcaNNet.default | Neural Networks with a Principal Component Step |
| pcaNNet.formula | Neural Networks with a Principal Component Step |
| pickSizeBest | Backwards Feature Selection Helper Functions |
| pickSizeTolerance | Backwards Feature Selection Helper Functions |
| pickVars | Backwards Feature Selection Helper Functions |
| 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 and Sparse Partial Least Squares Discriminant Analysis |
| plsda.default | Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis |
| posPredValue | Calculate sensitivity, specificity and predictive values |
| posPredValue.default | Calculate sensitivity, specificity and predictive values |
| posPredValue.matrix | Calculate sensitivity, specificity and predictive values |
| posPredValue.table | 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.classDist | Compute and predict the distances to class centroids |
| predict.knn3 | Predictions from k-Nearest Neighbors |
| predict.knnreg | Predictions from k-Nearest Neighbors Regression Model |
| predict.list | Extract predictions and class probabilities from train objects |
| predict.nullModel | Fit a simple, non-informative model |
| predict.pcaNNet | Neural Networks with a Principal Component Step |
| predict.plsda | Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis |
| predict.preProcess | Pre-Processing of Predictors |
| predict.sbf | Selection By Filtering (SBF) |
| predict.splsda | Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis |
| predict.train | Extract predictions and class probabilities from train objects |
| predictors | List predictors used in the model |
| predictors.bagEarth | List predictors used in the model |
| predictors.bagFDA | List predictors used in the model |
| predictors.BinaryTree | List predictors used in the model |
| predictors.blackboost | List predictors used in the model |
| predictors.classbagg | List predictors used in the model |
| predictors.earth | List predictors used in the model |
| predictors.fda | List predictors used in the model |
| predictors.formula | List predictors used in the model |
| predictors.gamboost | List predictors used in the model |
| predictors.gausspr | List predictors used in the model |
| predictors.gbm | List predictors used in the model |
| predictors.glmboost | List predictors used in the model |
| predictors.gpls | List predictors used in the model |
| predictors.knn3 | List predictors used in the model |
| predictors.ksvm | List predictors used in the model |
| predictors.lda | List predictors used in the model |
| predictors.list | List predictors used in the model |
| predictors.lm | List predictors used in the model |
| predictors.LogitBoost | List predictors used in the model |
| predictors.lssvm | List predictors used in the model |
| predictors.multinom | List predictors used in the model |
| predictors.mvr | List predictors used in the model |
| predictors.NaiveBayes | List predictors used in the model |
| predictors.nnet | List predictors used in the model |
| predictors.pamrtrained | List predictors used in the model |
| predictors.pcaNNet | List predictors used in the model |
| predictors.ppr | List predictors used in the model |
| predictors.RandomForest | List predictors used in the model |
| predictors.randomForest | List predictors used in the model |
| predictors.rda | List predictors used in the model |
| predictors.regbagg | List predictors used in the model |
| predictors.rfe | List predictors used in the model |
| predictors.rpart | List predictors used in the model |
| predictors.rvm | List predictors used in the model |
| predictors.slda | List predictors used in the model |
| predictors.superpc | List predictors used in the model |
| predictors.terms | List predictors used in the model |
| predictors.train | List predictors used in the model |
| predictors.Weka_classifier | List predictors used in the model |
| preProcess | Pre-Processing of Predictors |
| preProcess.default | 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 |
| rfe | Backwards Feature Selection |
| rfe.default | Backwards Feature Selection |
| rfeControl | Controlling the Feature Selection Algorithms |
| rfeIter | Backwards Feature Selection |
| rfFuncs | Backwards Feature Selection Helper Functions |
| rfSBF | Selection By Filtering (SBF) Helper Functions |
| roc | Compute the points for an ROC curve |
| sbf | Selection By Filtering (SBF) |
| sbf.default | Selection By Filtering (SBF) |
| sbf.formula | Selection By Filtering (SBF) |
| sbfControl | Control Object for Selection By Filtering (SBF) |
| sensitivity | Calculate sensitivity, specificity and predictive values |
| sensitivity.default | Calculate sensitivity, specificity and predictive values |
| sensitivity.matrix | Calculate sensitivity, specificity and predictive values |
| sensitivity.table | Calculate sensitivity, specificity and predictive values |
| spatialSign | Compute the multivariate spatial sign |
| spatialSign.data.frame | Compute the multivariate spatial sign |
| spatialSign.default | Compute the multivariate spatial sign |
| spatialSign.matrix | Compute the multivariate spatial sign |
| specificity | Calculate sensitivity, specificity and predictive values |
| specificity.default | Calculate sensitivity, specificity and predictive values |
| specificity.matrix | Calculate sensitivity, specificity and predictive values |
| specificity.table | Calculate sensitivity, specificity and predictive values |
| splsda | Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis |
| splsda.default | Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis |
| stripplot.rfe | Lattice functions for plotting resampling results of recursive feature selection |
| 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 |
| train.default | Fit Predictive Models over Different Tuning Parameters |
| train.formula | Fit Predictive Models over Different Tuning Parameters |
| trainControl | Control parameters for train |
| treebagFuncs | Backwards Feature Selection Helper Functions |
| treebagSBF | Selection By Filtering (SBF) Helper Functions |
| varImp | Calculation of variable importance for regression and classification models |
| varImp.bagEarth | Calculation of variable importance for regression and classification models |
| varImp.classbagg | Calculation of variable importance for regression and classification models |
| varImp.earth | Calculation of variable importance for regression and classification models |
| varImp.gbm | Calculation of variable importance for regression and classification models |
| varImp.lm | Calculation of variable importance for regression and classification models |
| varImp.mvr | Calculation of variable importance for regression and classification models |
| varImp.pamrtrained | Calculation of variable importance for regression and classification models |
| varImp.RandomForest | Calculation of variable importance for regression and classification models |
| varImp.randomForest | Calculation of variable importance for regression and classification models |
| varImp.regbagg | Calculation of variable importance for regression and classification models |
| varImp.rfe | Calculation of variable importance for regression and classification models |
| varImp.rpart | Calculation of variable importance for regression and classification models |
| varImp.train | Calculation of variable importance for regression and classification models |
| xyplot.rfe | Lattice functions for plotting resampling results of recursive feature selection |
| xyplot.train | Lattice functions for plotting resampling results |