Function reference • caret

All functions
`BloodBrain`	Blood Brain Barrier Data
`BoxCoxTrans()` `print(<BoxCoxTrans>)` `predict(<BoxCoxTrans>)`	Box-Cox and Exponential Transformations
`GermanCredit`	German Credit Data
`Sacramento`	Sacramento CA Home Prices
`as.matrix(<confusionMatrix>)`	Confusion matrix as a table
`avNNet()` `print(<avNNet>)` `predict(<avNNet>)`	Neural Networks Using Model Averaging
`bag()` `bagControl()` `predict(<bag>)` `print(<bag>)` `summary(<bag>)` `print(<summary.bag>)` `ldaBag` `plsBag` `nbBag` `ctreeBag` `svmBag` `nnetBag`	A General Framework For Bagging
`bagEarth()` `print(<bagEarth>)`	Bagged Earth
`bagFDA()` `print(<bagFDA>)`	Bagged FDA
`calibration()` `print(<calibration>)` `xyplot(<calibration>)` `ggplot(<calibration>)`	Probability Calibration Plot
`pickSizeBest()` `pickSizeTolerance()` `pickVars()` `caretFuncs` `ldaFuncs` `treebagFuncs` `gamFuncs` `rfFuncs` `lmFuncs` `nbFuncs` `lrFuncs`	Backwards Feature Selection Helper Functions
`caretSBF` `anovaScores()` `gamScores()`	Selection By Filtering (SBF) Helper Functions
`cars`	Kelly Blue Book resale data for 2005 model year GM cars
`classDist()` `predict(<classDist>)`	Compute and predict the distances to class centroids
`confusionMatrix()`	Create a confusion matrix
`confusionMatrix(<train>)`	Estimate a Resampled Confusion Matrix
`cox2`	COX-2 Activity Data
`createDataPartition()` `createFolds()` `createMultiFolds()` `createTimeSlices()` `groupKFold()` `createResample()`	Data Splitting functions
`densityplot(<rfe>)`	Lattice functions for plotting resampling results of recursive feature selection
`dhfr`	Dihydrofolate Reductase Inhibitors Data
`diff(<resamples>)` `summary(<diff.resamples>)` `compare_models()`	Inferential Assessments About Model Performance
`dotPlot()`	Create a dotplot of variable importance values
`dotplot(<diff.resamples>)`	Lattice Functions for Visualizing Resampling Differences
`downSample()`	Down- and Up-Sampling Imbalanced Data
`dummyVars()` `print(<dummyVars>)` `predict(<dummyVars>)` `contr.ltfr()` `class2ind()`	Create A Full Set of Dummy Variables
`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
`gafs(<default>)` `gafs(<recipe>)`	Genetic algorithm feature selection
`gafs_initial()` `gafs_lrSelection()` `gafs_spCrossover()` `gafs_raMutation()` `gafs_nlrSelection()` `gafs_rwSelection()` `gafs_tourSelection()` `gafs_uCrossover()`	Ancillary genetic algorithm functions
`getSamplingInfo()`	Get sampling info from a train model
`histogram(<train>)`	Lattice functions for plotting resampling results
`icr(<formula>)` `icr(<default>)` `predict(<icr>)`	Independent Component Regression
`index2vec()`	Convert indicies to a binary vector
`knn3()` `print(<knn3>)` `knn3Train()`	k-Nearest Neighbour Classification
`knnreg()` `print(<knnreg>)` `knnregTrain()`	k-Nearest Neighbour Regression
`learning_curve_dat()`	Create Data to Plot a Learning Curve
`lift()` `print(<lift>)` `xyplot(<lift>)` `ggplot(<lift>)`	Lift Plot
`maxDissim()` `minDiss()` `sumDiss()`	Maximum Dissimilarity Sampling
`mdrr`	Multidrug Resistance Reversal (MDRR) Agent Data
`modelLookup()` `checkInstall()` `getModelInfo()`	Tools for Models Available in `train`
`train_model_list`	A List of Available Models in train
`nearZeroVar()` `checkConditionalX()` `checkResamples()`	Identification of near zero variance predictors
`nullModel()` `predict(<nullModel>)`	Fit a simple, non-informative model
`oil`	Fatty acid composition of commercial oils
`oneSE()` `tolerance()`	Selecting tuning Parameters
`panel.lift2()`	Lattice Panel Functions for Lift Plots
`panel.needle()`	Needle Plot Lattice Panel
`pcaNNet()` `print(<pcaNNet>)` `predict(<pcaNNet>)`	Neural Networks with a Principal Component Step
`plot(<gafs>)` `ggplot(<gafs>)` `ggplot(<safs>)`	Plot Method for the gafs and safs Classes
`ggplot(<rfe>)` `plot(<rfe>)`	Plot RFE Performance Profiles
`ggplot(<train>)` `plot(<train>)`	Plot Method for the train Class
`plot(<varImp.train>)` `ggplot(<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()` `predict(<plsda>)`	Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
`defaultSummary()` `postResample()` `twoClassSummary()` `mnLogLoss()` `multiClassSummary()` `prSummary()`	Calculates performance across resamples
`pottery`	Pottery from Pre-Classical Sites in Italy
`prcomp(<resamples>)` `plot(<prcomp.resamples>)`	Principal Components Analysis of Resampling Results
`preProcess()` `predict(<preProcess>)`	Pre-Processing of Predictors
`predict(<bagEarth>)` `predict(<bagFDA>)`	Predicted values based on bagged Earth and FDA models
`predict(<gafs>)`	Predict new samples
`predict(<knn3>)`	Predictions from k-Nearest Neighbors
`predict(<knnreg>)`	Predictions from k-Nearest Neighbors Regression Model
`extractPrediction()` `extractProb()` `predict(<train>)`	Extract predictions and class probabilities from train objects
`predictors()`	List predictors used in the model
`print(<confusionMatrix>)`	Print method for confusionMatrix
`print(<train>)`	Print Method for the train Class
`recall()` `precision()` `F_meas()`	Calculate recall, precision and F values
`resampleHist()`	Plot the resampling distribution of the model statistics
`resampleSummary()`	Summary of resampled performance estimates
`resamples()` `sort(<resamples>)` `summary(<resamples>)` `as.matrix(<resamples>)` `as.data.frame(<resamples>)` `modelCor()` `print(<resamples>)`	Collation and Visualization of Resampling Results
`rfe()` `rfeIter()` `update(<rfe>)`	Backwards Feature Selection
`rfeControl()`	Controlling the Feature Selection Algorithms
`safs()`	Simulated annealing feature selection
`gafsControl()` `safsControl()`	Control parameters for GA and SA feature selection
`safs_initial()` `safs_perturb()` `safs_prob()` `caretSA` `treebagSA` `rfSA`	Ancillary simulated annealing functions
`sbf()` `predict(<sbf>)`	Selection By Filtering (SBF)
`sbfControl()`	Control Object for Selection By Filtering (SBF)
`scat`	Morphometric Data on Scat
`segmentationData`	Cell Body Segmentation
`negPredValue()` `posPredValue()` `sensitivity()`	Calculate sensitivity, specificity and predictive values
`spatialSign()`	Compute the multivariate spatial sign
`summary(<bagEarth>)` `summary(<bagFDA>)`	Summarize a bagged earth or FDA fit
`tecator`	Fat, Water and Protein Content of Meat Samples
`thresholder()`	Generate Data to Choose a Probability Threshold
`train()`	Fit Predictive Models over Different Tuning Parameters
`trainControl()`	Control parameters for train
`SLC14_1()` `SLC14_2()` `LPH07_1()` `LPH07_2()` `twoClassSim()`	Simulation Functions
`update(<safs>)`	Update or Re-fit a SA or GA Model
`update(<train>)`	Update or Re-fit a Model
`varImp()`	Calculation of variable importance for regression and classification models
`varImp(<gafs>)`	Variable importances for GAs and SAs
`var_seq()`	Sequences of Variables for Tuning
`xyplot(<resamples>)` `parallelplot(<resamples>)` `splom(<resamples>)` `densityplot(<resamples>)` `bwplot(<resamples>)` `dotplot(<resamples>)` `ggplot(<resamples>)`	Lattice Functions for Visualizing Resampling Results

Reference

All functions

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