A B C D E F G H I K L M N O P R S T U V X
absorp | Fat, Water and Protein Content of Meat Samples |
anovaScores | Selection By Filtering (SBF) Helper Functions |
as.matrix.confusionMatrix | Save Confusion Table Results |
as.table.confusionMatrix | Save Confusion Table Results |
avNNet | Neural Networks Using Model Averaging |
avNNet.default | Neural Networks Using Model Averaging |
avNNet.formula | Neural Networks Using Model Averaging |
bag | A General Framework For Bagging |
bag.default | A General Framework For Bagging |
bagControl | A General Framework For Bagging |
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 |
BoxCoxTrans | Box-Cox Transformations |
BoxCoxTrans.default | Box-Cox Transformations |
bwplot.diff.resamples | Lattice Functions for Visualizing Resampling Differences |
bwplot.resamples | Lattice Functions for Visualizing Resampling Results |
calibration | Probability Calibration Plot |
calibration.default | Probability Calibration Plot |
calibration.formula | Probability Calibration Plot |
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 |
checkConditionalX | Identification of near zero variance predictors |
checkResamples | Identification of near zero variance predictors |
classDist | Compute and predict the distances to class centroids |
classDist.default | Compute and predict the distances to class centroids |
cluster.resamples | Principal Components Analysis of Resampling Results |
confusionMatrix | Create a confusion matrix |
confusionMatrix.default | Create a confusion matrix |
confusionMatrix.rfe | Estimate a Resampled Confusion Matrix |
confusionMatrix.sbf | Estimate a Resampled Confusion Matrix |
confusionMatrix.table | Create a confusion matrix |
confusionMatrix.train | Estimate a Resampled Confusion Matrix |
contr.dummy | Create A Full Set of Dummy Variables |
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 |
createMultiFolds | Data Splitting functions |
createResample | Data Splitting functions |
createTimeSlices | Data Splitting functions |
ctreeBag | A General Framework For Bagging |
defaultSummary | Calculates performance across resamples |
densityplot.diff.resamples | Lattice Functions for Visualizing Resampling Differences |
densityplot.resamples | Lattice Functions for Visualizing Resampling Results |
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 |
diff.resamples | Inferential Assessments About Model Performance |
dotPlot | Create a dotplot of variable importance values |
dotplot.diff.resamples | Lattice Functions for Visualizing Resampling Differences |
dotplot.resamples | Lattice Functions for Visualizing Resampling Results |
downSample | Down- and Up-Sampling Imbalanced Data |
dummyVars | Create A Full Set of Dummy Variables |
dummyVars.default | Create A Full Set of Dummy Variables |
endpoints | Fat, Water and Protein Content of Meat 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 |
gamFuncs | Backwards Feature Selection Helper Functions |
gamScores | Selection By Filtering (SBF) Helper Functions |
generateExprVal.method.trimMean | Generate Expression Values from Probes |
GermanCredit | German Credit Data |
histogram.rfe | Lattice functions for plotting resampling results of recursive feature selection |
histogram.train | Lattice functions for plotting resampling results |
icr | Independent Component Regression |
icr.default | Independent Component Regression |
icr.formula | Independent Component Regression |
knn3 | k-Nearest Neighbour Classification |
knn3.data.frame | 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 |
ldaBag | A General Framework For Bagging |
ldaFuncs | Backwards Feature Selection Helper Functions |
ldaSBF | Selection By Filtering (SBF) Helper Functions |
levelplot.diff.resamples | Lattice Functions for Visualizing Resampling Differences |
lift | Lift Plot |
lift.default | Lift Plot |
lift.formula | Lift Plot |
lmFuncs | Backwards Feature Selection Helper Functions |
lmSBF | Selection By Filtering (SBF) Helper Functions |
logBBB | Blood Brain Barrier Data |
lrFuncs | Backwards Feature Selection Helper Functions |
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 |
modelLookup | Descriptions Of Models Available in train() |
nbBag | A General Framework For Bagging |
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 |
nnetBag | A General Framework For Bagging |
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.calibration | Probability Calibration Plot |
panel.lift | Lattice Panel Functions for Lift Plots |
panel.lift2 | Lattice Panel Functions for Lift Plots |
panel.needle | Needle Plot Lattice Panel |
parallelplot.resamples | Lattice Functions for Visualizing Resampling Results |
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.prcomp.resamples | Principal Components Analysis of Resampling Results |
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 |
plsBag | A General Framework For Bagging |
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 |
prcomp.resamples | Principal Components Analysis of Resampling Results |
predict.avNNet | Neural Networks Using Model Averaging |
predict.bag | A General Framework For Bagging |
predict.bagEarth | Predicted values based on bagged Earth and FDA models |
predict.bagFDA | Predicted values based on bagged Earth and FDA models |
predict.BoxCoxTrans | Box-Cox Transformations |
predict.classDist | Compute and predict the distances to class centroids |
predict.dummyVars | Create A Full Set of Dummy Variables |
predict.icr | Independent Component Regression |
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.rfe | Backwards Feature Selection |
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.dsa | List predictors used in the model |
predictors.earth | List predictors used in the model |
predictors.fda | List predictors used in the model |
predictors.foba | List predictors used in the model |
predictors.formula | List predictors used in the model |
predictors.gam | 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.glm | List predictors used in the model |
predictors.glmboost | List predictors used in the model |
predictors.glmnet | List predictors used in the model |
predictors.gpls | List predictors used in the model |
predictors.knn3 | List predictors used in the model |
predictors.knnreg | List predictors used in the model |
predictors.ksvm | List predictors used in the model |
predictors.lars | 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.logforest | List predictors used in the model |
predictors.logicBagg | List predictors used in the model |
predictors.LogitBoost | List predictors used in the model |
predictors.logreg | List predictors used in the model |
predictors.lssvm | List predictors used in the model |
predictors.mda | 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.penfit | List predictors used in the model |
predictors.ppr | List predictors used in the model |
predictors.qda | 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.sbf | List predictors used in the model |
predictors.sda | List predictors used in the model |
predictors.slda | List predictors used in the model |
predictors.smda | List predictors used in the model |
predictors.spls | List predictors used in the model |
predictors.splsda | 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.trocc | 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 |
R2 | Calculates performance across resamples |
resampleHist | Plot the resampling distribution of the model statistics |
resamples | Collation and Visualization of Resampling Results |
resamples.default | Collation and Visualization of Resampling Results |
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 |
RMSE | Calculates performance across resamples |
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) |
segmentationData | Cell Body Segmentation |
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 |
splom.resamples | Lattice Functions for Visualizing Resampling Results |
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 |
summary.diff.resamples | Inferential Assessments About Model Performance |
summary.resamples | Collation and Visualization of Resampling Results |
svmBag | A General Framework For Bagging |
tecator | Fat, Water and Protein Content of Meat 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 |
twoClassSummary | Calculates performance across resamples |
update.train | Update and Re-fit a Model |
upSample | Down- and Up-Sampling Imbalanced Data |
varImp | Calculation of variable importance for regression and classification models |
varImp.bagEarth | Calculation of variable importance for regression and classification models |
varImp.C5.0 | Calculation of variable importance for regression and classification models |
varImp.classbagg | Calculation of variable importance for regression and classification models |
varImp.cubist | Calculation of variable importance for regression and classification models |
varImp.dsa | Calculation of variable importance for regression and classification models |
varImp.earth | Calculation of variable importance for regression and classification models |
varImp.fda | Calculation of variable importance for regression and classification models |
varImp.gam | Calculation of variable importance for regression and classification models |
varImp.gbm | Calculation of variable importance for regression and classification models |
varImp.glmnet | Calculation of variable importance for regression and classification models |
varImp.JRip | Calculation of variable importance for regression and classification models |
varImp.lm | Calculation of variable importance for regression and classification models |
varImp.multinom | Calculation of variable importance for regression and classification models |
varImp.mvr | Calculation of variable importance for regression and classification models |
varImp.nnet | Calculation of variable importance for regression and classification models |
varImp.pamrtrained | Calculation of variable importance for regression and classification models |
varImp.PART | 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.calibration | Probability Calibration Plot |
xyplot.lift | Lift Plot |
xyplot.resamples | Lattice Functions for Visualizing Resampling Results |
xyplot.rfe | Lattice functions for plotting resampling results of recursive feature selection |
xyplot.train | Lattice functions for plotting resampling results |