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 |