# caret v6.0-72

0

Monthly downloads

by Max Kuhn

## Classification and Regression Training

Misc functions for training and plotting classification and
regression models.

## Functions in caret

Name | Description | |

caret-internal | Internal Functions | |

caretSBF | Selection By Filtering (SBF) Helper Functions | |

calibration | Probability Calibration Plot | |

avNNet | Neural Networks Using Model Averaging | |

BoxCoxTrans | Box-Cox and Exponential Transformations | |

as.matrix.confusionMatrix | Confusion matrix as a table | |

pickSizeBest | Backwards Feature Selection Helper Functions | |

bagFDA | Bagged FDA | |

diff.resamples | Inferential Assessments About Model Performance | |

downSample | Down- and Up-Sampling Imbalanced Data | |

createDataPartition | Data Splitting functions | |

dotplot.diff.resamples | Lattice Functions for Visualizing Resampling Differences | |

dotPlot | Create a dotplot of variable importance values | |

densityplot.rfe | Lattice functions for plotting resampling results of recursive feature selection | |

classDist | Compute and predict the distances to class centroids | |

confusionMatrix.train | Estimate a Resampled Confusion Matrix | |

dummyVars | Create A Full Set of Dummy Variables | |

featurePlot | Wrapper for Lattice Plotting of Predictor Variables | |

gafs_initial | Ancillary genetic algorithm functions | |

histogram.train | Lattice functions for plotting resampling results | |

GermanCredit | German Credit Data | |

getSamplingInfo | Get sampling info from a train model | |

filterVarImp | Calculation of filter-based variable importance | |

findLinearCombos | Determine linear combinations in a matrix | |

gafs.default | Genetic algorithm feature selection | |

format.bagEarth | Format 'bagEarth' objects | |

icr.formula | Independent Component Regression | |

findCorrelation | Determine highly correlated variables | |

knn3 | k-Nearest Neighbour Classification | |

lift | Lift Plot | |

modelLookup | Tools for Models Available in train | |

knnreg | k-Nearest Neighbour Regression | |

index2vec | Convert indicies to a binary vector | |

maxDissim | Maximum Dissimilarity Sampling | |

learing_curve_dat | Create Data to Plot a Learning Curve | |

train_model_list | A List of Available Models in train | |

nearZeroVar | Identification of near zero variance predictors | |

nullModel | Fit a simple, non-informative model | |

ggplot.train | Plot Method for the train Class | |

plotClassProbs | Plot Predicted Probabilities in Classification Models | |

panel.needle | Needle Plot Lattice Panel | |

oneSE | Selecting tuning Parameters | |

plot.varImp.train | Plotting variable importance measures | |

panel.lift2 | Lattice Panel Functions for Lift Plots | |

plot.gafs | Plot Method for the gafs and safs Classes | |

ggplot.rfe | Plot RFE Performance Profiles | |

pcaNNet | Neural Networks with a Principal Component Step | |

plotObsVsPred | Plot Observed versus Predicted Results in Regression and Classification Models | |

predict.knnreg | Predictions from k-Nearest Neighbors Regression Model | |

predict.knn3 | Predictions from k-Nearest Neighbors | |

preProcess | Pre-Processing of Predictors | |

predict.gafs | Predict new samples | |

predictors | List predictors used in the model | |

predict.bagEarth | Predicted values based on bagged Earth and FDA models | |

extractPrediction | Extract predictions and class probabilities from train objects | |

print.confusionMatrix | Print method for confusionMatrix | |

plsda | Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis | |

prcomp.resamples | Principal Components Analysis of Resampling Results | |

bagEarth | Bagged Earth | |

safs_initial | Ancillary simulated annealing functions | |

varImp.gafs | Variable importances for GAs and SAs | |

safs | Simulated annealing feature selection | |

var_seq | Sequences of Variables for Tuning | |

bag | A General Framework For Bagging | |

rfe | Backwards Feature Selection | |

rfeControl | Controlling the Feature Selection Algorithms | |

sbfControl | Control Object for Selection By Filtering (SBF) | |

spatialSign | Compute the multivariate spatial sign | |

varImp | Calculation of variable importance for regression and classification models | |

xyplot.resamples | Lattice Functions for Visualizing Resampling Results | |

resamples | Collation and Visualization of Resampling Results | |

resampleSummary | Summary of resampled performance estimates | |

print.train | Print Method for the train Class | |

resampleHist | Plot the resampling distribution of the model statistics | |

update.train | Update or Re-fit a Model | |

update.safs | Update or Re-fit a SA or GA Model | |

trainControl | Control parameters for train | |

SLC14_1 | Simulation Functions | |

gafsControl | Control parameters for GA and SA feature selection | |

sbf | Selection By Filtering (SBF) | |

summary.bagEarth | Summarize a bagged earth or FDA fit | |

train | Fit Predictive Models over Different Tuning Parameters | |

No Results! |

## Last month downloads

## Details

Date | 2016-10-31 |

URL | https://github.com/topepo/caret/ |

BugReports | https://github.com/topepo/caret/issues |

License | GPL (>= 2) |

RoxygenNote | 5.0.1 |

NeedsCompilation | yes |

Packaged | 2016-10-31 16:20:00 UTC; kuhna03 |

Repository | CRAN |

Date/Publication | 2016-11-01 10:15:32 |

suggests | BradleyTerry2 , Cubist , e1071 , earth (>= 2.2-3) , ellipse , fastICA , gam , ipred , kernlab , klaR , MASS , mda , mgcv , mlbench , MLmetrics , nnet , pamr , party (>= 0.9-99992) , pls , pROC , proxy , randomForest , RANN , spls , subselect , superpc , testthat (>= 0.9.1) |

imports | car , foreach , grDevices , methods , ModelMetrics (>= 1.1.0) , nlme , plyr , reshape2 , stats , stats4 , utils |

depends | ggplot2 , lattice (>= 0.20) , R (>= 2.10) |

Contributors | the Core team, Brenton Kenkel, Max Contributions from Jed Wing, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt, Tyler Hunt, Tony Cooper, Zachary Mayer, Michael Benesty, Reynald Lescarbeau, Andrew Ziem, Luca Scrucca, Yuan Tang, Can Candan |

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