Reference

All functions
agaricus.test	Test part from Mushroom Data Set
agaricus.train	Training part from Mushroom Data Set
callbacks	Callback closures for booster training.
cb.cv.predict()	Callback closure for returning cross-validation based predictions.
cb.early.stop()	Callback closure to activate the early stopping.
cb.evaluation.log()	Callback closure for logging the evaluation history
cb.gblinear.history()	Callback closure for collecting the model coefficients history of a gblinear booster during its training.
cb.print.evaluation()	Callback closure for printing the result of evaluation
cb.reset.parameters()	Callback closure for resetting the booster's parameters at each iteration.
cb.save.model()	Callback closure for saving a model file.
dim(<xgb.DMatrix>)	Dimensions of xgb.DMatrix
dimnames(<xgb.DMatrix>) `dimnames<-`(<xgb.DMatrix>)	Handling of column names of xgb.DMatrix
getinfo()	Get information of an xgb.DMatrix object
predict(<xgb.Booster>) predict(<xgb.Booster.handle>)	Predict method for eXtreme Gradient Boosting model
print(<xgb.Booster>)	Print xgb.Booster
print(<xgb.DMatrix>)	Print xgb.DMatrix
print(<xgb.cv.synchronous>)	Print xgb.cv result
setinfo()	Set information of an xgb.DMatrix object
slice() `[`(<xgb.DMatrix>)	Get a new DMatrix containing the specified rows of original xgb.DMatrix object
xgb.Booster.complete()	Restore missing parts of an incomplete xgb.Booster object.
xgb.DMatrix()	Construct xgb.DMatrix object
xgb.DMatrix.save()	Save xgb.DMatrix object to binary file
xgb.attr() `xgb.attr<-`() xgb.attributes() `xgb.attributes<-`()	Accessors for serializable attributes of a model.
xgb.create.features()	Create new features from a previously learned model
xgb.cv()	Cross Validation
xgb.dump()	Dump an xgboost model in text format.
xgb.gblinear.history()	Extract gblinear coefficients history.
xgb.importance()	Importance of features in a model.
xgb.load()	Load xgboost model from binary file
xgb.model.dt.tree()	Parse a boosted tree model text dump
`xgb.parameters<-`()	Accessors for model parameters.
xgb.ggplot.deepness() xgb.plot.deepness()	Plot model trees deepness
xgb.ggplot.importance() xgb.plot.importance()	Plot feature importance as a bar graph
xgb.plot.multi.trees()	Project all trees on one tree and plot it
xgb.plot.shap()	SHAP contribution dependency plots
xgb.plot.tree()	Plot a boosted tree model
xgb.save()	Save xgboost model to binary file
xgb.save.raw()	Save xgboost model to R's raw vector, user can call xgb.load to load the model back from raw vector
xgb.train() xgboost()	eXtreme Gradient Boosting Training
xgboost-deprecated	Deprecation notices.