# Combining Tree-Boosting with Gaussian Process and Mixed Effects Models

## Documentation for package ‘gpboost’ version 1.6.6

- DESCRIPTION file.
- Code demos. Use demo() to run them.

## Help Pages

agaricus.test | Test part from Mushroom Data Set
agaricus.train | Training part from Mushroom Data Set
bank | Bank Marketing Data Set
coords | Coordinates for example data for the GPBoost package
coords_test | Test coordinates for example data for the GPBoost package
dim.gpb.Dataset | Dimensions of an 'gpb.Dataset'
dimnames.gpb.Dataset | Handling of column names of 'gpb.Dataset'
dimnames<-.gpb.Dataset | Handling of column names of 'gpb.Dataset'
fit | Generic 'fit' method for a 'GPModel'
fit.GPModel | Fits a 'GPModel'
fitGPModel | Fits a 'GPModel'
getinfo | Get information of an 'gpb.Dataset' object
getinfo.gpb.Dataset | Get information of an 'gpb.Dataset' object
get_aux_pars | Get (estimated) auxiliary (additional) parameters of the likelihood
get_aux_pars.GPModel | Get (estimated) auxiliary (additional) parameters of the likelihood
get_coef | Get (estimated) linear regression coefficients
get_coef.GPModel | Get (estimated) linear regression coefficients
get_cov_pars | Get (estimated) covariance parameters
get_cov_pars.GPModel | Get (estimated) covariance parameters
get_nested_categories | Auxiliary function to create categorical variables for nested grouped random effects
gpb.convert_with_rules | Data preparator for GPBoost datasets with rules (integer)
gpb.cv | CV function for number of boosting iterations
gpb.Dataset | Construct 'gpb.Dataset' object
gpb.Dataset.construct | Construct Dataset explicitly
gpb.Dataset.create.valid | Construct validation data
gpb.Dataset.save | Save 'gpb.Dataset' to a binary file
gpb.Dataset.set.categorical | Set categorical feature of 'gpb.Dataset'
gpb.Dataset.set.reference | Set reference of 'gpb.Dataset'
gpb.dump | Dump GPBoost model to json
gpb.get.eval.result | Get record evaluation result from booster
gpb.grid.search.tune.parameters | Function for choosing tuning parameters
gpb.importance | Compute feature importance in a model
gpb.interprete | Compute feature contribution of prediction
gpb.load | Load GPBoost model
gpb.model.dt.tree | Parse a GPBoost model json dump
gpb.plot.importance | Plot feature importance as a bar graph
gpb.plot.interpretation | Plot feature contribution as a bar graph
gpb.plot.part.dep.interact | Plot interaction partial dependence plots
gpb.plot.partial.dependence | Plot partial dependence plots
gpb.save | Save GPBoost model (booster and GPModel)
gpb.train | Main training logic for GBPoost
gpboost | Train a GPBoost model
GPBoost_data | Example data for the GPBoost package
GPModel | Create a 'GPModel' object
GPModel_shared_params | Documentation for parameters shared by 'GPModel', 'gpb.cv', and 'gpboost'
group_data | Gouping data for example data for the GPBoost package
group_data_test | Test grouping data for example data for the GPBoost package
loadGPModel | Load a 'GPModel' from a file
neg_log_likelihood | Evaluate the negative log-likelihood
neg_log_likelihood.GPModel | Evaluate the negative log-likelihood
predict.gpb.Booster | Prediction function for 'gpb.Booster' objects
predict.GPModel | Make predictions for a 'GPModel'
predict_training_data_random_effects | Predict ("estimate") training data random effects for a 'GPModel'
predict_training_data_random_effects.GPModel | Predict ("estimate") training data random effects for a 'GPModel'
readRDS.gpb.Booster | readRDS for 'gpb.Booster' models
saveGPModel | Save a 'GPModel'
saveRDS.gpb.Booster | saveRDS for 'gpb.Booster' models
setinfo | Set information of an 'gpb.Dataset' object
setinfo.gpb.Dataset | Set information of an 'gpb.Dataset' object
set_optim_params | Set parameters for estimation of the covariance parameters
set_optim_params.GPModel | Set parameters for estimation of the covariance parameters
set_prediction_data | Set prediction data for a 'GPModel'
set_prediction_data.GPModel | Set prediction data for a 'GPModel'
slice | Slice a dataset
slice.gpb.Dataset | Slice a dataset
summary.GPModel | Summary for a 'GPModel'
X | Predictor variable data for example data for the GPBoost package
X_test | Test predictor variable data for example data for the GPBoost package
y | Response variable data for example data for the GPBoost package