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'
X                       Predictor variable data for example data for
                        the GPBoost package
X_test                  Test predictor variable data for example data
                        for the GPBoost package
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'
fit                     Generic 'fit' method for a 'GPModel'
fit.GPModel             Fits a 'GPModel'
fitGPModel              Fits a 'GPModel'
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
getinfo                 Get information of an 'gpb.Dataset' object
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.convert_with_rules
                        Data preparator for GPBoost datasets with rules
                        (integer)
gpb.cv                  CV function for number of boosting iterations
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
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.GPModel         Make predictions for a 'GPModel'
predict.gpb.Booster     Prediction function for 'gpb.Booster' objects
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
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'
setinfo                 Set information of an 'gpb.Dataset' object
slice                   Slice a dataset
summary.GPModel         Summary for a 'GPModel'
y                       Response variable data for example data for the
                        GPBoost package
