agaricus.test |
Test part from Mushroom Data Set |
agaricus.train |
Training part from Mushroom Data Set |
bank |
Bank Marketing Data Set |
coords |
Example data for the GPBoost package |
coords_test |
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 |
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 |
Example data for the GPBoost package |
group_data_test |
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 |
Example data for the GPBoost package |
X_test |
Example data for the GPBoost package |
y |
Example data for the GPBoost package |