Combining Tree-Boosting with Gaussian Process and Mixed Effects Models

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Documentation for package ‘gpboost’ version 1.5.1.1

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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