Combining Tree-Boosting with Gaussian Process and Mixed Effects Models
Get (estimated) covariance parameters
Get (estimated) linear regression coefficients
Dimensions of an gpb.Dataset
Get (estimated) linear regression coefficients
Get (estimated) auxiliary (additional) parameters of the likelihood
Get (estimated) covariance parameters
Get (estimated) auxiliary (additional) parameters of the likelihood
Handling of column names of gpb.Dataset
Fits a GPModel
Generic 'fit' method for a GPModel
Example data for the GPBoost package
Example data for the GPBoost package
Fits a GPModel
Auxiliary function to create categorical variables for nested grouped ...
Get information of an gpb.Dataset object
Shared parameter docs
Data preparator for GPBoost datasets with rules (integer)
CV function for number of boosting iterations
Construct Dataset explicitly
Construct validation data
Compute feature importance in a model
Construct gpb.Dataset object
Save gpb.Dataset to a binary file
Set categorical feature of gpb.Dataset
Set reference of gpb.Dataset
Dump GPBoost model to json
Get record evaluation result from booster
Function for choosing tuning parameters
Slice a dataset
Compute feature contribution of prediction
Load GPBoost model
Parse a GPBoost model json dump
Plot feature importance as a bar graph
Plot feature contribution as a bar graph
Plot interaction partial dependence plots
Plot partial dependence plots
Save GPBoost model
Main training logic for GBPoost
Train a GPBoost model
Documentation for parameters shared by GPModel, gpb.cv, and `gpboo...
Create a GPModel object
Example data for the GPBoost package
Example data for the GPBoost package
Load a GPModel from a file
Evaluate the negative log-likelihood
Evaluate the negative log-likelihood
Predict ("estimate") training data random effects for a GPModel
Predict ("estimate") training data random effects for a GPModel
Prediction function for gpb.Booster objects
Make predictions for a GPModel
readRDS for gpb.Booster models
Save a GPModel
saveRDS for gpb.Booster models
Set parameters for estimation of the covariance parameters
Set parameters for estimation of the covariance parameters
Set prediction data for a GPModel
Set prediction data for a GPModel
Set information of an gpb.Dataset object
Summary for a GPModel
Example data for the GPBoost package
Example data for the GPBoost package
Example data for the GPBoost package
An R package that allows for combining tree-boosting with Gaussian process and mixed effects models. It also allows for independently doing tree-boosting as well as inference and prediction for Gaussian process and mixed effects models. See <https://github.com/fabsig/GPBoost> for more information on the software and Sigrist (2022, JMLR) <https://www.jmlr.org/papers/v23/20-322.html> and Sigrist (2023, TPAMI) <doi:10.1109/TPAMI.2022.3168152> for more information on the methodology.