Package: gpboost 1.5.1.2

gpboost: Combining Tree-Boosting with Gaussian Process and Mixed Effects Models

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.

Authors:Fabio Sigrist [aut, cre], Tim Gyger [aut], Pascal Kuendig [aut], Benoit Jacob [cph], Gael Guennebaud [cph], Nicolas Carre [cph], Pierre Zoppitelli [cph], Gauthier Brun [cph], Jean Ceccato [cph], Jitse Niesen [cph], Other authors of Eigen for the included version of Eigen [ctb, cph], Timothy A. Davis [cph], Guolin Ke [ctb], Damien Soukhavong [ctb], James Lamb [ctb], Other authors of LightGBM for the included version of LightGBM [ctb], Microsoft Corporation [cph], Dropbox, Inc. [cph], Jay Loden [cph], Dave Daeschler [cph], Giampaolo Rodola [cph], Alberto Ferreira [ctb], Daniel Lemire [ctb], Victor Zverovich [cph], IBM Corporation [ctb], Keith O'Hara [cph], Stephen L. Moshier [cph], Jorge Nocedal [cph], Naoaki Okazaki [cph], Yixuan Qiu [cph], Dirk Toewe [cph]

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gpboost.pdf |gpboost.html✨
gpboost/json (API)

# Install 'gpboost' in R:
install.packages('gpboost', repos = c('https://fabsig.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/fabsig/gpboost/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • X - Example data for the GPBoost package
  • X_test - 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 - Example data for the GPBoost package
  • coords_test - Example data for the GPBoost package
  • group_data - Example data for the GPBoost package
  • group_data_test - Example data for the GPBoost package
  • y - Example data for the GPBoost package

On CRAN:

40 exports 1.31 score 5 dependencies 200 scripts 811 downloads

Last updated 22 days agofrom:31dce0f326. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 27 2024
R-4.5-win-x86_64OKAug 27 2024
R-4.5-linux-x86_64OKAug 27 2024
R-4.4-win-x86_64OKAug 27 2024
R-4.4-mac-x86_64OKAug 27 2024
R-4.4-mac-aarch64OKAug 27 2024
R-4.3-win-x86_64OKAug 27 2024
R-4.3-mac-x86_64OKAug 27 2024
R-4.3-mac-aarch64OKAug 27 2024

Exports:fitfitGPModelget_aux_parsget_coefget_cov_parsget_nested_categoriesgetinfogpb.convert_with_rulesgpb.cvgpb.Datasetgpb.Dataset.constructgpb.Dataset.create.validgpb.Dataset.savegpb.Dataset.set.categoricalgpb.Dataset.set.referencegpb.dumpgpb.get.eval.resultgpb.grid.search.tune.parametersgpb.importancegpb.interpretegpb.loadgpb.model.dt.treegpb.plot.importancegpb.plot.interpretationgpb.plot.part.dep.interactgpb.plot.partial.dependencegpb.savegpb.traingpboostGPModelloadGPModelneg_log_likelihoodpredict_training_data_random_effectsreadRDS.gpb.BoostersaveGPModelsaveRDS.gpb.Boosterset_optim_paramsset_prediction_datasetinfoslice

Dependencies:data.tablelatticeMatrixR6RJSONIO

Readme and manuals

Help Manual

Help pageTopics
Test part from Mushroom Data Setagaricus.test
Training part from Mushroom Data Setagaricus.train
Bank Marketing Data Setbank
Example data for the GPBoost packagecoords
Example data for the GPBoost packagecoords_test
Dimensions of an 'gpb.Dataset'dim.gpb.Dataset
Handling of column names of 'gpb.Dataset'dimnames.gpb.Dataset dimnames<-.gpb.Dataset
Generic 'fit' method for a 'GPModel'fit
Fits a 'GPModel'fit.GPModel
Fits a 'GPModel'fitGPModel
Get (estimated) auxiliary (additional) parameters of the likelihoodget_aux_pars
Get (estimated) auxiliary (additional) parameters of the likelihoodget_aux_pars.GPModel
Get (estimated) linear regression coefficientsget_coef
Get (estimated) linear regression coefficientsget_coef.GPModel
Get (estimated) covariance parametersget_cov_pars
Get (estimated) covariance parametersget_cov_pars.GPModel
Auxiliary function to create categorical variables for nested grouped random effectsget_nested_categories
Get information of an 'gpb.Dataset' objectgetinfo getinfo.gpb.Dataset
Data preparator for GPBoost datasets with rules (integer)gpb.convert_with_rules
CV function for number of boosting iterationsgpb.cv
Construct 'gpb.Dataset' objectgpb.Dataset
Construct Dataset explicitlygpb.Dataset.construct
Construct validation datagpb.Dataset.create.valid
Save 'gpb.Dataset' to a binary filegpb.Dataset.save
Set categorical feature of 'gpb.Dataset'gpb.Dataset.set.categorical
Set reference of 'gpb.Dataset'gpb.Dataset.set.reference
Dump GPBoost model to jsongpb.dump
Get record evaluation result from boostergpb.get.eval.result
Function for choosing tuning parametersgpb.grid.search.tune.parameters
Compute feature importance in a modelgpb.importance
Compute feature contribution of predictiongpb.interprete
Load GPBoost modelgpb.load
Parse a GPBoost model json dumpgpb.model.dt.tree
Plot feature importance as a bar graphgpb.plot.importance
Plot feature contribution as a bar graphgpb.plot.interpretation
Plot interaction partial dependence plotsgpb.plot.part.dep.interact
Plot partial dependence plotsgpb.plot.partial.dependence
Save GPBoost modelgpb.save
Main training logic for GBPoostgpb.train
Train a GPBoost modelgpboost
Example data for the GPBoost packageGPBoost_data
Create a 'GPModel' objectGPModel
Documentation for parameters shared by 'GPModel', 'gpb.cv', and 'gpboost'GPModel_shared_params
Example data for the GPBoost packagegroup_data
Example data for the GPBoost packagegroup_data_test
Load a 'GPModel' from a fileloadGPModel
Evaluate the negative log-likelihoodneg_log_likelihood
Evaluate the negative log-likelihoodneg_log_likelihood.GPModel
Predict ("estimate") training data random effects for a 'GPModel'predict_training_data_random_effects
Predict ("estimate") training data random effects for a 'GPModel'predict_training_data_random_effects.GPModel
Prediction function for 'gpb.Booster' objectspredict.gpb.Booster
Make predictions for a 'GPModel'predict.GPModel
readRDS for 'gpb.Booster' modelsreadRDS.gpb.Booster
Save a 'GPModel'saveGPModel
saveRDS for 'gpb.Booster' modelssaveRDS.gpb.Booster
Set parameters for estimation of the covariance parametersset_optim_params
Set parameters for estimation of the covariance parametersset_optim_params.GPModel
Set prediction data for a 'GPModel'set_prediction_data
Set prediction data for a 'GPModel'set_prediction_data.GPModel
Set information of an 'gpb.Dataset' objectsetinfo setinfo.gpb.Dataset
Slice a datasetslice slice.gpb.Dataset
Summary for a 'GPModel'summary.GPModel
Example data for the GPBoost packageX
Example data for the GPBoost packageX_test
Example data for the GPBoost packagey