Package: gpboost 1.5.4
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:
gpboost_1.5.4.tar.gz
gpboost_1.5.4.tar.gz(r-4.5-noble)gpboost_1.5.4.tar.gz(r-4.4-noble)
gpboost_1.5.4.tgz(r-4.4-emscripten)gpboost_1.5.4.tgz(r-4.3-emscripten)
gpboost.pdf |gpboost.html✨
gpboost/json (API)
# Install 'gpboost' in R: |
install.packages('gpboost', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/fabsig/gpboost/issues
- 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
Last updated 23 days agofrom:27c83bd445. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 25 2024 |
R-4.5-linux-x86_64 | OK | Nov 25 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