Package: drf 1.1.0
drf: Distributional Random Forests
An implementation of distributional random forests as introduced in Cevid & Michel & Meinshausen & Buhlmann (2020) <arxiv:2005.14458>.
Authors:
drf_1.1.0.tar.gz
drf_1.1.0.zip(r-4.5)drf_1.1.0.zip(r-4.4)drf_1.1.0.zip(r-4.3)
drf_1.1.0.tgz(r-4.4-x86_64)drf_1.1.0.tgz(r-4.4-arm64)drf_1.1.0.tgz(r-4.3-x86_64)drf_1.1.0.tgz(r-4.3-arm64)
drf_1.1.0.tar.gz(r-4.5-noble)drf_1.1.0.tar.gz(r-4.4-noble)
drf_1.1.0.tgz(r-4.4-emscripten)drf_1.1.0.tgz(r-4.3-emscripten)
drf.pdf |drf.html✨
drf/json (API)
| # Install 'drf' in R: |
| install.packages('drf', repos = c('https://lorismichel.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/lorismichel/drf/issues
Last updated 4 years agofrom:0cfbbd0747. Checks:OK: 1 NOTE: 8. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 05 2024 |
| R-4.5-win-x86_64 | NOTE | Nov 05 2024 |
| R-4.5-linux-x86_64 | NOTE | Nov 05 2024 |
| R-4.4-win-x86_64 | NOTE | Nov 05 2024 |
| R-4.4-mac-x86_64 | NOTE | Nov 05 2024 |
| R-4.4-mac-aarch64 | NOTE | Nov 05 2024 |
| R-4.3-win-x86_64 | NOTE | Nov 05 2024 |
| R-4.3-mac-x86_64 | NOTE | Nov 05 2024 |
| R-4.3-mac-aarch64 | NOTE | Nov 05 2024 |
Exports:drfget_sample_weightsget_treesplit_frequenciesvariable_importancevariableImportance
Dependencies:clidata.tablefansifastDummiesgluelatticelifecyclemagrittrMatrixpillarpkgconfigRcppRcppEigenrlangstringistringrtibbletransportutf8vctrs
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Distributional Random Forests | drf |
| Given a trained forest and test data, compute the training sample weights for each test point. | get_sample_weights |
| Retrieve a single tree from a trained forest object. | get_tree |
| A default leaf_stats for forests classes without a leaf_stats method that always returns NULL. | leaf_stats.default |
| Calculate summary stats given a set of samples for regression forests. | leaf_stats.drf |
| Compute the median heuristic for the MMD bandwidth choice | medianHeuristic |
| Plot a DRF tree object. | plot.drf_tree |
| Predict with a drf forest | predict.drf |
| Print a DRF forest object. | print.drf |
| Print a DRF tree object. | print.drf_tree |
| Calculate which features the forest split on at each depth. | split_frequencies |
| Calculate a simple measure of 'importance' for each feature. | variable_importance |
| Variable importance based on MMD | variableImportance |
| Weighted quantiles | weighted.quantile |
