Package: drf 1.3.1
drf: Distributional Random Forests
An implementation of distributional random forests as introduced in Cevid & Michel & Naf & Meinshausen & Buhlmann (2022) <doi:10.48550/arXiv.2005.14458>.
Authors:
drf_1.3.1.tar.gz
drf_1.3.1.zip(r-4.7-x86_64)drf_1.3.1.zip(r-4.7-arm64)drf_1.3.1.zip(r-4.6-x86_64)drf_1.3.1.zip(r-4.6-arm64)drf_1.3.1.zip(r-4.5-x86_64)
drf_1.3.1.tgz(r-4.6-x86_64)drf_1.3.1.tgz(r-4.6-arm64)drf_1.3.1.tgz(r-4.5-x86_64)drf_1.3.1.tgz(r-4.5-arm64)
drf_1.3.1.tar.gz(r-4.7-arm64)drf_1.3.1.tar.gz(r-4.7-x86_64)drf_1.3.1.tar.gz(r-4.6-arm64)drf_1.3.1.tar.gz(r-4.6-x86_64)
drf_1.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
drf/json (API)
| # Install 'drf' in R: |
| install.packages('drf', repos = c('https://jeffnaef.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/lorismichel/drf/issues
Last updated from:45267445a0. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 180 | ||
| linux-devel-x86_64 | OK | 156 | ||
| source / vignettes | OK | 272 | ||
| linux-release-arm64 | OK | 163 | ||
| linux-release-x86_64 | OK | 163 | ||
| macos-release-arm64 | OK | 203 | ||
| macos-release-x86_64 | OK | 384 | ||
| macos-oldrel-arm64 | OK | 177 | ||
| macos-oldrel-x86_64 | OK | 461 | ||
| windows-devel-arm64 | OK | 436 | ||
| windows-devel-x86_64 | OK | 179 | ||
| windows-release-arm64 | OK | 416 | ||
| windows-release-x86_64 | OK | 190 | ||
| windows-oldrel-x86_64 | OK | 164 | ||
| wasm-release | OK | 168 |
Exports:drfget_sample_weightsget_treesplit_frequenciesvariable_importancevariableImportance
Dependencies:clidata.tablefastDummiesgluelatticelifecyclemagrittrMatrixpillarpkgconfigRcppRcppEigenrlangstringistringrtibbleutf8vctrs
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 from Distributional Random Forests object | 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 |