Quantile Regression Forests is a tree-based ensemble method for estimation of conditional quantiles. It is particularly well suited for high-dimensional data. Predictor variables of mixed classes can be handled. The package is dependent on the package 'randomForest', written by Andy Liaw.
| Version: | 1.3-7 |
| Depends: | randomForest, RColorBrewer |
| Imports: | stats, parallel |
| Suggests: | gss, knitr, rmarkdown |
| Published: | 2017-12-19 |
| Author: | Nicolai Meinshausen |
| Maintainer: | Loris Michel <michel at stat.math.ethz.ch> |
| BugReports: | http://github.com/lorismichel/quantregForest/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| URL: | http://github.com/lorismichel/quantregForest |
| NeedsCompilation: | yes |
| In views: | MachineLearning |
| CRAN checks: | quantregForest results |
| Reference manual: | quantregForest.pdf |
| Package source: | quantregForest_1.3-7.tar.gz |
| Windows binaries: | r-devel: quantregForest_1.3-7.zip, r-release: quantregForest_1.3-7.zip, r-oldrel: quantregForest_1.3-7.zip |
| macOS binaries: | r-release (arm64): quantregForest_1.3-7.tgz, r-oldrel (arm64): quantregForest_1.3-7.tgz, r-release (x86_64): quantregForest_1.3-7.tgz, r-oldrel (x86_64): quantregForest_1.3-7.tgz |
| Old sources: | quantregForest archive |
| Reverse imports: | CondIndTests, ConformalSmallest, ParallelDSM, soilassessment |
| Reverse suggests: | fscaret, ModelMap |
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