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: 1.3-7.1 Depends: randomForest, RColorBrewer Imports: stats, parallel Suggests: gss, knitr, rmarkdown Published: 2024-10-07 DOI: 10.32614/CRAN.package.quantregForest Author: Nicolai Meinshausen [aut], Loris Michel [cre] Maintainer: Loris Michel <michel at stat.math.ethz.ch> BugReports: https://github.com/lorismichel/quantregForest/issues License: GPL-2 | GPL-3 [expanded from: GPL] URL: https://github.com/lorismichel/quantregForest NeedsCompilation: yes In views: MachineLearning CRAN checks: quantregForest results Documentation: Reference manual: quantregForest.pdf Downloads: Package source: quantregForest_1.3-7.1.tar.gz Windows binaries: r-devel: quantregForest_1.3-7.1.zip, r-release: quantregForest_1.3-7.1.zip, r-oldrel: quantregForest_1.3-7.1.zip macOS binaries: r-release (arm64): quantregForest_1.3-7.1.tgz, r-oldrel (arm64): quantregForest_1.3-7.1.tgz, r-release (x86_64): quantregForest_1.3-7.1.tgz, r-oldrel (x86_64): quantregForest_1.3-7.1.tgz Old sources: quantregForest archive Reverse dependencies: Reverse imports: CondIndTests, ConformalSmallest, curvir, geomod Reverse suggests: flowml, fscaret, ModelMap, probably, soilassessment, tidyfit, trtf Linking:Please use the canonical form https://CRAN.R-project.org/package=quantregForest to link to this page.
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