quantregForest: Quantile Regression Forests

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
DOI: 10.32614/CRAN.package.quantregForest
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 dependencies:

Reverse imports: CondIndTests, ConformalSmallest, curvir, geomod
Reverse suggests: flowml, fscaret, ModelMap, probably, soilassessment, tidyfit


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