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: 1.3-6
Depends: randomForest, RColorBrewer
Imports: stats, parallel
Suggests: gss
Published: 2017-11-14
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

Downloads:

Reference manual: quantregForest.pdf
Package source: quantregForest_1.3-6.tar.gz
Windows binaries: r-devel: quantregForest_1.3-6.zip, r-release: quantregForest_1.3-6.zip, r-oldrel: quantregForest_1.3-6.zip
OS X El Capitan binaries: r-release: quantregForest_1.3-6.tgz
OS X Mavericks binaries: r-oldrel: quantregForest_1.3-6.tgz
Old sources: quantregForest archive

Reverse dependencies:

Reverse imports: CondIndTests
Reverse suggests: fscaret, GSIF, ModelMap

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