conTree: Contrast Trees and Boosting

Contrast trees represent a new approach for assessing the accuracy of many types of machine learning estimates that are not amenable to standard (cross) validation methods; see "Contrast trees and distribution boosting", Jerome H. Friedman (2020) <doi:10.1073/pnas.1921562117>. In situations where inaccuracies are detected, boosted contrast trees can often improve performance. Functions are provided to to build such trees in addition to a special case, distribution boosting, an assumption free method for estimating the full probability distribution of an outcome variable given any set of joint input predictor variable values.

Version:	0.3-1
Depends:	R (≥ 3.5)
Imports:	stats, graphics
Suggests:	randomForest, knitr, rmarkdown
Published:	2023-11-22
DOI:	10.32614/CRAN.package.conTree
Author:	Jerome Friedman [aut, cph], Balasubramanian Narasimhan [aut, cre]
Maintainer:	Balasubramanian Narasimhan <naras at stanford.edu>
BugReports:	https://github.com/bnaras/conTree/issues
License:	Apache License 2.0
URL:	https://jhfhub.github.io/conTree_tutorial/
NeedsCompilation:	yes
Materials:	README NEWS
CRAN checks:	conTree results

Documentation:

Reference manual:	conTree.pdf
Vignettes:	conTree

Downloads:

Package source:	conTree_0.3-1.tar.gz
Windows binaries:	r-devel: conTree_0.3-1.zip, r-release: conTree_0.3-1.zip, r-oldrel: conTree_0.3-1.zip
macOS binaries:	r-release (arm64): conTree_0.3-1.tgz, r-oldrel (arm64): conTree_0.3-1.tgz, r-release (x86_64): conTree_0.3-1.tgz, r-oldrel (x86_64): conTree_0.3-1.tgz
Old sources:	conTree archive

Linking:

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