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
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
Old sources: conTree archive

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