Contrast Trees and Boosting R package conTree version 0.3-1 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. Software Depends: R (>= 3.5) Imports: stats, graphics Suggests: randomForest, knitr, rmarkdown Balasubramanian Narasimhan <naras@stanford.edu> Comprehensive R Archive Network (CRAN) Jerome Friedman [aut, cph], Balasubramanian Narasimhan [aut, cre] Apache License 2.0 2023-11-22 application/tgz https://CRAN.R-project.org/package=conTree doi:10.32614/CRAN.package.conTree