Contrast Trees and Boosting
Overview
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.
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From NEWS.md in the release
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 16includes a package overview page
- Examples that run
- 100%1 run in a plain check
- Exported functions with examples
- 8%
- Documented parameters
- 97%
- Return-value docs
- 100%
- Help pages citing references
- 13%
Downloads
Repository
Forks over time
Releases over time
3 development-tooling and community-health practices detected across 2 families in the upstream repository
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Dependencies
Installing conTree adds no packages beyond those that come with R.
Code & Tests
Datasets
People & History
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- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 0.3-1Latest
- 0.32023-06-07
- RR 4.3.0 released · 2023-04-21
Package metadata
- First published
- 2023-06-07
- Total releases
- 2 / 3 yrs
- License
- Apache License 2.0 OSI
- Minimum R
- ≥ 3.5
- Bundled data
- 1.7 MB / 3 files
- Download size
- 1.9 MB
Cite
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