Fuzzy Forests
Overview
Fuzzy forests, a new algorithm based on random forests, is designed to reduce the bias seen in random forest feature selection caused by the presence of correlated features. Fuzzy forests uses recursive feature elimination random forests to select features from separate blocks of correlated features where the correlation within each block of features is high and the correlation between blocks of features is low. One final random forest is fit using the surviving features. This package fits random forests using the 'randomForest' package and allows for easy use of 'WGCNA' to split features into distinct blocks. See D. Conn, Ngun, T., C. Ramirez, and G. Li (2019) doi:10.18637/jss.v091.i09 for further details.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 18includes a package overview page
- Examples that run
- 100%8 run in a plain check
- Exported functions with examples
- 56%
- Documented parameters
- 87%
- Return-value docs
- 89%
- Help pages citing references
- 33%
Downloads
Dependencies
Code & Tests
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- RR 4.6.0 released · 2026-04-24
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- 1.0.8Latest
- 1.0.72020-02-05 · diff ↗
- 1.0.62019-10-27 · diff ↗
- RR 3.6.0 released · 2019-04-26
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- 1.0.42017-11-12 · diff ↗
- 1.0.3
- RR 3.4.0 released · 2017-04-21
Package metadata
- First published
- 2016-02-18
- Total releases
- 9 / 10 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.2.1
- Bundled data
- 602 KB / 3 files
- Download size
- 651 KB
Cite
Cite this package
Run in R for the authors' entry:
citation("fuzzyforest")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.18637/jss.v091.i09.
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From data release v2026-10-20, which the citation names so these numbers can be found later. More on citing and the projects behind them.