Dforest: Decision Forest

Provides R-implementation of Decision forest algorithm, which combines the predictions of multiple independent decision tree models for a consensus decision. In particular, Decision Forest is a novel pattern-recognition method which can be used to analyze: (1) DNA microarray data; (2) Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) data; and (3) Structure-Activity Relation (SAR) data. In this package, three fundamental functions are provided, as (1)DF_train, (2)DF_pred, and (3)DF_CV. run Dforest() to see more instructions. Weida Tong (2003) <doi:10.1021/ci020058s>.

Version: 0.4.2
Depends: R (≥ 3.0)
Imports: rpart, ggplot2, methods, stats
Published: 2017-11-28
DOI: 10.32614/CRAN.package.Dforest
Author: Leihong Wu, Weida Tong (Weida.tong@fda.hhs.gov)
Maintainer: Leihong Wu <leihong.wu at fda.hhs.gov>
License: GPL-2
NeedsCompilation: no
CRAN checks: Dforest results


Reference manual: Dforest.pdf


Package source: Dforest_0.4.2.tar.gz
Windows binaries: r-devel: Dforest_0.4.2.zip, r-release: Dforest_0.4.2.zip, r-oldrel: Dforest_0.4.2.zip
macOS binaries: r-release (arm64): Dforest_0.4.2.tgz, r-oldrel (arm64): Dforest_0.4.2.tgz, r-release (x86_64): Dforest_0.4.2.tgz, r-oldrel (x86_64): Dforest_0.4.2.tgz
Old sources: Dforest archive


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