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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 |
| 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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