Package: Dforest 0.4.2
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>.
Authors:
Dforest_0.4.2.tar.gz
Dforest_0.4.2.zip(r-4.5)Dforest_0.4.2.zip(r-4.4)Dforest_0.4.2.zip(r-4.3)
Dforest_0.4.2.tgz(r-4.4-any)Dforest_0.4.2.tgz(r-4.3-any)
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Dforest.pdf |Dforest.html✨
Dforest/json (API)
| # Install 'Dforest' in R: |
| install.packages('Dforest', repos = c('https://lhnctr.r-universe.dev', 'https://cloud.r-project.org')) |
- data_dili - QSAR dataset with DILI endpoint for demo
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 7 years agofrom:729bce9030. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 02 2024 |
| R-4.5-win | OK | Nov 02 2024 |
| R-4.5-linux | OK | Nov 02 2024 |
| R-4.4-win | OK | Nov 02 2024 |
| R-4.4-mac | OK | Nov 02 2024 |
| R-4.3-win | OK | Nov 02 2024 |
| R-4.3-mac | OK | Nov 02 2024 |
Exports:cal_MCCCon_DTDF_accDF_ConfPlotDF_ConfPlot_accuDF_CVDF_CVsummaryDF_dataFsDF_dataPreDF_easyDF_perfDF_predDF_trainDF_TrainsummaryDforestPred_DT
Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangrpartscalestibbleutf8vctrsviridisLitewithr
