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:Leihong Wu <[email protected]>, Weida Tong

Dforest_0.4.2.tar.gz
Dforest_0.4.2.zip(r-4.7)Dforest_0.4.2.zip(r-4.6)Dforest_0.4.2.zip(r-4.5)
Dforest_0.4.2.tgz(r-4.6-any)Dforest_0.4.2.tgz(r-4.5-any)
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Dforest_0.4.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
Dforest/json (API)

# Install 'Dforest' in R:
install.packages('Dforest', repos = c('https://lhnctr.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • data_dili - QSAR dataset with DILI endpoint for demo

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 8 scripts 252 downloads 16 exports 18 dependencies

Last updated from:729bce9030. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK116
source / vignettesOK147
linux-release-x86_64OK109
macos-release-arm64OK134
macos-oldrel-arm64OK163
windows-develOK77
windows-releaseOK84
windows-oldrelOK282
wasm-releaseOK98

Exports:cal_MCCCon_DTDF_accDF_ConfPlotDF_ConfPlot_accuDF_CVDF_CVsummaryDF_dataFsDF_dataPreDF_easyDF_perfDF_predDF_trainDF_TrainsummaryDforestPred_DT

Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglifecycleR6RColorBrewerrlangrpartS7scalesvctrsviridisLitewithr