Package: RandomGaussianNB 0.2.4
RandomGaussianNB: Randomized Feature and Bootstrap-Enhanced Gaussian Naive Bayes Classifier
Provides an accessible and efficient implementation of a randomized feature and bootstrap-enhanced Gaussian naive Bayes classifier. The method combines stratified bootstrap resampling with random feature subsampling and aggregates predictions via posterior averaging. Support is provided for mixed-type predictors and parallel computation. Methods are described in Srisuradetchai (2025) <doi:10.3389/fdata.2025.1706417> "Posterior averaging with Gaussian naive Bayes and the R package RandomGaussianNB for big-data classification".
Authors:
RandomGaussianNB_0.2.4.tar.gz
RandomGaussianNB_0.2.4.zip(r-4.7)RandomGaussianNB_0.2.4.zip(r-4.6)RandomGaussianNB_0.2.4.zip(r-4.5)
RandomGaussianNB_0.2.4.tgz(r-4.6-any)RandomGaussianNB_0.2.4.tgz(r-4.5-any)
RandomGaussianNB_0.2.4.tar.gz(r-4.7-any)RandomGaussianNB_0.2.4.tar.gz(r-4.6-any)
RandomGaussianNB_0.2.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
RandomGaussianNB/json (API)
NEWS
| # Install 'RandomGaussianNB' in R: |
| install.packages('RandomGaussianNB', repos = c('https://patchanok-tu.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:0231a86ecd. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 108 | ||
| source / vignettes | OK | 143 | ||
| linux-release-x86_64 | OK | 101 | ||
| macos-release-arm64 | OK | 158 | ||
| macos-oldrel-arm64 | OK | 160 | ||
| windows-devel | OK | 57 | ||
| windows-release | OK | 90 | ||
| windows-oldrel | OK | 54 | ||
| wasm-release | OK | 84 |
Exports:random_gaussian_nb
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Predict from a random_gaussian_nb model | predict.random_gaussian_nb |
| Train a Random Naive Bayes Model via Bootstrap + Random Subspace (Mixed Types) | fitted.random_gaussian_nb nobs.random_gaussian_nb plot.random_gaussian_nb print.random_gaussian_nb random_gaussian_nb str.random_gaussian_nb summary.random_gaussian_nb |
