Introduction | Installation | Main functions | Naïve Bayes Model | Parameter estimation | General usage | Appendix
Package: naivebayes 1.0.0
naivebayes: High Performance Implementation of the Naive Bayes Algorithm
In this implementation of the Naive Bayes classifier following class conditional distributions are available: 'Bernoulli', 'Categorical', 'Gaussian', 'Poisson', 'Multinomial' and non-parametric representation of the class conditional density estimated via Kernel Density Estimation. Implemented classifiers handle missing data and can take advantage of sparse data.
Authors:
naivebayes_1.0.0.tar.gz
naivebayes_1.0.0.zip(r-4.7-any)naivebayes_1.0.0.zip(r-4.6-any)naivebayes_1.0.0.zip(r-4.5-any)
naivebayes_1.0.0.tgz(r-4.6-any)naivebayes_1.0.0.tgz(r-4.5-any)
naivebayes_1.0.0.tar.gz(r-4.7-any)naivebayes_1.0.0.tar.gz(r-4.6-any)
naivebayes_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
naivebayes/json (API)
| # Install 'naivebayes' in R: |
| install.packages('naivebayes', repos = c('https://majkamichal.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/majkamichal/naivebayes/issues
Pkgdown/docs site:https://majkamichal.github.io
classification-modeldatasciencemachine-learningnaive-bayes
Last updated from:06ace975ba. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 117 | ||
| source / vignettes | OK | 227 | ||
| linux-release | OK | 109 | ||
| macos-release | OK | 155 | ||
| macos-oldrel | OK | 154 | ||
| windows-devel | OK | 64 | ||
| windows-release | OK | 70 | ||
| windows-oldrel | OK | 61 | ||
| wasm-release | OK | 98 |
Exports:%class%%prob%bernoulli_naive_bayesgaussian_naive_bayesget_cond_distmultinomial_naive_bayesnaive_bayesnonparametric_naive_bayespoisson_naive_bayestables
Dependencies:
