This is an extremely fast implementation of a Naive Bayes classifier. This package is currently the only package that supports a Bernoulli distribution, a Multinomial distribution, and a Gaussian distribution, making it suitable for both binary features, frequency counts, and numerical features. Another feature is the support of a mix of different event models. Only numerical variables are allowed, however, categorical variables can be transformed into dummies and used with the Bernoulli distribution. The implementation is largely based on the paper "A comparison of event models for Naive Bayes anti-spam e-mail filtering" written by K.M. Schneider (2003) <doi:10.3115/1067807>. Any issues can be submitted to: <https://github.com/mskogholt/fastNaiveBayes/issues>.
| Version: | 2.1.0 |
| Depends: | R (≥ 3.2.0) |
| Imports: | Matrix, stats |
| Suggests: | knitr, rmarkdown, testthat |
| Published: | 2019-08-28 |
| Author: | Martin Skogholt |
| Maintainer: | Martin Skogholt <m.skogholt at gmail.com> |
| BugReports: | https://github.com/mskogholt/fastNaiveBayes/issues |
| License: | GPL-3 |
| URL: | https://github.com/mskogholt/fastNaiveBayes |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | fastNaiveBayes results |
| Reference manual: | fastNaiveBayes.pdf |
| Vignettes: |
Fast Naive Bayes |
| Package source: | fastNaiveBayes_2.1.0.tar.gz |
| Windows binaries: | r-devel: fastNaiveBayes_2.1.0.zip, r-devel-gcc8: fastNaiveBayes_2.1.0.zip, r-release: fastNaiveBayes_2.1.0.zip, r-oldrel: fastNaiveBayes_2.1.0.zip |
| OS X binaries: | r-release: fastNaiveBayes_2.1.0.tgz, r-oldrel: fastNaiveBayes_2.1.0.tgz |
| Old sources: | fastNaiveBayes archive |
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