fastNaiveBayes: Extremely Fast Implementation of a Naive Bayes Classifier

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. This implementation offers a huge performance gain compared to other implementations in R. The execution times were compared on a data set of tweets and this package was found to be around 283 to 34,841 times faster for the Bernoulli event models and 17 to 60 times faster for the Multinomial model. See the vignette for more details. For the Gaussian distribution this package was found to be between 2.8 and 1679 times faster. 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: <>.

Version: 1.1.2
Depends: R (≥ 3.2.0)
Imports: Matrix, stats
Suggests: knitr, rmarkdown, testthat
Published: 2019-04-16
Author: Martin Skogholt
Maintainer: Martin Skogholt <m.skogholt at>
License: GPL-3
NeedsCompilation: no
Materials: README NEWS
CRAN checks: fastNaiveBayes results


Reference manual: fastNaiveBayes.pdf
Vignettes: Fast Naive Bayes
Package source: fastNaiveBayes_1.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: fastNaiveBayes_1.1.2.tgz, r-oldrel: fastNaiveBayes_1.1.1.tgz
Old sources: fastNaiveBayes archive


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