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. 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:10.3115/1067807.1067848>. Any issues can be submitted to: <https://github.com/mskogholt/fastNaiveBayes/issues>.

Version: 2.2.0
Depends: R (≥ 3.2.0)
Imports: Matrix, stats
Suggests: knitr, rmarkdown, testthat
Published: 2020-02-23
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

Downloads:

Reference manual: fastNaiveBayes.pdf
Vignettes: Fast Naive Bayes
Package source: fastNaiveBayes_2.2.0.tar.gz
Windows binaries: r-devel: fastNaiveBayes_2.2.0.zip, r-devel-gcc8: fastNaiveBayes_2.2.0.zip, r-release: fastNaiveBayes_2.2.0.zip, r-oldrel: fastNaiveBayes_2.2.0.zip
OS X binaries: r-release: fastNaiveBayes_2.2.0.tgz, r-oldrel: fastNaiveBayes_2.2.0.tgz
Old sources: fastNaiveBayes archive

Reverse dependencies:

Reverse suggests: quanteda.textmodels

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