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.

Version: 1.0.0
Suggests: knitr, Matrix
Published: 2024-03-16
DOI: 10.32614/CRAN.package.naivebayes
Author: Michal Majka ORCID iD [aut, cre]
Maintainer: Michal Majka <michalmajka at>
License: GPL-2
NeedsCompilation: no
Citation: naivebayes citation info
Materials: NEWS
In views: MachineLearning, MissingData
CRAN checks: naivebayes results


Reference manual: naivebayes.pdf
Vignettes: An Introduction to Naivebayes


Package source: naivebayes_1.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): naivebayes_1.0.0.tgz, r-oldrel (arm64): naivebayes_1.0.0.tgz, r-release (x86_64): naivebayes_1.0.0.tgz, r-oldrel (x86_64): naivebayes_1.0.0.tgz
Old sources: naivebayes archive

Reverse dependencies:

Reverse imports: MLFS, ModTools, nproc, PrInCE, promor
Reverse suggests: discrim, FRESA.CAD, quanteda.textmodels, superml


Please use the canonical form to link to this page.