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 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: 0.9.7
Suggests: knitr, Matrix
Published: 2020-03-08
Author: Michal Majka
Maintainer: Michal Majka <michalmajka at hotmail.com>
BugReports: https://github.com/majkamichal/naivebayes/issues
License: GPL-2
URL: https://github.com/majkamichal/naivebayes, https://majkamichal.github.io/naivebayes/
NeedsCompilation: no
Citation: naivebayes citation info
Materials: NEWS
In views: MachineLearning, MissingData
CRAN checks: naivebayes results

Downloads:

Reference manual: naivebayes.pdf
Vignettes: An Introduction to Naivebayes
Package source: naivebayes_0.9.7.tar.gz
Windows binaries: r-devel: naivebayes_0.9.7.zip, r-release: naivebayes_0.9.7.zip, r-oldrel: naivebayes_0.9.7.zip
macOS binaries: r-release (arm64): naivebayes_0.9.7.tgz, r-release (x86_64): naivebayes_0.9.7.tgz, r-oldrel: naivebayes_0.9.7.tgz
Old sources: naivebayes archive

Reverse dependencies:

Reverse depends: fasi
Reverse imports: ModTools, nproc, PrInCE
Reverse suggests: discrim, FRESA.CAD, quanteda.textmodels, superml

Linking:

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