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
Author:	Michal Majka [aut, cre]
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

Documentation:

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

Downloads:

Package source:	naivebayes_1.0.0.tar.gz
Windows binaries:	r-devel: naivebayes_1.0.0.zip, r-release: naivebayes_1.0.0.zip, r-oldrel: naivebayes_1.0.0.zip
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

Linking:

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