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 |
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 |
Reference manual: | naivebayes.pdf |
Vignettes: |
An Introduction to Naivebayes |
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 imports: | MLFS, nproc, PrInCE, promor |
Reverse suggests: | discrim, FRESA.CAD, quanteda.textmodels, superml |
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