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 |
| 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-oldrel (arm64): naivebayes_0.9.7.tgz, r-release (x86_64): naivebayes_0.9.7.tgz, r-oldrel (x86_64): naivebayes_0.9.7.tgz |
| Old sources: | naivebayes archive |
| Reverse depends: | fasi |
| Reverse imports: | MLFS, ModTools, npcs, nproc, PrInCE |
| Reverse suggests: | discrim, FRESA.CAD, quanteda.textmodels, superml |
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