Package: fastNaiveBayes
Type: Package
Title: Extremely Fast Implementation of a Naive Bayes Classifier
Version: 1.1.1
Author: Martin Skogholt
Maintainer: Martin Skogholt <m.skogholt@gmail.com>
Description: This is an extremely fast implementation of a Naive Bayes classifier. 
    This package is currently the only package that supports a Bernoulli distribution, 
    a Multinomial distribution, and a Gaussian distribution, making it suitable for 
    both binary features, frequency counts, and numerical features. Another unique 
    feature is the support of a mix of different event models. Only numerical variables are allowed, 
    however, categorical variables can be transformed into dummies and used with 
    the Bernoulli distribution. This implementation offers a huge performance 
    gain compared to the 'e1071' implementation in R. The execution times were compared 
    on a data set of tweets and was found to be around 1135 times faster. Compared to other implementations 
    the minimum speed up was found to be 12.5 times faster for the Bernoulli distribution. See the vignette 
    for more details. This performance gain is only realized using a Bernoulli 
    event model. Furthermore, the Multinomial event model implementation is even slightly faster, 
    but incomparable since it was not implemented in 'e1071'. Compared to other implementations of a 
    Multinomial distribution, this package was found to give a speed up of 12.2 times.
   The implementation is largely based on the paper
    "A comparison of event models for Naive Bayes anti-spam e-mail filtering" written by 
    K.M. Schneider (2003) <doi:10.3115/1067807>.
    Any issues can be submitted to: <https://github.com/mskogholt/fastNaiveBayes/issues>.
Depends: R (>= 3.2.0)
License: GPL-3
Encoding: UTF-8
LazyData: FALSE
Imports: Matrix, stats
Suggests: mlbench, knitr, rmarkdown, testthat
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-03-31 16:52:23 UTC; Martin
Repository: CRAN
Date/Publication: 2019-03-31 17:10:02 UTC
