Title: Fast Computation of Multivariate M-estimators
Author: Lutz Duembgen, Klaus Nordhausen, Heike Schumacher
Description: The package implements the new algorithm for fast computation of M-scatter matrices using a partial Newton-Raphson procedure for several estimators.

Author: Lutz Duembgen, Klaus Nordhausen, Heike Schuhmacher

Title: Fast Computation of Multivariate M-Estimators
Description: Implements the new algorithm for fast computation of M-scatter matrices using a partial Newton-Raphson procedure for several estimators. The algorithm is described in Duembgen, Nordhausen and Schuhmacher (2016) <doi:10.1016/j.jmva.2015.11.009>.

Packaged: 2018-05-24 12:34:29 UTC; Klaus Nordhausen

Title: Extremely Fast Implementation of a Naive Bayes Classifier
Description: This is an extremely fast implementation of a Naive Bayes classifier.
The package currently supports a Bernoulli distribution, a Multinomial distribution, and
a Gaussian distribution, making it suitable for both binary features,
frequency counts, and numerical features. Only numerical variables are allowed,
however, categorical variables can be transformed into dummies and used with
the Bernoulli distribution. The implementation is 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>. 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 331 times faster. See the vignette
for more details. This performance gain is only realized using a Bernoulli
event model. Furthermore, the Multinomial event model is equally fast and

Package: fastNaiveBayes
Type: Package
Title: Extremely Fast Implementation of a Naive Bayes Classifier
Version: 1.0.1
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 330 times faster. 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'
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>.
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: 2019-03-07 22:41:53 UTC; Martin
Repository: CRAN
Date/Publication: 2019-03-08 11:22:43 UTC