The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
fastNaiveBayes 2.2.1
Bug Fixes
- Fix bug with class prediction of a single level
fastNaiveBayes 2.2.0
New Features
- update functionality to iteratively train models
- Poisson event model
Bug Fixes
- Threshold parameter not working for gaussian
Other Changes
fastNaiveBayes 2.1.1
New Features
Bug Fixes
fastNaiveBayes 2.1.0
New Features
- New naming structure
- User can specify priors
Bug Fixes
- y not as factor caused vague error
Other Changes
fastNaiveBayes 1.1.2
New Features
- threshold in all predict functions to ensure a minimum
probability
- Added tweets and tweetsDTM datasets as example data and for time
comparisons
- Changed Gaussian model to achieve a huge speed-up
- Removed inefficiencies for both the Bernoulli and Multinomial
models. Much faster now.
Bug Fixes
- With 2x1 matrices error were thrown
Other Changes
- Removed std_threshold in Gaussian model, not necessary since the
introduction of the above threshold feature
- Changed comparison to other packages in vignette
fastNaiveBayes 1.1.1
New Features
- Detect distribution. Automatically determine the distributions of a
matrix for use with mixed Naive Bayes model
- A threshold for the standard deviation for the Gaussian event model.
This way one can ensure that probabilities are real numbers and not
NaN’s due to standard deviation being 0.
Bug Fixes
Other Changes
- Expanded unit tests.
- Changed comparison to other packages in vignette
- small change to bernoulli predict function
fastNaiveBayes 1.0.1
Bug Fixes
- Fixed bug in Gaussian predict function.
Other Changes
- Changed Readme
- Changed description
- Added unit tests and Travis-ci
fastNaiveBayes 1.0.0
Initial Release of package
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.