Package: MLPUGS
Type: Package
Title: Multi-Label Prediction Using Gibbs Sampling (and Classifier
        Chains)
Version: 0.2.0
Date: 2016-07-05
Authors@R: person("Mikhail", "Popov", email = "mikhail@mpopov.com",
                  role = c("aut", "cre"), comment = "@bearloga on Twitter")
Maintainer: Mikhail Popov <mikhail@mpopov.com>
Description: An implementation of classifier chains (CC's) for multi-label
    prediction. Users can employ an external package (e.g. 'randomForest',
    'C50'), or supply their own. The package can train a single set of CC's or
    train an ensemble of CC's -- in parallel if running in a multi-core
    environment. New observations are classified using a Gibbs sampler since
    each unobserved label is conditioned on the others. The package includes
    methods for evaluating the predictions for accuracy and aggregating across
    iterations and models to produce binary or probabilistic classifications.
URL: https://github.com/bearloga/MLPUGS
BugReports: https://github.com/bearloga/MLPUGS/issues
Depends: R (>= 3.1.2)
Suggests: knitr, progress, C50, randomForest
VignetteBuilder: knitr
LazyData: true
License: MIT + file LICENSE
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2016-07-06 00:24:49 UTC; mpopov
Author: Mikhail Popov [aut, cre] (@bearloga on Twitter)
Repository: CRAN
Date/Publication: 2016-07-06 09:43:54
Built: R 4.6.0; ; 2026-04-25 20:29:03 UTC; unix
