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
ByteCompile: yes
License: MIT + file LICENSE
RoxygenNote: 5.0.1
Repository: https://bearloga.r-universe.dev
Date/Publication: 2020-03-06 17:45:11 UTC
RemoteUrl: https://github.com/bearloga/mlpugs
RemoteRef: HEAD
RemoteSha: 4737d1d72f8e12827472222ee39c4de59969aaae
NeedsCompilation: no
Packaged: 2026-07-15 07:42:43 UTC; root
Author: Mikhail Popov [aut, cre] (@bearloga on Twitter)