Package: mlquantify
Type: Package
Title: Algorithms for Class Distribution Estimation
Version: 0.2.0
Authors@R: c(
    person("Andre", "Maletzke", email = "andregustavom@gmail.com", role = c("aut","cre")),
    person("Everton", "Cherman", email = "evertoncherman@gmail.com", role = "ctb"),
    person("Denis", "dos Reis", email = "denismr@gmail.com", role = "ctb"),
    person("Gustavo", "Batista", email = "g.batista@unsw.edu.au", role = "ths"))
Maintainer: Andre Maletzke <andregustavom@gmail.com>
Description: Quantification is a prominent machine learning task that
        has received an increasing amount of attention in the last
        years. The objective is to predict the class distribution of a
        data sample. This package is a collection of machine learning
        algorithms for class distribution estimation. This package
        include algorithms from different paradigms of quantification.
        These methods are described in the paper: A. Maletzke, W.
        Hassan, D. dos Reis, and G. Batista. The importance of the test
        set size in quantification assessment. In Proceedings of the
        Twenty-Ninth International Joint Conference on Artificial
        Intelligence, IJCAI20, pages 2640–2646, 2020.
        <doi:10.24963/ijcai.2020/366>.
License: GPL (>= 2.0)
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Author: Andre Maletzke [aut, cre], Everton Cherman [ctb], Denis dos
        Reis [ctb], Gustavo Batista [ths]
RoxygenNote: 7.1.1
BugReports: https://github.com/andregustavom/mlquantify/issues
URL: https://github.com/andregustavom/mlquantify
Imports: caret, randomForest, stats, FNN
Suggests: CORElearn
Config/pak/sysreqs: libicu-dev
Repository: https://andregustavom.r-universe.dev
Date/Publication: 2022-01-20 13:36:12 UTC
RemoteUrl: https://github.com/andregustavom/mlquantify
RemoteRef: HEAD
RemoteSha: 5d7c9ea9ca8ef5992c922cfed4b9a5e31e46ac54
Packaged: 2026-10-06 10:01:18 UTC; root