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
Packaged: 2022-01-20 12:57:28 UTC; andregustavom
Repository: CRAN
Date/Publication: 2022-01-20 14:02:41 UTC
Built: R 4.6.0; ; 2026-04-30 13:52:16 UTC; unix
