Package: mcboost
Type: Package
Title: Multi-Calibration Boosting
Version: 0.4.2
Authors@R: 
    c(person(given = "Florian",
             family = "Pfisterer",
             role = c("cre", "aut"),
             email = "pfistererf@googlemail.com",
             comment = c(ORCID = "0000-0001-8867-762X")),
      person(given = "Susanne",
             family = "Dandl",
             role = "ctb",
             email = "susanne.dandl@stat.uni-muenchen.de",
             comment = c(ORCID = "0000-0003-4324-4163")),
      person(given = "Christoph",
             family = "Kern",
             role = "ctb",
             email = "c.kern@uni-mannheim.de",
             comment = c(ORCID = "0000-0001-7363-4299")),
      person(given = "Carolin", 
             family = "Becker",
             role = "ctb"),
      person(given = "Bernd",
             family = "Bischl",
             role = "ctb",
             email = "bernd_bischl@gmx.net",
             comment = c(ORCID = "0000-0001-6002-6980"))
    )
Maintainer: Florian Pfisterer <pfistererf@googlemail.com>
Description: Implements 'Multi-Calibration Boosting' (2018) <https://proceedings.mlr.press/v80/hebert-johnson18a.html> and
    'Multi-Accuracy Boosting' (2019) <arXiv:1805.12317> for the multi-calibration of a machine learning model's prediction.
    'MCBoost' updates predictions for sub-groups in an iterative fashion in order to mitigate biases like poor calibration or large accuracy differences across subgroups.
    Multi-Calibration works best in scenarios where the underlying data & labels are unbiased, but resulting models are.
    This is often the case, e.g. when an algorithm fits a majority population while ignoring or under-fitting minority populations.
License: LGPL (>= 3)
URL: https://github.com/mlr-org/mcboost
BugReports: https://github.com/mlr-org/mcboost/issues
Encoding: UTF-8
Depends: R (>= 3.1.0)
Imports: backports, checkmate (>= 2.0.0), data.table (>= 1.13.6), mlr3
        (>= 0.10), mlr3misc (>= 0.8.0), mlr3pipelines (>= 0.3.0), R6
        (>= 2.4.1), rmarkdown, rpart, glmnet
Suggests: curl, lgr, formattable, tidyverse, PracTools, mlr3learners,
        mlr3oml, neuralnet, paradox, knitr, ranger, xgboost, covr,
        testthat (>= 3.1.0)
RoxygenNote: 7.2.1
VignetteBuilder: knitr
Collate: 'AuditorFitters.R' 'MCBoost.R' 'PipelineMCBoost.R'
        'PipeOpLearnerPred.R' 'PipeOpMCBoost.R' 'Predictor.R'
        'ProbRange.R' 'helpers.R' 'zzz.R'
NeedsCompilation: no
Packaged: 2022-08-17 20:43:41 UTC; flo
Author: Florian Pfisterer [cre, aut] (<https://orcid.org/0000-0001-8867-762X>),
  Susanne Dandl [ctb] (<https://orcid.org/0000-0003-4324-4163>),
  Christoph Kern [ctb] (<https://orcid.org/0000-0001-7363-4299>),
  Carolin Becker [ctb],
  Bernd Bischl [ctb] (<https://orcid.org/0000-0001-6002-6980>)
Repository: CRAN
Date/Publication: 2022-08-18 12:30:02 UTC
Built: R 4.1.2; ; 2022-08-19 11:05:22 UTC; unix
