DESCRIPTION

Package: mcboost
Type: Package
Title: Multi-Calibration Boosting
Version: 0.4.3-9000
Authors@R:
    c(person(given = "Florian",
             family = "Pfisterer",
             role = "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")),
      person(given = "Sebastian",
             family = "Fischer",
             role = c("ctb", "cre"),
             email = "sebf.fischer@gmail.com")
    )
Description: Implements 'Multi-Calibration Boosting' (2018) <https://proceedings.mlr.press/v80/hebert-johnson18a.html> and
    'Multi-Accuracy Boosting' (2019) <doi:10.48550/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)
Roxygen: list(markdown = TRUE, r6 = TRUE)
RoxygenNote: 7.3.1
VignetteBuilder: knitr
Collate:
    'AuditorFitters.R'
    'MCBoost.R'
    'PipelineMCBoost.R'
    'PipeOpLearnerPred.R'
    'PipeOpMCBoost.R'
    'Predictor.R'
    'ProbRange.R'
    'helpers.R'
    'zzz.R'