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"))
)
curl, lgr, formattable, tidyverse, PracTools, mlr3learners,
mlr3oml, neuralnet, paradox, testthat, knitr, ranger,
rmarkdown, survival, xgboost, covr
'AuditorFitters.R' 'MCBoost.R' 'MCBoostSurv.R'
'PipelineMCBoost.R' 'PipeOpLearnerPred.R' 'PipeOpMCBoost.R'
'PipeOpMCBoostSurv.R' 'Predictor.R' 'ProbRange.R'
'ProbRange2D.R' 'helpers.R' 'zzz.R'
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>)

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
curl, lgr, formattable, tidyverse, PracTools, mlr3learners,
mlr3oml, neuralnet, paradox, knitr, ranger, xgboost, covr,
testthat (>= 3.1.0)
'AuditorFitters.R' 'MCBoost.R' 'PipelineMCBoost.R'
'PipeOpLearnerPred.R' 'PipeOpMCBoost.R' 'Predictor.R'
'ProbRange.R' 'helpers.R' 'zzz.R'

A number of biomedical problems involve performing many hypothesis tests, with an attendant need to apply stringent thresholds. Often the data take the form of a series of predictor vectors, each of which must be compared with a single response vector, perhaps with nuisance covariates. Parametric tests of association are often used, but can result in inaccurate type I error at the extreme thresholds, even for large sample sizes. Furthermore, standard two-sided testing can reduce power compared to the doubled p-value, due to asymmetry in the null distribution. Exact (permutation) testing approaches are attractive, but can be computationally intensive and cumbersome. MCC is an approximation to exact association testing of two vectors that is accurate and fast enough for standard use in high-throughput settings, and can easily provide standard two-sided or doubled p-values.