calibrationband: Calibration Bands

Package to assess the calibration of probabilistic classifiers using confidence bands for monotonic functions. Besides testing the classical goodness-of-fit null hypothesis of perfect calibration, the confidence bands calculated within that package facilitate inverted goodness-of-fit tests whose rejection allows for a sought-after conclusion of a sufficiently well-calibrated model. The package creates flexible graphical tools to perform these tests. For construction details see also Dimitriadis, Dümbgen, Henzi, Puke, Ziegel (2022) <doi:10.48550/arXiv.2203.04065>.

Version: 0.2.1
Depends: R (≥ 3.3)
Imports: Rcpp, ggplot2, tibble, dplyr, tidyr, sp, methods, base, stats, magrittr, rlang, tidyselect
LinkingTo: Rcpp
Published: 2022-08-09
Author: Timo Dimitriadis [aut], Alexander Henzi [aut], Marius Puke [aut, cre]
Maintainer: Marius Puke <marius.puke at uni-hohenheim.de>
License: GPL-3
URL: https://github.com/marius-cp/calibrationband, https://marius-cp.github.io/calibrationband/
NeedsCompilation: yes
Citation: calibrationband citation info
Materials: README NEWS
CRAN checks: calibrationband results

Documentation:

Reference manual: calibrationband.pdf

Downloads:

Package source: calibrationband_0.2.1.tar.gz
Windows binaries: r-devel: calibrationband_0.2.1.zip, r-release: calibrationband_0.2.1.zip, r-oldrel: calibrationband_0.2.1.zip
macOS binaries: r-release (arm64): calibrationband_0.2.1.tgz, r-oldrel (arm64): calibrationband_0.2.1.tgz, r-release (x86_64): calibrationband_0.2.1.tgz
Old sources: calibrationband archive

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