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) <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:
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