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Transforms your uncalibrated Machine Learning scores to well-calibrated prediction estimates that can be interpreted as probability estimates. The implemented BBQ (Bayes Binning in Quantiles) model is taken from Naeini (2015, ISBN:0-262-51129-0). Please cite this paper: Schwarz J and Heider D, Bioinformatics 2019, 35(14):2458-2465.
| Version: | 0.1.2 |
| Depends: | R (≥ 2.10.0) |
| Imports: | ggplot2, pROC, reshape2, parallel, foreach, stats, fitdistrplus, doParallel |
| Published: | 2019-08-19 |
| DOI: | 10.32614/CRAN.package.CalibratR |
| Author: | Johanna Schwarz, Dominik Heider |
| Maintainer: | Dominik Heider <heiderd at mathematik.uni-marburg.de> |
| License: | LGPL-3 |
| NeedsCompilation: | no |
| Citation: | CalibratR citation info |
| CRAN checks: | CalibratR results |
| Reference manual: | CalibratR.pdf |
| Package source: | CalibratR_0.1.2.tar.gz |
| Windows binaries: | r-devel: CalibratR_0.1.2.zip, r-release: CalibratR_0.1.2.zip, r-oldrel: CalibratR_0.1.2.zip |
| macOS binaries: | r-release (arm64): CalibratR_0.1.2.tgz, r-oldrel (arm64): CalibratR_0.1.2.tgz, r-release (x86_64): CalibratR_0.1.2.tgz, r-oldrel (x86_64): CalibratR_0.1.2.tgz |
| Old sources: | CalibratR archive |
| Reverse suggests: | ENMTools |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.