CalibratR: Mapping ML Scores to Calibrated Predictions

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
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

Documentation:

Reference manual: CalibratR.pdf

Downloads:

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-release (x86_64): CalibratR_0.1.2.tgz, r-oldrel: CalibratR_0.1.2.tgz
Old sources: CalibratR archive

Reverse dependencies:

Reverse suggests: ENMTools

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