hmeasure: The H-Measure and Other Scalar Classification Performance Metrics

Classification performance metrics that are derived from the ROC curve of a classifier. The package includes the H-measure performance metric as described in <http://link.springer.com/article/10.1007/s10994-009-5119-5>, which computes the minimum total misclassification cost, integrating over any uncertainty about the relative misclassification costs, as per a user-defined prior. It also offers a one-stop-shop for other scalar metrics of performance, including sensitivity, specificity and many others, and also offers plotting tools for ROC curves and related statistics.

Version:	1.0-2
Depends:	R (≥ 2.10)
Suggests:	MASS, class, testthat
Published:	2019-02-26
DOI:	10.32614/CRAN.package.hmeasure
Author:	Christoforos Anagnostopoulos and David J. Hand
Maintainer:	Christoforos Anagnostopoulos <christoforos.anagnostopoulos06 at imperial.ac.uk>
License:	MIT + file LICENSE
URL:	http://www.hmeasure.net
NeedsCompilation:	no
Materials:	README NEWS
CRAN checks:	hmeasure results

Documentation:

Reference manual:	hmeasure.pdf
Vignettes:	hmeasure

Downloads:

Package source:	hmeasure_1.0-2.tar.gz
Windows binaries:	r-devel: hmeasure_1.0-2.zip, r-release: hmeasure_1.0-2.zip, r-oldrel: hmeasure_1.0-2.zip
macOS binaries:	r-release (arm64): hmeasure_1.0-2.tgz, r-oldrel (arm64): hmeasure_1.0-2.tgz, r-release (x86_64): hmeasure_1.0-2.tgz, r-oldrel (x86_64): hmeasure_1.0-2.tgz
Old sources:	hmeasure archive

Reverse dependencies:

Reverse depends:

fscaret

Linking:

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