Statistical Tests for Assessing Trinormal ROC Data

Several statistical test functions as well as a function for exploratory data analysis to investigate classifiers allocating individuals to one of three disjoint and ordered classes. In a single classifier assessment the discriminatory power is compared to classification by chance. In a comparison of two classifiers the null hypothesis corresponds to equal discriminatory power of the two classifiers. See also "ROC Analysis for Classification and Prediction in Practice" by Nakas, Bantis and Gatsonis (2023), ISBN 9781482233704.


trinROC

This package helps to assess three-class Receiver Operating Characteristic (ROC) type data. It provides several statistical test functions as well as a function for exploratory data analysis to investigate classifiers allocating individuals to one of three disjoint and ordered classes. In a single classifier assessment the discriminatory power is compared to classification by chance. In a comparison of two classifiers the null hypothesis corresponds to equal discriminatory power of the two classifiers.
See also "ROC Analysis for Classification and Prediction in Practice" by Nakas, Bantis and Gatsonis (2023), ISBN 9781482233704.

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Reference manual

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install.packages("trinROC")

0.7 by Reinhard Furrer, 2 years ago


https://www.math.uzh.ch/pages/trinROC/


Report a bug at https://git.math.uzh.ch/reinhard.furrer/trinROC/-/issues


Browse source code at https://github.com/cran/trinROC


Authors: Samuel Noll [aut] , Reinhard Furrer [aut, cre] , Benjamin Reiser [ctb] , Christos T. Nakas [ctb] , Annina Cincera [aut]


Documentation:   PDF Manual  


LGPL-2.1 license


Imports ggplot2, rgl, gridExtra

Suggests testthat, knitr, rmarkdown, MASS, reshape


See at CRAN