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MKclass (version 0.5)

Statistical Classification

Description

Performance measures and scores for statistical classification such as accuracy, sensitivity, specificity, recall, similarity coefficients, AUC, GINI index, Brier score and many more. Calculation of optimal cut-offs and decision stumps (Iba and Langley (1991), ) for all implemented performance measures. Hosmer-Lemeshow goodness of fit tests (Lemeshow and Hosmer (1982), ; Hosmer et al (1997), ). Statistical and epidemiological risk measures such as relative risk, odds ratio, number needed to treat (Porta (2014), ).

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Install

install.packages('MKclass')

Monthly Downloads

214

Version

0.5

License

LGPL-3

Issues

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Maintainer

Matthias Kohl

Last Published

September 17th, 2023

Functions in MKclass (0.5)

predValues

Compute PPV and NPV.
perfScores

Compute Performance Scores for Binary Classification
rrCI

Compute Approximate Confidence Interval for RR.
risks

Compute RR, OR and Other Risk Measures
optCutoff

Compute the Optimal Cutoff for Binary Classification
MKclass-package

Statistical Classification.
pairwise.auc

Compute pairwise AUCs
perfMeasures

Compute Performance Measures or Binary Classification
AUC

Compute AUC
confMatrix

Compute Confusion Matrix
AUC.test

AUC-Test
HLgof.test

Hosmer-Lemeshow goodness of fit tests.
or2rr

Transform OR to RR
decisionStump

Compute Decision Stumps