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MKclass (version 0.3)

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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Version

Install

install.packages('MKclass')

Monthly Downloads

233

Version

0.3

License

LGPL-3

Maintainer

Matthias Kohl

Last Published

October 10th, 2020

Functions in MKclass (0.3)

AUC.test

AUC-Test
HLgof.test

Hosmer-Lemeshow goodness of fit tests.
perfScores

Compute Performance Scores for Binary Classification
predValues

Compute PPV and NPV.
optCutoff

Compute the Optimal Cutoff for Binary Classification
or2rr

Transform OR to RR
risks

Compute RR, OR and Other Risk Measures
rrCI

Compute Approximate Confidence Interval for RR.
AUC

Compute AUC
pairwise.auc

Compute pairwise AUCs
perfMeasures

Compute Performance Measures or Binary Classification
MKclass-package

Statistical Classification.
decisionStump

Compute Decision Stumps
confMatrix

Compute Confusion Matrix