Package: emplikAUC 0.4

emplikAUC: Empirical Likelihood Ratio Test/Confidence Interval for AUC or pAUC

Test hypotheses and construct confidence intervals for AUC (area under Receiver Operating Characteristic curve) and pAUC (partial area under ROC curve), from the given two samples of test data with disease/healthy subjects. The method used is based on TWO SAMPLE empirical likelihood and PROFILE empirical likelihood, as described in <https://www.ms.uky.edu/~mai/research/eAUC1.pdf>.

Authors:Mai Zhou [aut, cre, cph], Xue Ding [aut], Yumin Zhao [aut]

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# Install 'emplikAUC' in R:
install.packages('emplikAUC', repos = c('https://maizhou.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 621 downloads 17 exports 2 dependencies

Last updated 10 months agofrom:56b29b57a4. Checks:9 OK. Indexed: yes.

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Exports:EL2paucT1T2el2test4aucel2testPaucel2testPaucTeltest4aucONEeltest4paucONEeltest4paucTfindLnewfindULNEWfindUnewmyEstPaucTquantONEsmooth0smooth3smooth3vecsmooth5vecsmoothLvec

Dependencies:emplik2rootSolve

Readme and manuals

Help Manual

Help pageTopics
Testing one pAUC(p1, p2) and two quantile values together by Empirical Likelihood.EL2paucT1T2
Testing one AUC value by Empirical likelihood.el2test4auc
Testing one pAUC(0, p) value by Empirical likelihood.el2testPauc
Testing one pAUC(0, p) value and one quantile: F(tau) = 1-p together by Empirical Likelihood.el2testPaucT
Testing one AUC value by Empirical likelihood.eltest4aucONE
Testing one pAUC value by Empirical likelihood.eltest4paucONE
Testing one pAUC and one quantile together by Empirical Likelihood.eltest4paucT
Finding the Lower bound of a confidence interval for theta by repeatedly testing the hypothesis for the parameter theta.findLnew
Finding the Upper and Lower bound of a confidence interval for theta by repeatedly testing the hypothesis for the parameter theta.findULNEW
Finding the Upper bound of a confidence interval for 'theta' by repeatedly testing the hypothesis for the parameter 'theta'.findUnew
Given the x, y 2-sample data, first estimate the (1-partial)-th quantile of X sample, then estimate the pAUC(0, partial), with the plug-in estimated quantile.myEstPaucT
Smoothed quantile estimation from the given x-sample.quantONE
Non-Smoothed indicator function 0.5I[x=y] + I[x < y].smooth0
Smoothed indicator function I[x < y], which is the integration of the Epanechnikov kernal.smooth3
Smoothed indicator function I[x < const], which is the integration of the Epanechnikov kernal.smooth3vec
Smoothed indicator function I[x < const], which is the integration of the Quartic kernal.smooth5vec
Smoothed indicator function I[x < const], linear connecting the 1 and 0 on [-eps, eps].smoothLvec