The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
easyROC: An Interactive Web-tool for ROC Curve Analysis Using R Language Environment
Open accessAbstract
ROC curve analysis is a fundamental tool for evaluating the performance of a marker in a number of research areas, e.g., biomedicine, bioinformatics, engineering etc., and is frequently used for discriminating cases from controls.There are a number of analysis tools which are used to guide researchers through their analysis.Some of these tools are commercial and provide basic methods for ROC curve analysis while others offer advanced analysis techniques and a command-based user interface, such as the R environment.The R environmentg includes comprehensive tools for ROC curve analysis; however, using a command-based interface might be challenging and time consuming when a quick evaluation is desired; especially for non-R users, physicians etc.Hence, a quick, comprehensive, free and easy-to-use analysis tool is required.For this purpose, we developed a user-friendly webtool based on the R language.This tool provides ROC statistics, graphical tools, optimal cutpoint calculation, comparison of several markers, and sample size estimation to support researchers in their decisions without writing R codes.easyROC can be used via any device with an internet connection independently of the operating system.The web interface of easyROC is constructed with the R package shiny.This tool is freely available through www.biosoft.hacettepe.
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DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988) · Biometrics · doi:10.2307/2531595 · Cited by 22,649
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697
Hanley, J. A., & McNeil, B. J. (1983) · Radiology · doi:10.1148/radiology.148.3.6878708 · Cited by 7,016
Approximate is Better than “Exact” for Interval Estimation of Binomial Proportions
Agresti, A., & Coull, B. A. (1998) · The American Statistician · doi:10.1080/00031305.1998.10480550 · Cited by 3,223
ROCR: visualizing classifier performance in R
Sing, T., Sander, O., Beerenwinkel, N., & Lengauer, T. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti623 · Cited by 3,200
The Split-Apply-Combine Strategy for Data Analysis
Wickham, H. (2011) · Journal of Statistical Software · doi:10.18637/jss.v040.i01 · Cited by 2,705
Signal detection theory and ROC analysis
Egan, J. P. (1975) · Academic Press eBooks · Cited by 1,659
Bamber, D. (1975) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(75)90001-2 · Cited by 1,439
Cited by
Lánczky, A., & Győrffy, B. (2021) · Journal of Medical Internet Research · doi:10.2196/27633 · Cited by 1,694
Metadata
- Authors
- Goksuluk, D., Korkmaz, S., Zararsiz, G., & Karaagaoglu, A.
- Venue
- The R Journal
- Publisher
- The R Foundation
- Published
- 2016
- Volume
- 8
- Issue
- 2
- Pages
- 213
- ISSN
- 2073-4859 (ISSN-L)
- OpenAlex
- W2788915030
- Cited by
- 377
- References
- 27
- FWCI
- 17.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
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