Adrianingsih, N. Y.; Budiantara, I. N.; Purnomo, J. D. T. (2021). mixture model nonparametric regression and its application. Journal of Physics: Conference Series, 1842(1), 012044. https://doi.org/10.1088/1742-6596/1842/1/012044
Altunkaynak, B.; Gamgam, H. (2020). npordtests: an R package of nonparametric tests for equality of location against ordered alternatives. R Journal, 12(1). https://doi.org/10.32614/rj-2020-022
Bai, P.; Chen, Y.; Chen, L.; Zhang, X.; Wang, X.; Wang, X. (2024). Research on the influence of bad working state on air traffic control effect based on multi-independent sample Kruskal-Wallis test. Journal of Air Transport Management, 120, 102653. https://doi.org/10.1016/j.jairtraman.2024.102653
Bower, R.; Hager, J.; Cherniakov, C.; Gupta, S.; Cipolli, W. (2023). A case for nonparametrics. The American Statistician, 77(2), pp. 212-219. https://doi.org/10.1080/00031305.2022.2141858
Carranza, A. M.; Carranza, D. L.; León, S. L. (2023). Aplicación de las escalas de medición ordinal para interpretar coeficientes de la correlación en investigación científica. Revista Científica Searching de Ciencias Humanas y Sociales, 5(1), pp. 48-60. https://doi.org/10.46363/searching.v5i1.4
David, F. N.; Cramer, H. (1947). Mathematical methods of statistics. Biometrika, 34(3/4). https://doi.org/10.2307/2332454
de Farias, L.; de Carvalho, W. R. (2020). R-Shiny as an interface for data visualization and data analysis on the brazilian digital library of theses and dissertations (BDTD). Publications, 8(2). https://doi.org/10.3390/PUBLICATIONS8020024
Emons, W.; Sijtsma, K. (2023). Nonparametric statistical methods. International Encyclopedia of Education, 4.th ed. Elsevier, pp. 657-669. https://doi.org/10.1016/B978-0-12-818630-5.10073-9
Ewens, W. J.; Brumberg, K. (2023). Non-parametric Tests. Introductory Statistics for Data Analysis, Switzerland: Springer Nature, pp. 205-217. https://doi.org/10.1007/978-3-031-28189-1_14
Goodman, L. A.; Kruskal, W. H. (1954). Measures of association for cross classifications. Journal of the American Statistical Association, 49(268), 732-764. https://doi.org/10.2307/2281536
Goodman, L. A.; Kruskal, W. H. (1979). Measures of Association for Cross Classifications. EE. UU.: Springer. https://doi.org/10.1007/978-1-4612-9995-0_1
Kendall, M. G. (1938). A new measure of rank correlation. Biometrika, 30(1–2), pp. 81-93. https://doi.org/10.1093/biomet/30.1-2.81
Khedr, A.; Hilal, S. (2021). Interactive visualization for statistical modelling through a Shiny app in R. 2021 International Conference on Data Analytics for Business and Industry. Baréin: IEEE, pp. 332-337. https://doi.org/10.1109/ICDABI53623.2021.9655841
Kloke, J.; McKean, J. (2024). Nonparametric statistical methods using R. EE. UU.: Chapman y Hall/CRC. https://doi.org/10.1201/9781003039617
Kvålseth, T. O. (2018). Measuring association between nominal categorical variables: an alternative to the Goodman-Kruskal lambda. Journal of Applied Statistics, 45(6), pp. 1118-1132. https://doi.org/10.1080/02664763.2017.1346066
Madrid de la Vega, H. (2023). Gauss vs. Cramer. El Cálculo y Su Enseñanza, 19(2), pp. 1-14. https://doi.org/10.61174/recacym.v19i2.212
Mair, P.; Wilcox, R. (2020). Robust statistical methods in R using the WRS2 package. Behavior Research Methods, 52(2), pp. 464-488. https://doi.org/10.3758/s13428-019-01246-w
McHugh, M. L. (2012). Interrater reliability: the kappa statistic. Biochemia Medica, 22, pp. 276-282. https://doi.org/10.11613/BM.2012.031
Mendivelso, F. (2022). Prueba no paramétrica de correlación de Spearman. Revista Médica Sanitas, 24(1). https://doi.org/10.26852/01234250.578
Miot, H. A. (2018). Análise de correlação em estudos clínicos e experimentais. Jornal Vascular Brasileiro, 17(4), pp. 275-279. https://doi.org/10.1590/1677-5449.174118
Nahm, F. S. (2016). Nonparametric statistical tests for the continuous data: the basic concept and the practical use. Korean Journal of Anesthesiology, 69(1), pp. 8-14. https://doi.org/10.4097/kjae.2016.69.1.8
Newson, R. (2002). Parameters behind “nonparametric” statistics: Kendall’s tau, Somers’ D and median differences. The Stata Journal: Promoting Communications on Statistics and Stata, 2(1), pp. 45-64. https://doi.org/10.1177/1536867X0200200103
Ozhan, A.; Tombaz, M.; Konu, O. (2021). SmulTCan: a Shiny application for multivariate survival analysis of TCGA data with gene sets. Computers in Biology and Medicine, 137. https://doi.org/10.1016/j.compbiomed.2021.104793
Polanco, C. (2024). Non-parametric statistic methods. Fundamentals of mathematics. Medical research: theory and cases, pp. 45–56. Bentham Science Publishers. https://doi.org/10.2174/9789815223132124010009
Puerto, J. A. (2018). Influencia del tiempo de revenido en la resistencia al desgaste abrasivo de un acero aisi/sae 1045 templado desde temperaturas intercríticas. Universidad Libre de Colombia, 7. https://repository.unilibre.edu.co/handle/10901/15470
R Core Team. (2025). R Core Team, R: a language and environment for statistical computing. R foundation for statistical computing. https://www.R-project.org/
Ratner, B. (2009). The correlation coefficient: its values range between +1/−1, or do they? Journal of Targeting, Measurement and Analysis for Marketing, 17(2), pp. 139-142. https://doi.org/10.1057/jt.2009.5
Schober, P.; Boer, C.; Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & Analgesia, 126(5), pp. 1763-1768. https://doi.org/10.1213/ANE.0000000000002864
Sepulveda, M. V. (2025). Kendallknight: an R package for efficient implementation of Kendall’s correlation coefficient computation. PLOS One, 20(6), e0326090. https://doi.org/10.1371/journal.pone.0326090
Shryock, D. F.; Wright, M. C.; van Mantgem, P. J.; Esque, T. C. (2023). Best practices for distributing and deploying U.S. geological survey Shiny applications. U.S. Geological Survey Techniques and Methods. https://doi.org/10.3133/tm7B2
Siegel, A. F.; Wagner, M. R. (2022). Chapter 16 - nonparametrics: testing with ordinal data or nonnormal distributions. Practical Business Statistics, pp. 511-529. https://doi.org/10.1016/B978-0-12-820025-4.00016-6
Spencer, N.; Smeeton, N. C.; Sprent, P. (2025). Applied nonparametric statistical methods. EE. UU.: Chapman and Hall/CRC. https://doi.org/10.1201/9780429326172
Suich, R. C.; Turek, R. J. (2003). An asymptotic partial correlation test for the Goodman‐Kruskal λ. British Journal of Mathematical and Statistical Psychology, 56(1), pp. 111-117. https://doi.org/10.1348/000711003321645377
Wadgave, U.; Ravindra Khairnar, M. (2019). Parametric test for non-normally distributed continuous data: for and against. Electronic Physician, 11(2), pp. 7468-7470. https://doi.org/10.19082/7468
Więckowska, B.; Kubiak, K. B.; Jóźwiak, P.; Moryson, W.; Stawińska-Witoszyńska, B. (2022). Cohen’s Kappa coefficient as a measure to assess classification improvement following the addition of a new marker to a regression model. International Journal of Environmental Research and Public Health, 19(16), 10213. https://doi.org/10.3390/ijerph191610213
Wiedermann, W.; Bonifay, W.; Huang, F. L. (2023). Advanced categorical data analysis in prevention science. Prevention Science, 24(3). https://doi.org/10.1007/s11121-022-01485-y
Wilcox, R. R.; Rousselet, G. A. (2023). An updated guide to robust statistical methods in neuroscience. Current Protocols, 3(3). https://doi.org/10.1002/cpz1.719
Withers, S. D. (2009). Categorical data analysis. International Encyclopedia of Human Geography, 1-12. https://doi.org/10.1016/B978-008044910-4.00409-0
Xia, Q.; Mudaranthakam, D. P.; Chollet-Hinton, L.; Chen, R.; Krebill, H.; Kuo, H.; Koestler, D. C. (2022). shinyOPTIK, a user-friendly R Shiny Application for visualizing cancer risk factors and mortality across the University of Kansas Cancer Center Catchment Area. JCO Clinical Cancer Informatics, 6. https://doi.org/10.1200/CCI.21.00118
