References

1: Abramowitz, M., Stegun, I.A., editors. (1972). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Dover Publications. URL: https://personal.math.ubc.ca/~cbm/aands/intro.htm.
2: Arnold, T.B., Emerson, J.W. (2011). Nonparametric goodness-of-fit tests for discrete null distributions. The R Journal, 3(2):34–39. DOI: 10.32614/RJ-2011-016.
3: Beirlant, J., Goegebeur, Y., Teugels, J., Segers, J. (2004). Statistics of Extremes: Theory and Applications. Wiley. DOI: 10.1002/0470012382.
4: Bezdek, J.C., Ehrlich, R., Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computer and Geosciences, 10(2–3):191–203. DOI: 10.1016/0098-3004(84)90020-7.
5: Billingsley, P. (1995). Probability and Measure. John Wiley & Sons.
6: Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer-Verlag. URL: https://www.microsoft.com/en-us/research/people/cmbishop/.
7: Blum, A., Hopcroft, J., Kannan, R. (2020). Foundations of Data Science. Cambridge University Press. URL: https://www.cs.cornell.edu/jeh/book.pdf.
8: Box, G.E.P., Cox, D.R. (1964). An analysis of transformations. Journal of the Royal Statistical Society. Series B (Methodological), 26(2):211–252.
9: Bullen, P.S. (2003). Handbook of Means and Their Inequalities. Springer Science+Business Media, Dordrecht.
10: Campello, R.J.G.B., Moulavi, D., Zimek, A., Sander, J. (2015). Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Transactions on Knowledge Discovery from Data, 10(1):5:1–5:51. DOI: 10.1145/2733381.
11: Chambers, J.M., Hastie, T. (1991). Statistical Models in S. Wadsworth & Brooks/Cole.
12: Clauset, A., Shalizi, C.R., Newman, M.E.J. (2009). Power-law distributions in empirical data. SIAM Review, 51(4):661–703. DOI: 10.1137/070710111.
13: Connolly, T., Begg, C. (2015). Database Systems: A Practical Approach to Design, Implementation, and Management. Pearson.
14: Conover, W.J. (1972). A Kolmogorov goodness-of-fit test for discontinuous distributions. Journal of the American Statistical Association, 67(339):591–596. DOI: 10.1080/01621459.1972.10481254.
15: Cramér, H. (1946). Mathematical Methods of Statistics. Princeton University Press. URL: https://archive.org/details/in.ernet.dli.2015.223699.
16: Dasu, T., Johnson, T. (2003). Exploratory Data Mining and Data Cleaning. John Wiley & Sons.
17: Date, C.J. (2003). An Introduction to Database Systems. Pearson.
18: Deisenroth, M.P., Faisal, A.A., Ong, C.S. (2020). Mathematics for Machine Learning. Cambridge University Press. URL: https://mml-book.github.io/.
19: Dekking, F.M., Kraaikamp, C., Lopuhaä, H.P., Meester, L.E. (2005). A Modern Introduction to Probability and Statistics: Understanding Why and How. Springer.
20: Devroye, L., Györfi, L., Lugosi, G. (1996). A Probabilistic Theory of Pattern Recognition. Springer. DOI: 10.1007/978-1-4612-0711-5.
21: Deza, M.M., Deza, E. (2014). Encyclopedia of Distances. Springer.
22: Ester, M., Kriegel, H.P., Sander, J., Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. KDD'96, pp. 226–231.
23: Forbes, C., Evans, M., Hastings, N., Peacock, B. (2010). Statistical Distributions. Wiley.
24: Freedman, D., Diaconis, P. (1981). On the histogram as a density estimator: L₂ theory. Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete, 57:453–476.
25: Friedl, J.E.F. (2006). Mastering Regular Expressions. O'Reilly.
26: Gagolewski, M. (2015). Data Fusion: Theory, Methods, and Applications. Institute of Computer Science, Polish Academy of Sciences. ISBN 978-83-63159-20-7. DOI: 10.5281/zenodo.6960306.
27: Gagolewski, M. (2015). Spread measures and their relation to aggregation functions. European Journal of Operational Research, 241(2):469–477. DOI: 10.1016/j.ejor.2014.08.034.
28: Gagolewski, M. (2021). genieclust: Fast and robust hierarchical clustering. SoftwareX, 15:100722. URL: https://genieclust.gagolewski.com, DOI: 10.1016/j.softx.2021.100722.
29: Gagolewski, M. (2022). stringi: Fast and portable character string processing in R. Journal of Statistical Software, 103(2):1–59. URL: https://stringi.gagolewski.com, DOI: 10.18637/jss.v103.i02.
30: Gagolewski, M., Bartoszuk, M., Cena, A. (2016). Przetwarzanie i analiza danych w języku Python (Data Processing and Analysis in Python). PWN. ISBN 978-83-01-18940-2. in Polish.
31: Gagolewski, M., Bartoszuk, M., Cena, A. (2021). Are cluster validity measures (in)valid? Information Sciences, 581:620–636. DOI: 10.1016/j.ins.2021.10.004.
32: Gentle, J.E. (2003). Random Number Generation and Monte Carlo Methods. Springer-Verlag.
33: Gentle, J.E. (2009). Computational Statistics. Springer-Verlag.
34: Gentle, J.E. (2017). Matrix Algebra: Theory, Computations and Applications in Statistics. Springer.
35: Gentle, J.E. (2020). Theory of Statistics. book draft. URL: https://mason.gmu.edu/~jgentle/books/MathStat.pdf.
36: Goldberg, D. (1991). What every computer scientist should know about floating-point arithmetic. ACM Computing Surveys, 21(1):5–48. URL: https://perso.ens-lyon.fr/jean-michel.muller/goldberg.pdf.
37: Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learning. MIT Press. URL: https://www.deeplearningbook.org/.
38: Grabisch, M., Marichal, J.-L., Mesiar, R., Pap, E. (2009). Aggregation Functions. Cambridge University Press.
39: Gumbel, E.J. (1939). La probabilité des hypothèses. Comptes Rendus de l'Académie des Sciences Paris, 209:645–647.
40: Harris, C.R., et al. (2020). Array programming with NumPy. Nature, 585(7825):357–362. DOI: 10.1038/s41586-020-2649-2.
41: Hart, E.M., et al. (2016). Ten simple rules for digital data storage. PLOS Computational Biology, 12(10):1–12. DOI: 10.1371/journal.pcbi.1005097.
42: Hastie, T., Tibshirani, R., Friedman, J. (2017). The Elements of Statistical Learning. Springer-Verlag. URL: https://hastie.su.domains/ElemStatLearn/.
43: Higham, N.J. (2002). Accuracy and Stability of Numerical Algorithms. SIAM, Philadelphia, PA. URL: https://dx.doi.org/10.1137/1.9780898718027.
44: Hopcroft, J.E., Ullman, J.D. (1979). Introduction to Automata Theory, Languages, and Computation. Addison-Wesley.
45: Hunter, J.D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3):90–95.
46: Hyndman, R.J., Athanasopoulos, G. (2021). Forecasting: Principles and Practice. OTexts. URL: https://otexts.com/fpp3/.
47: Hyndman, R.J., Fan, Y. (1996). Sample quantiles in statistical packages. American Statistician, 50(4):361–365. DOI: 10.2307/2684934.
48: Kleene, S.C. (1951). Representation of events in nerve nets and finite automata. Technical Report RM-704, The RAND Corporation, Santa Monica, CA. URL: https://www.rand.org/content/dam/rand/pubs/research_memoranda/2008/RM704.pdf.
49: Knuth, D.E. (1992). Literate Programming. CSLI.
50: Knuth, D.E. (1997). The Art of Computer Programming II: Seminumerical Algorithms. Addison-Wesley.
51: Kuchling, A.M. (2022). Regular Expression HOWTO. URL: https://docs.python.org/3/howto/regex.html.
52: Lee, J. (2011). A First Course in Combinatorial Optimisation. Cambridge University Press.
53: Ling, R.F. (1973). A probability theory of cluster analysis. Journal of the American Statistical Association, 68(341):159–164. DOI: 10.1080/01621459.1973.10481356.
54: Little, R.J.A., Rubin, D.B. (2002). Statistical Analysis with Missing Data. John Wiley & Sons.
55: Lloyd, S.P. (1957 (1982)). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28:128–137. Originally a 1957 Bell Telephone Laboratories Research Report; republished in 1982. DOI: 10.1109/TIT.1982.1056489.
56: McKinney, W. (2022). Python for Data Analysis. O'Reilly. URL: https://wesmckinney.com/book/.
57: Modarres, M., Kaminskiy, M.P., Krivtsov, V. (2016). Reliability Engineering and Risk Analysis: A Practical Guide. CRC Press.
58: Müllner, D. (2011). Modern hierarchical, agglomerative clustering algorithms. arXiv:1109.2378 [stat.ML]. URL: https://arxiv.org/abs/1109.2378v1.
59: Nelsen, R.B. (1999). An Introduction to Copulas. Springer-Verlag.
60: Newman, M.E.J. (2005). Power laws, Pareto distributions and Zipf's law. Contemporary Physics, pages 323–351. DOI: 10.1080/00107510500052444.
61: Oetiker, T., et al. (2021). The Not So Short Introduction to LaTeX 2ε. URL: https://tobi.oetiker.ch/lshort/lshort.pdf.
62: Olver, F.W.J., et al. (2022). NIST Digital Library of Mathematical Functions. URL: https://dlmf.nist.gov/.
63: Ord, J.K., Fildes, R., Kourentzes, N. (2017). Principles of Business Forecasting. Wessex Press.
64: Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
65: Poore, G.M. (2019). Codebraid: Live code in pandoc Markdown. In: Proc. 18th Python in Science Conf., pp. 54–61. URL: https://conference.scipy.org/proceedings/scipy2019/pdfs/geoffrey_poore.pdf.
66: Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P. (2007). Numerical Recipes. The Art of Scientific Computing. Cambridge University Press.
67: Pérez-Fernández, R., Baets, B. De, Gagolewski, M. (2019). A taxonomy of monotonicity properties for the aggregation of multidimensional data. Information Fusion, 52:322–334. DOI: 10.1016/j.inffus.2019.05.006.
68: Rabin, M., Scott, D. (1959). Finite automata and their decision problems. IBM Journal of Research and Development, 3:114–125.
69: Ritchie, D.M., Thompson, K.L. (1970). QED text editor. Technical Report 70107-002, Bell Telephone Laboratories, Inc. URL: https://wayback.archive-it.org/all/20150203071645/http://cm.bell-labs.com/cm/cs/who/dmr/qedman.pdf.
70: Robert, C.P., Casella, G. (2004). Monte Carlo Statistical Methods. Springer-Verlag.
71: Rousseeuw, P.J., Ruts, I., Tukey, J.W. (1999). The bagplot: A bivariate boxplot. The American Statistician, 53(4):382–387. DOI: 10.2307/2686061.
72: Rubin, D.B. (1976). Inference and missing data. Biometrika, 63(3):581–590.
73: Sandve, G.K., Nekrutenko, A., Taylor, J., Hovig, E. (2013). Ten simple rules for reproducible computational research. PLOS Computational Biology, 9(10):1–4. DOI: 10.1371/journal.pcbi.1003285.
74: Smith, S.W. (2002). The Scientist and Engineer's Guide to Digital Signal Processing. Newnes. URL: https://www.dspguide.com/.
75: Steiglitz, K. (1996). A Digital Signal Processing Primer: With Applications to Digital Audio and Computer Music. Pearson.
76: Tijms, H.C. (2003). A First Course in Stochastic Models. Wiley.
77: Tufte, E.R. (2001). The Visual Display of Quantitative Information. Graphics Press.
78: Tukey, J.W. (1962). The future of data analysis. Annals of Mathematical Statistics, 33(1):1–67. URL: https://projecteuclid.org/journalArticle/Download?urlId=10.1214%2Faoms%2F1177704711, DOI: 10.1214/aoms/1177704711.
79: Tukey, J.W. (1977). Exploratory Data Analysis. Addison-Wesley.
80: van Buuren, S. (2018). Flexible Imputation of Missing Data. CRC Press. URL: https://stefvanbuuren.name/fimd/.
81: van der Loo, M., de Jonge, E. (2018). Statistical Data Cleaning with Applications in R. John Wiley & Sons.
82: Virtanen, P., et al. (2020). SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods, 17:261–272. DOI: 10.1038/s41592-019-0686-2.
83: Waskom, M.L. (2021). seaborn: Statistical data visualization. Journal of Open Source Software, 6(60):3021. DOI: 10.21105/joss.03021.
84: Wickham, H. (2011). The split-apply-combine strategy for data analysis. Journal of Statistical Software, 40(1):1–29. DOI: 10.18637/jss.v040.i01.
85: Wickham, H. (2014). Tidy data. Journal of Statistical Software, 59(10):1–23. DOI: 10.18637/jss.v059.i10.
86: Wierzchoń, S.T., Kłopotek, M.A. (2018). Modern Algorithms for Cluster Analysis. Springer. DOI: 10.1007/978-3-319-69308-8.
87: Wilson, G., et al. (2014). Best practices for scientific computing. PLOS Biology, 12(1):1–7. DOI: 10.1371/journal.pbio.1001745.
88: Wilson, G., et al. (2017). Good enough practices in scientific computing. PLOS Computational Biology, 13(6):1–20. DOI: 10.1371/journal.pcbi.1005510.
89: Xie, Y. (2015). Dynamic Documents with R and knitr. Chapman and Hall/CRC.