参考文献

[1]
茆诗松, 程依明, and 濮晓龙, 高等数理统计, 2nd ed. 北京: 高等教育出版社, 2006.
[2]
L. D. Brown, T. T. Cai, and A. DasGupta, “Interval estimation for a binomial proportion,” Statistical Science, no. 2, pp. 101–133, 2001,Available: https://projecteuclid.org/euclid.ss/1009213286
[3]
C. J. Geyer and G. D. Meeden, “Fuzzy and randomized confidence intervals and p-values,” Statistical Science, vol. 20, no. 4, pp. 358–366, Nov. 2005,Available: https://www.jstor.org/stable/20061193
[4]
C. R. Blyth and D. W. Hutchinson, “Table of neyman-shortest unbiased confidence intervals for the binomial parameter,” Biometrika, vol. 47, no. 3/4, pp. 381–391, 1960,Available: https://www.jstor.org/stable/2333308
[5]
B. D. Ripley, “Statistical methods need software: A view of statistical computing,” Opening Lecture Royal Statistical Society. Plymouth, Sep. 04, 2002. Accessed: Nov. 09, 2019. [Online]. Available: https://www.stats.ox.ac.uk/~ripley/RSS2002.pdf
[6]
M. Tsagris and M. Papadakis, “Taking r to its limits: 70+ tips,” PeerJ Preprints, vol. 6, p. e26605v1, 2018, doi: 10.7287/peerj.preprints.26605v1.
[7]
J. M. Chambers, S, R, and Data Science,” The R Journal, vol. 12, no. 1, pp. 462–476, 2020, doi: 10.32614/RJ-2020-028.
[8]
Y. Xie, Bookdown: Authoring books and technical documents with R markdown. Boca Raton, Florida: Chapman; Hall/CRC, 2016.Available: https://github.com/rstudio/bookdown
[9]
J. Allaire et al., Rmarkdown: Dynamic documents for r. 2021.Available: https://CRAN.R-project.org/package=rmarkdown
[10]
Y. Xie, Dynamic documents with R and knitr, 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC, 2015.Available: https://yihui.org/knitr/
[11]
Y. Xie, TinyTeX: A lightweight, cross-platform, and easy-to-maintain LaTeX distribution based on TeX Live,” TUGboat, no. 1, pp. 30–32, 2019,Available: https://tug.org/TUGboat/Contents/contents40-1.html
[12]
H. Wickham, ggplot2: Elegant graphics for data analysis, 2nd ed. New York: Springer-Verlag, 2016.Available: https://ggplot2-book.org/
[13]
B. D. Ripley and K. Hornik, “Date-time classes,” R News, vol. 1, no. 2, pp. 8–11, 2001,Available: https://cran.r-project.org/doc/Rnews/Rnews_2001-2.pdf
[14]
G. Grothendieck and T. Petzoldt, R Help Desk: Date and time classes in R,” R News, vol. 4, no. 1, pp. 29–32, 2004,Available: https://www.r-project.org/doc/Rnews/Rnews_2004-1.pdf
[15]
M. P. J. van der Loo, “The stringdist package for approximate string matching,” The R Journal, vol. 6, pp. 111–122, 2014,Available: https://CRAN.R-project.org/package=stringdist
[16]
K. Hornik, R FAQ: Frequently asked questions on R.” 2020.Available: https://CRAN.R-project.org/doc/FAQ/R-FAQ.html
[17]
P. Murrell, “Integrating grid graphics output with base graphics output,” R News, vol. 3, no. 2, pp. 7–12, 2003.
[18]
P. Murrell and R. Ihaka, “An approach to providing mathematical annotation in plots,” Journal of Computational and Graphical Statistics, vol. 9, no. 3, pp. 582–599, 2000.
[19]
Y. Qiu, showtext: Using system fonts in R graphics,” The R Journal, vol. 7, no. 1, pp. 99–108, Jun. 2015, doi: 10.32614/RJ-2015-008.
[20]
W. Chang, A. Kryukov, and P. Murrell, Fontcm: Computer modern font for use with extrafont package. 2014.Available: https://github.com/wch/fontcm
[21]
E. Torres-Manzanera, Xkcd: Plotting ggplot2 graphics in an XKCD style. 2018.
[22]
R. Stauffer, G. J. Mayr, M. Dabernig, and A. Zeileis, “Somewhere over the rainbow: How to make effective use of colors in meteorological visualizations,” Bulletin of the American Meteorological Society, vol. 96, no. 2, pp. 203–216, 2009, doi: 10.1175/BAMS-D-13-00155.1.
[23]
A. Zeileis, K. Hornik, and P. Murrell, “Escaping RGBland: Selecting colors for statistical graphics,” Computational Statistics & Data Analysis, vol. 53, no. 9, pp. 3259–3270, 2009, doi: 10.1016/j.csda.2008.11.033.
[24]
A. Zeileis et al., colorspace: A toolbox for manipulating and assessing colors and palettes,” arXiv.org E-Print Archive, arXiv 1903.06490, 2019.Available: http://arxiv.org/abs/1903.06490
[25]
E. Neuwirth, RColorBrewer: ColorBrewer palettes. 2014.Available: https://CRAN.R-project.org/package=RColorBrewer
[26]
Z. Gu, R. Eils, and M. Schlesner, “Complex heatmaps reveal patterns and correlations in multidimensional genomic data,” Bioinformatics, 2016.
[27]
P. Kampstra, beanplot: A boxplot alternative for visual comparison of distributions,” Journal of Statistical Software, vol. 28, no. 1, pp. 1–9, 2008,Available: http://www.jstatsoft.org/v28/c01/
[28]
Y. Tang, “Autoplotly: An r package for automatic generation of interactive visualizations for statistical results,” Journal of Open Source Software, vol. 3, 2018,Available: https://doi.org/10.21105/joss.00657
[29]
Y. Tang, M. Horikoshi, and W. Li, ggfortify: Unified interface to visualize statistical results of popular r packages,” The R Journal, vol. 8, no. 2, pp. 474–485, 2016, doi: 10.32614/RJ-2016-060.
[30]
Y. Xie, animation: An R package for creating animations and demonstrating statistical methods,” Journal of Statistical Software, vol. 53, no. 1, pp. 1–27, 2013,Available: http://www.jstatsoft.org/v53/i01/
[31]
X. Pu and M. Kay, “A probabilistic grammar of graphics,” in Proceedings of the 2020 CHI conference on human factors in computing systems, 2020, pp. 1–13. doi: 10.1145/3313831.3376466.
[32]
P. Kasprzak, L. Mitchell, O. Kravchuk, and A. Timmins, Six Years of Shiny in Research - Collaborative Development of Web Tools in R,” The R Journal, vol. 12, no. 2, pp. 155–162, 2021, doi: 10.32614/RJ-2021-004.
[33]
L. M. Leemis, “Relationships among common univariate distributions,” The American Statistician, vol. 40, no. 2, pp. 143–146, 1986,Available: https://www.jstor.org/stable/2684876
[34]
M. L. Eaton, “Chapter 8: The wishart distribution,” in Multivariate statistics, vol. 53, Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2007, pp. 302–333. doi: 10.1214/lnms/1196285114.
[35]
陈希孺, 数理统计引论. 北京: 科学出版社, 1981.
[36]
C. J. Clopper and E. S. Pearson, “The use of confidence or fiducial limits illustrated in the case of the binomial,” Biometrika, vol. 26, no. 4, pp. 404–413, Dec. 1934, doi: 10.1093/biomet/26.4.404.
[37]
J. Cohen, “The earth is round (\(p < .05\)),” American Psychologist, vol. 49, no. 12, pp. 997–1003, 1994, doi: 10.1037/0003-066x.49.12.997.
[38]
A. I. McLeod, Kendall: Kendall rank correlation and mann-kendall trend test. 2011.Available: http://www.stats.uwo.ca/faculty/aim
[39]
B. Wheeler, SuppDists: Supplementary distributions. 2020.Available: https://CRAN.R-project.org/package=SuppDists
[40]
P. Savicky, Pspearman: Spearman’s rank correlation test. 2014.Available: https://CRAN.R-project.org/package=pspearman
[41]
宋泽熙, “两个二项总体成功概率的比较,” 中国校外教育(理论), vol. z1, p. 81, 2011, doi: 10.3969/j.issn.1004-8502-B.2011.z1.0919.
[42]
韦博成, “《红楼梦》前80回与后40回某些文风差异的统计分析(两个独立二项总体等价性检验的一个应用),” 应用概率统计, vol. 25, no. 4, pp. 441–448, 2009, doi: 10.3969/j.issn.1001-4268.2009.04.012.
[43]
E. B. Wilson, “Probable inference, the law of succession, and statistical inference,” Journal of the American Statistical Association, vol. 22, no. 158, pp. 209–212, Jun. 1927, doi: 10.1080/01621459.1927.10502953.
[44]
R. G. Newcombe, “Interval estimation for the difference between independent proportions: Comparison of eleven methods,” Statistics in Medicine, vol. 17, no. 8, pp. 873–890, 1998, doi: 10.1002/(SICI)1097-0258(19980430)17:8<873::AID-SIM779>3.0.CO;2-I.
[45]
T. W. Epps and L. B. Pulley, “A test for normality based on the empirical characteristic function,” Biometrika, vol. 70, no. 3, pp. 723–726, 1983, doi: 10.2307/2336512.
[46]
P. Lafaye de Micheaux and V. A. Tran, PoweR: A reproducible research tool to ease monte carlo power simulation studies for goodness-of-fit tests in R,” Journal of Statistical Software, vol. 69, no. 3, pp. 1–42, 2016, doi: 10.18637/jss.v069.i03.
[47]
"Student", “The probable error of a mean,” Biometrika, vol. 6, pp. 1–25, 1908.
[48]
C. C. Heyde, E. Seneta, P. Crépel, S. E. Fienberg, and J. Gani, Statisticians of the centuries. New York, NY: Springer-Verlag, 2001. doi: 10.1007/978-1-4613-0179-0.
[49]
P. L. HSU, “Contribution to the theory of "student’s" \(T\)-test as applied to the problem of two samples,” Statistical Research Memoirs, vol. 2, pp. 1–24, 1938.
[50]
S.-H. Kim and A. S. Cohen, “On the behrens-fisher problem: A review,” Journal of Educational and Behavioral Statistics, vol. 23, no. 4, pp. 356–377, 1998, doi: 10.2307/1165281.
[51]
P. L. HSU, Collected papers. New York, NY: Springer-Verlag, 1983.
[52]
T. Hothorn, K. Hornik, M. A. van de Wiel, and A. Zeileis, “Implementing a class of permutation tests: The coin package,” Journal of Statistical Software, vol. 28, no. 8, pp. 1–23, 2008, doi: 10.18637/jss.v028.i08.
[53]
A. Kuznetsova, P. B. Brockhoff, and R. H. B. Christensen, lmerTest package: Tests in linear mixed effects models,” Journal of Statistical Software, vol. 82, no. 13, pp. 1–26, 2017, doi: 10.18637/jss.v082.i13.
[54]
A. Zeileis and T. Hothorn, “Diagnostic checking in regression relationships,” R News, vol. 2, no. 3, pp. 7–10, 2002,Available: https://CRAN.R-project.org/doc/Rnews/
[55]
茆诗松, 周纪芗, and 陈颖, 试验设计, 1st ed. 北京: 中国统计出版社, 2004.
[56]
R. I. Kabacoff, R in action: Data analysis and graphics with r, 2nd ed. Shelter Island, NY: Manning PUblications Co., 2015.Available: https://github.com/kabacoff/RiA2
[57]
J. Cohen, Statistical power analysis for the behavioral sciences, 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates, 1988.Available: https://www.utstat.toronto.edu/~brunner/oldclass/378f16/readings/CohenPower.pdf
[58]
P. Berger and R. Maurer, Experimental design: With application in management, engineering, and the sciences., 1st ed. Duxbury, 2002.
[59]
P. Berger, R. Maurer, and G. B. Celli, Experimental design: With application in management, engineering, and the sciences., 2nd ed. New York, NY: Springer International Publishing, 2018. doi: 10.1007/978-3-319-64583-4.
[60]
M. Fritz and P. D. Berger, Improving the user experience through practical data analytics: Gain meaningful insight and increase your bottom line, 1st ed. Morgan Kaufmann, 2015.
[61]
J. Lawson, Design and analysis of experiments with r, 1st ed. Boca Raton, Florida: Chapman; Hall/CRC, 2014.Available: http://www.mvstat.net/mvksa/mvksa.pdf
[62]
G. E. P. Box, J. S. Hunter, and W. G. Hunter, Statistics for experimenters: Design, innovation, and discovery, 2nd ed. Hoboken, New Jersey: John Wiley & Sons, Inc, 2005.
[63]
R. Kohavi, D. Tang, and Y. Xu, Trustworthy online controlled experiments: A practical guide to a/b testing. Cambridge, United Kingdom: Cambridge University Press, 2020.Available: https://experimentguide.com/
[64]
G. Beall, “The transformation of data from entomological field experiments so that the analysis of variance becomes applicable,” Biometrika, vol. 32, no. 3/4, pp. 243–262, 1942, doi: 10.2307/2332128.
[65]
J. Fox and S. Weisberg, An R companion to applied regression, Third. Thousand Oaks CA: Sage, 2019.Available: https://socialsciences.mcmaster.ca/jfox/Books/Companion/
[66]
A. J. Dobson and A. G. Barnett, An introduction to generalized linear models, Fourth. Boca Raton, Florida: Chapman; Hall/CRC, 2018.Available: https://www.crcpress.com/p/book/9781138741515
[67]
P. McCullagh and J. Nelder, Generalized linear models, Second. London: Chapman; Hall/CRC, 1989.Available: https://www.crcpress.com/p/book/9780412317606
[68]
D. W. Hosmer and S. Lemeshow, Applied logistic regression, Second. New York, NY: John Wiley & Sons, 2000.
[69]
K. E. Train, Discrete choice methods with simulation, Second. New York, NY: Cambridge University Press, 2009.
[70]
H. Ishwaran and J. S. Rao, “Spike and slab variable selection: Frequentist and bayesian strategies,” Ann. Statist., vol. 33, no. 2, pp. 730–773, 2005,Available: http://arXiv.org/abs/math/0505633v1
[71]
A. Hasan, Z. Wang, and A. S. Mahani, “Fast estimation of multinomial logit models: R package mnlogit,” Journal of Statistical Software, vol. 75, no. 3, pp. 1–24, 2016, doi: 10.18637/jss.v075.i03.
[72]
B.-H. Mevik and R. Wehrens, “The pls package: Principal component and partial least squares regression in r,” Journal of Statistical Software, vol. 18, no. 2, pp. 1–23, 2007, doi: 10.18637/jss.v018.i02.
[73]
P. F. Thall and S. C. Vail, “Some covariance models for longitudinal count data with overdispersion,” Biometrics, vol. 46, no. 3, pp. 657–671, 1990,Available: https://www.jstor.org/stable/2532086
[74]
M. A. Espeland and S. L. Hui, “A general approach to analyzing epidemiologic data that contain misclassification errors,” Biometrics, vol. 43, no. 4, pp. 1001–1012, 1987,Available: https://www.jstor.org/stable/2531553
[75]
C. Kleiber and A. Zeileis, Applied econometrics with R. New York: Springer-Verlag, 2008.Available: https://CRAN.R-project.org/package=AER
[76]
W. N. Venables and B. D. Ripley, Modern applied statistics with S, Fourth. New York, NY: Springer-Verlag, 2002.Available: http://www.stats.ox.ac.uk/pub/MASS4
[77]
Statisticat and LLC., LaplacesDemon: Complete environment for bayesian inference. 2021.Available: https://www.bayesian-inference.com/
[78]
Y.-S. Su and M. Yajima, R2jags: Using r to run JAGS. 2020.Available: https://CRAN.R-project.org/package=R2jags
[79]
D. S. Young, Handbook of regression methods. Boca Raton, FL: Chapman; Hall/CRC, 2017.
[80]
M. H. Kutner, C. J. Nachtsheim, J. Neter, and W. Li, Applied linear statistical models, Fifth. New York, NY: McGraw-Hill/Irwin, 2005.
[81]
Stan Development Team, Bayesian statistics using Stan. 2019.Available: https://github.com/stan-dev/stan-book
[82]
A. E. Gelfand, S. E. Hills, A. Racine-Poon, and A. F. M. Smith, “Illustration of bayesian inference in normal data models using gibbs sampling,” Journal of the American Statistical Association, vol. 85, no. 412, pp. 972–985, 1990, doi: 10.1080/01621459.1990.10474968.
[83]
Terry M. Therneau and Patricia M. Grambsch, Modeling survival data: Extending the Cox model. New York: Springer, 2000.
[84]
D. R. Brillinger, Time series: Data analysis and theory. Philadelphia, PA, USA: Society for Industrial; Applied Mathematics, 2001.
[85]
R. A. Maronna, R. D. Martin, and V. J. Yohai, Robust statistics, theory and methods. John Wiley & Sons, Ltd, 2006.
[86]
P. R. Winters, “Forecasting sales by exponentially weighted moving averages,” Management Science, vol. 6, no. 3, pp. 324–342, 1960, doi: 10.1287/mnsc.6.3.324.
[87]
C. C. Holt, “Forecasting seasonals and trends by exponentially weighted moving averages,” International Journal of Forecasting, vol. 20, no. 1, pp. 5–10, 2004, doi: 10.1016/j.ijforecast.2003.09.015.
[88]
E. J. Pebesma and R. S. Bivand, “Classes and methods for spatial data in R,” R News, vol. 5, no. 2, pp. 9–13, 2005,Available: https://cran.r-project.org/doc/Rnews/Rnews_2005-2.pdf
[89]
H. Wickham et al., “Welcome to the tidyverse,” Journal of Open Source Software, vol. 4, no. 43, p. 1686, 2019, doi: 10.21105/joss.01686.
[90]
D. Lüdecke, P. Waggoner, and D. Makowski, insight: A unified interface to access information from model objects in r,” Journal of Open Source Software, vol. 4, no. 38, p. 1412, 2019, doi: 10.21105/joss.01412.
[91]
D. Makowski, M. Ben-Shachar, and D. Lüdecke, bayestestR: Describing effects and their uncertainty, existence and significance within the bayesian framework,” Journal of Open Source Software, vol. 4, no. 40, p. 1541, 2019, doi: 10.21105/joss.01541.
[92]
L. Breiman, “Statistical modeling: The two cultures (with comments and a rejoinder by the author),” Journal of the American Statistical Association, vol. 16, no. 3, pp. 199–231, Dec. 2001, doi: 10.1214/ss/1009213726.
[93]
N. L. Johnson and S. Kotz, Leading personalities in statistical sciences: From the seventeenth century to the present. New York, NY: John Wiley & Sons, 1997.
[94]
A. J. Dobson, An introduction to statistical modelling, 1st ed. London: Chapman; Hall/CRC, 1983. doi: 10.1007/978-1-4899-3174-0.
[95]
J. H. Friedman, “Greedy function approximation: A gradient boosting machine.” Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001,Available: https://projecteuclid.org/euclid.aos/1013203451
[96]
A. Fu, B. Narasimhan, and S. Boyd, CVXR: An R package for disciplined convex optimization,” Journal of Statistical Software, vol. 94, no. 14, pp. 1–34, 2020, doi: 10.18637/jss.v094.i14.
[97]
J. Ypma, R interface to NLopt. 2020.Available: https://github.com/jyypma/nloptr
[98]
M. Binois and V. Picheny, GPareto: An R package for gaussian-process-based multi-objective optimization and analysis,” Journal of Statistical Software, vol. 89, no. 8, pp. 1–30, 2019, doi: 10.18637/jss.v089.i08.
[99]
S. Theußl, F. Schwendinger, and K. Hornik, ROI: An extensible R optimization infrastructure,” Journal of Statistical Software, vol. 94, no. 15, pp. 1–64, 2020, doi: 10.18637/jss.v094.i15.
[100]
L. Scrucca, GA: A package for genetic algorithms in R,” Journal of Statistical Software, vol. 53, no. 4, pp. 1–37, 2013,Available: https://www.jstatsoft.org/v53/i04/
[101]
L. Scrucca, “On some extensions to GA package: Hybrid optimisation, parallelisation and islands evolution,” The R Journal, vol. 9, no. 1, pp. 187–206, 2017,Available: https://journal.r-project.org/archive/2017/RJ-2017-008/
[102]
M. Gilli, D. Maringer, and E. Schumann, Numerical methods and optimization in finance, Second. Waltham, MA, USA: Elsevier/Academic Press, 2019.Available: http://www.enricoschumann.net/NMOF/
[103]
J. C. Nash, “On best practice optimization methods in r,” Journal of Statistical Software, vol. 60, no. 2, pp. 1–14, 2014, doi: 10.18637/jss.v060.i02.
[104]
B. A. Turlach, quadprog: Functions to solve quadratic programming problems. 2019.Available: https://CRAN.R-project.org/package=quadprog
[105]
H. W. Borchers, Pracma: Practical numerical math functions. 2021.Available: https://CRAN.R-project.org/package=pracma
[106]
R. Varadhan and P. Gilbert, BB: An R package for solving a large system of nonlinear equations and for optimizing a high-dimensional nonlinear objective function,” Journal of Statistical Software, vol. 32, no. 4, pp. 1–26, 2009,Available: https://www.jstatsoft.org/v32/i04/
[107]
K. Deb, “Multi-objective optimization,” in Search methodologies: Introductory tutorials in optimization and decision support techniques, E. K. Burke and G. Kendall, Eds. Boston, MA: Springer US, 2005, pp. 273–316. doi: 10.1007/0-387-28356-0_10.
[108]
R. Tibshirani, “Regression shrinkage and selection via the Lasso,” Journal of the Royal Statistical Society. Series B (Methodological), vol. 58, no. 1, pp. 267–288, 1996,Available: http://www.jstor.org/stable/2346178
[109]
B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression,” The Annals of Statistics, vol. 32, no. 2, pp. 407–499, 2004, doi: 10.1214/009053604000000067.
[110]
Y. Kim, H. Choi, and H.-S. Oh, “Smoothly clipped absolute deviation on high dimensions,” Journal of the American Statistical Association, vol. 103, no. 484, pp. 1665–1673, 2008, doi: 10.1198/016214508000001066.
[111]
C.-H. Zhang, “Nearly unbiased variable selection under minimax concave penalty,” The Annals of Statistics, vol. 38, no. 2, pp. 894–942, 2010, doi: 10.1214/09-AOS729.
[112]
K. Soetaert and F. Meysman, “Reactive transport in aquatic ecosystems: Rapid model prototyping in the open source software R,” Environmental Modelling & Software, vol. 32, pp. 49–60, 2012.
[113]
A. Couture-Beil, J. T. Schnute, R. Haigh, S. N. Wood, and B. J. Cairns, PBSddesolve: Solver for delay differential equations. 2019.Available: https://CRAN.R-project.org/package=PBSddesolve
[114]
P. B. Denton, S. J. Parke, T. Tao, and X. Zhang, “Eigenvectors from eigenvalues,” 2019,Available: https://arxiv.org/pdf/1908.03795.pdf
[115]
S. Boyd and L. Vandenberghe, Introduction to applied linear algebra: Vectors, matrices, and least squares. New York, NY: Cambridge University Press, 2018.Available: https://web.stanford.edu/~boyd/vmls/vmls.pdf
[116]
D. M. Bates and D. G. Watts, Nonlinear regression analysis and its applications. New York, NY: John Wiley & Sons, 1988.Available: https://doi.org/10.1002/9780470316757.app2
[117]
D. C. Hoaglin and R. E. Welsch, “The hat matrix in regression and ANOVA,” The American Statistician, vol. 32, no. 1, pp. 17–22, 1978,Available: https://www.jstor.org/stable/2683469
[118]
M. M. Andersen and S. Højsgaard, Ryacas: A computer algebra system in R,” Journal of Open Source Software, vol. 4, no. 42, 2019,Available: https://doi.org/10.21105/joss.01763
[119]
A. Meurer et al., SymPy: Symbolic computing in python,” PeerJ Computer Science, vol. 3, p. e103, Jan. 2017, doi: 10.7717/peerj-cs.103.
[120]
R. Ihaka and R. Gentleman, R: A language for data analysis and graphics,” Journal of Computational and Graphical Statistics, vol. 5, no. 3, pp. 299–314, 1996.
[121]
F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
[122]
S. Raschka and V. Mirjalili, Python machine learning, 2nd ed. Birmingham, UK: Packt Publishing, 2017.
[123]
Y. Xie, J. J. Allaire, and G. Grolemund, R markdown: The definitive guide. Boca Raton, Florida: Chapman; Hall/CRC, 2018.Available: https://bookdown.org/yihui/rmarkdown
[124]
K. Ushey, J. Allaire, and Y. Tang, Reticulate: Interface to python. 2021.Available: https://github.com/rstudio/reticulate