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]
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
[3]
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.
[4]
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.
[5]
Y. Xie, Bookdown: Authoring books and technical documents with R markdown. Boca Raton, Florida: Chapman; Hall/CRC, 2016.Available: https://github.com/rstudio/bookdown
[6]
J. Allaire et al., Rmarkdown: Dynamic documents for r. 2021.Available: https://CRAN.R-project.org/package=rmarkdown
[7]
Y. Xie, Dynamic documents with R and knitr, 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC, 2015.Available: https://yihui.org/knitr/
[8]
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
[9]
H. Wickham, ggplot2: Elegant graphics for data analysis, 2nd ed. New York: Springer-Verlag, 2016.Available: https://ggplot2-book.org/
[10]
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
[11]
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
[12]
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
[13]
K. Hornik, R FAQ: Frequently asked questions on R.” 2020.Available: https://CRAN.R-project.org/doc/FAQ/R-FAQ.html
[14]
P. Murrell, “Integrating grid graphics output with base graphics output,” R News, vol. 3, no. 2, pp. 7–12, 2003.
[15]
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.
[16]
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.
[17]
W. Chang, A. Kryukov, and P. Murrell, Fontcm: Computer modern font for use with extrafont package. 2014.Available: https://github.com/wch/fontcm
[18]
E. Torres-Manzanera, Xkcd: Plotting ggplot2 graphics in an XKCD style. 2018.
[19]
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.
[20]
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.
[21]
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
[22]
E. Neuwirth, RColorBrewer: ColorBrewer palettes. 2014.Available: https://CRAN.R-project.org/package=RColorBrewer
[23]
Z. Gu, R. Eils, and M. Schlesner, “Complex heatmaps reveal patterns and correlations in multidimensional genomic data,” Bioinformatics, 2016.
[24]
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/
[25]
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
[26]
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.
[27]
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/
[28]
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.
[29]
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.
[30]
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
[31]
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.
[32]
陈希孺, 数理统计引论. 北京: 科学出版社, 1981.
[33]
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.
[34]
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.
[35]
A. I. McLeod, Kendall: Kendall rank correlation and mann-kendall trend test. 2011.Available: http://www.stats.uwo.ca/faculty/aim
[36]
B. Wheeler, SuppDists: Supplementary distributions. 2020.Available: https://CRAN.R-project.org/package=SuppDists
[37]
P. Savicky, Pspearman: Spearman’s rank correlation test. 2014.Available: https://CRAN.R-project.org/package=pspearman
[38]
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.
[39]
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.
[40]
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.
[41]
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.
[42]
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.
[43]
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.
[44]
"Student", “The probable error of a mean,” Biometrika, vol. 6, pp. 1–25, 1908.
[45]
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.
[46]
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.
[47]
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.
[48]
P. L. HSU, Collected papers. New York, NY: Springer-Verlag, 1983.
[49]
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.
[50]
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.
[51]
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/
[52]
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.
[53]
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
[54]
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
[55]
P. Berger and R. Maurer, Experimental design: With application in management, engineering, and the sciences., 1st ed. Duxbury, 2002.
[56]
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.
[57]
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.
[58]
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
[59]
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.
[60]
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/
[61]
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.
[62]
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/
[63]
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
[64]
P. McCullagh and J. Nelder, Generalized linear models, Second. London: Chapman; Hall/CRC, 1989.Available: https://www.crcpress.com/p/book/9780412317606
[65]
D. W. Hosmer and S. Lemeshow, Applied logistic regression, Second. New York, NY: John Wiley & Sons, 2000.
[66]
K. E. Train, Discrete choice methods with simulation, Second. New York, NY: Cambridge University Press, 2009.
[67]
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
[68]
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.
[69]
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.
[70]
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
[71]
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
[72]
C. Kleiber and A. Zeileis, Applied econometrics with R. New York: Springer-Verlag, 2008.Available: https://CRAN.R-project.org/package=AER
[73]
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
[74]
Statisticat and LLC., LaplacesDemon: Complete environment for bayesian inference. 2021.Available: https://www.bayesian-inference.com/
[75]
Y.-S. Su and M. Yajima, R2jags: Using r to run JAGS. 2020.Available: https://CRAN.R-project.org/package=R2jags
[76]
D. S. Young, Handbook of regression methods. Boca Raton, FL: Chapman; Hall/CRC, 2017.
[77]
M. H. Kutner, C. J. Nachtsheim, J. Neter, and W. Li, Applied linear statistical models, Fifth. New York, NY: McGraw-Hill/Irwin, 2005.
[78]
Stan Development Team, Bayesian statistics using Stan. 2019.Available: https://github.com/stan-dev/stan-book
[79]
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.
[80]
Terry M. Therneau and Patricia M. Grambsch, Modeling survival data: Extending the Cox model. New York: Springer, 2000.
[81]
D. R. Brillinger, Time series: Data analysis and theory. Philadelphia, PA, USA: Society for Industrial; Applied Mathematics, 2001.
[82]
R. A. Maronna, R. D. Martin, and V. J. Yohai, Robust statistics, theory and methods. John Wiley & Sons, Ltd, 2006.
[83]
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.
[84]
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.
[85]
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
[86]
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.
[87]
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.
[88]
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.
[89]
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.
[90]
A. J. Dobson, An introduction to statistical modelling, 1st ed. London: Chapman; Hall/CRC, 1983. doi: 10.1007/978-1-4899-3174-0.
[91]
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
[92]
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.
[93]
J. Ypma, R interface to NLopt. 2020.Available: https://github.com/jyypma/nloptr
[94]
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.
[95]
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.
[96]
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/
[97]
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/
[98]
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/
[99]
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.
[100]
B. A. Turlach, quadprog: Functions to solve quadratic programming problems. 2019.Available: https://CRAN.R-project.org/package=quadprog
[101]
H. W. Borchers, Pracma: Practical numerical math functions. 2021.Available: https://CRAN.R-project.org/package=pracma
[102]
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/
[103]
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.
[104]
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
[105]
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.
[106]
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.
[107]
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.
[108]
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.
[109]
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
[110]
P. B. Denton, S. J. Parke, T. Tao, and X. Zhang, “Eigenvectors from eigenvalues,” 2019,Available: https://arxiv.org/pdf/1908.03795.pdf
[111]
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
[112]
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
[113]
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
[114]
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
[115]
A. Meurer et al., SymPy: Symbolic computing in python,” PeerJ Computer Science, vol. 3, p. e103, Jan. 2017, doi: 10.7717/peerj-cs.103.
[116]
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.
[117]
F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
[118]
S. Raschka and V. Mirjalili, Python machine learning, 2nd ed. Birmingham, UK: Packt Publishing, 2017.
[119]
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
[120]
K. Ushey, J. Allaire, and Y. Tang, Reticulate: Interface to python. 2021.Available: https://github.com/rstudio/reticulate

  1. 译文摘自 Eric D. Kolaczyk↩︎

  2. https://channel9.msdn.com/Events/useR-international-R-User-conference/useR2016/jailbreakr-Get-out-of-Excel-free↩︎

  3. https://resources.rstudio.com/wistia-rstudio-essentials-2/how-to-excel-without-using-excel↩︎

  4. https://www.nytimes.com/2009/01/07/technology/business-computing/07program.html↩︎

  5. https://docs.github.com/en/packages/using-github-packages-with-your-projects-ecosystem/configuring-docker-for-use-with-github-packages↩︎

  6. 来自 R 社区论坛收集的智语 fortunes::fortune(324)↩︎

  7. 表格中带 *** 标记的类型,用户不能轻易获得↩︎

  8. https://www.datacamp.com/courses/manipulating-time-series-data-in-r-with-xts-zoo↩︎

  9. https://www.fstpackage.org/↩︎

  10. https://rstudio-education.github.io/hopr/dataio.html↩︎

  11. https://brucezhaor.github.io/blog/2016/08/04/batch-process-txt-to-mysql↩︎

  12. https://bookdown.org/yihui/rmarkdown/language-engines.html#sql [rstudio-spark]: https://spark.rstudio.com/ [rmarkdown-teaching-demo]: https://stackoverflow.com/questions/35459166↩︎

  13. https://d.cosx.org/d/419974↩︎

  14. https://bookdown.org/yihui/rmarkdown/params-knit.html↩︎

  15. https://homepage.divms.uiowa.edu/~luke/R/regexp.html↩︎

  16. 参考刘思喆的两篇博文: 利用 R 函数生成差异化密码在 R 中各种码的转换↩︎

  17. Jeroen Ooms 已经确认 RCurl 早已经不再维护,取代它的是 curl/httr,不要使用不再维护的 R 包 https://frie.codes/curl-vs-rcurl/↩︎

  18. 推荐的学习正则表达式的路径可以见统计之都论坛 https://d.cosx.org/d/420410↩︎

  19. https://stat.ethz.ch/R-manual/R-devel/library/base/html/Quotes.html↩︎

  20. useBytes = TRUE 表示把字符看作字节。字符、字节和比特的关系是,一个字节 byte 八个比特 bit,一个英文字符 character 用一个字节表示,而一个中、日、韩文字符需要两个字节表示↩︎

  21. Thomas Lumley (2003) Standard nonstandard evaluation rules. https://developer.r-project.org/nonstandard-eval.pdf↩︎

  22. https://stackoverflow.com/questions/18258690/take-randomly-sample-based-on-groups↩︎

  23. https://stackoverflow.com/questions/1296646/how-to-sort-a-dataframe-by-multiple-columns↩︎

  24. https://stackoverflow.com/questions/3505701/grouping-functions-tapply-by-aggregate-and-the-apply-family↩︎

  25. https://trinkerrstuff.wordpress.com/2018/02/14/easily-make-multi-tabbed-xlsx-files-with-openxlsx/↩︎

  26. https://statcompute.wordpress.com/2018/09/03/playing-map-and-reduce-in-r-by-group-calculation/↩︎

  27. https://statcompute.wordpress.com/2018/09/08/playing-map-and-reduce-in-r-subsetting/↩︎

  28. https://cartesianfaith.com/2015/09/17/from-functional-programming-to-mapreduce-in-r/↩︎

  29. https://digitheadslabnotebook.blogspot.com/2010/01/pivot-tables-in-r.html↩︎

  30. https://cartesianfaith.files.wordpress.com/2015/12/rowe-modeling-data-with-functional-programming-in-r.pdf↩︎

  31. 2016年国际 R 语言大会上的介绍https://github.com/snoweye/user2016.demo 和2018年 JSM 会 上的介绍 https://github.com/RBigData/R_JSM2018↩︎

  32. https://stackoverflow.com/questions/22959635/↩︎

  33. Paul 在 DSC 2001 大会上的幻灯片 见https://www.stat.auckland.ac.nz/~paul/Talks/dsc2001.pdf↩︎

  34. http://varianceexplained.org/r/why-I-use-ggplot2/↩︎

  35. https://simplystatistics.org/2016/02/11/why-i-dont-use-ggplot2/↩︎

  36. https://github.com/ricardo-bion/medium_visualization↩︎

  37. https://leonawicz.github.io/↩︎

  38. https://stat.ethz.ch/pipermail/r-help/2007-October/142420.html↩︎

  39. https://www.stat.auckland.ac.nz/~paul/R/CM/CMR.html↩︎

  40. https://www.stat.auckland.ac.nz/~paul/Reports/maori/maori.html↩︎

  41. https://developer.r-project.org/Blog/public/2019/04/01/hcl-based-color-palettes-in-grdevices/index.html↩︎

  42. https://mbostock.github.io/protovis/ex/crimea-rose-full.html↩︎

  43. 其实是轴须图 rug plot,只因样子看起来像铺在地上的毛毯,故而称之为地毯图,对应于 R 内置的 rug() 函数或 ggplot2 提供的图层 geom_rug(),更多解释详见 https://en.wikipedia.org/wiki/Rug_plot↩︎

  44. 函数来自余光创的博客 — 3D 版邪恶的曲线 ,此处借用 gganimate 将其动态化,前方高能,少儿不宜,R 还能这么不正经的玩。↩︎

  45. https://plotly.com/r/reference/#layout-scene-annotations-items-annotation-font↩︎

  46. https://plotly-r.com/control-modebar.html↩︎

  47. 完整的列表见 https://github.com/plotly/plotly.js/blob/master/src/components/modebar/buttons.js↩︎

  48. 设置下载图片的尺寸,还可设置为 PNG 格式,SVG 格式图片,可借助 rsvgrsvg_pdf() 函数转化为 PDF 格式 https://github.com/ropensci/plotly/issues/1556#issuecomment-505833092↩︎

  49. https://plotly.com/r/logos/↩︎

  50. (about problems with creating a suitable lattice panel function) R-help (August 2008)↩︎

  51. (on the difference of Lattice (which eventually was called grid) and Trellis) DSC 2001, Wien (March 2001)↩︎

  52. (about the fact that lattice objects have to be print()ed) R-help (May 2005)↩︎

  53. https://xiangyunhuang.github.io/bookdown-kableExtra/↩︎

  54. https://zh.wikipedia.org/wiki/DOI↩︎

  55. https://thecoatlessprofessor.com/programming/r/sending-an-email-from-r-with-blastula-to-groups-of-students/↩︎

  56. https://bookdown.org/yihui/rmarkdown-cookbook/table-other.html↩︎

  57. https://stackoverflow.com/questions/50094698/rmarkdown-beamer-presentation-option-clash-clash-for-xcolor↩︎

  58. https://resources.rstudio.com/rstudio-conf-2020/we-re-hitting-r-a-million-times-a-day-so-we-made-a-talk-about-it-heather-nolis-dr-jacqueline-nolis↩︎

  59. https://stat.ethz.ch/pipermail/r-sig-mixed-models/2012q3/018817.html↩︎

  60. https://stat.ethz.ch/pipermail/r-help/2000-August/007778.html↩︎

  61. https://personal.utdallas.edu/~herve/Abdi-Lillie2007-pretty.pdf↩︎

  62. https://stat.ethz.ch/pipermail/r-help/2004-February/045597.html↩︎

  63. https://stat.ethz.ch/pipermail/r-help/2005-April/070508.html↩︎

  64. https://stat.ethz.ch/pipermail/r-help/2009-May/390164.html↩︎

  65. radix 排序翻译过来叫桶排序或基数排序,详细描述见 ?sort↩︎

  66. https://stat.ethz.ch/pipermail/r-sig-mixed-models/2011q4/017392.html↩︎

  67. https://stat.ethz.ch/pipermail/r-help/2006-March/101596.html↩︎

  68. 来源于 Exegeses on Linear Models↩︎

  69. https://socialsciences.mcmaster.ca/jfox/Books/Companion/appendices.html↩︎

  70. https://data.library.virginia.edu/getting-started-with-multivariate-multiple-regression/↩︎

  71. https://stat.ethz.ch/pipermail/r-help/2013-May/354311.html↩︎

  72. https://stat.ethz.ch/pipermail/r-help/2012-January/301501.html↩︎

  73. https://stat.ethz.ch/pipermail/r-help/2005-July/075649.html↩︎

  74. Department of Environmental Science and Policy at the University of California, Davis. Ecology, Geography, and Agriculture↩︎

  75. Institute for Geoinformatics of the University of Münster.↩︎

  76. Statistical Modeling of Networks in R https://user2010.org/Invited/handcockuser2010.pdf↩︎

  77. Network Analysis and Visualization with R and igraph https://kateto.net/networks-r-igraph with PDF↩︎

  78. 以 MacOS 为例安装 symphony 软件

    brew tap coin-or-tools/coinor
    brew install symphony
    ↩︎
  79. https://rwalk.xyz/solving-quadratic-progams-with-rs-quadprog-package/↩︎

  80. CentOS 系统默认没有安装 tree 软件,需要先安装才能使用此命令 sudo dnf install -y tree↩︎

  81. zip 格式的文件需要额外安装 zip 和 unzip 两款软件实现压缩和解压缩。↩︎

  82. 在 CentOS 7 上打造 R 语言编程环境↩︎

  83. libcurl-dev 是一个虚包 virtual package,由 libcurl4-openssl-dev 或 libcurl4-nss-dev 或 libcurl4-gnutls-dev 实际提供,选择其中一个安装即可。↩︎

  84. https://mc-stan.org/users/documentation/case-studies/qr_regression.html↩︎

  85. https://stat.ethz.ch/pipermail/r-help/2004-March/048688.html↩︎

  86. https://github.com/libarchive/libarchive/wiki/FormatTar↩︎

  87. 继 Rtools35 之后, RTools40 主要为 R 3.6.0 准备的,包含有 GCC 8 及其它编译R包需要的工具包,详情请看的幻灯片↩︎

  88. https://github.com/rwinlib/utils↩︎

  89. https://ww2.coastal.edu/kingw/statistics/R-tutorials/↩︎

  90. 早些时候,在 R Markdown 中设置 python.reticulate = TRUE 调用 reticulate 包,带来的副作用是不支持交叉引用的 https://d.cosx.org/d/420680-python-reticulate-true。RStudio 1.2 已经很好地集成了 reticulate,对 Python 的支持更加到位了 https://blog.rstudio.com/2018/10/09/rstudio-1-2-preview-reticulated-python/。截至本文写作时间 2022年01月11日 使用 reticulate 版本 1.22,本文没有对之前的版本进行测试。↩︎

  91. 朱俊辉的帖子 — 在 R 中使用 gluon https://d.cosx.org/d/419785-r-gluon↩︎

  92. https://stat.ethz.ch/pipermail/r-help/2008-November/180820.html↩︎