Bibliografia

Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, Winston Chang, and Richard Iannone. 2018. Rmarkdown: Dynamic Documents for r. https://CRAN.R-project.org/package=rmarkdown.

Breiman, Leo. 1996. “Bagging Predictors.” Machine Learning 24 (2): 123–40. https://doi.org/10.1007/BF00058655.

———. 1998. “Arcing Classifier (with Discussion and a Rejoinder by the Author).” Ann. Statist. 26 (3): 801–49. https://doi.org/10.1214/aos/1024691079.

Chan, Chung-hong, and Thomas J. Leeper. 2018. Rio: A Swiss-Army Knife for Data i/o. https://CRAN.R-project.org/package=rio.

Duda, Richard O., Peter E. Hart, and David G. Stork. 2001. Pattern Classification. Wiley. https://books.google.com?id=YoxQAAAAMAAJ.

Firke, Sam. 2018. Janitor: Simple Tools for Examining and Cleaning Dirty Data. https://CRAN.R-project.org/package=janitor.

Fisher, R. A. 1936. “The Use of Multiple Measurements in Taxonomic Problems.” Annals of Eugenics 7 (2): 179–88. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x.

Friedman, Jerome H. 1989. “Regularized Discriminant Analysis.” Journal of the American Statistical Association 84 (405): 165–75. https://doi.org/10.2307/2289860.

Greenwell, Brandon, Bradley Boehmke, Jay Cunningham, and GBM Developers. 2019. Gbm: Generalized Boosted Regression Models. https://CRAN.R-project.org/package=gbm.

Ho, Tin Kam. 1995. “Random Decision Forests.” In Proceedings of 3rd International Conference on Document Analysis and Recognition, 1:278–82. IEEE.

Hornik, Kurt, Christian Buchta, and Achim Zeileis. 2009. “Open-Source Machine Learning: R Meets Weka.” Computational Statistics 24 (2): 225–32. https://doi.org/10.1007/s00180-008-0119-7.

Hothorn, Torsten, Kurt Hornik, and Achim Zeileis. 2006. “Unbiased Recursive Partitioning: A Conditional Inference Framework.” Journal of Computational and Graphical Statistics 15 (3): 651–74. https://doi.org/10.1198/106186006X133933.

Hothorn, Torsten, and Achim Zeileis. 2015. “Partykit: A Modular Toolkit for Recursive Partytioning in r.” Journal of Machine Learning Research 16: 3905–9. http://jmlr.org/papers/v16/hothorn15a.html.

Jed Wing, Max Kuhn. Contributions from, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt, Tony Cooper, Zachary Mayer, et al. 2018. Caret: Classification and Regression Training. https://CRAN.R-project.org/package=caret.

Kavakiotis, Ioannis, Olga Tsave, Athanasios Salifoglou, Nicos Maglaveras, Ioannis Vlahavas, and Ioanna Chouvarda. 2017. “Machine Learning and Data Mining Methods in Diabetes Research.” Computational and Structural Biotechnology Journal 15: 104–16.

Kearns, M., and L. G. Valiant. 1989. “Crytographic Limitations on Learning Boolean Formulae and Finite Automata.” Annual ACM Symposium on Theory of Computing, 433. http://search.ebscohost.com/login.aspx?direct=true&db=edb&AN=73725380&lang=pl&site=eds-live&scope=site.

Kuhn, Max, and Ross Quinlan. 2018. C50: C5.0 Decision Trees and Rule-Based Models. https://CRAN.R-project.org/package=C50.

Liaw, Andy, and Matthew Wiener. 2002. “Classification and Regression by randomForest.” R News 2 (3): 18–22. https://CRAN.R-project.org/doc/Rnews/.

Meyer, David, Evgenia Dimitriadou, Kurt Hornik, Andreas Weingessel, and Friedrich Leisch. 2019. E1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. https://CRAN.R-project.org/package=e1071.

Peters, Andrea, and Torsten Hothorn. 2018. Ipred: Improved Predictors. https://CRAN.R-project.org/package=ipred.

Quinlan, J Ross. 1993. C4. 5: Programs for Machine Learning. Morgan Kaufmann.

R Core Team. 2018. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Schapire, Robert E. 1990. “The Strength of Weak Learnability.” Machine Learning 5 (2): 197–227. https://doi.org/10.1007/BF00116037.

Team, The FoRt Student Project. 2015. CHAID: CHi-Squared Automated Interaction Detection.

Templ, Matthias, Alexander Kowarik, Andreas Alfons, and Bernd Prantner. 2019. VIM: Visualization and Imputation of Missing Values. https://CRAN.R-project.org/package=VIM.

Therneau, Terry, and Beth Atkinson. 2018. Rpart: Recursive Partitioning and Regression Trees. https://CRAN.R-project.org/package=rpart.

Torgo, Luis. 2013. DMwR: Functions and Data for "Data Mining with r". https://CRAN.R-project.org/package=DMwR.

Trevor Hastie, S original by, Robert Tibshirani. Original R port by Friedrich Leisch, Kurt Hornik, and Brian D. Ripley. 2017. Mda: Mixture and Flexible Discriminant Analysis. https://CRAN.R-project.org/package=mda.

van Buuren, Stef, and Karin Groothuis-Oudshoorn. 2018. Mice: Multivariate Imputation by Chained Equations. https://CRAN.R-project.org/package=mice.

Venables, W. N., and B. D. Ripley. 2002. Modern Applied Statistics with s. Fourth. New York: Springer. http://www.stats.ox.ac.uk/pub/MASS4.

Weihs, Claus, Uwe Ligges, Karsten Luebke, and Nils Raabe. 2005. “klaR Analyzing German Business Cycles.” In Data Analysis and Decision Support, edited by D. Baier, R. Decker, and L. Schmidt-Thieme, 335–43. Berlin: Springer-Verlag.

Welch, B. L. 1939. “Note on Discriminant Functions.” Biometrika 31 (1/2): 218–20. https://doi.org/10.2307/2334985.

Wood, S. N. 2003. “Thin-Plate Regression Splines.” Journal of the Royal Statistical Society (B) 65 (1): 95–114.

Xie, Yihui. 2018a. Bookdown: Authoring Books and Technical Documents with r Markdown. https://CRAN.R-project.org/package=bookdown.

———. 2018b. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://CRAN.R-project.org/package=knitr.