Bibliografia

Bazarbash, Majid. 2019. «FinTech in Financial Inclusion Machine Learning Applications in Assessing Credit Risk». IMF Working Paper, n.º 109. https://www.imf.org/~/media/Files/Publications/WP/2019/WPIEA2019109.ashx.

Boehmke, Bradley, y Brandon Greenwell. 2020. Hands-On Machine Learning with R. Taylor & Francis Group. https://bradleyboehmke.github.io/HOML/.

Frost, Jon, Leonardo Gambacorta, Yi Huang, Hyun Song Shin, y Pablo Zbinden. 2019. «BigTech and the changing structure of financial intermediation». BIS Working Papers, n.º 779. https://www.bis.org/publ/work779.htm.

Hastie, Trevor, Robert Tibshirani, y Jerome Friedman. 2008. The Elements of Statistical Learning. Data Mining, Inference, and Prediction. Springer. https://hastie.su.domains/Papers/ESLII.pdf.

Petropoulos, Anastasios, Vasilis Siakoulis, Evaggelos Stavroulakis, y Aristotelis Klamargias. 2018. «A robust machine learning approach for credit risk analysis of large loan-level datasets using deep learning and extreme gradient boosting». Irving Fisher Committee. https://www.bis.org/ifc/publ/ifcb49_49.pdf.

Wickham, Hadley, y Garrett Grolemund. 2017. R for Data Science. O’REILLY. https://r4ds.had.co.nz/.

Wickham, Hadley, Danielle Navarro, y Thomas Lin Pedersen. 2016. ggplot2: Elegant Graphics for Data Analysis. Springer. https://ggplot2-book.org/.

Wooldridge, Jeffrey. 2012. Introductory Econometrics: A Modern Approach. Vol. 5th edition. South-Western College Publishing.

Xie, Yihui, J. J. Allaire, y Garrett Grolemund. 2021. R Markdown: The Definitive Guide. CRC Press. Chapman; Hall Book. https://bookdown.org/yihui/rmarkdown/.

Xie, Yihui, Christophe Dervieux, y Emily Riederer. 2021. bookdown: Authoring Books and Technical Documents with R Markdown. CRC Press. Chapman; Hall Book. https://bookdown.org/yihui/bookdown/.

———. 2022. R Markdown Cookbook. CRC Press. Chapman; Hall Book. https://bookdown.org/yihui/rmarkdown-cookbook/.