1.7 Main references and credits

The following great reference books have been used extensively for preparing these notes:

  • James et al. (2013) (linear regression, logistic regression, PCA, clustering),
  • Peña (2002) (linear regression, logistic regression, PCA, clustering),
  • Bartholomew et al. (2008) (PCA).

The icons used in the notes were designed by madebyoliver, freepik, and roundicons from Flaticon.

In addition, these notes are possible due to the existence of these incredible pieces of software: Xie (2016a), Xie (2016b), Allaire et al. (2016), and R Core Team (2015).

All material in these notes is licensed under CC BY-NC-SA 4.0.

References

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning. Vol. 103. Springer Texts in Statistics. Springer, New York. doi:10.1007/978-1-4614-7138-7.

Peña, Daniel. 2002. Análisis de Datos Multivariantes. Madrid: McGraw-Hill.

Bartholomew, David J, Fiona Steele, Jane Galbraith, and Irini Moustaki. 2008. Analysis of Multivariate Social Science Data. CRC press.

Xie, Yihui. 2016a. Bookdown: Authoring Books with R Markdown. https://github.com/rstudio/bookdown.

Xie, Yihui. 2016b. Knitr: A General-Purpose Package for Dynamic Report Generation in R. https://CRAN.R-project.org/package=knitr.

Allaire, JJ, Joe Cheng, Yihui Xie, Jonathan McPherson, Winston Chang, Jeff Allen, Hadley Wickham, Aron Atkins, and Rob Hyndman. 2016. Rmarkdown: Dynamic Documents for R. http://rmarkdown.rstudio.com.

R Core Team. 2015. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.