Strategic Thinking - Automated In R

Recently, I had read an article on R-bloggers, titled Classifying Breast Cancer as Benign or Malignent using RTextTools by Timothy P. Jurka, who is the author of both that article and the RTextTools package. Having reproduced the results using the author's R code successfully, I was motivated to explore the usefulness of this package.

Also, there is an excellent book by Conway & White (2012), Machine Learning for Hackers, that shows the reader how to build a Bayesian Spam Classifier (let's called it the Benchmark). I was interested to find out how a spam classifier model built using RTextTools would compare with the Benchmark.

Many of the forecasting packages in R requires a time series that is covariance stationary. For those who are not familiar with this term, there is an excellent online textbook by Hyndman and Athanasopoulos, Forecasting: Principles and Practice. Click here to go directly to that chapter.

The first step, therefore, in making a predictive or analytic procedure is to analyze a time series to see if it is already covariance stationary.

I write R scripts on both my laptop and desktop, so the main issue I have is making sure that the R scripts are updated on these devices.