has shot through the roof in recent years, totaling over 13000 packages. Many of these implement novel statistical / machine learning models. It is easy to port / link to other applications. R doesn't try to do everything itself. If you prefer models implemented in C, C++, tensorflow , keras , python , stan , or Weka , you can access these applications without leaving R.1 R packages are built by people who do data analysis. This is in contrast to computer science. R provides the tooling to allow user experience to be a big focus of a package. The S language is very mature. S is the precursor to R, and R inherits some of the statistical routines found in S. Why use R for modeling? [1] In fact, this is actually one of the core principles for R. It is an interface to other languages. 4 / 42