In recent years, data science and machine learning fields have experienced a rise in the use of Python and R [1, 2]. Python is often regarded as a tool with the greatest amount of libraries and tools designed for machine learning, artificial intelligence, and data engineering. Conversely, R remains a go-to language for statistical analysis and advanced visualization, thanks to packages along the lines of stats [3], caret [4], ggplot2 [5] or shiny [6]. In the evolving landscape of data science, combining multiple programming languages has become a popular strategy to take advantage of the strengths of each. For example, research has explored integrating Julia and Python for scientific computing to use Julia's computational efficiency alongside Python [7]. Similarly, the integration of Stata and Python has been examined to enhance machine learning applications, as shown in [8], which details how Stata's recent integration with Python allows for optimal tuning of machine learning models using Python's scikit-learn library.

We give novel Python and R interfaces for the (Java) Tetrad project for causal modeling, search, and estimation. The Tetrad project is a mainstay in the literature, having been under consistent development for over 30 years. Some of its algorithms are now classics, like PC and FCI; others are recent developments. It is increasingly the case, however, that researchers need to access the underlying Java code from Python or R. Existing methods for doing this are inadequate. We provide new, up-to-date methods using the JPype Python-Java interface and the Reticulate Python-R interface, directly solving these issues. With the addition of some simple tools and the provision of working examples for both Python and R, using JPype and Reticulate to interface Python and R with Tetrad is straightforward and intuitive.

As far as rivalries go, R vs Python can almost reach the levels of the glory days of Barca vs Madrid, Stones vs Beatles, or Sega vs Nintendo. Just dare to venture onto Twitter asking which language is best for data science to witness two tightly entrenched camps. Or at least that's what seemingly hundreds of Medium articles would like you believe. In reality, beyond some good-natured and occasionally entertaining joshing, the whole debate is rather silly. Because the question itself is wrong.

In recent months Florian Wetschoreck published a story on Toward Data Science's Medium channel that attracted the attention of many data scientists on LinkedIn thanks to its very provocative title: "RIP correlation. Let's see what it is and how to use it in R. The Predictive Power Score (PPS) is a normalized index (it ranges from 0 to 1) that tells us how much the variable x (be it numerical or categorical) could be used to predict the variable y (numerical or categorical). The higher the PPS index, the more the variable x is decisive in predicting the variable y. Basically, PPS is an asymmetric nonlinear index that is applicable to all types of variables for predictive purposes. Behind the scene it implements Decision Trees as learning algorithms due to their robustness to outliers and poor data pre-processing.

As far as rivalries go, R vs Python can almost reach the levels of the glory days of Barca vs Madrid, Stones vs Beatles, or Sega vs Nintendo. Just dare to venture onto Twitter asking which language is best for data science to witness two tightly entrenched camps. Or at least that's what seemingly hundreds of Medium articles would like you believe. In reality, beyond some good-natured and occasionally entertaining joshing, the whole debate is rather silly. Because the question itself is wrong.