R has extremely rich and diverse modeling capabilities. However, many packages have a variety of interfaces and differing syntactical conventions. Using them in the context of Hadley’s tidy data conventions can be difficult. This talk will discuss the process of making more modular and programming friendly code for modeling activities. Using the `caret` package as an example, a broad roadmap will be discussed for making the transition to more focused packages that use tidy ideas. The concept of writing a modeling _specification_ that can be used in different compute engines will also be discussed.
Max Kuhn is a software engineer at RStudio. He is currently working on improving R’s modeling capabilities. He was a Director of Nonclinical Statistics at Pfizer Global R&D in Connecticut. He was applying models in the pharmaceutical and diagnostic industries for over 18 years. Max has a Ph.D. in Biostatistics. Max is the author of numerous R packages for techniques in machine learning and reproducible research and is an Associate Editor for the Journal of Statistical Software. He, and Kjell Johnson, wrote the book Applied Predictive Modeling, which won the Ziegel award from the American Statistical Association, which recognizes the best book reviewed in Technometrics in 2015. Their latest book, Feature Engineering and Selection, was published in 2019.