Join us at rstudio::conf(2022) to sharpen your R skills. | July 25-28th in D.C.
July 25-28th in D.C.
7/25 - 7/28 in D.C.
The premier IDE for R
RStudio anywhere using a web browser
Put Shiny applications online
Shiny, R Markdown, Tidyverse and more
Next level training for you and your team
Do, share, teach and learn data science
An easy way to access R packages
Let us host your Shiny applications
A single home for R & Python Data Science Teams
Scale, develop, and collaborate across R & Python
Easily share your insights
Control and distribute packages
RStudio Public Package Manager
RStudio Package Manager
tidymodels/stacks, Or, In Preparation for Pesto: A Grammar for Stacked Ensemble Modeling
January 21, 2021
Through a community survey conducted over the summer, the RStudio tidymodels team learned that users felt the #1 priority for future development in the tidymodels package ecosystem should be ensembling, a statistical modeling technique involving the synthesis of multiple learning algorithms to improve predictive performance. This December, we were delighted to announce the initial release of stacks, a package for tidymodels-aligned ensembling. A particularly statistically-involved pesto recipe will help us get a sense for how the package works and how it advances the tidymodels package ecosystem as a whole.
Simon Couch, Grant Fleming, Chelsea Parlett, Richard Vogg, Alan Feder Q&A
Simon Couch, Richard Vogg, and Alan Feder Q&A
Simon Couch is an R developer and statistics student at Reed College, where he is entering the final semester of his undergraduate degree. He co-authors and maintains R packages including broom, infer, and stacks, leads trainings and workshops as an RStudio-certified tidyverse trainer, and researches in algorithmic data privacy. He interned on the RStudio tidymodels team in summer 2020, and is currently applying to doctoral programs in statistics.