A couple small changes can greatly speed up the hyperparameter tuning process with tidymodels.

Recent optimizations have made fits on small datasets much, much snappier.

Some short reflections on working on the {infer} R package.

Writing iterative code with ‘+’ rather than ‘%>%’ was a tough transition my first time around.

On the tension between documenting R packages exhaustively and maintainably.

Weighing the pros and cons of several possible schemas for naming the core functions in {stacks}.

Why {stacks} requires (at least) four separate functions to build an ensemble model rather than wrapping them all up into one.

Introducing a set of blog posts reflecting on the development process of the {stacks} package.

Some pointers on running R scripts and committing their results to a GitHub repository on a regular interval using Actions.

Introducing ensemble learning to the tidymodels.

In a few different roles over the past few years, I’ve come across the problem of programatically generating some kind of PDF reports from data. Here are some tips/tricks I’ve come across while making that happen.

Model stacking is an ensembling technique that involves training a model to combine the outputs of many diverse statistical models. The stacks package implements a grammar for tidymodels-aligned model stacking.