supernote! #FromTheWorldToTheWorld @supernote.com

After two weeks, I'm finally done! In this post, I explain different approaches for solving linear regression in R: directly, using QR, singular value and Cholesky decompositions, and do some benchmarking for comparison with in-built approaches. thomvolker.github.io/blog/2506_re...

@maxkasy.bsky.social uses POs in his bandit and reinforcement learning slides here maxkasy.github.io/home/ML_Oxfo...

#rstats and #QuartoPub PSA: @vincentab.bsky.social's {tinytable} is the absolute best table making package out there for LaTeX output (it natively supports tabularray!), and it's phenomenal for HTML. It has fully replaced {gt} and {kableExtra} for me vincentarelbundock.github.io/tinytable/

I wrote this up here: aeturrell.com/blog/posts/c...

Those who used to follow me on Twitter know that I tediously made the distinction between a model and an estimation method. In one thread, I considered regressions in a treatment effect context: Y on 1, D, X Y on 1, D, X, D*(X - Xbar) It is common to refer to the first as "OLS."

Says a lot that it's often easier to process/analyze European data using FRED than using European sources