Vincent Arel-Bundock

looks great!

What’s a multiverse good for anyway? Julia M. Rohrer, Jessica Hullman, and Andrew Gelman Multiverse analysis has become a fairly popular approach, as indicated by the present special issue on the matter. Here, we take one step back and ask why one would conduct a multiverse analysis in the first place. We discuss various ways in which a multiverse may be employed – as a tool for reflection and critique, as a persuasive tool, as a serious inferential tool – as well as potential problems that arise depending on the specific purpose. For example, it fails as a persuasive tool when researchers disagree about which variations should be included in the analysis, and it fails as a serious inferential tool when the included analyses do not target a coherent estimand. Then, we take yet another step back and ask what the multiverse discourse has been good for and whether any broader lessons can be drawn. Ultimately, we conclude that the multiverse does remain a valuable tool; however, we urge against taking it too seriously.

New preprint! So, what's a multiverse analysis good for anyway?>

With @jessicahullman.bsky.social and @statmodeling.bsky.social

juliarohrer.com/wp-content/u...

What's new in the world of Generalized Additive Models YouTube video by Bottom of the Heap

🚨 GAMs have moved on—so it’s time for an update.

On March 3, 2026 (17:00–19:00 CET) I’ll be livestreaming an updated introduction to Generalized Additive Models in R

📺 YouTube livestream link: youtube.com/live/A9U8e1K...

#RStats #mgcv #GAMs #gratia #statistics 🧪

I know you prefaced with: "if the slope is significant." I'm saying that's not always the case, so (a) there's a high risk these plots are misleading, (b) they focus our eyes on unimportant features, and (c) add basically no new information.

Counterpoint: they two points would be colored differently on a J-N plot but they are indistinguishable.

Hard disagree github.com/vincentarelb...

One of the most important statistical packages made in Econ in the last decade

I wish I could speed more things than just code production. Like, say, IRB approval.

lol

Things are grim. But in more frivolous news...

@jamesbrandecon.bsky.social and I have been chipping away at `dbreg`, a 📦 for running big regression models on database backends. For the right kinds of problems, the speed-ups are near magical.

Website: grantmcdermott.com/dbreg/

#rstats

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FYI, broom-ified shortcuts: `modelsummary::get_gof()` and `modelsummary::get_estimates()`. Obviously, if your students use other `easystats` stuff, it's worth introducing them to the ecosystem, especially since they are doing the actual "work" here.

Finished teaching my new Advance Stats for Psych graduate course today with a heavy emphasis on both DAGs and shifting away from coefficient interpretation and towards models as prediction machines.

Both went great!

The latter was extremely helpful for logistic regression (for obvious reasons 😵‍💫)!

Thanks, that's very useful and interesting!

hmm, can't think of a direct solution now, but it sounds pretty easy to achieve this by manipulating the draws a bit. I posted this minimal "solution" to the forum.

Yes, in principle, I think that should work. You are just missing: c()

My poster got allocated prime real estate at the conference: High traffic, captive audience.

GitHub - jordandeklerk/moderndid: Python package implementing modern DiD estimators with diagnostic tools and sensitivity analysis. Python package implementing modern DiD estimators with diagnostic tools and sensitivity analysis. - jordandeklerk/moderndid

There is a new-ish Python package, moderndid, that implements almost all of the "modern" DiD estimators:
github.com/jordandekler...

very exciting stuff. Congrats!

Look under your tree! 🌲 🎁

There's a major #rstats #rdatatable release waiting!

A tremendous thanks to all involved and especially those contributing to some major (performance-maintaining!!) rewrites around the non-API issue.

cran.r-project.org/web/packages...

looks great!

Oh yeah, I'm all in for validation and code review. Essential!

I mainly use claude and codex from the command line, and gpt web interface.

Have you tried other similar tools? Copilot is the one I use the least among them. FWIW, care about "correct" code, I code a lot, and can't imagine doing it without these tools anymore. (Not a fan of the dismissive language in the original post.)

I'm hoping they'll become better at continuous update. also, the new "Skills" feature in Claude (and similar elsewhere) might be useful. Developers could package docs, prompts, and instructions to guide LLMs.

That's one primary motivation for marginaleffects.com !

FWIW, I have heard many academics (esp. at small conferences or lower-resources institutions) talk about silicone samples as direct substitutes for human samples. If synthetic data is "free" and big name companies use it all the time, many authors will try to get it past peer review.

very excited about this

We're doing the final sprint, and I think we're able to send the PDF of the forthcoming Bayesian Workflow book to the publisher in the next two weeks (500+ pages), which would mean it would be published some time next year

Congrats, that's amazing!

Good suggestions. Thanks, I appreciate it!