jonny over on masto doing god's work:
> anthropic [is running] an ENTIRE AD CAMPAIGN around "Claude code is written with claude code" and after the source was leaked that has got to be the funniest self-own in the history of advertising because OH BOY IT SHOWS.
neuromatch.social/@jonny/11632...
Things I wish I could show past me. (Some speakers at an early career seminar I attended as a PhD student once told the entire audience that the only way to make it in science was to publish at least 1 first author Nature paper.)
Statistical Rethinking 2026 is done: 20 new lectures emphasizing logical and critical statistical workflow, from basics of probability theory to causal inference to reliable computation to sensitivity. It's all free, made just for you. Lecture list and links: github.com/rmcelreath/s...
The intersection between runapp and Slay the Spire fans is real!
"Model to Meaning" just received a super generous review in JASA.
Check it out! (I'm blushing 😊)
Reminder: You can get the paper copy from CRC, and the full **free** version will remain online forever at marginaleffects.com
www.tandfonline.com/doi/full/10....
Dark times call for desperate measures, and with my career being a little hosed, I'm writing a book! It's about teaching R users to write code like software engineers do, and the hope is that I can use it partially as content marketing for my coaching offerings. Here's the first chapter:
#R #R-lang
screenshot of my post
Big new blogpost!
My guide to data visualization, which includes a very long table of contents, tons of charts, and more.
--> Why data visualization matters and how to make charts more effective, clear, transparent, and sometimes, beautiful.
www.scientificdiscovery.dev/p/salonis-gu...
the UI design principles and the two UI approaches
My paper "Examining the Interface Design of Tidyverse" is now published in ANZJS. This paper calls attention to the importance of UI/UX principles & approaches to statisiticians and #rstats developers.
Many thanks to reviewers who made this paper better!
🔗 onlinelibrary.wiley.com/share/author...
new blog post:
Of course, someone has to write imperative code to build reproducible data science pipelines. It doesn’t have to be you.
brodrigues.co/posts/2025-1...
Looks like a good guide - the general data cleaning part is a lean intro to some very common issues in all sorts of data. Would be great if every phd who touches raw data was offered a short course in these basics (in R or Python or whatever HipsterScript) cleaning-data-r.ala.org.au/2_general-cl...
tl;dr Collinearity is a form of lack of information that is appropriately reflected in the output of your statistical model. When collinearity is associated with interpretational difficulties, these difficulties aren’t caused by the collinearity itself. Rather, they reveal that the model was poorly specified (in that it answers a question different to the one of interest), that the analyst overly focuses on significance rather than estimates and the uncertainty about them or that the analyst took a mental shortcut in interpreting the model that could’ve also led them astray in the absence of collinearity. If you do decide to “deal with” collinearity, make sure you can still answer the question of interest.
Was asked about collinearity again, so here's Vahove's 2019 post on why it isn't a problem that needs a solution. Design the model(s) to answer a formal question and free your mind janhove.github.io/posts/2019-0...
Galaxy brain meme format outlining options for working with data: CSV file, parquet & duckdb, databases, and remote SSH sessions
I'm excited to speak this afternoon at #useR2025 on outgrowing your laptop with #Positron for #rstats users!
You can check out my slides at juliasilge.github.io/useR-2025/
Me too please :).
Really nice new paper by Jingyu Zhang, Oliver Lüdtke, and Alexander Robitzsch on the performance of doubly robust estimators of the ATE. A great example of clear writing and reporting, useful visualization through tables, and a review of modern literature. osf.io/5uj2f_v2
