I'm analyzing Medicare data -- my first real experience with a large dataset, where the number of observations of interest to me is in the millions. We have repeated measures/clusters to worry about, each ranging from 2 to 10 observations, give or take.

I'm struggling with performance issues in pretty much every approach I take to this dataset. One outcome of interest is a proportion. zoib is painfully slow, even when I take a (stratified) random sample of 2% of rows -- in an hour it's only 4% done fitting my null model. Boundary values (0,1) are common in the data, ruling out "transform and just do lmer."

What general tools are available for modeling bigger datasets in R? Because of data privacy agreements I'm required to do all of the computing on-prem, so unfortunately I don't know that I can take advantage of high throughput computing on other servers, if it were even workable in this case.

#rstats #lme4 #zoib

[blog] A Better R Programming Experience Thanks to Tree-sitter

Did you know that thnaks to Tree-sitter's support for R help you get

✨ reformatting through Air and linting through Jarl;

✨ auto-completion or help on hover in the Positron IDE;

✨ better search for R on GitHub;
✨ and more!

By @maelle, edited by @etiennebacher, @davis, @steffilazerte

https://ropensci.org/blog/2026/04/02/tree-sitter-overview/

#RStats

Actually, let me try something different than just asking for money for TDOV:

If you have any possible leads or hiring power for a job that hires for WFH customer/tech support out of Washington state PLEASE message or DM me and if you don't please share this.