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for anyone unfamiliar with Steve's toolbox, the point of these models is to allow independent estimation of task-level psychological factors (i.e., stimulus sensitivity and response bias) from metacognitive factors (e.g., metacognitive sensitivity and bias) see this paper for reference:

beyond a *ton* of efficiency upgrades 🚀, the package allows for arbitrary hierarchical structure, easy interfacing to other packages in the Stan ecosystem, simple computation of model-implied estimates (e.g., mean confidence, type 1/type 2 ROCs), and a bunch of other cool features

one thing I'm particularly happy about is the ability to plug in arbitrary signal distributions- for example, we allow for metacognitive signal detection with the Gumbel-min distribution (cc @singmann.bsky.social) osf.io/preprints/ps...

First CRAN submission is a different kind of programming. R CMD check on three platforms you've never touched, edge cases you'd never think of locally. After that the way you write code just shifts. Welcome to it.

idk I felt that @hadley.nz & the devtools ecosystem with pkgdown/github actions really made things smooth for me! most of the corrections I had to make were smaller things like \donttest vs \dontrun and example runtime