the amount of interesting DSLs coming from the #rstats community is genuinely amazing. TIL about @djnavarro ‘s jaysire package for behavioral experiment design https://djnavarro.github.io/jaysire/
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You can see how it could be combined by a description of the statistical design, and you’d have a DSL that end-to-end describes, runs, *and* analyses an experiment. It is mind blowing to think we’re close to being able to do this
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What should be humbling about this for CS educators is that in CS classrooms, DSLs are still thought to be esoteric and fancy. But to paraphrase Ivan Sutherland, the
#rstats community as a whole doesn’t know this is supposed to be A Hard Thing, so they just do it! Incredible.3 proslijeđena tweeta 10 korisnika označava da im se sviđaPrikaži ovu nit -
Like, let me give you another example I also learned of today. There's this package in R called tfautograph which takes an R function, analyses it, and then emits tensorflow code that "does the right thing":https://t-kalinowski.github.io/tfautograph/articles/tfautograph.html …
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in CS-speak, this is a "source-to-source compiler"; in R, this is an (admittedly awesome) package that someone built to solve their problem!
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you'll say: "wait wait, autograph is also a thing in python". But it's a thing _inside tensorflow_. in R, tfautograph was built by the community. It's a critical distinction that is kind of the whole point here.
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