What is your favorite "best practice" when designing and implementing computational models? Mine is: As far as possible, isolate stochasticity.
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Interesting. Are there other ways to isolate stochasticity?
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Well you're an R user (I forget which new term one), so I'm not sure. But, testing tools named something like "QuickCheck" are really useful. I wrote this one for Python a while back:https://github.com/jbn/pytest-caprng …
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