[ANN] MathChecker.jl: Find problems in floating-point calculations

I’m always surprised this isn’t a bigger topic of conversation among Julia users: how to find where problems are coming from in numerics. The gap between our pretty equations and algorithms on one hand, and practical code implementations on the other can simultaneously be frightening and invisible. Most importantly, we need to know: Where do NaNs, Infs, and subnormal numbers come from, or go to? Where do we lose precision — due to cancellation or absorption, inexact rounding, or simply mixin...