I'd be interested in seeing things you think are done correctly as well. We're working to build a statistics package in go as well (github.com/gonum/stat). It's good that people are taking a fresh look. Our capabilities are clearly limited at the moment, and the features of Go are quite different from those of Julia, but there's still a lot that can be learned in common.
To echo what I said in the post, I think using Nullable as the representation of missing values is the right strategy. I think it achieves sufficiently strong performance while also providing a lot of other desirable features.
As for other things that we've done well, I think the Distributions package is one of the best designed interfaces to probability distributions I've ever seen in any programming language. I don't know if Go supports value types, but the existence of value types in Julia allows each distribution to have its own distinct type without having to pay any cost for maintaining object identity. When combined with Julia's ability to do multiple dispatch, the Distributions package allows one to encode analytic knowledge about probability distributions directly into the type system. And existence of parametric types lets you talk about things like location-scale families in a natural way.
I'd also encourage you to look at the optimization libraries that have been built for Julia. The language makes it easy to do automatic differentiation, which is probably the most under-utilized tool in all of scientific computing. In general, I think there's really high quality work being done on optimization -- which I see as essential for enabling ad hoc statistical modeling.
