Julia has language-level support for missing values (https://docs.julialang.org/en/v1/manual/missing/ https://docs.julialang.org/en/v1/manual/missing/) which ac

Julia has language-level support for missing values (https://docs.julialang.org/en/v1/manual/missing/ https://docs.julialang.org/en/v1/manual/missing/) which acts like the solution here, but generally for any value that can be put in an array by doing `Union{Missing,T}`. The array type will automatically optimize by having a bitmap of represented values (for any singleton type this works, not just Missing). There were lots of pre-v1.0 design discussions on this vs sentinels, such as https://discourse.julialang.org/t/representing-nullable-values/4729 https://discourse.julialang.org/t/representing-nullable-valu... which can be dug up. The conclusion was more about the safety than performance, since sentinel values are fundamentally unsafe in generally since they can take a valid value and make it "special" (like in the blog post, minimum integer). Thus Julia has the following 3 choices: * Use `nothing`, which throws an error at each operation like `nothing + 1`. This requires `if x === nothing` handling everywhere, is the safest option, but is generally considered clunky for statistics use. As `nothing` is a singleton type, it has the same optimizations as what's mentioned in the blog post. * Use `missing`, which propagates, i.e. `missing + 1 === missing`. This allows for any `Union{Missing,T}` to use a scheme similar to the blog post: as `nothing` is a singleton type, it has the same optimizations as what's mentioned in the blog post. * Use a sentinel of your choice, risks known and dependent on the type. And you can build your stats libraries however you so choose, though JuliaStats has strongly gravitated towards the middle option as something close in ergonomics to R's NA but while having a bit more safety guarantees and generality.