May be documentation will need some time to catch up vis-a-vis the likes of R, Python etc., Understandably since it is a new language this may probably remain so for quite some time? For e.g., when I was trying to find out how to generate "n" random numbers in a uniform distribution from a range of -x to +x. In R the code is as simple as runif(n,-x,x). In matlab, it is a little bit contrived by offsetting and multiplying with the interval etc., But in Julia, the details do not go beyond the standard random functions that do not let you do this and no pointers to documentation beyond. But finally I was lucky to find an entry in the mailing list where I figured out I needed to use the "Distributions" package using the "using" command. Even then , the results were delivered in multiple precision points -> 1.1234, 1.123456 etc.,
In R computing an outer product while passing a custom function is easy. Something like outer(x,y, some function of (x,y)) is pretty straight forward. Not sure how to achieve this in Julia.
Nevertheless, as the language grows and as more idioms are documented and widely used, lif will be simpler I guess.
In R computing an outer product while passing a custom function is easy. Something like outer(x,y, some function of (x,y)) is pretty straight forward. Not sure how to achieve this in Julia.
Nevertheless, as the language grows and as more idioms are documented and widely used, lif will be simpler I guess.