> idiosyncratic scientist’s decision to write their analysis in Perl.
In my experience, looking at "scientist" produced code, the programming language matters very little. It is not hard to produce something completely inscrutable and non-replicable in Python and R the same way it's been done for ages using SAS, Stata, MatLab etc.
I still see people rolling out their regression computations using matrix inversion and calculating averages as `sum(x)/n`.
I really like PDL when I can use it. I have had problems building it from source on Windows in the past, but it is actually a very well thought out library.
Also worth mentioning, you can get a lot of mileage out of GSL[1].
In my experience, looking at "scientist" produced code, the programming language matters very little. It is not hard to produce something completely inscrutable and non-replicable in Python and R the same way it's been done for ages using SAS, Stata, MatLab etc.
I still see people rolling out their regression computations using matrix inversion and calculating averages as `sum(x)/n`.
I really like PDL when I can use it. I have had problems building it from source on Windows in the past, but it is actually a very well thought out library.
Also worth mentioning, you can get a lot of mileage out of GSL[1].
[1]: https://www.gnu.org/software/gsl/