> 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.
It’s possible to write inscrutable code in any language, but some languages sure make it easier.
Syntax issues aside, the main advantage of Julia/Python/R (the latter’s syntax might even be worse than Perl’s) for scientific computing is their ecosystems. A language for a particular use case is only as good as the packages available for that use case. The scientific package ecosystems for Ju/Py/R are far richer than that of Perl, simply because their userbases are much larger. Thus, a scientist using Perl would likely be forced to roll a lot of their own functions, which makes the code idiosyncratic and more likely to contain bugs. (To use one of your examples, people might implement OLS regression by manually computing the hat matrix because no stats package exists for the language they’re using. Now imagine their language lacks something more complicated, like a robust MCMC sampler package à la PyMC3 or STAN, and they have to roll that themselves. Yikes.)
And that’s not even getting into the value of the languages for interactive scientific computing, which is how most of it gets done these days. For instance, there’s no official Jupyter notebook support for Perl (although unofficial plugins exist, they don’t support inline graphics/dataframes/other widgets), and the REPLs for Julia/Python/R are much more modern and fully-featured than PDL2.
BTW, I agree the GSL is great for building standalone tools, but it’s totally irrelevant for any interactive work.
It’s possible to write inscrutable code in any language, but some languages sure make it easier.
Syntax issues aside, the main advantage of Julia/Python/R (the latter’s syntax might even be worse than Perl’s) for scientific computing is their ecosystems. A language for a particular use case is only as good as the packages available for that use case. The scientific package ecosystems for Ju/Py/R are far richer than that of Perl, simply because their userbases are much larger. Thus, a scientist using Perl would likely be forced to roll a lot of their own functions, which makes the code idiosyncratic and more likely to contain bugs. (To use one of your examples, people might implement OLS regression by manually computing the hat matrix because no stats package exists for the language they’re using. Now imagine their language lacks something more complicated, like a robust MCMC sampler package à la PyMC3 or STAN, and they have to roll that themselves. Yikes.)
And that’s not even getting into the value of the languages for interactive scientific computing, which is how most of it gets done these days. For instance, there’s no official Jupyter notebook support for Perl (although unofficial plugins exist, they don’t support inline graphics/dataframes/other widgets), and the REPLs for Julia/Python/R are much more modern and fully-featured than PDL2.
BTW, I agree the GSL is great for building standalone tools, but it’s totally irrelevant for any interactive work.