I mean that someone using Perl for scientific computing makes it harder for others to collaborate on the project, which in turn makes the project less scientifically valuable.
This is due to two main reasons. For one, Perl’s incredible syntactical flexibility makes it easy to write “clever” one liners that are hard to comprehend. Speaking from experience, scientists tend to be the sort of people who value “clever” code over “clean” code.
Secondly, the Perl package ecosystem for numerical/scientific methods just isn’t as fully featured as the Julia/Python/R ecosystems. This leads to individuals having to reimplement methods themselves, which leads to idiosyncrasies and likely bugs. For instance, I see no way to generate Wishart random variables (or RVs from other distributions beyond the common ones) in PDL. Julia, Python (via SciPy), and R all have full featured support for many different distributions beyond the common ones.
A Perl user would thus have to implement this themselves. Someone else reading their code would have to both familiarize themselves with the custom function’s syntax (as opposed to immediately recognizing the standardized scipy.stats.wishart, which behaves like any other scipy probability distribution class) and likely check for any bugs, since a standard package is far more likely to be correct than some random one-off function. I’ve had the unfortunate experience of working with someone who refused to use off-the-shelf libraries for numerical methods, and unsurprisingly their code was not only hard to read (since there was no standardization) but also full of bugs.
This is due to two main reasons. For one, Perl’s incredible syntactical flexibility makes it easy to write “clever” one liners that are hard to comprehend. Speaking from experience, scientists tend to be the sort of people who value “clever” code over “clean” code.
Secondly, the Perl package ecosystem for numerical/scientific methods just isn’t as fully featured as the Julia/Python/R ecosystems. This leads to individuals having to reimplement methods themselves, which leads to idiosyncrasies and likely bugs. For instance, I see no way to generate Wishart random variables (or RVs from other distributions beyond the common ones) in PDL. Julia, Python (via SciPy), and R all have full featured support for many different distributions beyond the common ones.
A Perl user would thus have to implement this themselves. Someone else reading their code would have to both familiarize themselves with the custom function’s syntax (as opposed to immediately recognizing the standardized scipy.stats.wishart, which behaves like any other scipy probability distribution class) and likely check for any bugs, since a standard package is far more likely to be correct than some random one-off function. I’ve had the unfortunate experience of working with someone who refused to use off-the-shelf libraries for numerical methods, and unsurprisingly their code was not only hard to read (since there was no standardization) but also full of bugs.