The core libraries available in R are some of the most well-reviewed, carefully written, and correct codes available.
There are a huge amount of available libraries (thousands!) of variable quality thanks to the open nature of the project. But commercial software has problems too, especially with new and niche products. And when something goes wrong in those cases, you can't see why for yourself. Worse, other independent experts would not have the chance to either.
He is probably comparing R to SAS (which are the two most popular statistical programming languages). SAS doesn't really have libraries, instead you buy additional packages from SAS, which are very reliable and well supported, but expensive.
My company shuns R (although I personally like it), primarily because of this issue. If we need to run a rare or uncommon statistical procedure, it is a lot easier to trust the SAS procedure, rather than an open source R package written by some grad student.
True,
Though if you need to run a rare or uncommon stat procedure, SAS is not likely to have it in the core, and then you are back to using what "some grad student wrote".
I am shunning SciPy and to a less extent NumPy for the same reason. I have reason to believe the developers are not experts in numerical linear algebra and some of the documentation also do not lend confidence.
yes.. but for less adopted or emerging platforms, you have to be more conscious of the source of the library, and should look at the source to verify its functionalities
There are a huge amount of available libraries (thousands!) of variable quality thanks to the open nature of the project. But commercial software has problems too, especially with new and niche products. And when something goes wrong in those cases, you can't see why for yourself. Worse, other independent experts would not have the chance to either.