That is a good practice for any statistical software you are using. You should validate your R code with Python or Julia.

Agreed. Whenever I'm using new statistical software, I validate it against one I know.

I validate my C++ code by comparing against a Python implementation

this is just a normal sanity check and is not a strike against Julia (or R)

Isn't it always a good idea to test your implementations against a known correct implementation? Like, if I were testing, say, a SHA implementation, I would also test against the results from an independent implementation. How is this an issue with Julia the language?

The way you frame it makes it sound like the author was saying Julia is a less trustworthy language, which isn't what they're saying at all. Here's the quote in context:

> Still, if you are new to (Julia) programming and statistics then most likely you should calibrate your tools first. Before you run some statistical analysis you may want to try it out on an example from a textbook written by an expert (not me though) and see if you get the same (or at least comparable) result on your own. Although this is a sound approach, I suspect you are more prone to visit some statistical blog or internet forum and go with the examples that are contained there. One such option is rseek.org, i.e. a search engine for the R programming language.

> ... Once I got both outputs that are similar enough I can be fairly sure I did right. Otherwise I should investigate where the differences come from and possibly make some necessary adjustments.

It doesn't say to compare it against R, it says that if you are new to programming and statistics you should check your work against known-good answers or against a second implementation before rushing ahead, and it gives R as an example of a place to find code that you can use to check your work.

This isn't advice about one language being better, it's the usual advice to solve the same problem in two different ways to make sure you got it right!

An alternative interpretation of the comment you're responding to is that you can do the same things in R, so no reason to switch.

just because someone programmed something in R successfuly one time to compare against doesn't suddenly make it a better language.

The entire point of my comment was that the initial commenter wasn't necessarily saying R is a better language. Somehow you've interpreted my comment as "R is a better language" which is quite far from what it says.

I suspect what is being said is to compare existing stat textbook but more likely existing stat result of a web site using r. Not to run r but run these and compare the result.

I did the same to r using ibm spss. Not that I trust that more. Just to make sure when newly program you have used the right parameters. If they compare, you finish learning and switch over. A cautious approach when your job is at stake.

well, after https://yuri.is/not-julia/, the above is a very valid statement.

"I suppose SAS is too expensive, so R will have to do"?