<p>CV has a policy, which is explicit at <a href="https://stats.stackexchange.com/help/on-topic">https://stats.stackexchange.com/help/on-topic</a></p>

<blockquote>
  <p>If your question is about Programming, ask on Stack Overflow. If the language is statistically oriented (such as R, SAS, Stata, SPSS, etc.), then decide based on the nature of your question: if it needs statistical expertise to understand or answer, ask it here; if it's about an algorithm, routine data processing, or details of the language, then please refer to the collection of links to resources we maintain.</p>
</blockquote>

<p>That's fairly described as rather negative about anything where the code rather than the statistics is the focus of concern. It's germane that many postings on CV are bounced towards SO for that reason. (Some come the other way, too.) </p>

<p>Your question, however, combines several related issues that are perhaps better kept distinct. </p>

<ul>
<li>You are making some broad statements about how far and how well R packages are documented. Many active members on CV would have views on that, but it is at best context, and not suitable for a debate on CV Meta. I just note in passing other views such as this from @joran (50K reputation on SO) in <a href="https://stats.stackexchange.com/questions/58966/why-and-when-create-a-r-package">Why and when create a R package?</a>  </li>
</ul>

<blockquote>
  <p>R documentation is absolutely not the place to learn stat methods. Even vignettes assume a certain level of sophistication. Too many complaints about minimal documentation in R really amount to complaining that the docs aren't spoon feeding them statistical knowledge.</p>
</blockquote>

<ul>
<li><p>The policy of SO on different kinds of R questions is a matter for SO. The stated aim of SO is to support professional and enthusiastic programmers, and many questions on SO that are about someone else's code get little or no support (and indeed sometimes rather hostile or negative reactions). </p></li>
<li><p>That leaves CV policy and the questions you raise but do not really address are Is our policy wrong? Why should we change? What should be our new rules if those questions are convincingly argued? In fact, our policy is to be positive about questions in which statistical analysis is the focus of concern, and we often encourage posters to bring that to the fore (or be migrated to SO otherwise). </p></li>
</ul>

<p>I agree with you that many of the issues arising with R also affect use of other software too (SAS, Stata, etc.). In practice users of such software also have access to support from the vendors and there are healthy software-specific lists and forums that are broad-minded about software-specific questions. Then again R-help remains a very active list.</p>
