<p>I have initially thought that I agree with @gung (and even upvoted his answer) but the discussion in the comments showed that I actually disagree.</p>

<p><strong>I think this question (after the clarifying edits) is on-topic.</strong></p>

<p>@Gung writes:</p>

<blockquote>
  <p>If the question were rephrased to ask about general computational strategies for large datasets, or about variable selection, then it would become on-topic, in my opinion. It would be fine if the question still makes reference to software as part of providing informative context for their situation.</p>
</blockquote>

<p>I think this is what <em>already</em> happened. The OP wants to run logistic regression with elastic net penalty -- a very clear &amp; meaningful statistical setup. The dataset is too large and the standard tool (<code>glmnet</code>) fails to work -- a very clear obstacle. This person comes to our forum and essentially asks: "What can I do?". This <em>must</em> be on-topic.</p>

<p>@Gung replies that </p>

<blockquote>
  <p>In this case, 'try package y in software z' is the kind of answer the OP seems to want</p>
</blockquote>

<p>and I agree but I do not think this makes it off-topic. Imagine there <em>is</em> a more efficient algorithm of fitting this model that does not require so much memory. An answer describing this algorithm without mentioning the software package is pretty much useless because <em>nobody</em> wants to re-implement stuff like that from scratch. Everybody would want to have a working implementation.</p>

<p>Of course an answer "try package y in software z" and nothing else would be a bad answer. But a possibility of bad answer does not make the question off-topic. A description of the methodology <em>plus</em> a package recommendation would be a fine answer.</p>
