<p>These two threads seem closely related:</p>

<ul>
<li><p><a href="https://stats.stackexchange.com/q/17897/22228">What are alternatives to p-values</a> - an earlier thread, which originally was asked from quite a "software implementation in R" point of view, but received a rather more general response.</p></li>
<li><p><a href="https://stats.stackexchange.com/q/200500/22228">ASA discusses limitations of p-values - what are the alternatives?</a> - a more recent thread, written in response to the recent ASA report on p-values and statistical significance, which is perhaps a bit more wider ranging, but whose answers bear some similarity to the earlier question.</p></li>
</ul>

<p>Because they quote different stimulus material which the answers responded to, it wouldn't be possible to merge the two threads - answers to one question would not fit as answers to the other. But it is hard to see that the two threads are sufficiently distinct to justify both remaining open.</p>

<p>I'm minded to suggest the former thread should be closed as a duplicate - there is some incongruity between the OP's search for "how to do something other than p-values in R" and the type of answers that it received, which seems a bit of a weakness. The second question is completely software agnostic and has a better focus on statistical issues, and arguably it's an advantage that it is more on top of current issues e.g. the ASA report (though being "topical" can arguably be a disadvantage as well, because it means the thread might "date" less well as academic discussion moves on beyond the ASA report).</p>
