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    <p>It depends on what exactly you want to test. Next to Dirks recommendations, <code>svUnit</code> or the <code>RUnit</code> package VitoshKa mentioned, I'd like to add a few things :</p> <ul> <li>Indeed, set the seed, but make sure you try the function with different seeds as well. Some functions fail only once every ten times you try. Especially when optimization is involved, this becomes crucial. <code>replicate()</code> is a nice function to use in this context.</li> <li>Think very well about the input you want to test. You should test a number of "odd" cases that don't really resemble the "perfect" dataset. I always test at least 10 (simulated) datasets of different sizes. </li> <li>Fool-proof the function: I also throw in some data types that are not the ones the function is meant for. Wrong type input is likely going to happen at one point, and the last thing you want is a function returning a bogus result without a warning. If you use that function later on in some other code, debugging that code can and will! be hell. Been there, done that, bought the t-shirt...</li> </ul> <p>An example on extended testing of datasets: what would you like to see as output in these cases? Is this the result you'd expect? Not according to the test you did.</p> <pre><code>&gt; test3 &lt;- rep(12,100000) # data with only 1 value &gt; expected &lt;- c(12, 12, 12) &gt; actual &lt;- dtheta(test3) Error in quantile.default(x, qs) : 'probs' outside [0,1] &gt; test4 &lt;- rbinom(100000,30,0.5) # large dataset with a limited amount of values &gt; expected &lt;- quantile(test4,c(0.35, 0.50, 0.65)) &gt; actual &lt;- dtheta(test4) &gt; expected %in% signif(actual,2) [1] FALSE TRUE TRUE &gt; test5 &lt;- runif(100,0,100) # small dataset. &gt; expected &lt;- c(35, 50, 65) &gt; actual &lt;- dtheta(test5) &gt; expected %in% signif(actual,2) [1] FALSE FALSE FALSE </code></pre> <p>edit : corrected code so tests are a bit more senseful.</p>
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    1. CO+1 Joris for testing input. I would add to test the *output* as well. The output of your functions must be *predictable* and *precisely defined*. Unfortunately on this point, R's basic functionality sometimes just sucks. Never know exactly what the function returns unless you go to documentation again, and again, and again...
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