title: Huber-White robust standard errors for a GLMM - R
body: <p>I have discovered some heteroscedasticity in my model that I would like to compensate for with more robust standard errors. I have tried to use the Huber-White robust standard errors from the <code>merDeriv</code> package in R but I beleive these only work for a GLMM with a binomial distribution. Is there a way I could achieve the same thing for a Negative Binomial distribition?</p>
  <p>Model:</p>
  <pre><code>library(lme4)
  model &lt;- glmer.nb(Jobs ~ 1 + Month + Year + (1|Region), data = df)
  </code></pre>
  <p>Huber-White robust standard errors:</p>
  <pre><code>library(merDeriv)
  bread.glmerMod(model)
  </code></pre>
  <p>Error:</p>
  <pre><code>Error in vcov.lmerMod(object, full = full) : estfun.lmerMod() only works for lmer() models.
  </code></pre>
  <p>Thank you for any help!</p>