title: Zero-inflated negative binomial distribution function NaN warning
body: <p>I am trying to fit my data to a zero-inflated negative binomial model but one of my 3 independent variables (Exposure) seems to be causing NaNs to be produced (see very end of zeroinfl call) when the SE is being calculated in the summary function. I have also tried running a negative binomial hurdle model and am running into a similar issue.</p>
  
  <pre><code>str(eggTreat)
  'data.frame':   455 obs. of  4 variables:
   $ Exposure : Factor w/ 2 levels "C","E": 2 2 2 2 2 2 2 2 2 2 ...
   $ hi_lo    : Factor w/ 2 levels "hi","lo": 2 2 2 2 2 2 2 2 2 2 ...
   $ Egg_count: int  0 0 0 0 0 0 0 0 0 0 ...
   $ Food     : Factor w/ 2 levels "1.5A5YS","5ASMQ": 2 2 2 2 2 2 2 2 2 2 ...
  </code></pre>
  
  <pre><code>mod.zeroinfl &lt;- zeroinfl(Egg_count ~ Food+Exposure+hi_lo | Food+Exposure+hi_lo, data=eggTreat,
  +                          dist="negbin")
  &gt; summary(mod.zeroinfl)
  
  Call:
  zeroinfl(formula = Egg_count ~ Food + Exposure + hi_lo | Food + Exposure + hi_lo, data = eggTreat, dist = "negbin")
  
  Pearson residuals:
       Min       1Q   Median       3Q      Max 
  -0.65632 -0.47163 -0.28588  0.02976  9.00804 
  
  Count model coefficients (negbin with log link):
              Estimate Std. Error z value Pr(&gt;|z|)    
  (Intercept) -0.04435    0.14393  -0.308   0.7580    
  Food        -1.12486    0.22267  -5.052 4.38e-07 ***
  Exposure    -2.34990    0.38684  -6.075 1.24e-09 ***
  hi_lo       -0.44893    0.19524  -2.299   0.0215 *  
  Log(theta)  -0.24387    0.22639  -1.077   0.2814    
  
  Zero-inflation model coefficients (binomial with logit link):
                Estimate Std. Error z value Pr(&gt;|z|)
  (Intercept) -1.830e+01         NA      NA       NA
  Food        -5.768e+00  5.628e+04       0        1
  Exposure     4.612e-01         NA      NA       NA
  hi_lo       -7.477e+00  9.963e+05       0        1
  ---
  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
  
  Theta = 0.7836 
  Number of iterations in BFGS optimization: 21 
  Log-likelihood: -350.2 on 9 Df
  Warning message:
  In sqrt(diag(object$vcov)) : NaNs produced
  </code></pre>
  
  <pre><code>function (object, ...) 
  {
    object$residuals &lt;- residuals(object, type = "pearson")
    kc &lt;- length(object$coefficients$count)
    kz &lt;- length(object$coefficients$zero)
    se &lt;- sqrt(diag(object$vcov))
  </code></pre>