Running examples in 'repeated-Ex.R' failed
The error most likely occurred in:

> ### Name: gnlmix
> ### Title: Generalized Nonlinear Regression with a Random Parameter
> ### Aliases: gnlmix
> ### Keywords: models
> 
> ### ** Examples
> 
> 
> dose <- c(9,12,4,9,11,10,2,11,12,9,9,9,4,9,11,9,14,7,9,8)
> #y <- rgamma(20,shape=2+0.3*dose,scale=2)+rep(rnorm(4,0,4),rep(5,4))
> y <- c(8.674419, 11.506066, 11.386742, 27.414532, 12.135699,  4.359469,
+        1.900681, 17.425948,  4.503345,  2.691792,  5.731100, 10.534971,
+       11.220260,  6.968932,  4.094357, 16.393806, 14.656584,  8.786133,
+       20.972267, 17.178012)
> resp <- restovec(matrix(y, nrow=4, byrow=TRUE), name="y")
> reps <- rmna(resp, tvcov=tvctomat(matrix(dose, nrow=4, byrow=TRUE), name="dose"))
> 
> # same linear normal model with random normal intercept fitted four ways
> # compare with growth::elliptic(reps, model=~dose, preg=c(0,0.6), pre=4)
> glmm(y~dose, nest=individuals, data=reps)

Call:  glmm(y ~ dose, nest = individuals, data = reps)

Coefficients:
(Intercept)         dose           sd  
     8.7102       0.2491       3.0862  

Degrees of Freedom: 18 Total (i.e. Null);  16 Residual
Null Deviance:	    131.3 
Residual Deviance: 129.3 	AIC: 137.3
> gnlmm(reps, mu=~dose, pmu=c(8.7,0.25), psh=3.5, psd=3)

Call:
gnlmm(reps, mu = ~dose, pmu = c(8.7, 0.25), psh = 3.5, psd = 3)

normal distribution

 with normal mixing distribution on identity scale
 (10 point Gauss-Hermite integration)

Response: y 

Log likelihood function:
{
    t <- sh1(p)
    wt * (t + (y - mu4(p))^2/exp(t))/2
}

Location function:
~dose

Log shape function:
p[1] * rep(1, n)

-Log likelihood    64.65019 
Degrees of freedom 16 
AIC                68.65019 
Iterations         18 

Location parameters:
             estimate      se
(Intercept)    8.7106  4.4459
dose           0.2491  0.4467

Mixing standard deviation:
   estimate     se
      3.088  1.838

Shape parameters:
      estimate      se
p[1]     3.442  0.3532

Correlations:
         1        2        3        4
1  1.00000 -0.89424  0.08947 -0.04067
2 -0.89424  1.00000 -0.09830  0.04489
3  0.08947 -0.09830  1.00000 -0.18945
4 -0.04067  0.04489 -0.18945  1.00000
> gnlmix(reps, mu=~a+b*dose+rand, random="rand", pmu=c(8.7,0.25),
+ 	pshape=3.44, pmix=2.3)