galamm-mixed-resp.md
In galamm: Generalized Additive Latent and Mixed Models

Covariate measurement error model works

Code
  anova(mod, mod0)
Output
  Data: diet
  Models:
  mod0: formula0
  mod: formula
       npar    AIC    BIC  logLik deviance  Chisq Df Pr(>Chisq)
  mod0    9 2764.0 2805.5 -1373.0   2002.9                     
  mod    12 2768.3 2823.6 -1372.2   2002.9 1.7058  3     0.6357

Mixed response and heteroscedastic error works

Code
  print(summary(mod), digits = 2)
Output
  GALAMM fit by maximum marginal likelihood.
  Formula: y ~ x + (1 | id)
     Data: mresp_hsced
  Weights: ~(0 + isgauss | grp)

       AIC      BIC   logLik deviance df.resid 
   12356.9  12388.3  -6173.4  31426.4     3995

  Random effects:
   Groups Name        Variance Std.Dev.
   id     (Intercept) 0        0       
  Number of obs: 4000, groups:  id, 1000

  Variance function:
      a     b 
  1.000 0.078

  Fixed effects:
              Estimate Std. Error z value Pr(>|z|)
  (Intercept)    0.015      0.063    0.24  8.1e-01
  x              0.996      0.112    8.93  4.4e-19