ToDo

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Change the terms object to be the terms for the fixed-effects only so
that the drop1 method doesn't try to drop the grouping factors for the
random effects.
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Modify the effect of the verbose setting in the Nelder-Mead optimizer.
In particular, it should count evaluations but define an "iteration"
as a change in the best value encountered so far.
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The paper by Sophia Rabe-Hesketh et al describes a spherical form
of the Gauss-Hermite quadrature formula.  Look that up and use it.

Because the Gauss-Hermite quadrature is formed as a sum, it is
necessary to divide the contributions to the deviance according to
the levels of the random effects.  This means that it is only
practical to use AGQ when the response vector can be split into
sections that are conditionally independent. As far as I can see
this will mean a single grouping factor only.
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Allow for a matrix of responses in lmer so multiple fits can be
performed without needing to regenerate the model matrices.
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Determine what a "coef" function should do for multiple, possibly
  non-nested, grouping  factors.
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- add nicer (more realistic?) pedigree examples and tests

- document print(<mer>) including an example  print(<lmer>, corr = FALSE)
  and one with many fixed effects (*) and     print(<lmer>, symbolic.cor = TRUE)