[R] difference between lme and lmer in df calculation

[Jarrett Byrnes]

Hello all.  I'm currently working with mixed models, and have noticed  
a curious difference between the nlme and lmer packages.  While I  
realize that model selection with mixed models is a tricky issue, the  
two packages currently produce different AIC scores for the same  
model, but they systematically differ by 2.  In looking at the logLik  
values for each method, I find that they indeed differ by 1.  So, the  
following code:

utils::data(npk, package="MASS")

library(lme4)
a<-lmer(yield ~ 1+(1|block), data=npk)
logLik(a)

library(nlme)
b<-lme(yield ~ 1, random=~1|block, data=npk)
logLik(b)

produces a df of 2 for a, and a df of 3 for b.  I'm guessing that lmer  
is not accounting for the level-1 variance.  Is this the case, and, if  
so, will this be fixed?

I see that this issue was brought up sometime back.  Is there a reason  
it has not been addressed?
https://stat.ethz.ch/pipermail/r-help/2006-March/102520.html

Incidentally, I'm also curious what folk think about the approach to  
using the conditional AIC value as posted here
https://stat.ethz.ch/pipermail/r-help/2008-February/154389.html

Thanks!

-Jarrett