I have fit a few mixed effects models (particularly longitudinal models) using lme4 in R but would like to really master the models and the code that goes with them.
However, before diving in with both feet (and buying some books) I want to be sure that I am learning the right library. I have used lme4 up to now because I just found it easier than nlme, but if nlme is better for my purposes then I feel I should use that.
I'm sure neither is "better" in a simplistic way, but I would value some opinions or thoughts. My main criteria are:

Easy to use (I'm a psychologist by training, and not particularly versed in statistics or coding, but I'm learning)
Good features for fitting longitudinal data (if there is a difference here- but this is what I mainly use them for)
Good (easy to interpret) graphical summaries, again not sure if there is a difference here but I often produce graphs for people even less technical than I, so nice clear plots are always good (I'm very fond of the xyplot function in lattice() for this reason).


Both packages use Lattice as the backend, but nlme has some nice features like groupedData() and lmList() that are lacking in lme4 (IMO). From a practical perspective, the two most important criteria seem, however, that


lme4 extends nlme with other link functions: in nlme, you cannot fit outcomes whose distribution is not gaussian, lme4 can be used to fit mixed-effects logistic regression, for example.
in nlme, it is possible to specify the variance-covariance matrix for the random effects (e.g. an AR(1)); it is not possible in lme4.


Now, lme4 can easily handle very huge number of random effects (hence, number of individuals in a given study) thanks to its C part and the use of sparse matrices. The nlme package has somewhat been superseded by lme4 so I won't expect people spending much time developing add-ons on top of nlme. Personally, when I have a continuous response in my model, I tend to use both packages, but I'm now versed to the lme4 way for fitting GLMM.

Rather than buying a book, take a look first at the Doug Bates' draft book on R-forge: lme4: Mixed-effects Modeling with R.
