repeated measures anova, sphericity, epsilon, etc

I have 3 questions (below).

Background: I am teaching an introductory statistics course in which we are
covering (among other things) repeated measures anova. This time around
teaching it, we are using R for all of our computations. We are starting by
covering the univariate approach to repeated measures anova.

Doing a basic repeated measures anova (univariate approach) using aov()
seems straightforward (e.g.:

+> myModel<-aov(myDV~myFactor+Error(Subjects/myFactor),data=myData)
+> summary(myModel)

Where I am currently stuck is how best to deal with the issue of the
assumption of homogeneity of treatment differences (in other words, the
sphericity assumption) - both how to test it in R and how to compute
corrected df for the F-test if the assumption is violated.

Back when I taught this course using SPSS it was relatively straightforward
- we would look at Mauchly's test of sphericity - if it was significant,
then we would use one of the corrected F-tests (e.g. Greenhouse-Geisser or
Huynh-Feldt) that were spat out automagically by SPSS.

I gather from searching the r-help archives, searching google, and searching
through various books on R, that the only way of using mauchly.test() in R
is on a multivariate model object (e.g. mauchly.test cannot handle an aov()
object).

Question 1: how do you (if you do so), test for sphericity in a repeated
measures anova using R, when using aov()? (or do you test the sphericity
assumption using a different method)?

Question 2: Can someone point me to an example (on the web, in a book,
wherever) showing how to perform a repeated measures anova using the
multivariate approach in R?

Question 3: Are there any existing R functions for calculating adjusted df
for Greenhouse-Geisser, Huynh-Feldt (or calculating epsilon), or is it up to
me to write my own function?

Thanks in advance for any suggestions,