R help - May 2008 - select a range od distributions for simulation experiment

Sorry if this is a bit of topic but I was wondering if there was any standard
set of distributions /and parameters that can be used in a simulation
experiment to sort of cover all the bases? I basically need to run a
simulation using a series of distribtions with the same mean, but covering a
whole range of extremes: uniform and normal of course, but then also some
with extreme outliers, a bimodal one etc. What  would be the most sensible
way to go about the selection of a set of (e.g. 10, 20) distributions to
sample from?
Hope this makes sense and thanks!
Maia
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I admit, that was a very half-baked way to ask a question. Here instead is a
version of code I have, to make it clearer:

oldpar<-par(mfrow=c(4,4), mar=c(2,2,1,1))
sds<- data.frame(sd.cells=numeric(0), sd.margins=numeric(0))
for (i in 1:2){
	for (j in 1:2) {
dist<-rbeta(100000, j*0.5,i*0.5)
plot(density(dist), main="", xlab="", ylab="")
s<-sample(dist, 25)
s<-100*s/sum(s)
hist(s, main="", xlab="", ylab="")
pop<-matrix(s, c(5,5))
mosaicplot(pop, main="", xlab="", ylab="")
mars<-c(margin.table(pop,1), margin.table(pop,2))
hist(mars, main="", xlab="", ylab="",
probability=TRUE)
lines(density(mars))
rug(c(margin.table(pop,1), margin.table(pop,2)), col="red")
sds<-rbind(sds,c(sd(s),sd(mars)))
	}
}
par(oldpar)
sds


This loops through four different beta distributions (1st. graph), and in
each case samples 25 values (2.nd graph), to become "cell sizes" in a
5x5
crosstabulation of 100 (cells arranged randomly) (3rd graph) after which I
am interested in the marginal distributions (both margins together in this
case, but this is irrelevant to my question) (4th graph). 
I am only visualising this at this point, but i am actually interested in
the table of standard deviations of cell sizes and marginal totals (sds).

What I would like is to loop this through a series of distributions, which
would give me a whole range of cell size distributions, from all of them
being aproximatley equaly sized, to  extremes, where e.g. most cells would
have values close to zero and only one or two would contain the majority -
the sort of extremes that arise e.g. if you change the distribution code
into this: 
dist<-rbeta(100000, j*0.01,i*0.1) 

the idea is to get a whole range of posible combinations  of sds of cell and
margin distributions

Hope this makes a little more sense!

cheers,
maia
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