I think the `gamlss' package can do this.
Simon
On Fri, 16 May 2008, Markus Loecher wrote:
> Dear list members,
> while I appreciate the possibility to deal with overdispersion for count
> data either by specifying the family argument to be quasipoisson() or
> negative.binomial(), it estimates j
Dear list members,
while I appreciate the possibility to deal with overdispersion for count
data either by specifying the family argument to be quasipoisson() or
negative.binomial(), it estimates just one overdispersion parameter for the
entire data set.
In my applications I often would like the es
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