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maximum likelihood estimation package

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Not clear whether this should be merged, but we wanted you to see. I was not able to easily reproduce any problems without underflow control code, so Mike is going...

for example, consider ``` library(bbmle) set.seed(101) z = rpois(100,lambda=5) m1 = mle2(z~dpois(lambda=L),start=list(L=4),data=data.frame(z)) qAICc(m1,nobs=100,dispersion=1.2) qAICc(m1,m1,nobs=100,dispersion=1.2) ## !! ```

When profiling following an mle2() fit, profiling often finds a new better MLE. Currently profile() then stops and returns the new MLE. This is useful, but it seems like it...

For a specific application I would like to be able to calculate confidence and prediction intervals. Would it be hard to incorporate these in the predict method of bbmle?

as far as I can tell, profiling is using the same starting values as the original call. This is inefficient and particularly bad when the likelihood surface is sufficiently weird...