Dear all,

I am trying to use the 'glm' package as part of a semiparametric technique that involves weighting a likelihood in various ways, i.e.

L(theta;data)=Sum_i=1,..,n (W_i)(log L(theta;data_i))

Where W_i can be a kernel weighting function, or W_i can be an indicator of 'non-missingness' divided by a propensity score.

In a Monte Carlo exercise, the option glm(..., weights=W_i,) works very well for the Gaussian design, but if I were to change it to a Binomial design it doesn't (I have read previous posts in this mailing list explaining why).

My question is: Is there a way or a package that uses the glm library and allows me to set 'weights' the way I need?

Thanks in advance for your suggestions.

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