poLCA
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Add complex survey correction to estimation
Would it be legitimate to use survey::svymle to maximize the likelihood at each iteration (see here) of the optimization algorithm to apply complex survey adjustments (weights, clustering, stratification) to the results?
I would be interested in this too. I wonder whether using the ML from svmle might impact the ability to use information criterions as outlines in https://www.statisticalinnovations.com/wp-content/uploads/Vermunt2007.pdf?