# How can I calculate covariance of quasi-Poisson-distributed variables in R?

I have two time series of count data which I believe are correlated. Is there an equivalent to cov() that assumes an overdispersed Poisson rather than a normal distribution? If there is a significance test for this (noting that both series increase over time but that doesn't prove correlation), so much the better. I wondered if vcov() might help but it focuses on parameters of a solved model. More broadly, I'm looking for a measure of the similarity between two variables which is appropriate for quasi-Poisson data.

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The cov() function in R just calculates the empirical correlation, there is no assumption about normality in that. Of course, if you go longer, like doing inference about the correlation, you need a model, and normality might simplify the analysis. Or you could just bootstrap!