# Generalized Linear Model estimating multiple parameters concurrently

Is there an extension of the generalized linear model that is able to estimate (using maximum likelihood) more than one parameter of a distribution at once. For example, I know that for a gamma distribution with shape parameter $k$ and scale parameter $\theta$, a GLM can estimate the coefficient matrix $\beta$ for predicting $\theta$ from given covariates, responses, and $k$. Is there a way to estimate both parameters as a linear combination of predictor variables ?

In chapter 10 of Generalized Linear Models (McCullagh & Nelder, 1989), Chapter 10 deals with joint modelling of mean and dispersion from covariates. However, they do not go into detail about a general algorithm to do so.