Cluster-weighted models using Stata

The cluster-weighted model (CWM) is a member of the family of mixture of regression models and is also known as mixtures of regressions with random covariates. CWMs refer to the framework of model-based clustering and have their natural application when the research interest requires modeling the relationship between a response variable and a set of covariates using a regression-based approach such as a generalized linear model and the sample is suspected to be composed by heterogeneous latent classes. Software for estimating these models is not yet available in Stata. The aim of this article is to introduce the Stata package \code{cwmglm}, which allows fitting CWMs based on the most common generalized linear models with random covariates. Moreover, \code{cwmglm} allows the estimation of parsimonious models of Gaussian distributions, with the parametrization of the variance-covariance matrix based on the eigenvalue decomposition. These features are completely new for Stata users. The \code{cwmglm} package features goodness-of-fit, bootstrapping and model selection tools. We illustrate the use of \code{cwmglm} with real and simulated datasets