A Stata package for cluster-weighted modeling

The cluster-weighted model (CWM) is a member of the family of the mixtures of regression models, and is also referred to in the literature as the mixture of regression with random covariates. These models extend finite mixture models by allowing the researcher to model the marginal distribution of regression covariates along with the conditional distribution. The attention on CWMs is increasing; indeed, software for estimating these kinds of models is available to R users but not for Stata users. Thus, the aim of this presentation is to introduce the Stata package cwmglm. This package extends the capabilities of fmm by introducing more advanced mixture models based on maximum likelihood estimation and the expectation maximization (EM) algorithm. cwmglm allows users to fit CWMs based on the most common generalized linear models (GLM) with random covariates. The supported GLM families are Gaussian, Poisson and binomial, while the allowed marginal distributions for the covariates are multivariate normal, multinomial, binomial, and Poisson. cwmglm extends the current capabilities in the estimation of CWMs by allowing users to evaluate model