Title: Fast estimation of generalized linear latent and mixed models for categorical, count, and continuous observed variables
Abstract: We introduce a fast method for approximate maximum likelihood estimation of generalized linear latent and mixed models models with a combination of categorical, count and continuous data. The method is compared against alternative approaches in simulation studies with multiple group models, non-linear latent growth models, two-tier longitudinal models and joint models for process and performance data. An overview of the R implementation lamle is given and some remaining estimation challenges are outlined.