Latent class analysis (LCA) and latent transition analysis (LTA) can be extended in a variety of ways. Advanced features, such as covariates, outcomes, moderators, and inverse propensity score weights, can be added to “baseline” static and dynamic latent class models. All of the models discussed here add advanced features to baseline models. Over time, these pages will includes information about recent research on latent class moderation, adding inverse propensity score weights to models, multilevel latent class models, and associative latent transition analysis, among other topics. Note that these models all fall under the broad classification of loglinear modeling with latent variables.