This code fits a 4-class, latent-class model for marijuana use and attitudes using 7 binary indicators of the latent class variable. It includes a grouping variable for year, and observations came from 3 different years. Measurement invariance across groups is imposed such that analogous item-response probabilities within classes are restricted to be equal to each other across groups.

These web apps use R Shiny to make it easier to understand output from complex models, such as multilevel latent class analysis.