latents: Multilevel Latent Profile, Class and Transition Models

Provides functions for fitting multilevel latent profile analysis, latent class, and latent transition models for continuous, categorical, and mixed-type variables. Models are estimated by expectation-maximization, handle missing data, and provide standard and robust standard errors. Model selection is supported by information criteria, entropy, classification diagnostics, and a parametric bootstrap likelihood ratio test. Covariates can be related to latent class membership using three-step methods with correction for classification error. Latent transition models estimate transitions between latent profiles across ordered occasions and are compatible with the 'tna' package for network and sequence analysis. The multilevel mixture formulation follows Vermunt (2003) <doi:10.1111/j.0081-1750.2003.t01-1-00131.x>.

install.packages("latents")

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