Automate Latent Growth Mixture Modelling in 'Mplus'
Fit Group-Based Trajectory Models (GBTM) for class enumeration.
Fit Growth Curve Models (GCM)
Fit Latent Class Growth Analysis (LCGA) models to refine covariance st...
Select best-fitting model from a list of Latent Growth Models (LGM)
Get fit indices from Latent Growth Models (LGM)
Refine Polynomial Order in Latent Growth Modelling (LGM)
Plot individual trajectories of outcome - Spaghetti plot
Create Mplus model objects for Latent Growth Modelling (LGM)
Run Latent Growth Models (LGM) and replicate the best loglikelihood va...
Provide a suite of functions for conducting and automating Latent Growth Modeling (LGM) in 'Mplus', including Growth Curve Model (GCM), Growth-Based Trajectory Model (GBTM) and Latent Class Growth Analysis (LCGA). The package builds upon the capabilities of the 'MplusAutomation' package (Hallquist & Wiley, 2018) to streamline large-scale latent variable analyses. “MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus.” Structural Equation Modeling, 25(4), 621–638. <doi:10.1080/10705511.2017.1402334> The workflow implemented in this package follows the recommendations outlined in Van Der Nest et al. (2020). “An Overview of Mixture Modeling for Latent Evolutions in Longitudinal Data: Modeling Approaches, Fit Statistics, and Software.” Advances in Life Course Research, 43, Article 100323. <doi:10.1016/j.alcr.2019.100323>.