Multilevel Latent Time Series Models with 'R' and 'Stan'
Create Missings for Approximation of Continuous Time Dynamic Models
Fit Bayesian Multilevel Manifest or Latent Time-Series Models
Create TeX Model Formula from mlts model object
Create Path Diagrams from mlts model object
Build a multilevel latent time series model
Plot Paths for Two-Level VAR Model
Plot results of mlts
Generate Posterior Predictive Samples for Multilevel Latent Time Serie...
Posterior Predictive Checks for Multilevel Latent Time Series Models
Simulate data from mlts model
Get Standardized Estimates for an mlts Model
mlts: Multilevel Latent Time Series Models with 'R' and 'Stan'
Create a summary of a fitted model with class mltsfit
Fit multilevel manifest or latent time-series models, including popular Dynamic Structural Equation Models (DSEM). The models can be set up and modified with user-friendly functions and are fit to the data using 'Stan' for Bayesian inference. Path models and formulas for user-defined models can be easily created with functions using 'knitr'. Asparouhov, Hamaker, & Muthen (2018) <doi:10.1080/10705511.2017.1406803>.