galamm

galamm provides a common ground between structural equation modeling and mixed effect models by estimating generalized additive latent and mixed models in R.

Key Features:

• Random effects structures: Supports estimation of models with an arbitrary number of crossed or nested random effects.
• Smoothing splines: Incorporates smoothing splines for semiparametric modeling.
• Mixed response types: Handles mixed response types within the modeling framework.
• Factor structures: Supports factor structures for latent variable modeling.
• Heteroscedastic residuals: Accommodates heteroscedastic residuals in model estimation.
• Missing data: Handles data missing at random.
• Computational efficiency: Uses sparse matrix methods and automatic differentiation for efficient computation.

Scientific Applications:

• Multilevel semiparametric latent variable modeling: Enables multilevel semiparametric latent variable modeling in R.

Methodology:

Estimation is implemented using sparse matrix methods and automatic differentiation.