galamm: Generalized Additive Latent and Mixed Models

Estimates generalized additive latent and mixed models using maximum marginal likelihood, as defined in Sorensen et al. (2023) <doi:10.1007/s11336-023-09910-z>, which is an extension of Rabe-Hesketh and Skrondal (2004)'s unifying framework for multilevel latent variable modeling <doi:10.1007/BF02295939>. Efficient computation is done using sparse matrix methods, Laplace approximation, and automatic differentiation. The framework includes generalized multilevel models with heteroscedastic residuals, mixed response types, factor loadings, smoothing splines, crossed random effects, and combinations thereof. Syntax for model formulation is close to 'lme4' (Bates et al. (2015) <doi:10.18637/jss.v067.i01>) and 'PLmixed' (Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>).

Package overview README.md Computational Scaling Generalized Linear Mixed Models with Factor Structures Heteroscedastic Linear Mixed Models Interactions Between Latent and Observed Covariates Introduction Linear Mixed Models with Factor Structures Models with Mixed Response Types Optimization Semiparametric Latent Variable Modeling

Vignettes Man pages API and functions Files

Package details
Author	Øystein Sørensen [aut, cre] (ORCID: <https://orcid.org/0000-0003-0724-3542>), Douglas Bates [ctb], Ben Bolker [ctb], Martin Maechler [ctb], Allan Leal [ctb], Fabian Scheipl [ctb], Steven Walker [ctb], Simon Wood [ctb]
Maintainer	Øystein Sørensen <oystein.sorensen@psykologi.uio.no>
License	GPL (>= 3)
Version	0.2.2
URL	https://github.com/LCBC-UiO/galamm https://lcbc-uio.github.io/galamm/
Package repository	View on CRAN
Installation	Install the latest version of this package by entering the following in R: `install.packages("galamm")`