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 ).

References

sorensenLongitudinalModelingAgeDependent2023galamm

See also

Useful links:

• https://github.com/LCBC-UiO/galamm

• https://lcbc-uio.github.io/galamm/

• Report bugs at https://github.com/LCBC-UiO/galamm/issues

Author

Maintainer: Øystein Sørensen oystein.sorensen@psykologi.uio.no (ORCID)

Other contributors:

• Douglas Bates [contributor]

• Ben Bolker [contributor]

• Martin Maechler [contributor]

• Allan Leal [contributor]

• Fabian Scheipl [contributor]

• Steven Walker [contributor]

• Simon Wood [contributor]