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>).

Version: 0.1.1
Depends: R (≥ 3.5.0)
Imports: lme4, Matrix, memoise, methods, mgcv, nlme, Rcpp, Rdpack, stats
LinkingTo: Rcpp, RcppEigen
Suggests: covr, gamm4, ggplot2, knitr, PLmixed, rmarkdown, testthat (≥ 3.0.0)
Published: 2023-10-13
Author: Øystein Sørensen ORCID iD [aut, cre], 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 at psykologi.uio.no>
BugReports: https://github.com/LCBC-UiO/galamm/issues
License: GPL (≥ 3)
URL: https://github.com/LCBC-UiO/galamm, https://lcbc-uio.github.io/galamm/
NeedsCompilation: yes
SystemRequirements: C++17
Citation: galamm citation info
Materials: README
CRAN checks: galamm results

Documentation:

Reference manual: galamm.pdf
Vignettes: Introduction
Generalized Linear Mixed Models with Factor Structures
Interactions Between Latent and Observed Covariates
Linear Mixed Models with Factor Structures
Heteroscedastic Linear Mixed Models
Models with Mixed Response Types
Optimization
Semiparametric Latent Variable Modeling

Downloads:

Package source: galamm_0.1.1.tar.gz
Windows binaries: r-devel: galamm_0.1.1.zip, r-release: galamm_0.1.1.zip, r-oldrel: galamm_0.1.1.zip
macOS binaries: r-release (arm64): galamm_0.1.1.tgz, r-oldrel (arm64): galamm_0.1.1.tgz, r-release (x86_64): galamm_0.1.1.tgz
Old sources: galamm archive

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