galamm: Generalized Additive Latent and Mixed Models

[osorensen]

Submitting Author Name: Øystein Sørensen Submitting Author Github Handle: !--author1-->@osorensen<!--end-author1-- Repository: https://github.com/LCBC-UiO/galamm Version submitted: 0.1.1.9000 Submission type: Stats Badge grade: gold Editor: !--editor-->@noamross<!--end-editor-- Reviewers: @nicholasjclark, @eric-pedersen

Due date for @nicholasjclark: 2024-11-10 Due date for @eric-pedersen: 2024-11-23

Archive: TBD Version accepted: TBD Language: en

• Paste the full DESCRIPTION file inside a code block below:

Package: galamm
Title: Generalized Additive Latent and Mixed Models
Version: 0.1.1.9000
Authors@R: c(
    person(given = "Øystein",
           family = "Sørensen",
           role = c("aut", "cre"),
           email = "oystein.sorensen@psykologi.uio.no",
           comment = c(ORCID = "0000-0003-0724-3542")),
    person(given = "Douglas", family = "Bates", role = "ctb"),       
    person(given = "Ben", family = "Bolker", role = "ctb"),
    person(given = "Martin", family = "Maechler", role = "ctb"),
    person(given = "Allan", family = "Leal", role = "ctb"),
    person(given = "Fabian", family = "Scheipl", role = "ctb"),
    person(given = "Steven", family = "Walker", role = "ctb"),
    person(given = "Simon", family = "Wood", role = "ctb")
           )
Description: 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>).
License: GPL (>= 3)
URL: https://github.com/LCBC-UiO/galamm, https://lcbc-uio.github.io/galamm/
BugReports: https://github.com/LCBC-UiO/galamm/issues
Encoding: UTF-8
Imports: 
    lme4,
    Matrix,
    memoise,
    methods,
    mgcv,
    nlme,
    Rcpp,
    Rdpack,
    stats
Depends:
    R (>= 3.5.0)
LinkingTo:
    Rcpp,
    RcppEigen
LazyData: true
Roxygen: list(markdown = TRUE, roclets = c ("namespace", "rd", "srr::srr_stats_roclet"))
RoxygenNote: 7.2.3
Suggests:
    covr,
    gamm4,
    knitr,
    PLmixed,
    rmarkdown,
    testthat (>= 3.0.0)
Config/testthat/edition: 3
VignetteBuilder: knitr, rmarkdown
RdMacros: Rdpack
NeedsCompilation: yes
SystemRequirements: C++17

Scope

• Please indicate which of our statistical package categories this package falls under. (Please check one appropriate box below):

  Statistical Packages

  • [ ] Bayesian and Monte Carlo Routines
  • [ ] Dimensionality Reduction, Clustering, and Unsupervised Learning
  • [ ] Machine Learning
  • [x] Regression and Supervised Learning
  • [ ] Exploratory Data Analysis (EDA) and Summary Statistics
  • [ ] Spatial Analyses
  • [ ] Time Series Analyses

Pre-submission Inquiry

• [x] A pre-submission inquiry has been approved in issue 614

General Information

• Who is the target audience and what are scientific applications of this package? The target audience is applied statisticians and quantitative scientists, particularly those working on the social sciences. The package is motivated by longitudinal studies in cognitive neuroscience, but it is applicable wherever a measurement model (of factor analysis type) needs to be combined with hierarchical modeling.

• Paste your responses to our General Standard G1.1 here, describing whether your software is:

This is the first implementation of the algorithm developed in Sørensen, Fjell, and Walhovd (2023).

• (If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research? Not applicable.

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• [ ] I/we have run autotest checks on the package, and ensured no tests fail. Running autotest gives some errors, but they were waived in the pre-review issue.
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• [x] does not violate the Terms of Service of any service it interacts with.
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