Package: galamm 0.2.1.9000
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>).
Authors:
galamm_0.2.1.9000.tar.gz
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galamm.pdf |galamm.html✨
galamm/json (API)
# Install 'galamm' in R: |
install.packages('galamm', repos = c('https://lcbc-uio.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/lcbc-uio/galamm/issues
- cognition - Simulated Data with Measurements of Cognitive Abilities
- diet - Diet Data
- epilep - Epilepsy Data
- hsced - Example Data with Heteroscedastic Residuals
- latent_covariates - Simulated Data with Latent and Observed Covariates Interaction
- latent_covariates_long - Simulated Longitudinal Data with Latent and Observed Covariates Interaction
- lifespan - Simulated Dataset with Lifespan Trajectories of Three Cognitive Domains
- mresp - Simulated Mixed Response Data
- mresp_hsced - Simulated Mixed Response Data with Heteroscedastic Residuals
generalized-additive-modelshierarchical-modelsitem-response-theorylatent-variable-modelsstructural-equation-models
Last updated 2 months agofrom:f254af8238. Checks:OK: 9. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 21 2024 |
R-4.5-win-x86_64 | OK | Nov 21 2024 |
R-4.5-linux-x86_64 | OK | Nov 21 2024 |
R-4.4-win-x86_64 | OK | Nov 21 2024 |
R-4.4-mac-x86_64 | OK | Nov 21 2024 |
R-4.4-mac-aarch64 | OK | Nov 21 2024 |
R-4.3-win-x86_64 | OK | Nov 21 2024 |
R-4.3-mac-x86_64 | OK | Nov 21 2024 |
R-4.3-mac-aarch64 | OK | Nov 21 2024 |
Exports:extract_optim_parametersfactor_loadingsfixefgalammgalamm_controlplot_smoothranefresponsesslt2t2lVarCorr
Dependencies:bootcachemfastmaplatticelme4MASSMatrixmemoisemgcvminqanlmenloptrrbibutilsRcppRcppEigenRdpackrlang
Computational Scaling
Rendered fromscaling.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-09-16
Started: 2023-10-16
Generalized Linear Mixed Models with Factor Structures
Rendered fromglmm_factor.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-09-16
Started: 2023-08-11
Heteroscedastic Linear Mixed Models
Rendered fromlmm_heteroscedastic.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-09-16
Started: 2023-08-13
Interactions Between Latent and Observed Covariates
Rendered fromlatent_observed_interaction.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-09-22
Started: 2023-09-25
Introduction
Rendered fromgalamm.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-09-16
Started: 2023-08-15
Linear Mixed Models with Factor Structures
Rendered fromlmm_factor.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-09-16
Started: 2023-08-11
Models with Mixed Response Types
Rendered frommixed_response.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-09-16
Started: 2023-08-13
Optimization
Rendered fromoptimization.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-09-16
Started: 2023-08-14
Semiparametric Latent Variable Modeling
Rendered fromsemiparametric.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-09-16
Started: 2023-08-14