gllvm
Generalized Linear Latent Variable Models
Analysis of multivariate data using generalized linear latent variable models (gllvm). Estimation is performed using either the Laplace method, variational approximations, or extended variational approximations, implemented via TMB (doi:10.18637/jss.v070.i05).
- Version2.0
- R versionunknown
- LicenseGPL-2
- Needs compilation?Yes
- gllvm citation info
- Last release11/26/2024
Documentation
- VignetteAnalysing multivariate abundance data using gllvm
- VignetteAnalysing high-dimensional microbial community data using gllvm
- VignetteIntroduction to gllvm Part 1: Ordination
- VignetteIntroduction to gllvm Part 2: Species correlations
- VignetteHow to use the quadratic response model
- VignetteOrdination with predictors
- VignettePhylogenetic random effects
- VignetteAnalysing sparse ecological percent cover data using gllvm
- VignetteCorrelation structures for latent variables and row effects
- MaterialREADME
- MaterialNEWS
- In ViewsEnvironmetrics
Team
Jenni Niku
MaintainerShow author detailsRiki Herliansyah
Show author detailsRolesAuthorBert van der Veen
Show author detailsRolesAuthorFrancis K.C. Hui
Show author detailsRolesAuthorDavid I. Warton
Show author detailsRolesAuthorWesley Brooks
Show author detailsRolesAuthorSara Taskinen
Pekka Korhonen
Show author detailsRolesAuthor
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- Depends1 package
- Imports7 packages
- Suggests8 packages
- Linking To2 packages
- Reverse Suggests1 package