gllvm 2.0: fast fitting of advanced ordination methods and joint species distribution models
Some of the key new features of gllvm include model-based constrained and concurrent ordination methods, capacity to account for nested/hierarchical sampling designs, and (phylogenetic) random effects. On top of this, other notable improvements include a great expansion of the response types that it can handle, enhanced capabilities of GLLVM inference, selection and prediction, and an easier-to-use interface for model fitting.
Authors
- Francis K. C. Hui (ORCID: https://orcid.org/0000-0003-0765-3533)
- Pekka Korhonen (ORCID: https://orcid.org/0000-0003-2650-3645)
- Bert van der Veen (ORCID: https://orcid.org/0000-0003-2263-3880)
- Jenni Niku (ORCID: https://orcid.org/0000-0002-7992-2598)
- Sara Taskinen (ORCID: https://orcid.org/0000-0001-9470-7258)
Institutions
- Australian National University (AU)
- Norwegian University of Science and Technology (NO)
- Department of Mathematical Sciences (RU)
- University of Jyväskylä (FI)
Publication Details
- Journal
- PeerJ
- Published
- 2025-12-12
- DOI
- https://doi.org/10.7717/peerj.20338
- Primary Topic
- Species Distribution and Climate Change
- Type
- article
- Field-Weighted Citation Impact
- 0.00