gllvm 2.0: fast fitting of advanced ordination methods and joint species distribution models

Over the past decade, joint species distribution models (JSDMs) and model-based ordination have emerged as powerful tools for the analysis of community ecology data. Generalized linear latent variable models (GLLVMs) offer a flexible framework for multivariate analysis of a wide range of data types, based on including a small number of latent variables to perform dimension reduction while accounting for residual correlation between species.

The R package gllvm implements a wide range of GLLVMs, with estimation performed via fast approximate likelihood-based techniques; including the recently proposed extended variational approximation, which is applicable to almost any combination of response type and link function. Since its original development and accompanying software paper, the gllvm package has undergone a significant overhaul, consolidating its place as a general framework for joint modeling of community ecology datasets.

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.