boral: Bayesian Models for Multivariate Ecological Data

Bayesian approaches for analyzing multivariate data in ecology, using Markov Chain Monte Carlo (MCMC) methods via JAGS. The boral package fits three types of models: independent column GLMs, latent variable models for ordination, and correlated GLMs with latent variables. It was authored by Francis K.C. Hui and is featured on the paperswithcode platform.

Use Cases

• Model-based unconstrained ordination based on latent variable models.
• Fitting independent Generalized Linear Models (GLMs) to each column of a multivariate response matrix.
• Accounting for residual correlation between response variables using correlated GLMs with latent variables.

Strengths

• Implements three distinct, well-defined Bayesian model types for multivariate analysis.
• Uses MCMC methods via JAGS for estimation, a standard in Bayesian inference.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

paperswithcode

Collection Method

Software package for statistical analysis.