mvgam

The goal of mvgam is to use a Bayesian framework to estimate parameters of Dynamic Generalized Additive Models (DGAMs) for time series with dynamic trend components. The package provides an interface to fit Bayesian DGAMs using either JAGS or Stan as the backend, but note that users are strongly encouraged to opt for Stan over JAGS. The formula syntax is based on that of the package mgcv to provide a familiar GAM modelling interface. There is also built-in support for the increasingly powerful marginaleffects package to make interpretation easy. The motivation for the package and some of its primary objectives are described in detail by Clark & Wells 2022 (published in Methods in Ecology and Evolution). An introduction to the package and some worked examples are also shown in the below seminar:

Ecological Forecasting with Dynamic Generalized Additive Models DGAMs)