sdmTMB: An R Package for Fast, Flexible, and User-Friendly Generalized Linear Mixed Effects Models with Spatial and Spatiotemporal Random Fields

<jats:title>Abstract</jats:title><jats:p>Geostatistical spatial or spatiotemporal data are common across scientific fields. However, appropriate models to analyse these data, such as generalised linear mixed effects models (GLMMs) with Gaussian Markov random fields (GMRFs), are computationally intensive and challenging for many users to implement. Here, we introduce the R package<jats:bold>sdmTMB</jats:bold>, which extends the flexible interface familiar to users of<jats:bold>lme4, glmmTMB</jats:bold>, and<jats:bold>mgcv</jats:bold>to include spatial and spatiotemporal latent GMRFs using an SPDE-(stochastic partial differential equation) based approach. SPDE matrices are constructed with<jats:bold>fmesher</jats:bold>and estimation is conducted via maximum marginal likelihood with<jats:bold>TMB</jats:bold>or via Bayesian inference with<jats:bold>tmbstan</jats:bold>and<jats:bold>rstan</jats:bold>. We describe the model and explore case studies that illustrate<jats:bold>sdmTMB</jats:bold>’s flexibility in implementing penalised smoothers, non-stationary processes (time-varying and spatially varying coefficients), hurdle models, cross-validation and anisotropy (directionally dependent spatial correlation). Finally, we compare the functionality, speed, and interfaces of related software, demonstrating that<jats:bold>sdmTMB</jats:bold>can be an order of magnitude faster than R-<jats:bold>INLA</jats:bold>. We hope<jats:bold>sdmTMB</jats:bold>will help open this useful class of models to a wider field of geostatistical analysts.</jats:p>