Spatial and spatiotemporal GLMMs with TMB

sdmTMB is an R package that implements spatial and spatiotemporal predictive-process GLMMs (Generalized Linear Mixed Effects Models) using Template Model Builder (TMB), R-INLA, and Gaussian Markov random fields. One common application is for species distribution models (SDMs).

Assuming you have a C++ compiler installed, you can install sdmTMB:

```
# install.packages("remotes")
remotes::install_github("pbs-assess/sdmTMB")
```

sdmTMB:

- Fits GLMMs with spatial, spatiotemporal, spatial and spatiotemporal, or AR1 spatiotemporal Gaussian Markov random fields with TMB. It can also fit spatially varying local trends through time as a random field.
- Uses formula interfaces for fixed effects and any time-varying effects (dynamic regression) (e.g.
`formula = y ~ 1 + x1 + (1 | g), time_varying = ~ 0 + x2`

), where`y`

is the response,`1`

represents an intercept,`0`

omits an intercept,`x1`

is a covariate with a constant effect,`(1 | g)`

is a random intercept across groups`g`

, and`x2`

is a covariate with a time-varying effect. - Can handle formulas with splines from mgcv. E.g.,
`y ~ s(x, k = 4)`

. - Can handle linear breakpoint or logistic threshold fixed effects:
`y ~ breakpt(x1)`

or`y ~ logistic(x2)`

. - Uses a
`family(link)`

format similar to`glm()`

, lme4, or glmmTMB. This includes Gaussian, Poisson, negative binomial, gamma, binomial, lognormal, Student-t, and Tweedie distributions with identity, log, inverse, and logit links. E.g.,`family = tweedie(link = "log")`

. - Has
`predict()`

and`residuals()`

methods. The residuals are randomized-quantile residuals similar to those implemented in the DHARMa package. The`predict()`

function can take a`newdata`

argument similar to`lm()`

or`glm()`

etc. The predictions are bilinear interpolated predictive-process predictions (i.e., they make smooth pretty maps). - Has a simulation function
`sdmTMB_sim()`

for simulation testing models and`sdmTMB_cv()`

for cross-validation testing of model accuracy or comparing across model configurations. - Includes functionality for estimating the centre of gravity or total biomass by time step for index standardization.
- Can optionally allow for anisotropy in the random fields (spatial correlation that is directionally dependent) and barriers (e.g., land for ocean species) to spatial correlation.
- Can generate an SPDE predictive-process mesh or can take any standard R-INLA mesh created externally as input.

The main function is `sdmTMB()`

. See `?sdmTMB`

and `?predict.sdmTMB`

for the most complete examples. Also see the vignettes (‘Articles’) on the documentation site.