Spatial and Spatiotemporal SPDE-Based GLMMs with 'TMB'
Run extra optimization on an already fitted object
Get TMB parameter list
Check if ggplot2 installed
Transform a mesh object into a mesh with correlation barriers
Replicate a prediction data frame over time
Add UTM coordinates to a data frame
DHARMa residuals
Calculate effects
Estimated marginal means with the emmeans package with sdmTMB
Extract MCMC samples from a model fit with tmbstan.
Additional families
Extract parameter simulations from the joint precision matrix
Extract a relative biomass/abundance index or a center of gravity
Calculate a population index via simulation from the joint precision m...
Construct an SPDE mesh for sdmTMB
Plot anisotropy from an sdmTMB model
Plot PC Matérn priors
Plot a smooth term from an sdmTMB model
Predict from an sdmTMB model
Prior distributions
Objects exported from other packages
Residuals method for sdmTMB models
Sanity check of an sdmTMB model
Fit a spatial or spatiotemporal GLMM with TMB
Cross validation with sdmTMB models
Simulate from a spatial/spatiotemporal model
Perform stacking with log scores on sdmTMB_cv() output
Optimization control options
Set delta model for ggeffects::ggpredict()
Simulate from a fitted sdmTMB model
Example fish survey data
Turn sdmTMB model output into a tidy data frame
Plot sdmTMB models with the visreg package
Implements spatial and spatiotemporal GLMMs (Generalized Linear Mixed Effect Models) using 'TMB', 'fmesher', and the SPDE (Stochastic Partial Differential Equation) Gaussian Markov random field approximation to Gaussian random fields. One common application is for spatially explicit species distribution models (SDMs). See Anderson et al. (2024) <doi:10.1101/2022.03.24.485545>.
Useful links