Bayesian Modeling of Spatial Count Data
Gamma-Count (GC) Distribution
Fit ICAR Spatial Gamma-Count Model
Fit Negative Binomial Spatial Model
Fit Poisson Spatial Model
Generate a Random Adjacency Matrix
Generate Data from GC Spatial Regression Model with Geospatial Depende...
Generate Data from GC Spatial Regression Model with Lattice Spatial Ef...
Generate Spatial Random Fields from CAR Models
Generate Spatial Random Fields from Matern Covariance Model
Generate Spatial Random Fields from ICAR Models
Provides a collection of functions for preparing data and fitting Bayesian count spatial regression models, with a specific focus on the Gamma-Count (GC) model. The GC model is well-suited for modeling dispersed count data, including under-dispersed or over-dispersed counts, or counts with equivalent dispersion, using Integrated Nested Laplace Approximations (INLA). The package includes functions for generating data from the GC model, as well as spatially correlated versions of the model. See Nadifar, Baghishani, Fallah (2023) <doi:10.1007/s13253-023-00550-5>.