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SpatGC: Bayesian Modeling of Spatial Count Data
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>.
Getting started
Browse package contents
Package details |
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| Author | Mahsa Nadifar [aut, cre] (<https://orcid.org/0000-0002-9130-125X>), Hossein Baghishani [aut] (<https://orcid.org/0000-0002-6396-0303>) |
| Maintainer | Mahsa Nadifar <mahsa.nst@gmail.com> |
| License | GPL (>= 2) |
| Version | 0.1.0 |
| URL | https://github.com/mahsanst/SpatGC |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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