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>.

Version:	0.1.0
Depends:	R (≥ 4.0)
Imports:	mvtnorm, stats, spdep, sf
Suggests:	INLA (≥ 23.06.15)
Published:	2024-04-25
Author:	Mahsa Nadifar [aut, cre], Hossein Baghishani [aut]
Maintainer:	Mahsa Nadifar <mahsa.nst at gmail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/mahsanst/SpatGC
NeedsCompilation:	no
Additional_repositories:	https://inla.r-inla-download.org/R/testing
Materials:	README NEWS
CRAN checks:	SpatGC results

Documentation:

Reference manual:

SpatGC.pdf

Downloads:

Package source:	SpatGC_0.1.0.tar.gz
Windows binaries:	r-devel: SpatGC_0.1.0.zip, r-release: SpatGC_0.1.0.zip, r-oldrel: SpatGC_0.1.0.zip
macOS binaries:	r-release (arm64): SpatGC_0.1.0.tgz, r-oldrel (arm64): SpatGC_0.1.0.tgz, r-release (x86_64): SpatGC_0.1.0.tgz, r-oldrel (x86_64): SpatGC_0.1.0.tgz

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