ZINB.GP: Bayesian Zero-Inflated Negative Binomial Gaussian Process Models

Fits Bayesian zero-inflated negative binomial regression models with Gaussian process random effects for spatial, temporal, or spatiotemporal count data. Provides Markov chain Monte Carlo sampling, configurable random effects in the zero-inflation and count components, and posterior predictive draws. Implements a full GP version of the methods described by He and Huang (2024) <doi:10.1016/j.jspi.2023.106098>.

Version:	1.0.0
Imports:	BayesLogit, LaplacesDemon, MASS, Matrix, msm, mvtnorm, stats
Suggests:	coda, knitr, posterior, rmarkdown, testthat (≥ 3.0.0)
Published:	2026-09-14
DOI:	10.32614/CRAN.package.ZINB.GP
Author:	Mahlon Scott [aut], Qing He [aut], Hsin-Hsiung Huang [aut, cre, cph]
Maintainer:	Hsin-Hsiung Huang <hsin.huang at ucf.edu>
BugReports:	https://github.com/KingJMS1/GP_ZINB_R/issues
License:	MIT + file LICENSE
Copyright:	see file COPYRIGHTS
URL:	https://github.com/KingJMS1/GP_ZINB_R, https://kingjms1.github.io/GP_ZINB_R/
NeedsCompilation:	no
Materials:	README, NEWS
CRAN checks:	ZINB.GP results

Documentation:

Reference manual:	ZINB.GP.html , ZINB.GP.pdf
Vignettes:	Oregon Landslides: A Spatiotemporal ZINB-GP Case Study (source, R code) Simulating and Fitting a Spatiotemporal ZINB-GP Model (source, R code)

Downloads:

Package source:	ZINB.GP_1.0.0.tar.gz
Windows binaries:	r-devel: ZINB.GP_1.0.0.zip, r-release: ZINB.GP_1.0.0.zip, r-oldrel: ZINB.GP_1.0.0.zip
macOS binaries:	r-release (arm64): ZINB.GP_1.0.0.tgz, r-oldrel (arm64): ZINB.GP_1.0.0.tgz, r-release (x86_64): ZINB.GP_1.0.0.tgz, r-oldrel (x86_64): ZINB.GP_1.0.0.tgz

Linking:

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