Bayesian Zero-Inflated Negative Binomial Gaussian Process Models
Overview
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.
Initial release
- Fits Bayesian zero-inflated negative binomial models with spatial, temporal, or spatiotemporal Gaussian process random effects.
- Supports Gaussian process effects in the zero-inflation component, count component, or both.
- Provides posterior draws for fixed effects, random effects, covariance parameters, dispersion, latent at-risk states, and predictive counts.
- Includes simulation and Oregon landslide case-study vignettes.
- Includes input validation and tests for every supported model configuration.
From NEWS.md in the release
Install
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- OK5 OK2026-09-15
Documentation
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- 25 (7 internal)
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- Exported functions with examples
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- Documented parameters
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- Return-value docs
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Checks run against github.com/kingjms1/gp_zinb_r on 2026-10-11.
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GitHub Pages site last deployed 2026-09-04 · The repository has a site deploy workflow
pkgdown site last built 2026-09-04, before release 1.0.0 (2026-09-14)
Dependencies
Installing ZINB.GP adds 16 packages (11 with compiled code, 8.2 MB of source tarballs). Heaviest: LaplacesDemon 2.0 MB, mvtnorm 1.0 MB, vctrs 1.0 MB.
Code & Tests
Datasets
People & History
- 1.0.0Latestfirst seen 2026-09-14 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First seen
- 2026-09-14
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Download size
- 1.6 MB
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