Bayesian ZINB-Ordinal Joint Model with CDPMM Random Effects

Fits a Bayesian joint model for longitudinal zero-inflated negative binomial (ZINB) and ordinal outcomes with shared random effects under a centered Dirichlet process mixture model (CDPMM) prior. Provides a blocked Gibbs sampler with Polya-Gamma (PG) and Chinese restaurant table data augmentation, plus helpers for simulation and posterior summaries based on the deviance information criterion (DIC), the widely applicable information criterion (WAIC) and the leave-one-out information criterion (LOOIC). The PG augmentation follows Polson, Scott and Windle (2013) , the zero-inflated negative binomial construction follows Neelon (2019) , and the centered Dirichlet process mixture prior follows Tang, Tang and Pan (2014) .

zinbcdpmm

Bayesian joint model for longitudinal zero-inflated negative binomial (ZINB) and ordinal outcomes with CDPMM random effects (Liang & Du).

贝叶斯联合模型：纵向零膨胀负二项 + 有序多分类，随机效应为中心化 Dirichlet 过程混合模型（CDPMM）。

Install / 安装

GitHub（当前）:

# install.packages("remotes")
remotes::install_github("Liang-ShuQing/zinb-ordinal-cdpmm-code", subdir = "zinbcdpmm")

CRAN（包被接受后）:

install.packages("zinbcdpmm")

Tiny example / 小例子

library(zinbcdpmm)
set.seed(1)
dat <- simulate_zinb_ordinal(n = 30, nis = 4, scenario = 2, re_dist = "normal")
fit <- fit_zinb_ordinal(dat, chain = 200, burn = 100, thin = 5, G = 4)
print(fit)
summary(fit)