Likelihood-Based Inference for Joint Modeling of Correlated Count and Binary Outcomes with Extra Variability and Zeros R package HZIP version 0.1.2 Inference approach for jointly modeling correlated count and binary outcomes. This formulation allows simultaneous modeling of zero inflation via the Bernoulli component while providing a more accurate assessment of the Hierarchical Zero-Inflated Poisson's parsimony (Lizandra C. Fabio, Jalmar M. F. Carrasco, Victor H. Lachos and Ming-Hui Chen, Likelihood-based inference for joint modeling of correlated count and binary outcomes with extra variability and zeros, 2026, under submission). Software Depends: R (>= 3.5) Imports: Rcpp, Formula, pscl, stats, tibble, dplyr, statmod, RcppParallel, ggplot2, numDeriv, rlang LinkingTo: Rcpp, RcppParallel, RcppArmadillo Jalmar M. F. Carrasco <carrasco.jalmar@ufba.br> Comprehensive R Archive Network (CRAN) Lizandra C. Fabio [aut], Jalmar M. F. Carrasco [aut, cre], Victor H. Lachos [aut], Ming-Hui Chen [aut] GPL-3 2026-06-02 application/tgz https://CRAN.R-project.org/package=HZIP doi:10.32614/CRAN.package.HZIP