HZIP: Likelihood-Based Inference for Joint Modeling of Correlated
Count and Binary Outcomes with Extra Variability and Zeros
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, 2025, under submission).
| Version: |
0.1.1 |
| Depends: |
R (≥ 3.5) |
| Imports: |
Rcpp, Formula, pscl, stats, tibble, dplyr, statmod, RcppParallel, cubature, VGAM, ggplot2 |
| LinkingTo: |
Rcpp, RcppParallel, RcppArmadillo |
| Published: |
2025-12-19 |
| DOI: |
10.32614/CRAN.package.HZIP |
| Author: |
Lizandra C. Fabio [aut],
Jalmar M. F. Carrasco [aut, cre],
Victor H. Lachos [aut],
Ming-Hui Chen [aut] |
| Maintainer: |
Jalmar M. F. Carrasco <carrasco.jalmar at ufba.br> |
| BugReports: |
https://github.com/carrascojalmar/HZIP/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/carrascojalmar/HZIP |
| NeedsCompilation: |
yes |
| CRAN checks: |
HZIP results |
Documentation:
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