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, 2026, under submission).


Reference manual

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install.packages("HZIP")

0.1.3 by Jalmar M. F. Carrasco, 11 days ago


https://github.com/carrascojalmar/HZIP


Report a bug at https://github.com/carrascojalmar/HZIP/issues


Browse source code at https://github.com/cran/HZIP


Authors: Lizandra C. Fabio [aut] , Jalmar M. F. Carrasco [aut, cre] , Victor H. Lachos [aut] , Ming-Hui Chen [aut]


Documentation:   PDF Manual  


GPL-3 license


Imports Rcpp, Formula, pscl, stats, tibble, dplyr, statmod, RcppParallel, ggplot2, numDeriv, rlang

Linking to Rcpp, RcppParallel, RcppArmadillo


See at CRAN