Analyze count time series with excess zeros. Two types of statistical models are supported: Markov regression by Yang et al. (2013) <doi:10.1016/j.stamet.2013.02.001> and state-space models by Yang et al. (2015) <doi:10.1177/1471082X14535530>. They are also known as observation-driven and parameter-driven models respectively in the time series literature. The functions used for Markov regression or observation-driven models can also be used to fit ordinary regression models with independent data under the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) assumption. Besides, the package contains some miscellaneous functions to compute density, distribution, quantile, and generate random numbers from ZIP and ZINB distributions.
Package details |
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Author | Ming Yang [aut, cre], Gideon Zamba [aut], Joseph Cavanaugh [aut] |
Maintainer | Ming Yang <mingyang@biostatstudio.com> |
License | GPL-3 |
Version | 1.1.0 |
URL | https://github.com/biostatstudio/ZIM |
Package repository | View on CRAN |
Installation |
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