Zero-Inflated Models (ZIM) for Count Time Series with Excess Zeros
Overview
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.
Installation
Stable Version
Install stable version from CRAN:
install.packages("ZIM")Development Version
Install development version from GitHub:
# install.packages("remotes")
remotes::install_github("mingstat/ZIM")References
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M Yang, GKD Zamba, JE Cavanaugh. Markov regression models for count time series with excess zeros: A partial likelihood approach. Statistical Methodology, 2013, 14:26–38. <doi:10.1016/j.stamet.2013.02.001>
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M Yang, JE Cavanaugh, GKD Zamba. State-space models for count time series with excess zeros. Statistical Modelling, 2015, 15(1):70–90. <doi:10.1177/1471082X14535530>