Analyze count time series with excess zeros.
Two types of statistical models are supported: Markov regression by Yang et al.
(2013)
Zero-Inflated Models (ZIM) for Count Time Series with Excess Zeros
Analyze count time series with excess zeros. Two types of statistical models are supported: Markov regression by Yang et al. (2013) and state-space models by Yang et al. (2015). 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.
# Install stable version from CRANinstall.packages("ZIM") # Install development version from GitHubdevtools::install_github("biostatstudio/ZIM") # Load package into Rlibrary(ZIM)
Yang, M., Zamba, G. K. D. and Cavanaugh, J. E. (2013). Markov Regression Models for Count Time Series with Excess Zeros: A Partial Likelihood Approach. Statistical Methodology, 14, 26-38.
Yang, M., Cavanaugh, J. E. and Zamba, G. K. D. (2015). State-Space Models for Count Time Series with Excess Zeros. Statistical Modelling, 15, 70-90.