Zero-Inflated Models (ZIM) for Count Time Series with Excess Zeros
Backshift Operator
The Zero-Inflated Negative Binomial Distribution
The Zero-Inflated Poisson Distribution
Auxiliary for Controlling DZIM Fitting
Particle Filtering for DZIM
Fitter Function for Dynamic Zero-Inflated Models
Trace Plots from DZIM
Fitting Dynamic Zero-Inflated Models
Simulate Data from DZIM
Particle Smoothing for DZIM
Function to Compute P-value.
Zero-Inflated Models for Count Time Series with Excess Zeros
Auxiliary for Controlling ZIM Fitting
Fitter Function for Zero-Inflated Models
Fitting Zero-Inflated Models
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.