ZIM: 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) <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.

Version: 1.1.0
Imports: MASS
Suggests: pscl, TSA
Published: 2018-08-28
Author: Ming Yang [aut, cre], Gideon Zamba [aut], Joseph Cavanaugh [aut]
Maintainer: Ming Yang <mingyang at biostatstudio.com>
BugReports: https://github.com/biostatstudio/ZIM/issues
License: GPL-3
URL: https://github.com/biostatstudio/ZIM
NeedsCompilation: no
Materials: README
In views: TimeSeries
CRAN checks: ZIM results


Reference manual: ZIM.pdf
Package source: ZIM_1.1.0.tar.gz
Windows binaries: r-devel: ZIM_1.1.0.zip, r-release: ZIM_1.1.0.zip, r-oldrel: ZIM_1.1.0.zip
macOS binaries: r-release: ZIM_1.1.0.tgz, r-oldrel: ZIM_1.1.0.tgz
Old sources: ZIM archive


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