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

Documentation:

Reference manual:

ZIM.pdf

Downloads:

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 (arm64): ZIM_1.1.0.tgz, r-oldrel (arm64): ZIM_1.1.0.tgz, r-release (x86_64): ZIM_1.1.0.tgz, r-oldrel (x86_64): ZIM_1.1.0.tgz
Old sources:	ZIM archive

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