dzim.fit

offset

control

<code><a rd-options="" href="/link/dzim.fit?package=ZIM&version=1.1.0" data-mini-rdoc="ZIM::dzim.fit">dzim.fit</a></code> is the basic computing engine called by <code><a rd-options="" href="/link/dzim?package=ZIM&version=1.1.0" data-mini-rdoc="ZIM::dzim">dzim</a></code> used to fit 
dynamic zero-inflated models. This should usually not be used directly unless by experienced users.

regression

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.

Ming Yang

Zero-Inflated Models (ZIM) for Count Time Series with Excess
Zeros

Gideon Zamba

Joseph Cavanaugh

dzim.fit function

<code><a rd-options='' href='dzim.fit'>dzim.fit</a></code> is the basic computing engine called by <code><a rd-options='' href='dzim'>dzim</a></code> used to fit 
dynamic zero-inflated models. This should usually not be used directly unless by experienced users.

dzim.fit: Fitter Function for Dynamic Zero-Inflated Models

Description

Usage

Arguments

See Also