Differential Abundance Analysis for Metagenomic Data via
Zero-Inflated Beta Regression
Detects abundance differences across clinical conditions. Besides, it takes the sparse nature of metagenomic data into account and handles compositional data efficiently.

XIAOLING PENG,GANG LI, ZHENQIU LIU, HONGLIANG CHEN

Zero Inflated Generalized Poisson (ZIGP) regression models
Adapt and analyze ZIGP regression models

Adapt and analyze GP, ZIP and ZIGP regression models

Adapt and analyze and compare GP, ZIP and ZIGP regression models

Fit and analyze and compare GP, ZIP and ZIGP regression models

Fit, analyze and compare GP, ZIP and ZIGP regression
models

Zero-Inflated Models for Count Time Series with Excess Zeros
Ming Yang, Gideon K. D. Zamba, and Joseph E. Cavanaugh
Fits observation-driven and parameter-driven models for zero-inflated time series

Fits observation-driven and parameter-driven models for zero-inflated time series.

Statistical Models for Count Time Series with Excess Zeros
Fits observation-driven and parameter-driven models for
count time series with excess zeros.

Zero-Inflated Models (ZIM) for Count Time Series with Excess
Zeros
c(
person("Ming", "Yang", email = "mingyang@biostatstudio.com", role = c("aut", "cre")),
person("Gideon", "Zamba", role = "aut"),
person("Joseph", "Cavanaugh", role = "aut")
)
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 [aut, cre],
Gideon Zamba [aut],
Joseph Cavanaugh [aut]