dataC

Simulated data was generated with x1 following a Bernoulli distribution with a success probability of 0.4, x2 following a standard normal distribution, and y following a zero-inflated Gaussian regression model.

datasets

Run a Gibbs sampler for hurdle models to analyze data showing an excess of zeros, which is common in zero-inflated count and semi-continuous models. The package includes the hurdle model under Gaussian, Gamma, inverse Gaussian, Weibull, Exponential, Beta, Poisson, negative binomial, logarithmic, Bell, generalized Poisson, and binomial distributional assumptions. The models described in Ganjali et al. (2024).

Taban Baghfalaki

Unified Zero-Inflated Hurdle Regression Models

Mojtaba Ganjali

Narayanaswamy Balakrishnan

dataC function

A data frame which contains x1, x2 and y.<dl>
 <dt>y</dt>
<dd>the response variable</dd> <dt>x1</dt>
<dd>Binary covariate</dd> <dt>x2</dt>
<dd>Continuous covariate</dd>
</dl>

Format

Simulated data from zero-inflated Gaussian regression model — dataC

A data frame which contains x1, x2 and y.<dl>
 <dt>y</dt>
<dd>the response variable</dd>

 <dt>x1</dt>
<dd>Binary covariate</dd>

 <dt>x2</dt>
<dd>Continuous covariate</dd>


</dl>

dataC: Simulated data from zero-inflated Gaussian regression model

Description

Usage

Arguments

Format

See Also