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zoib (version 1.0)

Bayesian Inference for Zero/One Inflated Beta Regression

Description

zoib fits the zero/one inflated beta regression model and obtains Bayesian Inference of the model via the Markov Chain Monte Carlo approach implemented in JAGS

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Version

Install

install.packages('zoib')

Monthly Downloads

271

Version

1.0

License

GPL (>= 2)

Maintainer

Fang Liu

Last Published

June 4th, 2014

Functions in zoib (1.0)

joint.2z01

Jointly modelling of multiple variables taking values from [0,1] when there are multiple random variables inthe linear predictors of the link functions
joint.1z0

Jointly modelling of multiple variables taking values from [0,1) when there is a single random variable inthe linear predictors of the link functions
joint.2z

Jointly modelling of multiple variables taking values from (0,1) when there are multiple random variables in the linear predictors of the link functions
joint.1z1

Jointly modelling of multiple variables taking values from (0,1] when there is a single random variable in the linear predictors of the link functions
sep.1z1

Seperately modelling of multiple response variables taking values from (0,1] when there a single random variable in the linear predictors of the link functions
joint.1z

Jointly modeling of multiple variables taking values from (0,1) when there is a single random variable in the linear predictors of the link functions
joint.1z01

Jointly modelling of multiple variables taking values from [0,1] when there is a single random variable in the linear predictors of the link functions
GasolineYield

Gasoline Yields Data
fixed0

Fixed-effects beta regression model with inflation at 0
AlcoholUse

California County-level Teenager Monthly Alcohol Use data
zoib

Bayesian Inference for Zero/One Inflated Beta Regression
zoib-package

Bayesian Inference For Zero/One-Inflated Beta Regression Model
BiRepeated

Data from a correlated bivariate beta distribution with repeated measures
sep.2z

Seperately modelling of multiple response variables taking values from (0,1) when there are multiple random variables in the linear predictors of the link functions
fixed

Fixed-effects beta regression with no inflation at 0 or 1
fixed01

Fixed-effects beta regression with inflation at 0 and 1
sep.1z0

Seperately modelling of multiple response variables taking values from [0,1) when there a single random variable in the linear predictors of the link functions
sep.2z0

Seperately modelling of multiple response variables taking values from [0,1) when there are multiple random variables in the linear predictors of the link functions
sep.1z

Seperately modelling of multiple response variables taking values from (0,1) when there a single random variable in the linear predictors of the link functions
sep.2z1

Seperately modelling of multiple response variables taking values from (0,1] when there are multiple random variables in the linear predictors of the link functions
joint.2z0

Jointly modelling of multiple variables taking values from [0,1) when there are multiple random variables in the linear predictors of the link functions
sep.2z01

Seperately modelling of multiple response variables taking values from [0,1] when there are multiple random variables in the linear predictors of the link functions
check.psrf

Convergence Check for Markov Chain Monte Carlo simulations via Potential Scale Reduction Factor
fixed1

Fixed-effects beta regression with inflation at 1
sep.1z01

Seperately modelling of multiple response variables taking values from [0,1] when there a single random variable in the linear predictors of the link functions
joint.2z1

Jointly modelling of multiple variables taking values from (0,1] when there are multiple random variables inthe linear predictors of the link functions