# ContRespPP

ContRespPP is an implementation of the Bayesian approach to using
predictive probability in an ANOVA construct with a continuous normal
response, when threshold values must be obtained for the question of
interest to be evaluated as successful (Sieck and Christensen
(2021). In this package, the Bayesian Mission Mean (BMM) is used to
evaluate a question of interest (that is, a mean that randomly selects
combination of factor levels based on their probability of occurring
instead of averaging over the factor levels, as in the grand mean).
Under this construct, in contrast to a Gibbs sampler (or
Metropolis-within-Gibbs sampler), a two-stage sampling method is
required. The nested sampler determines the conditional posterior
distribution of the model parameters, given Y, and the outside sampler
determines the marginal posterior distribution of Y (also commonly
called the predictive distribution for Y). This approach provides a
sample from the joint posterior distribution of Y and the model
parameters, while also accounting for the threshold value that must be
obtained in order for the question of interest to be evaluated as
successful.

## Installation

You can install the released version of ContRespPP from CRAN with:

`install.packages("ContRespPP")`

or from Github
with:

`devtools::install_github("jcliff89/ContRespPP")`

## How to Use

For details, please refer to the package vignette
`vignette("gibbs-sampler")`

.