CRAN/E | ContRespPP

ContRespPP

Predictive Probability for a Continuous Response with an ANOVA Structure

Installation

About

A 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) doi:10.1002/qre.2802). 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.

github.com/jcliff89/ContRespPP
Bug report File report

Key Metrics

Version 0.4.2
R ≥ 2.10
Published 2022-10-15 553 days ago
Needs compilation? no
License CC0
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Maintainer

Maintainer

Victoria Sieck

vcarrillo314@gmail.com

Authors

Victoria Sieck

aut / cre

Joshua Clifford

aut

Fletcher Christensen

aut

Material

README
NEWS
Reference manual
Package source

Vignettes

gibbs-sampler

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

ContRespPP archive

Depends

R ≥ 2.10

Imports

stats

Suggests

rjags
coda
knitr
devtools
rmarkdown
testthat ≥ 3.0.0