ContRespPP: Predictive Probability for a Continuous Response with an ANOVA Structure

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.

Version: 0.4.2
Depends: R (≥ 2.10)
Imports: stats
Suggests: rjags, coda, knitr, devtools, rmarkdown, testthat (≥ 3.0.0)
Published: 2022-10-15
Author: Victoria Sieck ORCID iD [aut, cre], Joshua Clifford ORCID iD [aut], Fletcher Christensen ORCID iD [aut]
Maintainer: Victoria Sieck <vcarrillo314 at>
License: CC0
NeedsCompilation: no
Materials: README NEWS
CRAN checks: ContRespPP results


Reference manual: ContRespPP.pdf
Vignettes: gibbs-sampler


Package source: ContRespPP_0.4.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): ContRespPP_0.4.2.tgz, r-oldrel (arm64): ContRespPP_0.4.2.tgz, r-release (x86_64): ContRespPP_0.4.2.tgz, r-oldrel (x86_64): ContRespPP_0.4.2.tgz
Old sources: ContRespPP archive


Please use the canonical form to link to this page.