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ContRespPP  

Predictive Probability for a Continuous Response with an ANOVA Structure
View on CRAN: Click here


Download and install ContRespPP package within the R console
Install from CRAN:
install.packages("ContRespPP")

Install from Github:
library("remotes")
install_github("cran/ContRespPP")

Install by package version:
library("remotes")
install_version("ContRespPP", "0.4.2")



Attach the package and use:
library("ContRespPP")
Maintained by
Victoria Sieck
[Scholar Profile | Author Map]
First Published: 2022-09-27
Latest Update: 2022-10-15
Description:
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) ). 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.
How to cite:
Victoria Sieck (2022). ContRespPP: Predictive Probability for a Continuous Response with an ANOVA Structure. R package version 0.4.2, https://cran.r-project.org/web/packages/ContRespPP. Accessed 30 Apr. 2025.
Previous versions and publish date:
0.4.1 (2022-09-27 12:20)
Other packages that cited ContRespPP R package
View ContRespPP citation profile
Other R packages that ContRespPP depends, imports, suggests or enhances
Complete documentation for ContRespPP
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