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ContRespPP
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]
[Scholar Profile | Author Map]
All associated links for this package
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 29 Oct. 2026.
Previous versions and publish date:
0.4.1 (2022-09-27 12:20), (2026-07-09 08:00)
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
Functions, R codes and Examples using
the ContRespPP R package
Some associated functions: exData . gibbs.sampler . gibbs.sampler.posterior . gibbs.sampler.posterior.rjags . gibbs.sampler.predictive . gibbs.sampler.predictive.rjags . print.ContRespPP . prob.creator . summary.ContRespPP .
Some associated R codes: data.R . gibbs_sampler.R . gibbs_sampler_posterior.R . gibbs_sampler_posterior_jags.R . gibbs_sampler_predictive.R . gibbs_sampler_predictive_jags.R . prob_creator.R . utils.R . Full ContRespPP package functions and examples
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