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BayesianMCPMod
View on CRAN: Click
here
Download and install BayesianMCPMod package within the R console
Install from CRAN:
install.packages("BayesianMCPMod")
Install from Github:
library("remotes")
install_github("cran/BayesianMCPMod")
Install by package version:
library("remotes")
install_version("BayesianMCPMod", "1.0.1")
Attach the package and use:
library("BayesianMCPMod")
Maintained by
Stephan Wojciekowski
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-01-08
Latest Update: 2024-01-08
Description:
Bayesian MCPMod (Fleischer et al. (2022)
) is an innovative method that improves the
traditional MCPMod by systematically incorporating historical data,
such as previous placebo group data. This R package offers functions
for simulating, analyzing, and evaluating Bayesian MCPMod trials with
normally distributed endpoints. It enables the assessment of trial
designs incorporating historical data across various true
dose-response relationships and sample sizes. Robust mixture prior
distributions, such as those derived with the Meta-Analytic-Predictive
approach (Schmidli et al. (2014) ), can be
specified for each dose group. Resulting mixture posterior
distributions are used in the Bayesian Multiple Comparison Procedure
and modeling steps. The modeling step also includes a weighted model
averaging approach (Pinheiro et al. (2014) ).
Estimated dose-response relationships can be bootstrapped and
visualized.
How to cite:
Stephan Wojciekowski (2024). BayesianMCPMod: Simulate, Evaluate, and Analyze Dose Finding Trials with Bayesian MCPMod. R package version 1.0.1, https://cran.r-project.org/web/packages/BayesianMCPMod. Accessed 22 Dec. 2024.
Previous versions and publish date:
1.0.0 (2024-01-08 17:50)
Other packages that cited BayesianMCPMod R package
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Other R packages that BayesianMCPMod depends,
imports, suggests or enhances
Complete documentation for BayesianMCPMod
Functions, R codes and Examples using
the BayesianMCPMod R package
Some associated functions: assessDesign . getBootstrapQuantiles . getContr . getCritProb . getESS . getModelFits . getPosterior . performBayesianMCP . performBayesianMCPMod . plot.modelFits . predict.modelFits . simulateData .
Some associated R codes: BMCPMod.R . bootstrapping.R . modelling.R . plot.R . posterior.R . s3methods.R . simulation.R . Full BayesianMCPMod package functions and examples
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