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BayesDIP
View on CRAN: Click
here
Download and install BayesDIP package within the R console
Install from CRAN:
install.packages("BayesDIP")
Install from Github:
library("remotes")
install_github("cran/BayesDIP")
Install by package version:
library("remotes")
install_version("BayesDIP", "0.1.1")
Attach the package and use:
library("BayesDIP")
Maintained by
Chen Wang
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-02-02
Latest Update: 2023-02-02
Description:
Provide early termination phase II trial designs with a
decreasingly informative prior (DIP) or a regular Bayesian prior
chosen by the user. The program can determine the minimum planned
sample size necessary to achieve the user-specified admissible
designs. The program can also perform power and expected sample size
calculations for the tests in early termination Phase II trials.
See Wang C and Sabo RT (2022) ;
Sabo RT (2014) .
How to cite:
Chen Wang (2023). BayesDIP: Bayesian Decreasingly Informative Priors for Early Termination Phase II Trials. R package version 0.1.1, https://cran.r-project.org/web/packages/BayesDIP
Previous versions and publish date:
No previous versions
Other packages that cited BayesDIP R package
View BayesDIP citation profile
Other R packages that BayesDIP depends,
imports, suggests or enhances
Functions, R codes and Examples using
the BayesDIP R package
Some associated functions: OneSampleBernoulli.Design . OneSampleBernoulli . OneSampleNormal1.Design . OneSampleNormal1 . OneSampleNormal2.Design . OneSampleNormal2 . OneSamplePoisson.Design . OneSamplePoisson . TwoSampleBernoulli.Design . TwoSampleBernoulli .
Some associated R codes: OneSampleBernoulli.Design.R . OneSampleBernoulli.R . OneSampleNormal1.Design.R . OneSampleNormal1.R . OneSampleNormal2.Design.R . OneSampleNormal2.R . OneSamplePoisson.Design.R . OneSamplePoisson.R . TwoSampleBernoulli.Design.R . TwoSampleBernoulli.R . Full BayesDIP package functions and examples
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