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bayesassurance
View on CRAN: Click
here
Download and install bayesassurance package within the R console
Install from CRAN:
install.packages("bayesassurance")
Install from Github:
library("remotes")
install_github("cran/bayesassurance")
Install by package version:
library("remotes")
install_version("bayesassurance", "0.1.0")
Attach the package and use:
library("bayesassurance")
Maintained by
Jane Pan
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-06-17
Latest Update: 2022-06-17
Description:
Computes Bayesian assurance under various
settings characterized by different assumptions and objectives, including
precision-based conditions, credible intervals, and goal functions.
All simulation-based functions included in this package rely on a two-stage
Bayesian method that assigns two distinct priors to evaluate the
probability of observing a positive outcome, which addresses subtle
limitations that take place when using the standard single-prior approach.
For more information, please refer to Pan and Banerjee (2021)
.
How to cite:
Jane Pan (2022). bayesassurance: Bayesian Assurance Computation. R package version 0.1.0, https://cran.r-project.org/web/packages/bayesassurance. Accessed 03 Feb. 2025.
Previous versions and publish date:
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Complete documentation for bayesassurance
Functions, R codes and Examples using
the bayesassurance R package
Some associated functions: assurance_nd_na . bayes_adcock . bayes_goal_func . bayes_sim . bayes_sim_betabin . bayes_sim_unbalanced . bayes_sim_unknownvar . gen_Xn . gen_Xn_longitudinal . pwr_curve . pwr_freq .
Some associated R codes: bayes_adcock.R . bayes_assurance.R . bayes_goal_func.R . bayes_sim.R . bayes_sim_betabinomial.R . bayes_sim_unbalanced.R . bayes_sim_unknownvar.R . frequentist.R . genXn_longitudinal.R . gen_Xn.R . pwr_curves.R . Full bayesassurance package functions and examples
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