Authors@R: c(person("Jong Hee", "Chung", role = c("aut", "cre"), email = "jochung947@gmail.com"),
person("Yong Bin", "Lim", role = "aut"),
person("Donghoh", "Kim", role = "ctb", email = "donghoh.kim@gmail.com"))
Description: For a balanced design of experiments, this package calculates the sample size required to detect a certain standardized effect size, under a significance level. This package also provides three graphs; detectable standardized effect size vs power, sample size vs detectable standardized effect size, and sample size vs power, which show the mutual relationship between the sample size, power and the detectable standardized effect size. The detailed procedure is described in R. V. Lenth (2006-9) <https://homepage.divms.uiowa.edu/~rlenth/Power/>, Y. B. Lim (1998), M. A. Kastenbaum, D. G. Hoel and K. O. Bowman (1970) <doi:10.2307/2334851>, and Douglas C. Montgomery (2013, ISBN: 0849323312).

Title: Bayesian Adaptive Designs for Phase II Trials with Binary
Author: Annette Kopp-Schneider, Manuel Wiesenfarth, Ulrich Abel
Description: Tools and workflow to choose design parameters in Bayesian adaptive single-arm phase II trial designs with binary endpoint (response, success) with possible stopping for efficacy and futility at interim analyses. Also contains routines to determine and visualize operating characteristics. See Kopp-Schneider et al. (2018) <doi:10.1002/bimj.201700209>.

Description: A Bayesian regression model for discrete response, where the conditional distribution is modelled via a discrete Weibull distribution. This package provides an implementation of Metropolis-Hastings and Reversible-Jumps algorithms to draw samples from the posterior. It covers a wide range of regularizations through any two parameter prior. Examples are Laplace (Lasso), Gaussian (ridge), Uniform, Cauchy and customized priors like a mixture of priors. An extensive visual toolbox is included to check the validity of the results as well as several measures of goodness-of-fit.
Title: Bayesian Inference for Discrete Weibull Regression
Imports: coda, parallel, foreach, doParallel, MASS, methods, graphics,