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  "Description": "Bayesian quantitative decision-making for proof-of-concept\nclinical trials with Go, NoGo and Gray decisions. A study is\nspecified by its sample sizes, conjugate prior distributions,\nexternal data borrowed through power priors, a concurrent,\nexternal, hypothetical or fixed control group, and a decision\ncriterion based on the posterior or posterior predictive\nprobability of the treatment effect. The package computes the\ndecision for observed data, the operating characteristics of a\ndesign under scenarios for the true parameters, the decision\nthresholds that control the probabilities of false Go and false\nNoGo decisions, and the observed data that lead to each\ndecision. One or two binary endpoints (Beta-binomial and\nDirichlet-multinomial models), one or two continuous endpoints\n(Normal-Inverse-chi-squared and Normal-Inverse-Wishart models)\nand one time-to-event endpoint evaluated by the milestone\nsurvival probability or the hazard ratio (exponential model\nwith Gamma priors) are supported, with larger or smaller\neffects as favorable. The posterior distributions have closed\nforms, so Markov chain Monte Carlo (MCMC) sampling is not\nneeded; the probabilities are computed from closed-form\ndistribution functions, by exact summation, numerical\nintegration, a moment-matching approximation or Monte Carlo\nsimulation, with the main computations in C++. See Kang,\nYamaguchi, and Han (2026) <doi:10.1080/10543406.2026.2655410>\nfor the methodological framework.",
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