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  "Title": "Bayesian Quantitative Decision-Making Framework for Binary and\nContinuous Endpoints",
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  "Description": "Provides comprehensive methods to calculate posterior\nprobabilities, posterior predictive probabilities, and\nGo/NoGo/Gray decision probabilities for quantitative\ndecision-making under a Bayesian paradigm in clinical trials.\nThe package supports both single and two-endpoint analyses for\nbinary and continuous outcomes, with controlled, uncontrolled,\nand external designs. For single continuous endpoints, three\ncalculation methods are available: numerical integration (NI),\nMonte Carlo simulation (MC), and Moment-Matching approximation\n(MM). For two continuous endpoints, a bivariate\nNormal-Inverse-Wishart conjugate model is implemented with MC\nand MM methods. For two binary endpoints, a\nDirichlet-multinomial model is implemented. External designs\nincorporate historical data through power priors using exact\nconjugate representations (Normal-Inverse-Chi-squared for\nsingle continuous, Normal-Inverse-Wishart for two continuous,\nand Dirichlet for binary endpoints), enabling closed-form\nposterior computation without Markov chain Monte Carlo (MCMC)\nsampling. This approach significantly reduces computational\nburden while preserving complete Bayesian rigor. The package\nalso provides grid-search functions to find optimal Go and NoGo\nthresholds that satisfy user-specified operating characteristic\ncriteria for all supported endpoint types and study designs. S3\nprint() and plot() methods are provided for all decision\nprobability classes, enabling formatted display and\nvisualisation of Go/NoGo/Gray operating characteristics across\ntreatment scenarios. See Kang, Yamaguchi, and Han (2026)\n<doi:10.1080/10543406.2026.2655410> for the methodological\nframework.",
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