{ "_id": "6ab0fcd73d719c1f840856e6", "Package": "gpcihybridIImcmc", "Type": "Package", "Title": "Generalized Process Capability Indices for Hybrid Type-II\nCensored Data using MCMC", "Version": "0.1.0", "Authors@R": "c(\nperson(\"Shikhar\", \"Tyagi\", email = \"shikhar1093tyagi@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-1606-0844\")),\nperson(\"Vrijesh\", \"Tripathi\", email = \"vrijesh.tripathi@uwi.edu\", role = \"aut\")\n)", "Maintainer": "Shikhar Tyagi ", "Description": "Implements Bayesian Markov Chain Monte Carlo (MCMC)\nestimation using Metropolis-Hastings within Gibbs sampler for\nGeneralized Process Capability Indices (GPCIs) under Hybrid\nType-II censored lifetime data. Supports classical and\ngeneralized capability indices including Cpy, Cp, Cpk, Cpm,\nCpmk, Spmk, CpTk, Cpc, CNp, CNpk, CNpm, CNpmk, CNpmc, and\nCNpmkc. Calculates posterior point estimates, bias, mean\nsquared error (MSE), Bayes risk, Highest Posterior Density\n(HPD) credible intervals at 90%, 95%, and 99% levels,\nHeidelberger and Welch's MCMC convergence diagnostics, and\ncoverage probabilities. Accommodates user-defined probability\ndensity/mass functions, cumulative distribution functions, and\nsurvival functions. Based on methods described in Childs et al.\n(2003) , Kundu and Pradhan\n(2009) , Saha and Dey (2019)\n, Alotaibi et al. (2022)\n, Dey et al. (2017)\n, and Wu et al. 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