{ "_id": "6ab0fcc23d719c1f840856d6", "Package": "gpcihybridIIImpSam", "Type": "Package", "Title": "Process Capability Indices for Hybrid Type-II Data via\nImportance Sampling", "Version": "0.1.0", "Authors@R": "c(\nperson(\"Shikhar\", \"Tyagi\",\nemail = \"shikhar1093tyagi@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-1606-0844\"))\n)", "Maintainer": "Shikhar Tyagi ", "Description": "Evaluates Generalized Process Capability Indices (GPCIs)\nunder Hybrid Type-II censored lifetime data using Importance\nSampling (Sampling Importance Resampling, SIR). Implements\nBayesian parameter estimation and evaluates classical and\ngeneralized capability indices including Cpy, Cp, Cpk, Cpu,\nCpl, Cpm, Cpmk, Spmk, CpTk, Cpc, CNp, CNpk, CNpm, CNpmk, CNpmc,\nCNpmkc, and Vannman's Cp(u,v) family. Computes initial maximum\nlikelihood estimates under Hybrid Type-II censoring, parameter\nMCMC chains, GPCI posterior chains, posterior point estimates,\nbias, mean squared error (MSE), Bayes risk, Highest Posterior\nDensity (HPD) credible intervals at 90%, 95%, and 99% levels,\nHeidelberger and Welch's MCMC convergence diagnostics, and\nconvergence probabilities. Accommodates user-defined\nprobability density/mass functions, cumulative distribution\nfunctions, and survival functions. Goodness-of-fit testing for\nHybrid Type-II censored data is supported via 'gofPHCS'.\nMethods are based on Childs et al. (2003)\n, Kundu and Pradhan (2009)\n, Maiti et al. (2010)\n, Dey and Saha (2019)\n, Alotaibi et al. (2022)\n, Saha et al. (2022)\n, and Saha et al. 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