{ "_id": "6ac64f46f511ae58c09a9e94", "Package": "gpciProgTyIIImpSam", "Type": "Package", "Title": "Generalized Process Capability Indices for Progressive Type-II\nCensored Data using Importance Sampling", "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(\"Sumit\", \"Kumar\", email = \"stats.sumitbhal@gmail.com\", role = \"aut\"),\nperson(\"Arvind\", \"Pandey\", email = \"arvindmzu@gmail.com\", role = \"aut\"),\nperson(\"Bhupendra\", \"Singh\", email = \"bhupendra.rana@gmail.com\", role = \"aut\"),\nperson(\"Vrijesh\", \"Tripathi\", email = \"vrijesh.tripathi@uwi.edu\", role = \"aut\")\n)", "Maintainer": "Shikhar Tyagi ", "Description": "Implements Importance Sampling (Sampling Importance\nResampling, SIR) for Bayesian parameter estimation and\nGeneralized Process Capability Indices (GPCIs) under\nprogressive Type-II censored data. 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\nuncensored estimates, parameter MCMC chains, GPCI posterior\nchains, point estimates, posterior means, bias, mean squared\nerror (MSE), Bayes risk under loss functions, 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. Methods based on\nBalakrishnan and Aggarwala (2000)\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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