gpciProgTyIIImpSam: Generalized Process Capability Indices for Progressive Type-II
Censored Data using Importance Sampling
Implements Importance Sampling (Sampling Importance
Resampling, SIR) for Bayesian parameter estimation and Generalized
Process Capability Indices (GPCIs) under progressive Type-II
censored data. Evaluates classical and generalized capability
indices including Cpy, Cp, Cpk, Cpu, Cpl, Cpm, Cpmk, Spmk, CpTk,
Cpc, CNp, CNpk, CNpm, CNpmk, CNpmc, CNpmkc, and Vannman's Cp(u,v)
family. Computes initial uncensored estimates, parameter MCMC
chains, GPCI posterior chains, point estimates, posterior means,
bias, mean squared error (MSE), Bayes risk under loss functions,
Highest Posterior Density (HPD) credible intervals at 90%, 95%,
and 99% levels, Heidelberger and Welch's MCMC convergence
diagnostics, and convergence probabilities. Accommodates
user-defined probability density/mass functions, cumulative
distribution functions, and survival functions. Methods based on
Balakrishnan and Aggarwala (2000) <doi:10.1007/978-1-4612-1186-0>,
Maiti et al. (2010) <doi:10.1080/16843703.2010.11673233>,
Dey and Saha (2019) <doi:10.1007/s41872-019-00081-4>,
Alotaibi et al. (2022) <doi:10.1155/2022/3135264>,
Saha et al. (2022) <doi:10.1080/02664763.2021.1971632>, and
Saha et al. (2024) <doi:10.1142/S021853932450013X>.
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[aut, cre],
Sumit Kumar [aut],
Arvind Pandey [aut],
Bhupendra Singh [aut],
Vrijesh Tripathi [aut]