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) , Maiti et al. (2010) , Dey and Saha (2019) , Alotaibi et al. (2022) , Saha et al. (2022) , and Saha et al. (2024) .