Evaluates Generalized Process Capability Indices (GPCIs) under
Hybrid Type-II censored lifetime data using Importance Sampling
(Sampling Importance Resampling, SIR). Implements Bayesian parameter
estimation and 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 maximum likelihood estimates under Hybrid Type-II
censoring, parameter MCMC chains, GPCI posterior chains, posterior
point estimates, bias, mean squared error (MSE), Bayes risk, 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. Goodness-of-fit testing for Hybrid Type-II
censored data is supported via 'gofPHCS'. Methods are based on
Childs et al. (2003)