Importance Sampling Estimation of Generalized Process Capability Indices

Provides a comprehensive generalized framework for parameter estimation and Generalized Process Capability Indices (GPCIs) under uncensored data using Importance Sampling (ImpSam). Supports user-supplied probability density functions (PDF/PMF), cumulative distribution functions (CDF), and survival functions (SF). Computes classical and generalized capability indices including Cpy (Maiti et al., 2010 ), Spmk (Dey & Saha, 2019 ), CpTk (Saha et al., 2019), Cpc (Saha et al., 2022 ), CNpmc (Alotaibi et al., 2022 ), CNpmkc (Saha et al., 2024 ), CNpk (Saha et al., 2018 ), and Vannman's Cp(u,v) family. Generates parameter and GPCI MCMC chains via Sampling Importance Resampling (SIR) after burn-in and thinning. Provides point estimates, bias, MSE, risk values, Highest Posterior Density (HPD) intervals at 90, 95, and 99 percent levels of significance, Heidelberger and Welch MCMC convergence diagnostic, and convergence probability.