Provides a comprehensive framework for estimating Generalized Process
Capability Indices (GPCIs) using the Lindley approximation method for uncensored
data under Bayesian inference. Evaluates point estimates and posterior expectations
for classical and non-normal 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. Computes parametric and non-parametric bootstrap
confidence intervals at 90%, 95%, and 99% levels of significance. Supports MCMC
chain generation with burn-in and thinning, Highest Posterior Density (HPD)
intervals, Bias, MSE, Risk values, and Heidelberger and Welch's MCMC Convergence
Diagnostic with convergence probabilities.
References:
Lindley (1980)