Generalized Process Capability Indices and Bootstrap Confidence Intervals

A comprehensive, generalized framework for computing, estimating, and validating Generalized Process Capability Indices (GPCIs). Supports user-supplied probability density functions (PDF/PMF), cumulative distribution functions (CDF), survival functions (SF), and quantile functions with uncensored data parameter estimation via Maximum Likelihood Estimation (MLE). Provides classical and non-normal capability indices, including Cpy (Maiti, Saha and Nanda, 2010) , Spmk (Dey and Saha, 2019) , CpTk (Saha, Dey and Maiti, 2019) , Cpc (Saha, Dey and Nadarajah, 2022) , CNpmc (Alotaibi, Dey and Saha, 2022) , CNpmkc (Saha, Tripathi and Dey, 2024) , CNpk (Saha, Dey and Maiti, 2018) , and Vannman capability indices. Computes parametric and non-parametric bootstrap confidence intervals at 90%, 95%, and 99% confidence levels using percentile, normal, basic, BCa, BCp, and studentized bootstrap methods. Evaluates Highest Posterior Density (HPD) intervals and Heidelberger-Welch convergence diagnostics. References: Maiti, Saha and Nanda (2010) , Saha, Dey and Maiti (2018) , Dey and Saha (2019) , Saha, Dey and Maiti (2019) , Alotaibi, Dey and Saha (2022) , Saha, Dey and Nadarajah (2022) , Saha, Tripathi and Dey (2024) .