Package: gpcihybridIILinApp 0.1.0

gpcihybridIILinApp: Lindley Approximation for Capability Indices under Hybrid Censoring

Provides a comprehensive framework for estimating Generalized Process Capability Indices (GPCIs) under Hybrid Type-II censored lifetime data using Lindley's 3rd-order approximation method (Lindley, 1980 <doi:10.2307/2345271>). Supports user-supplied probability density/mass functions (PDF/PMF), cumulative distribution functions (CDF), survival functions (SF), and quantile functions. Computes Maximum Likelihood Estimates (MLE) using the 'MleCensoR' package (Childs et al., 2003 <doi:10.1007/BF02517803>; Balakrishnan & Kundu, 2013 <doi:10.1002/nav.21545>) and Bayesian posterior expectations for classical and non-normal capability indices, including Cpy (Maiti et al., 2010 <doi:10.1080/16843703.2010.11673233>), Spmk (Dey & Saha, 2019 <doi:10.1007/s41872-019-00081-4>), CpTk (Saha et al., 2019), Cpc (Saha et al., 2022 <doi:10.1080/02664763.2021.1971632>), CNpmc (Alotaibi et al., 2022 <doi:10.1155/2022/3135264>), CNpmkc (Saha et al., 2024 <doi:10.1142/S021853932450013X>), CNpk (Saha et al., 2018 <doi:10.1080/21681015.2018.1437793>), and Vannman's Cp(u,v) family. Generates posterior parameter and GPCI chains via sampling with burn-in and thinning, calculating Bias, Mean Squared Error (MSE), Bayes Risk (SEL and Linex), Highest Posterior Density (HPD) credible intervals at 90%, 95%, and 99% levels, and Heidelberger and Welch's MCMC Convergence Diagnostics (Heidelberger & Welch, 1983 <doi:10.1287/opre.31.6.1109>) with convergence probabilities. Evaluates parametric and non-parametric bootstrap confidence intervals (Percentile, Normal, Basic, BCp, BCa) at 90%, 95%, and 99% levels of significance. Integrates goodness-of-fit testing for Hybrid Type-II censored data via the 'gofPHCS' package.

Authors:Shikhar Tyagi [aut, cre], Sumit Kumar [aut], Arvind Pandey [aut], Bhupendra Singh [aut], Vrijesh Tripathi [aut]