Package: gpciLindleyApprox 0.1.0

gpciLindleyApprox: Lindley Approximation Method for Generalized Process Capability Indices

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) <doi:10.2307/2345271>, Maiti, Saha & Nanda (2010) <doi:10.1080/16843703.2010.11673233>, Saha, Dey & Maiti (2018) <doi:10.1080/21681015.2018.1437793>, Dey & Saha (2019) <doi:10.1007/s41872-019-00081-4>, Saha, Dey & Maiti (2019), Alotaibi, Dey & Saha (2022) <doi:10.1155/2022/3135264>, Saha, Dey & Nadarajah (2022) <doi:10.1080/02664763.2021.1971632>, Saha, Tripathi & Dey (2024) <doi:10.1142/S021853932450013X>.

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