Package: gpcihybridIIImpSam 0.1.0

gpcihybridIIImpSam: Process Capability Indices for Hybrid Type-II Data via Importance Sampling

Evaluates Generalized Process Capability Indices (GPCIs) under Hybrid Type-II censored lifetime data using Importance Sampling (Sampling Importance Resampling, SIR). Implements Bayesian parameter estimation and evaluates classical and generalized capability indices including Cpy, Cp, Cpk, Cpu, Cpl, Cpm, Cpmk, Spmk, CpTk, Cpc, CNp, CNpk, CNpm, CNpmk, CNpmc, CNpmkc, and Vannman's Cp(u,v) family. Computes initial maximum likelihood estimates under Hybrid Type-II censoring, parameter MCMC chains, GPCI posterior chains, posterior point estimates, bias, mean squared error (MSE), Bayes risk, Highest Posterior Density (HPD) credible intervals at 90%, 95%, and 99% levels, Heidelberger and Welch's MCMC convergence diagnostics, and convergence probabilities. Accommodates user-defined probability density/mass functions, cumulative distribution functions, and survival functions. Goodness-of-fit testing for Hybrid Type-II censored data is supported via 'gofPHCS'. Methods are based on Childs et al. (2003) <doi:10.1080/0266476032000053637>, Kundu and Pradhan (2009) <doi:10.1016/j.spl.2008.09.006>, Maiti et al. (2010) <doi:10.1080/16843703.2010.11673233>, Dey and Saha (2019) <doi:10.1007/s41872-019-00081-4>, Alotaibi et al. (2022) <doi:10.1155/2022/3135264>, Saha et al. (2022) <doi:10.1080/02664763.2021.1971632>, and Saha et al. (2024) <doi:10.1142/S021853932450013X>.

Authors:Shikhar Tyagi [aut, cre]