Lindley Approximation for Capability Indices under Progressive Censoring
Overview
Implements Bayesian parameter and Generalized Process Capability Indices (GPCIs) estimation using the Lindley approximation method (Lindley, 1980 doi:10.2307/2345271) under progressive Type-II censored data (Balakrishnan & Aggarwala, 2000 doi:10.1007/978-1-4612-1334-5). Evaluates point estimates and 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 doi:10.1007/s13198-019-00789-7), 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 (Vannman, 1995 doi:10.1111/j.1467-9574.1995.tb01472.x). Calculates point estimates, bias, mean squared error (MSE), Bayes risk under Linex and squared error loss, 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. Accommodates user-defined probability density/mass functions, cumulative distribution functions, and survival functions. Supports progressive parametric and non-parametric bootstrap confidence intervals (Efron, 1987 doi:10.1080/01621459.1987.10478410) at 90%, 95%, and 99% significance levels.
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- OK13 OKNow
- OK5 OK2026-08-27
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- 18includes a package overview page
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- Exported functions with examples
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- 98%
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- 0.1.1Latest
- 0.1.02026-08-26
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-08-26
- Total releases
- 2 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.0.0
- Download size
- 57 KB
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