Package: gpcihybridIILinApp
Type: Package
Title: Lindley Approximation for Capability Indices under Hybrid
        Censoring
Version: 0.1.0
Authors@R: c(
    person("Shikhar", "Tyagi",
           email = "shikhar1093tyagi@gmail.com",
           role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-1606-0844")),
    person("Sumit", "Kumar",
           email = "stats.sumitbhal@gmail.com",
           role = "aut"),
    person("Arvind", "Pandey",
           email = "arvindmzu@gmail.com",
           role = "aut"),
    person("Bhupendra", "Singh",
           email = "bhupendra.rana@gmail.com",
           role = "aut"),
    person("Vrijesh", "Tripathi",
           email = "vrijesh.tripathi@uwi.edu",
           role = "aut")
    )
Description: 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.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
Depends: R (>= 4.0.0)
Imports: stats, graphics, numDeriv, MleCensoR, gofPHCS
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
VignetteBuilder: knitr
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2026-09-21 09:41:23 UTC; root
Author: Shikhar Tyagi [aut, cre] (ORCID:
        <https://orcid.org/0000-0003-1606-0844>), Sumit Kumar [aut],
        Arvind Pandey [aut], Bhupendra Singh [aut], Vrijesh Tripathi
        [aut]
Maintainer: Shikhar Tyagi <shikhar1093tyagi@gmail.com>
Repository: https://shikhartyagi.r-universe.dev
Date/Publication: 2026-08-21 13:00:20 UTC
RemoteUrl: https://github.com/cran/gpcihybridIILinApp
RemoteRef: HEAD
RemoteSha: 34f9d8e37933c74bd1193f5d9bb3e66ecf80ad79