Package: gpciLindApproxProgII
Type: Package
Title: Lindley Approximation for Capability Indices under Progressive
        Censoring
Version: 0.1.1
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")
    )
Maintainer: Shikhar Tyagi <shikhar1093tyagi@gmail.com>
Description: 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.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
Depends: R (>= 4.0.0)
Imports: stats, graphics, ggplot2, numDeriv, boot, coda
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
VignetteBuilder: knitr
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2026-09-28 07:22:05 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]
Repository: https://shikhartyagi.r-universe.dev
Date/Publication: 2026-08-29 02:19:26 UTC
RemoteUrl: https://github.com/cran/gpciLindApproxProgII
RemoteRef: HEAD
RemoteSha: 56ae31f3b5fcb41c9e6ce4681d9daa41255c73ec