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<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>Lindley Approximation for Capability Indices under Progressive
Censoring</dc:title>
  <dc:title>R package gpciLindApproxProgII version 0.1.1</dc:title>
  <dc:description>Implements Bayesian parameter and Generalized Process Capability
    Indices (GPCIs) estimation using the Lindley approximation method (Lindley,
    1980 &lt;doi:10.2307/2345271&gt;) under progressive Type-II censored data
    (Balakrishnan &amp; Aggarwala, 2000 &lt;doi:10.1007/978-1-4612-1334-5&gt;). Evaluates
    point estimates and posterior expectations for classical and non-normal
    capability indices, including Cpy (Maiti et al., 2010
    &lt;doi:10.1080/16843703.2010.11673233&gt;), Spmk (Dey &amp; Saha, 2019
    &lt;doi:10.1007/s41872-019-00081-4&gt;), CpTk (Saha et al., 2019
    &lt;doi:10.1007/s13198-019-00789-7&gt;), Cpc (Saha et al.,
    2022 &lt;doi:10.1080/02664763.2021.1971632&gt;), CNpmc (Alotaibi et al., 2022
    &lt;doi:10.1155/2022/3135264&gt;), CNpmkc (Saha et al., 2024
    &lt;doi:10.1142/S021853932450013X&gt;), CNpk (Saha et al., 2018
    &lt;doi:10.1080/21681015.2018.1437793&gt;), and Vannman's Cp(u,v) family (Vannman,
    1995 &lt;doi:10.1111/j.1467-9574.1995.tb01472.x&gt;). 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 &amp;
    Welch, 1983 &lt;doi:10.1287/opre.31.6.1109&gt;) 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
    &lt;doi:10.1080/01621459.1987.10478410&gt;) at 90%, 95%, and 99% significance levels.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 4.0.0)</dc:relation>
  <dc:relation>Imports: stats, graphics, ggplot2, numDeriv, boot, coda</dc:relation>
  <dc:relation>Suggests: testthat (&gt;= 3.0.0), knitr, rmarkdown</dc:relation>
  <dc:creator>Shikhar Tyagi &lt;shikhar1093tyagi@gmail.com&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Shikhar Tyagi [aut, cre] (ORCID:
    &lt;https://orcid.org/0000-0003-1606-0844&gt;),
  Sumit Kumar [aut],
  Arvind Pandey [aut],
  Bhupendra Singh [aut],
  Vrijesh Tripathi [aut]</dc:contributor>
  <dc:rights>MIT + file LICENSE (https://CRAN.R-project.org/package=gpciLindApproxProgII/LICENSE)</dc:rights>
  <dc:date>2026-08-28</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=gpciLindApproxProgII</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.gpciLindApproxProgII</dc:identifier>
</oai_dc:dc>
