Package: gpci
Type: Package
Title: Generalized Process Capability Indices and Bootstrap Confidence
        Intervals
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: A comprehensive, generalized framework for computing,
        estimating, and validating Generalized Process Capability
        Indices (GPCIs). Supports user-supplied probability density
        functions (PDF/PMF), cumulative distribution functions (CDF),
        survival functions (SF), and quantile functions with uncensored
        data parameter estimation via Maximum Likelihood Estimation
        (MLE). Provides classical and non-normal capability indices,
        including Cpy (Maiti, Saha and Nanda, 2010)
        <doi:10.1080/16843703.2010.11673233>, Spmk (Dey and Saha, 2019)
        <doi:10.1007/s41872-019-00081-4>, CpTk (Saha, Dey and Maiti,
        2019) <doi:10.1007/s13198-019-00789-7>, Cpc (Saha, Dey and
        Nadarajah, 2022) <doi:10.1080/02664763.2021.1971632>, CNpmc
        (Alotaibi, Dey and Saha, 2022) <doi:10.1155/2022/3135264>,
        CNpmkc (Saha, Tripathi and Dey, 2024)
        <doi:10.1142/S021853932450013X>, CNpk (Saha, Dey and Maiti,
        2018) <doi:10.1080/21681015.2018.1437793>, and Vannman
        capability indices. Computes parametric and non-parametric
        bootstrap confidence intervals at 90%, 95%, and 99% confidence
        levels using percentile, normal, basic, BCa, BCp, and
        studentized bootstrap methods. Evaluates Highest Posterior
        Density (HPD) intervals and Heidelberger-Welch convergence
        diagnostics. References: Maiti, Saha and Nanda (2010)
        <doi:10.1080/16843703.2010.11673233>, Saha, Dey and Maiti
        (2018) <doi:10.1080/21681015.2018.1437793>, Dey and Saha (2019)
        <doi:10.1007/s41872-019-00081-4>, Saha, Dey and Maiti (2019)
        <doi:10.1007/s13198-019-00789-7>, Alotaibi, Dey and Saha (2022)
        <doi:10.1155/2022/3135264>, Saha, Dey and Nadarajah (2022)
        <doi:10.1080/02664763.2021.1971632>, Saha, Tripathi and Dey
        (2024) <doi:10.1142/S021853932450013X>.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
Depends: R (>= 4.0.0)
Imports: stats, ggplot2, numDeriv, boot
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
VignetteBuilder: knitr
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2026-10-01 12:26:54 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: 2026-08-31 12:30:02 UTC
RemoteUrl: https://github.com/cran/gpci
RemoteRef: HEAD
RemoteSha: 76697c41a9d8f0b117b68ffceb61707de193073b