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gpci: Generalized Process Capability Indices and Bootstrap Confidence Intervals

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>.

Version: 0.1.0
Depends: R (≥ 4.0.0)
Imports: stats, ggplot2, numDeriv, boot
Suggests: testthat (≥ 3.0.0), knitr, rmarkdown
Published: 2026-08-31
DOI: 10.32614/CRAN.package.gpci (may not be active yet)
Author: Shikhar Tyagi ORCID iD [aut, cre], Sumit Kumar [aut], Arvind Pandey [aut], Bhupendra Singh [aut], Vrijesh Tripathi [aut]
Maintainer: Shikhar Tyagi <shikhar1093tyagi at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: gpci results

Documentation:

Reference manual: gpci.html , gpci.pdf
Vignettes: Using Custom Distributions and Bootstrap Cross-Validation (source, R code)
Getting Started with gpci (source, R code)

Downloads:

Package source: gpci_0.1.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): gpci_0.1.0.tgz, r-oldrel (arm64): gpci_0.1.0.tgz, r-release (x86_64): gpci_0.1.0.tgz, r-oldrel (x86_64): gpci_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=gpci to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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