Package: gpci 0.1.0

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>.

Authors:Shikhar Tyagi [aut, cre], Sumit Kumar [aut], Arvind Pandey [aut], Bhupendra Singh [aut], Vrijesh Tripathi [aut]

gpci_0.1.0.tar.gz
gpci_0.1.0.zip(r-4.7-any)gpci_0.1.0.zip(r-4.6-any)gpci_0.1.0.zip(r-4.5-any)
gpci_0.1.0.tgz(r-4.6-any)gpci_0.1.0.tgz(r-4.5-any)
gpci_0.1.0.tar.gz(r-4.7-any)gpci_0.1.0.tar.gz(r-4.6-any)
gpci_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
gpci/json (API)

# Install 'gpci' in R:
install.packages('gpci', repos = c('https://shikhartyagi.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.30 score 353 downloads 22 exports 19 dependencies

Last updated from:76697c41a9. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK188
source / vignettesOK197
linux-releaseOK132
macos-releaseOK111
macos-oldrelOK117
windows-develOK91
windows-releaseOK106
windows-oldrelOK97
wasm-releaseOK116

Exports:boot_ciboot_cvcapabilitycompute_theoretical_momentsdefine_distributiondist_betadist_gammadist_logistic_exponentialdist_lognormaldist_normaldist_weibullfit_distributiongpci_bootgpci_cvgpci_distgpci_fitgpci_indexgpci_mcmcgpci_mlegpci_plotheidelberger_welchhpd_interval

Dependencies:bootclicpp11farverggplot2gluegtableisobandlabelinglifecyclenumDerivR6RColorBrewerrlangS7scalesvctrsviridisLitewithr

Getting Started with gpci
Setup | Simulating Process Data | Capability Analysis | Bootstrap Confidence Intervals | Plotting Results | 1. Process Density and Specification Limits | 2. Empirical CDF vs Fitted CDF | 3. Quantile-Quantile (Q-Q) Plot | 4. Process Run Chart | 5. Bootstrap Sampling Distributions and Confidence Intervals | 6. Forest Plot of CIs
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-08-31
Started: 2026-08-31

Using Custom Distributions and Bootstrap Cross-Validation
Setup | Defining a Custom Distribution | Fitting the Custom Distribution to Skewed Data | Robust Capability Analysis | Bootstrap Cross-Validation Diagnostics
Rendered fromcustom-distributions.Rmdusingknitr::rmarkdown

Last update: 2026-08-31
Started: 2026-08-31

Readme and manuals

Help Manual

Help pageTopics
Compute Bootstrap Confidence Intervals for Process Capability Indicesboot_ci gpci_boot
Bootstrap Cross-Validation for CI Quality Evaluationboot_cv gpci_cv
Built-in Distributionsbuiltin_dist dist_beta dist_gamma dist_logistic_exponential dist_lognormal dist_normal dist_weibull
Compute Process Capability Indicescapability gpci_fit
Coef Method for gpcifitcoef.gpcifit
Coef Method for gpcimcmc Objectscoef.gpcimcmc
Compute Theoretical Moments of a Distributioncompute_theoretical_moments
Confint Method for gpcifitconfint.gpcifit
Define a Process Distributiondefine_distribution gpci_dist
Fit Distribution Parameters under Uncensored Datafit_distribution gpci_mle
Dispatcher for Index Computationgpci_index
Bayesian MCMC Estimation of Generalized Process Capability Indicesgpci_mcmc
Generic Dispatcher for gpci Plotsgpci_plot
Heidelberger and Welch's MCMC Convergence Diagnosticheidelberger_welch
Highest Posterior Density (HPD) Interval Calculationhpd_interval
Plotting and Visualizations for Process Capability Fitsautoplot.gpcicv autoplot.gpcifit autoplot.gpcimcmc autoplot.gpci_ci autoplot.gpci_cv autoplot.gpci_mcmc autoplot.pci_fit plot.gpcicv plot.gpcifit plot.gpcimcmc plot.gpci_ci plot.gpci_cv plot.gpci_mcmc plot.pci_fit plot_gpci
Print Method for gpci_ciprint.gpci_ci
Print Method for gpci_cv / gpcicvprint.gpci_cv
Print Method for gpcimcmc Objectsprint.gpcimcmc
Print Method for pci_fit / gpcifitprint.pci_fit
Summary Method for gpci_cisummary.gpci_ci
Summary Method for gpci_cv / gpcicvsummary.gpci_cv
Summary Method for gpcimcmc Objectssummary.gpcimcmc
Summary Method for pci_fit / gpcifitsummary.pci_fit
Vcov Method for gpcifitvcov.gpcifit