Introduction | Example: Importance Sampling Analysis with User Functions | Visualizing Posterior Distributions
Package: gpciImpSam 0.1.0
gpciImpSam: Importance Sampling Estimation of Generalized Process Capability Indices
Provides a comprehensive generalized framework for parameter estimation and Generalized Process Capability Indices (GPCIs) under uncensored data using Importance Sampling (ImpSam). Supports user-supplied probability density functions (PDF/PMF), cumulative distribution functions (CDF), and survival functions (SF). Computes classical and generalized capability indices including Cpy (Maiti et al., 2010 <doi:10.1080/16843703.2010.11673233>), Spmk (Dey & Saha, 2019 <doi:10.1080/00949655.2019.1671980>), CpTk (Saha et al., 2019), 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. Generates parameter and GPCI MCMC chains via Sampling Importance Resampling (SIR) after burn-in and thinning. Provides point estimates, bias, MSE, risk values, Highest Posterior Density (HPD) intervals at 90, 95, and 99 percent levels of significance, Heidelberger and Welch MCMC convergence diagnostic, and convergence probability.
Authors:
gpciImpSam_0.1.0.tar.gz
gpciImpSam_0.1.0.zip(r-4.7-any)gpciImpSam_0.1.0.zip(r-4.6-any)gpciImpSam_0.1.0.zip(r-4.5-any)
gpciImpSam_0.1.0.tgz(r-4.6-any)gpciImpSam_0.1.0.tgz(r-4.5-any)
gpciImpSam_0.1.0.tar.gz(r-4.7-any)gpciImpSam_0.1.0.tar.gz(r-4.6-any)
gpciImpSam_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
gpciImpSam/json (API)
| # Install 'gpciImpSam' in R: |
| install.packages('gpciImpSam', repos = c('https://shikhartyagi.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:62f56f9839. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 105 | ||
| source / vignettes | OK | 147 | ||
| linux-release | OK | 104 | ||
| macos-release | OK | 116 | ||
| macos-oldrel | OK | 112 | ||
| windows-devel | OK | 62 | ||
| windows-release | OK | 74 | ||
| windows-oldrel | OK | 75 | ||
| wasm-release | OK | 83 |
Exports:capabilitycompute_theoretical_momentsdefine_distributiondist_exponentialdist_gammadist_logistic_exponentialdist_normaldist_weibullfit_distributiongpci_impsamheidelberger_welchhpd_intervalimpsam_gpci
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Compute Process Capability Indices (GPCIs) | capability |
| Theoretical Moments Computation for Process Distribution | compute_theoretical_moments |
| Define a Process Distribution | define_distribution |
| Standard Pre-defined Exponential Distribution | dist_exponential |
| Standard Pre-defined Gamma Distribution | dist_gamma |
| Pre-defined Logistic-Exponential Distribution | dist_logistic_exponential |
| Standard Pre-defined Normal Distribution | dist_normal |
| Standard Pre-defined Weibull Distribution | dist_weibull |
| Uncensored Data Parameter Estimation via Maximum Likelihood | fit_distribution |
| High-Level User Interface Function for Importance Sampling GPCIs | gpci_impsam |
| Heidelberger and Welch's MCMC Convergence Diagnostic | heidelberger_welch |
| Highest Posterior Density (HPD) Interval Calculation | hpd_interval |
| Importance Sampling MCMC Chain Generator for Process Capability Indices | impsam_gpci |
| Plot Method for gpciImpSam Objects | plot.gpciImpSam |
| Summary and Diagnostics for gpciImpSam Objects | summary.gpciImpSam |