Introduction | Example: Progressive Type-II Censored Analysis
Package: gpciProgTyII 0.1.0
gpciProgTyII: Generalized Process Capability Indices under Progressive Type-II Censoring
Provides a comprehensive generalized framework for parameter estimation and Generalized Process Capability Indices (GPCIs) under Progressive Type-II censored data using the MleCensoR package. Accepts 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:10.1007/s41872-019-00081-4>), 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. Evaluates parametric and non-parametric bootstrap confidence intervals at 90%, 95%, and 99% levels of significance. Computes Standard Errors, Mean Squared Error (MSE), Bias, and Coverage Probabilities for model parameters and capability indices. References: Balakrishnan & Aggarwala (2000) <doi:10.1007/978-1-4612-1186-0>, Maiti, Saha & Nanda (2010) <doi:10.1080/16843703.2010.11673233>, Saha, Dey & Maiti (2018) <doi:10.1080/21681015.2018.1437793>, Dey & Saha (2019) <doi:10.1007/s41872-019-00081-4>, Saha, Dey & Maiti (2019), Alotaibi, Dey & Saha (2022) <doi:10.1155/2022/3135264>, Saha, Dey & Nadarajah (2022) <doi:10.1080/02664763.2021.1971632>, Saha, Tripathi & Dey (2024) <doi:10.1142/S021853932450013X>.
Authors:
gpciProgTyII_0.1.0.tar.gz
gpciProgTyII_0.1.0.zip(r-4.7-any)gpciProgTyII_0.1.0.zip(r-4.6-any)gpciProgTyII_0.1.0.zip(r-4.5-any)
gpciProgTyII_0.1.0.tgz(r-4.6-any)gpciProgTyII_0.1.0.tgz(r-4.5-any)
gpciProgTyII_0.1.0.tar.gz(r-4.7-any)gpciProgTyII_0.1.0.tar.gz(r-4.6-any)
gpciProgTyII_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
gpciProgTyII/json (API)
| # Install 'gpciProgTyII' in R: |
| install.packages('gpciProgTyII', 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:d044899011. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 107 | ||
| source / vignettes | OK | 164 | ||
| linux-release | OK | 99 | ||
| macos-release | OK | 85 | ||
| macos-oldrel | OK | 105 | ||
| windows-devel | OK | 69 | ||
| windows-release | OK | 74 | ||
| windows-oldrel | OK | 67 | ||
| wasm-release | OK | 84 |
Exports:boot_ci_progcapability_progcoef.gpc_prog_fitconfint.gpc_prog_fitdist_exp_expdist_gammadist_logistic_expdist_normaldist_weibulleval_performancefit_prog_ty2gpc_bootgpc_fitgpc_indexgpc_simmake_gpc_distplot.gpc_prog_ciplot.gpc_prog_fitprint.gpc_prog_ciprint.gpc_prog_fitsummary.gpc_prog_cisummary.gpc_prog_fitvcov.gpc_prog_fit
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Bootstrap Confidence Intervals and Performance Evaluation for Progressive Type-II GPCI | boot_ci_prog gpc_boot print.gpc_prog_ci summary.gpc_prog_ci |
| Compute Generalized Process Capability Indices for Progressive Type-II Censored Data | capability_prog coef.gpc_prog_fit confint.gpc_prog_fit gpc_fit print.gpc_prog_fit summary.gpc_prog_fit vcov.gpc_prog_fit |
| Monte Carlo Simulation and Coverage Probability Evaluation | eval_performance gpc_sim |
| Parameter Estimation for Progressive Type-II Censored Data using MleCensoR | fit_prog_ty2 |
| Dispatcher for Index Value Retrieval | gpc_index |
| Custom Distribution Object Builder for Progressive Type-II GPCI | make_gpc_dist |
| Visualization Functions for Progressive Type-II GPCI | plot.gpc_prog_ci plot.gpc_prog_fit |
| Predefined Distribution Specifications | dist_exp_exp dist_gamma dist_logistic_exp dist_normal dist_weibull predefined_distributions |