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gpciLindApproxProgII: Lindley Approximation for Generalized Process Capability Indices under Progressive Type-II Censoring

Overview

The gpciLindApproxProgII package implements Bayesian estimation and Generalized Process Capability Indices (GPCIs) evaluation under Progressive Type-II Censoring using Lindley’s 3rd-order approximation method.

Features

Lindley 3rd-Order Approximation: Evaluates Bayesian posterior expectations for model parameters and 14 Generalized Process Capability Indices (Cpy, Cp, Cpk, Cpu, Cpl, Cpm, Cpmk, CpTk, Spmk, Cpc, CNpk, CNpmc, CNpmkc, Cp_uv).
Flexible Distributions: Built-in Normal, Weibull, Gamma, Logistic-Exponential, and Exponentiated-Exponential distributions, plus full support for custom user-supplied PDF, CDF, SF, and QF functions.
Sampling & Diagnostics: MCMC-style chain generation with customizable burn-in and thinning, Highest Posterior Density (HPD) credible intervals at 90%, 95%, and 99% levels, and Heidelberger-Welch convergence diagnostics.
Progressive Resampling: Parametric and non-parametric bootstrap confidence intervals (Percentile, Normal, Basic, BCp) at 90%, 95%, and 99% confidence levels.

Installation

# Install from source package archive (.tar.gz)
install.packages("gpciLindApproxProgII_0.1.0.tar.gz", repos = NULL, type = "source")

Quick Example

library(gpciLindApproxProgII)

# Observed failure times and progressive removal counts
x <- c(0.5, 1.2, 2.1, 3.4, 4.8)
r <- c(1, 0, 2, 0, 1)

# Fit model using Lindley approximation and chain generator
fit <- lindley_prog_gpci(
  x = x,
  r_removals = r,
  distribution = dist_weibull(),
  USL = 6,
  LSL = 0,
  chain_length = 500,
  burn_in = 100,
  thinning = 1,
  B = 100
)

# Print and summary
print(fit)
summary(fit)

# Plot posterior distributions and trace plots
plot(fit)

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.