AccSamplingDesign

An R package for designing and analyzing acceptance sampling plans
📦 Now available on CRAN! 🎉 —

Overview

The AccSamplingDesign package provides flexible tools to create and evaluate acceptance sampling plans in quality control, for both attributes (pass/fail) and variables (measurable) data. It supports optimization using nonlinear programming (NLP) to meet specified risks while minimizing the required sample size.

Key Features

• 🔍 Attribute Sampling (Binomial, Poisson): Decisions based on defect counts
  
• 📈 Variable Sampling (Normal, Beta): Including compositional data
  
• ⚙️ Risk-Based Optimization: Minimize sample size under alpha and beta constraints
  
• 📊 OC Curve Visualization: Plot Operating Characteristic curves
  
• 🔄 Custom Plan Comparison: Evaluate user-defined vs. optimized plans

Installation

``` r

Install from CRAN

install.packages("AccSamplingDesign")

Or install development version from GitHub

devtools::install_github("vietha/AccSamplingDesign")

Load the package

library(AccSamplingDesign) ```

Examples

📌 Attribute Sampling (Binomial)

``` r plan_attr <- optPlan( PRQ = 0.01, # Acceptable quality CRQ = 0.05, # Rejectable quality alpha = 0.02, # Producer's risk beta = 0.15, # Consumer's risk distribution = "binomial" )

summary(planattr) accProb(planattr, 0.03) # P(accept) if 3% defective plot(plan_attr) # OC curve ```

📌 Variable Sampling (Normal, Known Sigma)

``` r planvar <- optPlan( PRQ = 0.025, CRQ = 0.1, alpha = 0.05, beta = 0.10, distribution = "normal", sigmatype = "known" )

summary(planvar) plot(planvar) ```

📌 Variable Sampling (Normal, Unknown Sigma)

``` r planvar2 <- optPlan( PRQ = 0.025, CRQ = 0.1, alpha = 0.05, beta = 0.10, distribution = "normal", sigmatype = "unknown" )

summary(plan_var2) ```

📌 Variable Sampling (Beta, Known Theta)

``` r planbeta <- optPlan( PRQ = 0.05, CRQ = 0.2, alpha = 0.05, beta = 0.10, distribution = "beta", theta = 44000000, thetatype = "known", LSL = 0.00001 # Lower Specification Limit )

summary(planbeta) plot(planbeta) # By defect level plot(plan_beta, by = "mean") # By mean value ```

📌 Variable Sampling (Beta, Unknown Theta)

``` r planbeta <- optPlan( PRQ = 0.05, CRQ = 0.2, alpha = 0.05, beta = 0.10, distribution = "beta", theta = 44000000, thetatype = "unknown", LSL = 0.00001 )

summary(planbeta) plot(planbeta) # By defect level plot(plan_beta, by = "mean") # By mean value ```

📌 Compare Custom vs. Optimal Plans

``` r pd <- seq(0, 0.15, by = 0.001)

ocopt <- OCdata(plan = planattr, pd = pd)

mplan1 <- manualPlan(n = planattr$n, c = planattr$c - 1, distribution = "binomial") oc_alt1 <- OCdata(plan = mplan1, pd = pd)

plot(pd, ocopt$paccept, type = "l", col = "blue", lwd = 2, xlab = "Proportion Defective", ylab = "Probability of Acceptance", main = "OC Curves Comparison for Attributes Sampling Plan") lines(pd, ocalt1$paccept, col = "red", lwd = 2, lty = 2) legend("topright", legend = c("Optimal Plan", "Manual Plan (c - 1)"), col = c("blue", "red"), lty = c(1, 2), lwd = 2) ```

Additional Notes

This README provides a quick start for using the AccSamplingDesign package. For a full discussion of the statistical foundations, models, and optimization methods used, please refer to the foundation sources such as:

• Schilling, E.G., & Neubauer, D.V. (2017). Acceptance Sampling in Quality Control (3rd ed.). CRC Press.
  
• Wilrich, P.T. (2004). In Frontiers in Statistical Quality Control 7.
  
• Govindaraju, K., & Kissling, R. (2015). Quality Engineering, 27(1), 1–13.

Contributing

Contributions, suggestions, and bug reports are welcome!
Please use GitHub Issues or submit a pull request.