AccSamplingDesign: An R Package for Optimizing Acceptance Sampling Plans
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Truong, Ha
Miranda, Victor
Kissling, Roger
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The R Foundation
Abstract
This paper introduces an R package for the optimization and visualization of acceptance sampling plans. It supports classical distributions (Binomial, Poisson, Normal) and provides the first R implementation of Beta-based plans tailored for bounded, non-normal data such as proportions. The package leverages nonlinear programming to compute optimal designs significantly faster and employs less memory than traditional grid search methods, achieving over 1000× speedup for Beta-based plans and substantial improvements for Normal-based plans. The package also delivers stable performance across a wide range of sampling plan settings and constraints. Users can visualize operating characteristic curves, compare sampling plans, and apply the tools in real-world quality control settings. We outline the statistical foundation, software architecture, and key applications.
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0104 Statistics, 0803 Computer Software, 4612 Software engineering, 4905 Statistics
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The R Journal, ISSN: 2073-4859 (Online), The R Foundation, 18(1), 368-381. doi: 10.32614/rj-2026-007
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Creative Commons Attribution CC BY 4.0