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AccSamplingDesign 0.0.9.9000

Known-theta Beta variables plans now meet both risk constraints at the delivered (rounded-up) sample size. Previously, the soft-penalty constrained search could return a plan that already violated PR <= alpha or CR <= beta, and rounding the sample size up could not repair it. The search is now refined by solving the two binding risk equations (Nelder-Mead in log coordinates), the sample size is rounded up from the binding solution, and both risks are verified at the delivered size. Known-theta sample sizes are now the minimal feasible integers, e.g. the reported moisture case (PRQ = 0.005, CRQ = 0.01, USL = 0.05, theta = 500) changes from n = 112 (violating both risks) to n = 117. If the refinement does not converge, the constrained-search solution is delivered after verification, with a warning.
For known-theta Beta plans, the reported PR and CR, the OC curve and the plots now describe the delivered integer plan, and n equals sample_size. Unknown-theta plans keep the previous reporting semantics.
accProb() no longer fails for a Beta plan with acceptability constant k = 0 (floating-point cancellation made the closed-form discriminant marginally negative).

Added analytical Delta--MLE and Delta--MoM methods for Beta variables acceptance sampling when the precision parameter, theta, is unknown.
Delta--MLE is now the default unknown-theta method. The sample-size adjustment used in version 0.0.8 remains available as "gk_adjustment".

Added the citation for the AccSamplingDesign article published in The R Journal, volume 18, issue 1, pages 368--381 (doi:10.32614/RJ-2026-007).