Implements drift-aware risk-guaranteed acceptance sampling methods
for adaptive lot disposition. The package provides tools for classical
acceptance sampling, producer-risk and consumer-risk assessment,
exponentially weighted moving average drift monitoring, adaptive
tightening policies, decision fragility analysis, and safety-cost Pareto
frontier construction. Classical binomial acceptance-plan design is
described in the NIST/SEMATECH e-Handbook of Statistical Methods
< https://www.itl.nist.gov/div898/handbook/pmc/section2/pmc232.htm>.
Exponentially weighted moving averages follow Roberts (1959)
drasR implements Drift-Aware Risk-Guaranteed Acceptance Sampling for adaptive lot disposition under process drift.
The package extends classical acceptance sampling by adding:
# Development version
# install.packages("devtools")
# devtools::install_github("YOUR-GITHUB-USERNAME/drasR")
library(drasR)
plan <- find_plan(
aql = 0.01,
ltpd = 0.05,
alpha = 0.05,
beta = 0.10
)
plan
set.seed(123)
result <- run_dras_experiment(
n_reps = 100,
scenario = "sudden_deterioration",
seed = 123
)
result
tuned <- tune_dras_policy(
n_reps = 100,
scenario = "sudden_deterioration",
seed = 123
)
selected <- select_dras_policy(tuned)
selected
plot_dras_frontier(tuned, selected)
drasR helps users study how adaptive inspection can reduce false acceptance of bad lots under process drift while making the inspection-cost trade-off explicit.
This package is under active development. The current version focuses on simulation-based evaluation and methodological development.