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drasR

Drift-Aware Risk-Guaranteed Acceptance Sampling

drasR implements Drift-Aware Risk-Guaranteed Acceptance Sampling for adaptive lot disposition under process drift.

The package extends classical acceptance sampling by adding:

• producer-risk and consumer-risk assessment,
• EWMA-based process drift monitoring,
• adaptive inspection tightening,
• Monte Carlo evaluation,
• policy tuning,
• low-cost, balanced, and high-safety policy selection,
• safety-cost frontier visualisation.

Installation

# Development version
# install.packages("devtools")
# devtools::install_github("YOUR-GITHUB-USERNAME/drasR")

Basic example

library(drasR)

plan <- find_plan(
  aql = 0.01,
  ltpd = 0.05,
  alpha = 0.05,
  beta = 0.10
)

plan

Compare fixed acceptance sampling with DRAS

set.seed(123)

result <- run_dras_experiment(
  n_reps = 100,
  scenario = "sudden_deterioration",
  seed = 123
)

result

Tune DRAS policies

tuned <- tune_dras_policy(
  n_reps = 100,
  scenario = "sudden_deterioration",
  seed = 123
)

selected <- select_dras_policy(tuned)

selected

Plot safety-cost frontier

plot_dras_frontier(tuned, selected)

Interpretation

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.

Status

This package is under active development. The current version focuses on simulation-based evaluation and methodological development.

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.
Health stats visible at Monitor.