Package {drasR}

drasR: Drift-Aware Risk-Guaranteed Acceptance Sampling

Description

The drasR package provides tools for drift-aware, risk-guaranteed acceptance sampling and adaptive lot-disposition monitoring.

Details

It includes functions for classical acceptance sampling, producer-risk and consumer-risk assessment, EWMA-based drift monitoring, adaptive tightening rules, safety-cost frontier analysis, decision-fragility assessment, and audit-oriented reporting.

Author(s)

Maintainer: Muhammad Zahir Khan zahirstat007@gmail.com

See Also

Useful links:

• https://github.com/zerish12/drasR

• Report bugs at https://github.com/zerish12/drasR/issues

Apply a Fixed Acceptance Sampling Plan

Description

Apply a Fixed Acceptance Sampling Plan

Usage

apply_fixed_plan(lots, n, c, aql = 0.01)

Arguments

Value

Data frame with lot decisions.

Examples

plan <- find_plan(0.01, 0.05)
p <- simulate_defect_process(20)
lots <- simulate_lots(p)
apply_fixed_plan(lots, n = plan$n, c = plan$c)

Consumer Risk

Description

Computes consumer risk, defined as the probability of accepting a lot at the limiting quality level.

Usage

consumer_risk(n, c, ltpd)

Arguments

Value

Consumer risk.

Examples

consumer_risk(n = 132, c = 3, ltpd = 0.05)

Acceptance Decision Fragility

Description

Computes whether an accept-favouring decision changes under plausible uncertainty around an estimated defect probability.

Usage

decision_fragility(
  n,
  c,
  p_hat,
  uncertainty_width = 0.01,
  threshold = 0.5,
  grid_points = 101
)

Arguments

Value

Integer value: 1 if fragile, 0 if stable.

Examples

decision_fragility(n = 132, c = 3, p_hat = 0.03)

Apply EWMA Drift-Aware Acceptance Sampling

Description

Applies a drift-aware acceptance sampling policy using an EWMA statistic to trigger tightened inspection.

Usage

dras_ewma(
  lots,
  n0,
  c0,
  lambda = 0.2,
  warning_threshold = 0.03,
  tighten_factor = 1.5,
  aql = 0.01
)

Arguments

Value

Data frame with adaptive lot decisions.

Examples

plan <- find_plan(0.01, 0.05)
p <- simulate_defect_process(50, "sudden_deterioration")
lots <- simulate_lots(p)
dras_ewma(lots, n0 = plan$n, c0 = plan$c)

Monitor Real Lot Inspection Data Using DRAS

Description

Applies an EWMA-based DRAS monitoring rule to real lot-level inspection data prepared by prepare_lot_data(). Unlike simulation functions, this function does not require the true defect probability.

Usage

dras_monitor(
  data,
  baseline_sample_size,
  acceptance_number,
  lambda = 0.2,
  warning_threshold = 0.03,
  tighten_factor = 1.5,
  initial_rate = 0.01
)

Arguments

Value

A data frame with EWMA statistic, inspection mode, and recommended next sample size.

Examples

raw_data <- data.frame(
  lot_id = paste0("L", 1:6),
  sample_size = rep(100, 6),
  defects_found = c(1, 0, 1, 4, 5, 6)
)

prepared <- prepare_lot_data(
  raw_data,
  lot_id = "lot_id",
  sample_size = "sample_size",
  defects = "defects_found"
)

dras_monitor(prepared, baseline_sample_size = 132, acceptance_number = 3)

Evaluate Lot-Disposition Results

Description

Evaluates false acceptance, false rejection, inspection burden, and tightened-inspection frequency.

Usage

evaluate_sampling(results, method_name = "method")

Arguments

Value

One-row data frame of performance metrics.

Examples

plan <- find_plan(0.01, 0.05)
p <- simulate_defect_process(20)
lots <- simulate_lots(p)
res <- apply_fixed_plan(lots, plan$n, plan$c)
evaluate_sampling(res, "Fixed ASP")

Example Lot Inspection Data

Description

A simulated lot-level inspection dataset for demonstrating drift-aware risk-guaranteed acceptance sampling. The data represent 200 production lots under a sudden deterioration scenario, where the true defect probability increases halfway through the sequence.

Usage

example_lot_data

Format

A data frame with 200 rows and 4 variables:

lot

Lot index.

p_true

True lot-level defect probability used in simulation.

lot_size

Number of units in the lot.

true_defects

Simulated number of true defective units in the lot.

Details

This dataset is intended for examples, tests, and vignettes. It is not a real industrial dataset. Users can apply the same functions to their own lot-level inspection data containing defect rates or defect counts.

Examples

data(example_lot_data)
head(example_lot_data)

Find a Single Acceptance Sampling Plan

Description

Finds the smallest sample size and acceptance number satisfying producer-risk and consumer-risk constraints.

Usage

find_plan(aql, ltpd, alpha = 0.05, beta = 0.1, max_n = 500)

Arguments

Value

A data frame with sample size, acceptance number, producer risk, and consumer risk.

Examples

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

Compute a Safety-Cost Pareto Frontier

Description

Identifies Pareto-efficient policies where lower inspection burden and lower false acceptance are preferred.

Usage

pareto_frontier(
  x,
  cost_col = "mean_avg_sample_number",
  safety_col = "mean_false_accepts"
)

Arguments

Value

Data frame of Pareto-efficient rows.

Examples

df <- data.frame(cost = c(100, 120, 150), risk = c(5, 3, 2))
pareto_frontier(df, cost_col = "cost", safety_col = "risk")

Plot the DRAS Safety-Cost Frontier

Description

Plots the safety-cost trade-off from a DRAS tuning table. The x-axis represents inspection burden and the y-axis represents false-acceptance burden.

Usage

plot_dras_frontier(
  tuning_results,
  selected_policies = NULL,
  cost_col = "mean_avg_sample_number",
  safety_col = "mean_false_accepts",
  main = "DRAS Safety-Cost Frontier"
)

Arguments

Value

Invisibly returns tuning_results.

Examples

tuned <- tune_dras_policy(
  n_reps = 2,
  n_lots = 30,
  scenario = "sudden_deterioration",
  lambda_values = c(0.20, 0.40),
  warning_values = c(0.03),
  tighten_values = c(1.50, 2.00)
)
selected <- select_dras_policy(tuned)
plot_dras_frontier(tuned, selected)

Prepare Real Lot-Level Inspection Data

Description

Converts real industrial or pharmaceutical inspection data into a standard format suitable for DRAS analysis.

Usage

prepare_lot_data(
  data,
  lot_id,
  sample_size,
  defects,
  lot_size = NULL,
  date = NULL,
  supplier = NULL
)

Arguments

Value

A data frame in DRAS-ready format.

Examples

raw_data <- data.frame(
  lot_id = paste0("L", 1:5),
  date = as.Date("2026-01-01") + 0:4,
  supplier = c("A", "A", "A", "B", "B"),
  lot_size = rep(1000, 5),
  sample_size = rep(100, 5),
  defects_found = c(1, 0, 2, 5, 4)
)

prepare_lot_data(
  raw_data,
  lot_id = "lot_id",
  sample_size = "sample_size",
  defects = "defects_found",
  lot_size = "lot_size",
  date = "date",
  supplier = "supplier"
)

Probability of Accepting a Lot

Description

Computes the probability of accepting a lot under a binomial acceptance sampling plan.

Usage

prob_accept(n, c, p)

Arguments

Value

Probability of lot acceptance.

Examples

prob_accept(n = 132, c = 3, p = 0.01)

Producer Risk

Description

Computes producer risk, defined as the probability of rejecting a lot at the acceptable quality level.

Usage

producer_risk(n, c, aql)

Arguments

Value

Producer risk.

Examples

producer_risk(n = 132, c = 3, aql = 0.01)

Run a DRAS Monte Carlo Experiment

Description

Runs repeated simulations comparing fixed acceptance sampling with EWMA-based drift-aware risk-guaranteed acceptance sampling.

Usage

run_dras_experiment(
  n_reps = 100,
  n_lots = 200,
  scenario = c("stable", "sudden_deterioration", "gradual_drift"),
  aql = 0.01,
  ltpd = 0.05,
  alpha = 0.05,
  beta = 0.1,
  lambda = 0.4,
  warning_threshold = 0.02,
  tighten_factor = 2,
  seed = NULL
)

Arguments

Value

A data frame of summary results by method.

Examples

run_dras_experiment(n_reps = 10, scenario = "sudden_deterioration")

Select Low-Cost, Balanced, and High-Safety DRAS Policies

Description

Selects three representative policies from a DRAS tuning table: a low-cost policy, a balanced policy, and a high-safety policy.

Usage

select_dras_policy(
  tuning_results,
  cost_col = "mean_avg_sample_number",
  safety_col = "mean_false_accepts"
)

Arguments

Value

A data frame containing three selected DRAS policies.

Examples

tuned <- tune_dras_policy(
  n_reps = 2,
  n_lots = 30,
  scenario = "sudden_deterioration",
  lambda_values = c(0.20, 0.40),
  warning_values = c(0.03),
  tighten_values = c(1.50, 2.00)
)
select_dras_policy(tuned)

Simulate a Defect Process

Description

Simulates lot-level defect probabilities under stable, sudden deterioration, or gradual drift scenarios.

Usage

simulate_defect_process(
  n_lots = 200,
  scenario = c("stable", "sudden_deterioration", "gradual_drift"),
  p0 = 0.01,
  p1 = 0.06
)

Arguments

Value

Numeric vector of true defect probabilities.

Examples

simulate_defect_process(10, scenario = "gradual_drift")

Simulate Lot Inspection Data

Description

Simulate Lot Inspection Data

Usage

simulate_lots(p_true, lot_size = 1000)

Arguments

Value

Data frame of simulated lots.

Examples

p <- simulate_defect_process(20)
simulate_lots(p)

Summarise DRAS Monitoring Results

Description

Summarises the output from dras_monitor() to provide an audit-friendly overview of lot decisions, warnings, inspection modes, and defect rates.

Usage

summarise_dras_monitor(monitored_data, by_supplier = FALSE)

Arguments

Value

A data frame containing DRAS monitoring summary statistics.

Examples

raw_data <- data.frame(
  lot_id = paste0("L", 1:6),
  supplier = c("A", "A", "A", "B", "B", "B"),
  sample_size = rep(100, 6),
  defects_found = c(1, 0, 1, 4, 5, 6)
)

prepared <- prepare_lot_data(
  raw_data,
  lot_id = "lot_id",
  sample_size = "sample_size",
  defects = "defects_found",
  supplier = "supplier"
)

monitored <- dras_monitor(
  prepared,
  baseline_sample_size = 132,
  acceptance_number = 3
)

summarise_dras_monitor(monitored)

Tune DRAS Policies

Description

Searches over EWMA smoothing parameters, warning thresholds, and tightening factors to identify safety-cost trade-offs for DRAS.

Usage

tune_dras_policy(
  n_reps = 100,
  n_lots = 200,
  scenario = c("stable", "sudden_deterioration", "gradual_drift"),
  lambda_values = c(0.05, 0.1, 0.2, 0.3, 0.4),
  warning_values = c(0.02, 0.03, 0.04),
  tighten_values = c(1.25, 1.5, 2),
  aql = 0.01,
  ltpd = 0.05,
  alpha = 0.05,
  beta = 0.1,
  seed = NULL
)

Arguments

Value

A data frame of policy performance results.

Examples

tune_dras_policy(
  n_reps = 2,
  n_lots = 30,
  scenario = "sudden_deterioration",
  lambda_values = c(0.20, 0.40),
  warning_values = c(0.03),
  tighten_values = c(1.50)
)

Write a DRAS Audit Report

Description

Writes DRAS monitoring outputs and audit summaries to CSV files. This function is intended for industrial and quality-assurance workflows where lot-level monitoring decisions must be saved for review.

Usage

write_dras_report(monitored_data, output_dir, include_supplier_summary = TRUE)

Arguments

Value

Invisibly returns a character vector of written file paths.

Examples

raw_data <- data.frame(
  lot_id = paste0("L", 1:6),
  supplier = c("A", "A", "A", "B", "B", "B"),
  sample_size = rep(100, 6),
  defects_found = c(1, 0, 1, 4, 5, 6)
)

prepared <- prepare_lot_data(
  raw_data,
  lot_id = "lot_id",
  sample_size = "sample_size",
  defects = "defects_found",
  supplier = "supplier"
)

monitored <- dras_monitor(
  prepared,
  baseline_sample_size = 132,
  acceptance_number = 3
)

temp_dir <- tempfile("dras_report_")
write_dras_report(monitored, output_dir = temp_dir)